diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..513331f12f61f9bcd313eeb962d62c025804cf51 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,6 @@ +FROM tensorflow/tensorflow:2.4.1-gpu-jupyter + +RUN apt update && apt install -y nvidia-modprobe +RUN pip install adabelief-tf pandas imageio imageio-ffmpeg seaborn sklearn tensorflow_addons tqdm +RUN pip install keras_applications tensorflowjs +RUN pip install tf2onnx diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..72876f14eb49ec11242cff906da659769e879ae2 --- /dev/null +++ b/README.md @@ -0,0 +1,92 @@ +# [*mEye*](https://www.pupillometry.it): A Deep Learning Tool for Pupillometry + +> ⭐ MEYE is available on **MATLAB**! Check it out [here](matlab/README.md) + +> Check out [pupillometry.it](https://www.pupillometry.it) for a ready-to-use web-based mEye pupillometry tool! + +This branch provides the Python code to make predictions and train/finetune models. +If you are interested in the code of the pupillometry web app, check out the `gh-pages` branch. + +## Requirements +You need a Python 3 environment with the following packages installed: + + - tensorflow >= 2.4 + - imageio, imageio-ffmpeg + - scipy + - tqdm + +If you want to train models, you also need + + - adabelief_tf >= 0.2.1 + - pandas + - sklearn + +We provide a [Dockerfile](./Dockerfile) for building an image with docker. + +## Make Predictions with Pretrained Models + +You can make predictions with pretrained models on pre-recorded videos or webcam streams. + + 1. Download the [pretrained model](https://github.com/fabiocarrara/meye/releases/download/v0.1.1/meye-2022-01-24.h5). If you want to use the [old model](https://github.com/fabiocarrara/meye/releases/download/v0.1/meye-segmentation_i128_s4_c1_f16_g1_a-relu.hdf5), check out version [`v0.1` of this branch](https://github.com/fabiocarrara/meye/tree/v0.1). See available models in [Releases](https://github.com/fabiocarrara/meye/releases). + 2. Check out the `pupillometry-offline-videos.ipynb` notebook for a complete example of pupillometry data analysis. + 3. In alternative, we provide also the `predict.py` script that implements the basic loop to make predictions on video streams. E.g.: + + - ```bash + # input: webcam (default) + # prediction roi: biggest central square crop (default) + # outputs: predictions.mp4, predictions.csv (default) + predict.py path/to/model + ``` + + - ```bash + # input: video file + # prediction roi: left=80, top=80, right=208, bottom=208 + # outputs: video_with_predictions.mp4, pupil_metrics.csv + predict.py path/to/model path/to/video.mp4 -rl 80 -rt 80 -rr 208 -rb 208 -ov video_with_predictions.mp4 -oc pupil_metrics.csv + ``` + - ```bash + # check all parameters with + predict.py -h + ``` + +## Training Models + + 1. Download our dataset ([NN_human_mouse_eyes.zip](https://doi.org/10.5281/zenodo.4488164), 246.4 MB) or prepare your dataset following our dataset's structure. + > If you need to annotate your dataset, check out [pLabeler](https://github.com/LeonardoLupori/pLabeler), a MATLAB software for labeling pupil images. + + The dataset should be placed in `data/`. + + 2. If you are using a custom dataset, edit `train.py` to perform the train/validation/test split of your data. + + 3. Train with default parameters: + ```bash + python train.py -d data/ + ``` + + - For a list of available parameters, run + ```bash + python train.py -h + ``` + +## MATLAB support +Starting from MATLAB version 2021b, MEYE is also available for use on MATLAB! +A fully functional class and a tutorial for its use is available [here](matlab/README.md)! + + +## References + +### Dataset + [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4488164.svg)](https://doi.org/10.5281/zenodo.4488164) + +If you use our dataset, please cite: + + @dataset{raffaele_mazziotti_2021_4488164, + author = {Raffaele Mazziotti and Fabio Carrara and Aurelia Viglione and Lupori Leonardo and Lo Verde Luca and Benedetto Alessandro and Ricci Giulia and Sagona Giulia and Amato Giuseppe and Pizzorusso Tommaso}, + title = {{Human and Mouse Eyes for Pupil Semantic Segmentation}}, + month = feb, + year = 2021, + publisher = {Zenodo}, + version = {1.0}, + doi = {10.5281/zenodo.4488164}, + url = {https://doi.org/10.5281/zenodo.4488164} + } diff --git a/convert_to_onnx.sh b/convert_to_onnx.sh new file mode 100644 index 0000000000000000000000000000000000000000..efbc836741f861d2763fc9c2ef5cafc1126d8cb2 --- /dev/null +++ b/convert_to_onnx.sh @@ -0,0 +1,8 @@ +#/bin/bash + +# prerequisite: +# pip install tf2onnx + +RUNDIR="path/to/run/dir" + +python -m tf2onnx.convert --saved-model $RUNDIR/best_savedmodel --output $RUNDIR/best_model.onnx \ No newline at end of file diff --git a/convert_to_tfjs.py b/convert_to_tfjs.py new file mode 100644 index 0000000000000000000000000000000000000000..a9365cdd51971ff9da6e05b7f96d5025e51437af --- /dev/null +++ b/convert_to_tfjs.py @@ -0,0 +1,58 @@ +import argparse +import os +os.sys.path += ['expman'] +import json +from glob import glob +import subprocess +from tqdm import tqdm +import expman + + +def main(args): + + variants = ( + ('' , []), + # ('_qf16', ['--quantize_float16', '*']), + # ('_qu16', ['--quantize_uint16' , '*']), + # ('_qu8' , ['--quantize_uint8' , '*']), + ) + + converted_models = [] + + exps = expman.gather(args.run).filter(args.filter) + for exp_name, exp in tqdm(exps.items()): + # ckpt = exp.path_to('best_model.h5') + # ckpt = ckpt if os.path.exists(ckpt) else exp.path_to('last_model.h5') + ckpt = exp.path_to('best_savedmodel/') + + for suffix, extra_args in variants: + name = exp_name + suffix + out = os.path.join(args.output, name) if args.output else exp.path_to(f'tfjs_graph{suffix}') + + if args.force or not os.path.exists(out): + os.makedirs(out, exist_ok=True) + cmd = ['tensorflowjs_converter', + '--input_format', 'tf_saved_model', + '--output_format', 'tfjs_graph_model'] + extra_args + [ckpt, out] + subprocess.call(cmd) + + converted_models.append(name) + + js_output = 'models = ' + json.dumps(converted_models) + if args.output: + js_filename = os.path.join(args.output, 'models.js') + with open(js_filename, 'w') as f: + f.write(js_output) + else: + print(js_output) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Convert runs to tfjs') + parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) + parser.add_argument('run', default='runs/') + parser.add_argument('--output', help='output dir for models, defaults to run dir') + parser.add_argument('--force', action='store_true', default=False) + + args = parser.parse_args() + main(args) diff --git a/data/NN_human_mouse_eyes/annotation/annotations.csv b/data/NN_human_mouse_eyes/annotation/annotations.csv new file mode 100644 index 0000000000000000000000000000000000000000..3241dd26c15a565fcedc0bd4e11f1af470f7dbc3 --- /dev/null +++ b/data/NN_human_mouse_eyes/annotation/annotations.csv @@ -0,0 +1,11898 @@ +filename,eye,blink,exp,w,h,roi_x,roi_y,roi_w,sub +2P-fc2_save_20200806_1_0001.jpg,1,0,2p,334,446,85,60,128,1 +2P-fc2_save_20200806_1_0002.jpg,1,0,2p,334,446,85,60,128,1 +2P-fc2_save_20200806_1_0003.jpg,1,0,2p,334,446,85,60,128,1 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0000000000000000000000000000000000000000..56ef2a4fe8fdbebec726c35569128d20e8feb852 Binary files /dev/null and b/data/NN_human_mouse_eyes/annotation/png/2P-fc2_save_20200806_1_0048.png differ diff --git a/dataloader.py b/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..67ad7e0185aed1ae01a5c34a4b04b9cd68297970 --- /dev/null +++ b/dataloader.py @@ -0,0 +1,240 @@ +# dataloader.py + +import os +import math +import pandas as pd +import tensorflow as tf +from functools import partial + +def validate_data_files(data_df): + """Validate all files exist before starting training""" + missing_files = [] + + for _, row in data_df.iterrows(): + if not os.path.exists(row['filename']): + missing_files.append(row['filename']) + if not os.path.exists(row['target']): + missing_files.append(row['target']) + + if missing_files: + print("Missing files:") + for f in missing_files[:10]: + print(f" {f}") + if len(missing_files) > 10: + print(f" ... and {len(missing_files)-10} more") + raise FileNotFoundError(f"Found {len(missing_files)} missing files") + +def _get_pupil_position(pmap, datum, x_shape): + total_mass = tf.reduce_sum(pmap) + if total_mass > 0: + shape = tf.shape(pmap) + h, w = shape[0], shape[1] + ii, jj = tf.meshgrid(tf.range(h), tf.range(w), indexing='ij') + y = tf.reduce_sum(tf.cast(ii, 'float32') * pmap) / total_mass + x = tf.reduce_sum(tf.cast(jj, 'float32') * pmap) / total_mass + return tf.stack((y, x)) + + if 'roi_x' in datum and 'roi_y' in datum and 'roi_w' in datum: + roi_x = tf.cast(datum['roi_x'], 'float32') + roi_y = tf.cast(datum['roi_y'], 'float32') + half = tf.cast(datum['roi_w'] / 2, 'float32') + result = tf.stack((roi_y + half, roi_x + half)) + else: # fallback to center of the image + result = tf.cast(tf.stack((x_shape[0] / 2, x_shape[1] / 2)), dtype='float32') + + return result + +def additional_augmentations(image, mask, p=0.3): + # Keep original image with probability 1-p + if tf.random.uniform([]) > p: + return image, mask + + # Random noise augmentation + if tf.random.uniform([]) < 0.3: + noise = tf.random.normal(tf.shape(image), mean=0, stddev=0.05) + image = image + noise + image = tf.clip_by_value(image, 0, 1) + + # Random blur using gaussian filter + if tf.random.uniform([]) < 0.3: + kernel_size = 3 + sigma = tf.random.uniform([], 0, 1.0) + x = tf.range(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=tf.float32) + gaussian = tf.exp(-(x ** 2) / (2 * sigma ** 2)) + gaussian = gaussian / tf.reduce_sum(gaussian) + gaussian = tf.reshape(gaussian, [kernel_size, 1]) + gaussian_kernel = gaussian @ tf.transpose(gaussian) + gaussian_kernel = tf.reshape(gaussian_kernel, [kernel_size, kernel_size, 1, 1]) + image = tf.nn.conv2d(tf.expand_dims(image, 0), gaussian_kernel, + strides=[1,1,1,1], padding='SAME')[0] + + # Random sharpening + if tf.random.uniform([]) < 0.3: + kernel = tf.constant([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]], dtype=tf.float32) + kernel = tf.reshape(kernel, [3, 3, 1, 1]) + image = tf.nn.conv2d(tf.expand_dims(image, 0), kernel, + strides=[1,1,1,1], padding='SAME')[0] + image = tf.clip_by_value(image, 0, 1) + + return image, mask + +@tf.function +def load_datum(datum, x_shape=(128, 128, 1), augment=False): + try: + x = tf.io.read_file(datum['filename']) + y = tf.io.read_file(datum['target']) + + # HWC [0,1] float32 + x = tf.io.decode_image(x, channels=1, dtype='float32', expand_animations=False) + y = tf.io.decode_image(y, dtype='float32', expand_animations=False) + + # Get image dimensions + h = tf.shape(x)[0] + w = tf.shape(x)[1] + + # Extract pupil information + pupil_map = y[:, :, 0] + pupil_area = tf.reduce_sum(pupil_map) + pupil_pos_yx = _get_pupil_position(pupil_map, datum, x_shape) + + # Target size we want to achieve + target_size = tf.minimum(tf.minimum(h, w), x_shape[0]) + + if not augment: + # Calculate center crop + h_start = (h - target_size) // 2 + w_start = (w - target_size) // 2 + else: + # Random crop within safe bounds considering pupil position + h_start = tf.random.uniform([], 0, h - target_size + 1, dtype=tf.int32) + w_start = tf.random.uniform([], 0, w - target_size + 1, dtype=tf.int32) + + # Perform crop + x = tf.image.crop_to_bounding_box(x, h_start, w_start, target_size, target_size) + y = tf.image.crop_to_bounding_box(y, h_start, w_start, target_size, target_size) + + if augment: + # Rotation with arbitrary angles + k = tf.random.uniform([], 0, 4, dtype=tf.int32) + x = tf.image.rot90(x, k=k) + y = tf.image.rot90(y, k=k) + + # Flips with pupil position consideration + if tf.random.uniform([]) < 0.5: + x = tf.image.flip_left_right(x) + y = tf.image.flip_left_right(y) + + if tf.random.uniform([]) < 0.5: + x = tf.image.flip_up_down(x) + y = tf.image.flip_up_down(y) + + # Apply additional augmentations + x, y = additional_augmentations(x, y) + + # Calculate pupil visibility after transformation + new_pupil_map = y[:, :, 0] + new_pupil_area = tf.reduce_sum(new_pupil_map) + eye = (new_pupil_area / pupil_area) if pupil_area > 0 else 0. + + # Process eye and blink information + datum_eye = tf.cast(datum['eye'], 'float32') + datum_blink = tf.cast(datum['blink'], 'float32') + + # Handle blink cases + if datum_eye == 0: + datum_blink = 0. + if (datum_eye == 1) & (datum_blink == 0): + datum_eye = eye + + # Resize if needed + if target_size != x_shape[0]: + x = tf.image.resize(x, [x_shape[0], x_shape[0]]) + y = tf.image.resize(y, [x_shape[0], x_shape[0]]) + + y = y[:, :, :1] + y2 = tf.stack((datum_eye, datum_blink)) + + return x, y, y2 + + except Exception as e: + print(f"Error processing datum: {str(e)}") + raise + +def get_loader(dataframe, batch_size=8, shuffle=False, **kwargs): + categories = dataframe.exp.values + dataset = tf.data.Dataset.from_tensor_slices(dict(dataframe)) + + if shuffle: + dataset = dataset.shuffle(1000) + + dataset = dataset.map( + partial(load_datum, **kwargs), + num_parallel_calls=tf.data.AUTOTUNE, + deterministic=not shuffle + ) + dataset = dataset.batch(batch_size) + + def _pack_targets(*ins): + inputs = ins[0] + targets = {'mask': ins[1], 'tags': ins[2]} + return [inputs, targets] + + dataset = dataset.map( + _pack_targets, + num_parallel_calls=tf.data.AUTOTUNE, + deterministic=not shuffle + ) + dataset = dataset.prefetch(tf.data.AUTOTUNE) + return dataset, categories + +def load_datasets(dataset_dirs): + def _load_and_prepare_annotations(dataset_dir): + # Normalize path + dataset_dir = os.path.normpath(dataset_dir) + + data_path = os.path.join(dataset_dir, 'annotation', 'annotations.csv') + if not os.path.exists(data_path): + raise FileNotFoundError(f"Annotations file not found: {data_path}") + + data = pd.read_csv(data_path) + + # Create directories if they don't exist + png_dir = os.path.join(dataset_dir, 'annotation', 'png') + full_frames_dir = os.path.join(dataset_dir, 'fullFrames') + os.makedirs(png_dir, exist_ok=True) + os.makedirs(full_frames_dir, exist_ok=True) + + # Filter valid files before creating paths + valid_data = data[data.apply(lambda x: os.path.exists(os.path.join(full_frames_dir, os.path.basename(x['filename']))), axis=1)] + + if len(valid_data) == 0: + raise ValueError(f"No valid image files found in {full_frames_dir}") + + # Create target paths + valid_data['target'] = valid_data['filename'].apply( + lambda x: os.path.join(png_dir, os.path.splitext(os.path.basename(x))[0] + '.png') + ) + + valid_data['filename'] = valid_data['filename'].apply( + lambda x: os.path.join(full_frames_dir, os.path.basename(x)) + ) + + return valid_data + + all_data = [] + for d in dataset_dirs: + try: + dataset = _load_and_prepare_annotations(d) + all_data.append(dataset) + except Exception as e: + print(f"Error loading dataset from {d}: {str(e)}") + continue + + if not all_data: + raise ValueError("No valid datasets found in any of the provided directories") + + dataset = pd.concat(all_data) + dataset['sub'] = dataset['sub'].astype(str) + + print(f"Found {len(dataset)} valid image pairs") + return dataset \ No newline at end of file diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..b9c67153db23bf1607859d33f63f5b01b3274496 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,339 @@ +# -*- coding: utf-8 -*- +""" MEye: Semantic Segmentation """ + +import argparse +import os +os.sys.path += ['expman', 'models/deeplab'] +import expman + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +import numpy as np +import pandas as pd +import tensorflow as tf +from tensorflow.keras import backend as K +from tensorflow.keras.models import load_model +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score +from adabelief_tf import AdaBeliefOptimizer +from glob import glob +from tqdm import tqdm +from PIL import Image + +from deeplabv3p.models.deeplabv3p_mobilenetv3 import hard_swish +from dataloader import get_loader, load_datasets +from utils import visualize, visualizable + +from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph + +def iou_coef(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + intersection = K.sum(K.abs(y_true * y_pred), axis=[1, 2, 3]) + union = K.sum(y_true, axis=[1, 2, 3]) + K.sum(y_pred, axis=[1, 2, 3]) - intersection + return K.mean((intersection + 1e-6) / (union + 1e-6)) + +def dice_coef(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + intersection = K.sum(K.abs(y_true * y_pred), axis=[1, 2, 3]) + return K.mean((2. * intersection + 1e-6) / (K.sum(y_true, axis=[1, 2, 3]) + K.sum(y_pred, axis=[1, 2, 3]) + 1e-6)) + +def boundary_loss(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + dy_true, dx_true = tf.image.image_gradients(y_true) + dy_pred, dx_pred = tf.image.image_gradients(y_pred) + loss = tf.reduce_mean(tf.abs(dy_pred - dy_true) + tf.abs(dx_pred - dx_true)) + return loss * 0.5 + +def enhanced_binary_crossentropy(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + bce = tf.keras.losses.binary_crossentropy(y_true, y_pred) + boundary = boundary_loss(y_true, y_pred) + return bce + boundary + +def _filter_by_closeness(a, eps=10e-3): + keep = [] + prev = np.array([-1, -1]) + for row in a.drop('thr', axis=1).values: + if (np.abs(prev - row) > eps).any(): + keep.append(True) + prev = row + else: + keep.append(False) + return a[keep] + +def _weighted_roc_pr(y_true, y_scores, label, outdir, simplify=False): + # Convert to binary labels using 0.5 threshold for true values + y_true_binary = (y_true > 0.5).astype(int) + + npos = y_true_binary.sum() + nneg = len(y_true_binary) - npos + pos_weight = nneg / npos if npos > 0 else 1.0 + print(label, 'Tot:', len(y_true), 'P:', npos, 'N:', nneg, 'N/P:', pos_weight) + sample_weight = np.where(y_true_binary, pos_weight, 1) + + fpr, tpr, thr = roc_curve(y_true_binary, y_scores, sample_weight=sample_weight) + auc_score = auc(fpr, tpr) + print(label, 'AuROC:', auc_score) + + roc_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'roc_auc': auc_score}) + roc_metrics_file = os.path.join(outdir, '{}_roc_metrics.csv'.format(label)) + roc_metrics.to_csv(roc_metrics_file, index=False) + + roc = pd.DataFrame({'fpr': fpr, 'tpr': tpr, 'thr': thr}) + if simplify: + full_roc_file = os.path.join(outdir, '{}_roc_curve_full.csv.gz'.format(label)) + roc.to_csv(full_roc_file, index=False) + roc = _filter_by_closeness(roc) + + roc_file = os.path.join(outdir, '{}_roc_curve.csv'.format(label)) + roc.to_csv(roc_file, index=False) + + roc.plot(x='fpr', y='tpr', xlim=(0, 1), ylim=(0, 1)) + roc_plot_file = os.path.join(outdir, '{}_roc.pdf'.format(label)) + plt.savefig(roc_plot_file) + plt.close() + + precision, recall, thr = precision_recall_curve(y_true_binary, y_scores, sample_weight=sample_weight) + f1_score = 2 * precision * recall / (precision + recall) + pr_auc = auc(recall, precision) + + pr_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'pr_auc': pr_auc}) + pr_metrics_file = os.path.join(outdir, '{}_pr_metrics.csv'.format(label)) + pr_metrics.to_csv(pr_metrics_file, index=False) + + thr = np.append(thr, [thr[-1]]) + pr = pd.DataFrame({'precision': precision, 'recall': recall, 'f1_score': f1_score, 'thr': thr}) + if simplify: + full_pr_file = os.path.join(outdir, '{}_pr_curve_full.csv.gz'.format(label)) + pr.to_csv(full_pr_file, index=False) + pr = _filter_by_closeness(pr) + + pr_file = os.path.join(outdir, '{}_pr_curve.csv'.format(label)) + pr.to_csv(pr_file, index=False) + + pr.plot(x='recall', y='precision', xlim=(0, 1), ylim=(0, 1)) + pr_plot_file = os.path.join(outdir, '{}_pr.pdf'.format(label)) + plt.savefig(pr_plot_file) + plt.close() + + print(label, 'AuPR:', pr_auc, 'AvgP:', average_precision_score(y_true_binary, y_scores, sample_weight=sample_weight)) + +def get_flops(model): + concrete = tf.function(lambda inputs: model(inputs)) + concrete_func = concrete.get_concrete_function( + [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs]) + frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func) + with tf.Graph().as_default() as graph: + tf.graph_util.import_graph_def(graph_def, name='') + run_meta = tf.compat.v1.RunMetadata() + opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation() + flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd="op", options=opts) + + tf.compat.v1.reset_default_graph() + return flops.total_float_ops + +def evaluate(exp, force=False): + ckpt_path = exp.path_to('best_model.keras') + + # If the model doesn't exist and we're not forcing evaluation, just return + if not os.path.exists(ckpt_path): + print(f"Model file not found: {ckpt_path}") + return + + custom_objects = { + 'AdaBeliefOptimizer': AdaBeliefOptimizer, + 'iou_coef': iou_coef, + 'dice_coef': dice_coef, + 'hard_swish': hard_swish, + 'enhanced_binary_crossentropy': enhanced_binary_crossentropy, + 'boundary_loss': boundary_loss + } + + try: + model = tf.keras.models.load_model(ckpt_path, custom_objects=custom_objects) + except Exception as e: + print(f"Error loading model: {str(e)}") + return + + # get flops + flop_params_path = exp.path_to('flops_nparams.csv') + if force or not os.path.exists(flop_params_path): + model.compile() + # Add .keras extension to tmp_model + tf.keras.models.save_model(model, 'tmp_model.keras', overwrite=True, include_optimizer=False) + stripped_model = tf.keras.models.load_model('tmp_model.keras') + flops = get_flops(stripped_model) + nparams = stripped_model.count_params() + del stripped_model + # Clean up temporary model file + if os.path.exists('tmp_model.keras'): + os.remove('tmp_model.keras') + print('FLOPS:', flops) + print('#PARAMS:', nparams) + pd.DataFrame({'flops': flops, 'nparams': nparams}, index=[0]).to_csv(flop_params_path) + + model.compile( + loss='binary_crossentropy', + metrics={'mask': [iou_coef, dice_coef], 'tags': 'binary_accuracy'} + ) + + params = exp.params + np.random.seed(params.seed) + tf.random.set_seed(params.seed) + + data = load_datasets(params.data) + + # TRAIN/VAL/TEST SPLIT + if params.split == 'subjects': # by SUBJECTS + test_subjects = (3, 4, 19, 38, 45, 46, 51, 52) + test_data = data[data['sub'].isin(test_subjects)] + elif params.split == 'random': # 70-20-10 % + _, valtest_data = train_test_split(data, test_size=.3, shuffle=True) + _, test_data = train_test_split(valtest_data, test_size=.33) + + x_shape = (params.resolution, params.resolution, 1) + test_gen, test_categories = get_loader(test_data, batch_size=1, x_shape=x_shape) + + prediction_dir = exp.path_to('test_pred') + os.makedirs(prediction_dir, exist_ok=True) + + loss_per_sample = None + + def _get_test_predictions(test_gen, model): + x_masks = [] + y_masks, y_tags = [], [] + pred_masks, pred_tags = [], [] + loss_per_sample = [] + + for x, y in tqdm(test_gen, desc='TEST'): + try: + # Generate predictions first + p_mask, p_tags = model.predict_on_batch(x) + + # Append predictions and ground truth + pred_masks.append(p_mask) + pred_tags.append(p_tags) + y_masks.append(y['mask'].numpy()) + y_tags.append(y['tags'].numpy()) + x_masks.append(x.numpy()) + + # Calculate losses manually if needed + sample_loss = model.compiled_loss( + {'mask': y['mask'], 'tags': y['tags']}, + {'mask': p_mask, 'tags': p_tags} + ) + loss_per_sample.append(sample_loss.numpy()) + + except Exception as e: + print(f"Error processing batch: {str(e)}") + continue + + # Check if we have any successful predictions + if not pred_masks or not pred_tags or not y_masks or not y_tags or not x_masks: + raise ValueError("No predictions were collected - all batches failed") + + try: + loss_per_sample = np.array(loss_per_sample) + pred_masks = np.concatenate(pred_masks) + pred_tags = np.concatenate(pred_tags) + y_masks = np.concatenate(y_masks) + y_tags = np.concatenate(y_tags) + x_masks = np.concatenate(x_masks) + except Exception as e: + print(f"Error concatenating results: {str(e)}") + raise + + return loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags + + mask_metrics_path = exp.path_to('test_pred/mask_metrics.csv') + if force or not os.path.exists(mask_metrics_path): + if loss_per_sample is None: + loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) + + thrs = np.linspace(0, 1, 101) + ious = [iou_coef(y_masks, pred_masks).numpy() for thr in thrs] + dices = [dice_coef(y_masks, pred_masks).numpy() for thr in thrs] + + best_thr = max(zip(dices, thrs))[1] + + mask_metrics = pd.DataFrame({'iou': ious, 'dice': dices, 'thr': thrs}) + print(mask_metrics.max(axis=0)) + mask_metrics.to_csv(mask_metrics_path) + else: + mask_metrics = pd.read_csv(mask_metrics_path, index_col=0) + best_thr = mask_metrics.loc[mask_metrics.dice.idxmax(), 'thr'] + + if force: + if loss_per_sample is None: + loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) + _weighted_roc_pr(y_tags[:, 0], pred_tags[:, 0], 'all_eye', prediction_dir) + _weighted_roc_pr(y_tags[:, 1], pred_tags[:, 1], 'all_blink', prediction_dir) + + filenames = ('top_samples.png', 'bottom_samples.png', 'random_samples.png') + if force or any(not os.path.exists(os.path.join(prediction_dir, f)) for f in filenames): + if loss_per_sample is None: + loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) + + k = 5 + best_selector = [] + worst_selector = [] + random_selector = [] + + idx = np.arange(len(test_data)) + for cat in np.unique(test_categories): + cat_outdir = os.path.join(prediction_dir, cat) + os.makedirs(cat_outdir, exist_ok=True) + + selector = test_categories == cat + _weighted_roc_pr(y_tags[selector, 0], pred_tags[selector, 0], '{}_eye'.format(cat), cat_outdir) + _weighted_roc_pr(y_tags[selector, 1], pred_tags[selector, 1], '{}_blink'.format(cat), cat_outdir) + + # Fix the indexing here - loss_per_sample is 1D + cat_losses = loss_per_sample[selector] + rank = cat_losses.argsort() + topk, bottomk = rank[:k], rank[-k:] + + best_selector += idx[selector][topk].tolist() + worst_selector += idx[selector][bottomk].tolist() + random_selector += np.random.choice(idx[selector], k, replace=False).tolist() + +# topk-bottomk images + selectors = (best_selector, worst_selector, random_selector) + for selector, outfile in zip(selectors, filenames): + combined_m = np.concatenate((pred_masks[selector], y_masks[selector]), axis=-1)[:, :, :, ::-1] + combined_t = np.concatenate((pred_tags[selector], y_tags[selector]), axis=-1) + combined_y = (combined_m, combined_t) + out = os.path.join(prediction_dir, outfile) + visualize(x_masks[selector], combined_y, out=out, thr=best_thr, n_cols=k, width=10) + + for i, (xi, yi_mask) in enumerate(zip(x_masks[selector], combined_m)): + img = visualizable(xi, yi_mask, thr=best_thr) + img = (img * 255).astype(np.uint8) + out = os.path.join(prediction_dir, outfile[:-4]) + os.makedirs(out, exist_ok=True) + out = os.path.join(out, f'{i}.png') + Image.fromarray(img).save(out) + +def main(args): + try: + for exp in expman.gather(args.run).filter(args.filter): + print(exp) + evaluate(exp, force=args.force) + except Exception as e: + print(f"Error in main: {str(e)}") + raise + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Evaluate Run') + parser.add_argument('run', help='Run(s) directory') + parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) + parser.add_argument('--force', default=False, action='store_true', help='Force metrics recomputation') + + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/models/__pycache__/unet.cpython-311.pyc b/models/__pycache__/unet.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..70fb25790c6c0c39be100213aa5abaffaaae3c36 Binary files /dev/null and b/models/__pycache__/unet.cpython-311.pyc differ diff --git a/models/deeplab/inference/tflite/stb_image.h b/models/deeplab/inference/tflite/stb_image.h new file mode 100644 index 0000000000000000000000000000000000000000..a6202a31fb0fd41cff3d2f20370b10460ba7166a --- /dev/null +++ b/models/deeplab/inference/tflite/stb_image.h @@ -0,0 +1,7547 @@ +/* stb_image - v2.22 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk + + Do this: + #define STB_IMAGE_IMPLEMENTATION + before you include this file in *one* C or C++ file to create the implementation. + + // i.e. it should look like this: + #include ... + #include ... + #include ... + #define STB_IMAGE_IMPLEMENTATION + #include "stb_image.h" + + You can #define STBI_ASSERT(x) before the #include to avoid using assert.h. + And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free + + + QUICK NOTES: + Primarily of interest to game developers and other people who can + avoid problematic images and only need the trivial interface + + JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) + PNG 1/2/4/8/16-bit-per-channel + + TGA (not sure what subset, if a subset) + BMP non-1bpp, non-RLE + PSD (composited view only, no extra channels, 8/16 bit-per-channel) + + GIF (*comp always reports as 4-channel) + HDR (radiance rgbE format) + PIC (Softimage PIC) + PNM (PPM and PGM binary only) + + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) + - decode from arbitrary I/O callbacks + - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) + + Full documentation under "DOCUMENTATION" below. + + +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.22 (2019-03-04) gif fixes, fix warnings + 2.21 (2019-02-25) fix typo in comment + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED + + See end of file for full revision history. + + + ============================ Contributors ========================= + + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + Carmelo J Fdez-Aguera + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt JR Smith github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 +*/ + +#ifndef STBI_INCLUDE_STB_IMAGE_H +#define STBI_INCLUDE_STB_IMAGE_H + +// DOCUMENTATION +// +// Limitations: +// - no 12-bit-per-channel JPEG +// - no JPEGs with arithmetic coding +// - GIF always returns *comp=4 +// +// Basic usage (see HDR discussion below for HDR usage): +// int x,y,n; +// unsigned char *data = stbi_load(filename, &x, &y, &n, 0); +// // ... process data if not NULL ... +// // ... x = width, y = height, n = # 8-bit components per pixel ... +// // ... replace '0' with '1'..'4' to force that many components per pixel +// // ... but 'n' will always be the number that it would have been if you said 0 +// stbi_image_free(data) +// +// Standard parameters: +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result +// +// The return value from an image loader is an 'unsigned char *' which points +// to the pixel data, or NULL on an allocation failure or if the image is +// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels, +// with each pixel consisting of N interleaved 8-bit components; the first +// pixel pointed to is top-left-most in the image. There is no padding between +// image scanlines or between pixels, regardless of format. The number of +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. +// +// An output image with N components has the following components interleaved +// in this order in each pixel: +// +// N=#comp components +// 1 grey +// 2 grey, alpha +// 3 red, green, blue +// 4 red, green, blue, alpha +// +// If image loading fails for any reason, the return value will be NULL, +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// more user-friendly ones. +// +// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. +// +// =========================================================================== +// +// UNICODE: +// +// If compiling for Windows and you wish to use Unicode filenames, compile +// with +// #define STBI_WINDOWS_UTF8 +// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert +// Windows wchar_t filenames to utf8. +// +// =========================================================================== +// +// Philosophy +// +// stb libraries are designed with the following priorities: +// +// 1. easy to use +// 2. easy to maintain +// 3. good performance +// +// Sometimes I let "good performance" creep up in priority over "easy to maintain", +// and for best performance I may provide less-easy-to-use APIs that give higher +// performance, in addition to the easy-to-use ones. Nevertheless, it's important +// to keep in mind that from the standpoint of you, a client of this library, +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. +// +// Some secondary priorities arise directly from the first two, some of which +// provide more explicit reasons why performance can't be emphasized. +// +// - Portable ("ease of use") +// - Small source code footprint ("easy to maintain") +// - No dependencies ("ease of use") +// +// =========================================================================== +// +// I/O callbacks +// +// I/O callbacks allow you to read from arbitrary sources, like packaged +// files or some other source. Data read from callbacks are processed +// through a small internal buffer (currently 128 bytes) to try to reduce +// overhead. +// +// The three functions you must define are "read" (reads some bytes of data), +// "skip" (skips some bytes of data), "eof" (reports if the stream is at the end). +// +// =========================================================================== +// +// SIMD support +// +// The JPEG decoder will try to automatically use SIMD kernels on x86 when +// supported by the compiler. For ARM Neon support, you must explicitly +// request it. +// +// (The old do-it-yourself SIMD API is no longer supported in the current +// code.) +// +// On x86, SSE2 will automatically be used when available based on a run-time +// test; if not, the generic C versions are used as a fall-back. On ARM targets, +// the typical path is to have separate builds for NEON and non-NEON devices +// (at least this is true for iOS and Android). Therefore, the NEON support is +// toggled by a build flag: define STBI_NEON to get NEON loops. +// +// If for some reason you do not want to use any of SIMD code, or if +// you have issues compiling it, you can disable it entirely by +// defining STBI_NO_SIMD. +// +// =========================================================================== +// +// HDR image support (disable by defining STBI_NO_HDR) +// +// stb_image supports loading HDR images in general, and currently the Radiance +// .HDR file format specifically. You can still load any file through the existing +// interface; if you attempt to load an HDR file, it will be automatically remapped +// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// both of these constants can be reconfigured through this interface: +// +// stbi_hdr_to_ldr_gamma(2.2f); +// stbi_hdr_to_ldr_scale(1.0f); +// +// (note, do not use _inverse_ constants; stbi_image will invert them +// appropriately). +// +// Additionally, there is a new, parallel interface for loading files as +// (linear) floats to preserve the full dynamic range: +// +// float *data = stbi_loadf(filename, &x, &y, &n, 0); +// +// If you load LDR images through this interface, those images will +// be promoted to floating point values, run through the inverse of +// constants corresponding to the above: +// +// stbi_ldr_to_hdr_scale(1.0f); +// stbi_ldr_to_hdr_gamma(2.2f); +// +// Finally, given a filename (or an open file or memory block--see header +// file for details) containing image data, you can query for the "most +// appropriate" interface to use (that is, whether the image is HDR or +// not), using: +// +// stbi_is_hdr(char *filename); +// +// =========================================================================== +// +// iPhone PNG support: +// +// By default we convert iphone-formatted PNGs back to RGB, even though +// they are internally encoded differently. You can disable this conversion +// by calling stbi_convert_iphone_png_to_rgb(0), in which case +// you will always just get the native iphone "format" through (which +// is BGR stored in RGB). +// +// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per +// pixel to remove any premultiplied alpha *only* if the image file explicitly +// says there's premultiplied data (currently only happens in iPhone images, +// and only if iPhone convert-to-rgb processing is on). +// +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// + + +#ifndef STBI_NO_STDIO +#include +#endif // STBI_NO_STDIO + +#define STBI_VERSION 1 + +enum +{ + STBI_default = 0, // only used for desired_channels + + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 +}; + +#include +typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef STBIDEF +#ifdef STB_IMAGE_STATIC +#define STBIDEF static +#else +#define STBIDEF extern +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// PRIMARY API - works on images of any type +// + +// +// load image by filename, open file, or memory buffer +// + +typedef struct +{ + int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void *user); // returns nonzero if we are at end of file/data +} stbi_io_callbacks; + +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +// for stbi_load_from_file, file pointer is left pointing immediately after image +#endif + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + +#ifdef STBI_WINDOWS_UTF8 +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif + +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// +#ifndef STBI_NO_LINEAR + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + + #ifndef STBI_NO_STDIO + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); + #endif +#endif + +#ifndef STBI_NO_HDR + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); +#endif // STBI_NO_HDR + +#ifndef STBI_NO_LINEAR + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); +#endif // STBI_NO_LINEAR + +// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename); +STBIDEF int stbi_is_hdr_from_file(FILE *f); +#endif // STBI_NO_STDIO + + +// get a VERY brief reason for failure +// NOT THREADSAFE +STBIDEF const char *stbi_failure_reason (void); + +// free the loaded image -- this is just free() +STBIDEF void stbi_image_free (void *retval_from_stbi_load); + +// get image dimensions & components without fully decoding +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); +#endif + + + +// for image formats that explicitly notate that they have premultiplied alpha, +// we just return the colors as stored in the file. set this flag to force +// unpremultiplication. results are undefined if the unpremultiply overflow. +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); + +// indicate whether we should process iphone images back to canonical format, +// or just pass them through "as-is" +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); + +// flip the image vertically, so the first pixel in the output array is the bottom left +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); + +// ZLIB client - used by PNG, available for other purposes + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); +STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + +STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + + +#ifdef __cplusplus +} +#endif + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBI_INCLUDE_STB_IMAGE_H + +#ifdef STB_IMAGE_IMPLEMENTATION + +#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \ + || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ + || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ + || defined(STBI_ONLY_ZLIB) + #ifndef STBI_ONLY_JPEG + #define STBI_NO_JPEG + #endif + #ifndef STBI_ONLY_PNG + #define STBI_NO_PNG + #endif + #ifndef STBI_ONLY_BMP + #define STBI_NO_BMP + #endif + #ifndef STBI_ONLY_PSD + #define STBI_NO_PSD + #endif + #ifndef STBI_ONLY_TGA + #define STBI_NO_TGA + #endif + #ifndef STBI_ONLY_GIF + #define STBI_NO_GIF + #endif + #ifndef STBI_ONLY_HDR + #define STBI_NO_HDR + #endif + #ifndef STBI_ONLY_PIC + #define STBI_NO_PIC + #endif + #ifndef STBI_ONLY_PNM + #define STBI_NO_PNM + #endif +#endif + +#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) +#define STBI_NO_ZLIB +#endif + + +#include +#include // ptrdiff_t on osx +#include +#include +#include + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#include // ldexp, pow +#endif + +#ifndef STBI_NO_STDIO +#include +#endif + +#ifndef STBI_ASSERT +#include +#define STBI_ASSERT(x) assert(x) +#endif + +#ifdef __cplusplus +#define STBI_EXTERN extern "C" +#else +#define STBI_EXTERN extern +#endif + + +#ifndef _MSC_VER + #ifdef __cplusplus + #define stbi_inline inline + #else + #define stbi_inline + #endif +#else + #define stbi_inline __forceinline +#endif + + +#ifdef _MSC_VER +typedef unsigned short stbi__uint16; +typedef signed short stbi__int16; +typedef unsigned int stbi__uint32; +typedef signed int stbi__int32; +#else +#include +typedef uint16_t stbi__uint16; +typedef int16_t stbi__int16; +typedef uint32_t stbi__uint32; +typedef int32_t stbi__int32; +#endif + +// should produce compiler error if size is wrong +typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBI_NOTUSED(v) (void)(v) +#else +#define STBI_NOTUSED(v) (void)sizeof(v) +#endif + +#ifdef _MSC_VER +#define STBI_HAS_LROTL +#endif + +#ifdef STBI_HAS_LROTL + #define stbi_lrot(x,y) _lrotl(x,y) +#else + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) +#endif + +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) +// ok +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." +#endif + +#ifndef STBI_MALLOC +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) +#endif + +// x86/x64 detection +#if defined(__x86_64__) || defined(_M_X64) +#define STBI__X64_TARGET +#elif defined(__i386) || defined(_M_IX86) +#define STBI__X86_TARGET +#endif + +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) +// gcc doesn't support sse2 intrinsics unless you compile with -msse2, +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. +#define STBI_NO_SIMD +#endif + +#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD) +// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET +// +// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the +// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant. +// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not +// simultaneously enabling "-mstackrealign". +// +// See https://github.com/nothings/stb/issues/81 for more information. +// +// So default to no SSE2 on 32-bit MinGW. If you've read this far and added +// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2. +#define STBI_NO_SIMD +#endif + +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) +#define STBI_SSE2 +#include + +#ifdef _MSC_VER + +#if _MSC_VER >= 1400 // not VC6 +#include // __cpuid +static int stbi__cpuid3(void) +{ + int info[4]; + __cpuid(info,1); + return info[3]; +} +#else +static int stbi__cpuid3(void) +{ + int res; + __asm { + mov eax,1 + cpuid + mov res,edx + } + return res; +} +#endif + +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; +} +#endif + +#else // assume GCC-style if not VC++ +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; +} +#endif + +#endif +#endif + +// ARM NEON +#if defined(STBI_NO_SIMD) && defined(STBI_NEON) +#undef STBI_NEON +#endif + +#ifdef STBI_NEON +#include +// assume GCC or Clang on ARM targets +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) +#endif + +#ifndef STBI_SIMD_ALIGN +#define STBI_SIMD_ALIGN(type, name) type name +#endif + +/////////////////////////////////////////////// +// +// stbi__context struct and start_xxx functions + +// stbi__context structure is our basic context used by all images, so it +// contains all the IO context, plus some basic image information +typedef struct +{ + stbi__uint32 img_x, img_y; + int img_n, img_out_n; + + stbi_io_callbacks io; + void *io_user_data; + + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + + stbi_uc *img_buffer, *img_buffer_end; + stbi_uc *img_buffer_original, *img_buffer_original_end; +} stbi__context; + + +static void stbi__refill_buffer(stbi__context *s); + +// initialize a memory-decode context +static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +{ + s->io.read = NULL; + s->read_from_callbacks = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; +} + +// initialize a callback-based context +static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +{ + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; +} + +#ifndef STBI_NO_STDIO + +static int stbi__stdio_read(void *user, char *data, int size) +{ + return (int) fread(data,1,size,(FILE*) user); +} + +static void stbi__stdio_skip(void *user, int n) +{ + fseek((FILE*) user, n, SEEK_CUR); +} + +static int stbi__stdio_eof(void *user) +{ + return feof((FILE*) user); +} + +static stbi_io_callbacks stbi__stdio_callbacks = +{ + stbi__stdio_read, + stbi__stdio_skip, + stbi__stdio_eof, +}; + +static void stbi__start_file(stbi__context *s, FILE *f) +{ + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); +} + +//static void stop_file(stbi__context *s) { } + +#endif // !STBI_NO_STDIO + +static void stbi__rewind(stbi__context *s) +{ + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; +} + +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + +#ifndef STBI_NO_JPEG +static int stbi__jpeg_test(stbi__context *s); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNG +static int stbi__png_test(stbi__context *s); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_BMP +static int stbi__bmp_test(stbi__context *s); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_TGA +static int stbi__tga_test(stbi__context *s); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_HDR +static int stbi__hdr_test(stbi__context *s); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_test(stbi__context *s); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_GIF +static int stbi__gif_test(stbi__context *s); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNM +static int stbi__pnm_test(stbi__context *s); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +// this is not threadsafe +static const char *stbi__g_failure_reason; + +STBIDEF const char *stbi_failure_reason(void) +{ + return stbi__g_failure_reason; +} + +static int stbi__err(const char *str) +{ + stbi__g_failure_reason = str; + return 0; +} + +static void *stbi__malloc(size_t size) +{ + return STBI_MALLOC(size); +} + +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + +// stbi__err - error +// stbi__errpf - error returning pointer to float +// stbi__errpuc - error returning pointer to unsigned char + +#ifdef STBI_NO_FAILURE_STRINGS + #define stbi__err(x,y) 0 +#elif defined(STBI_FAILURE_USERMSG) + #define stbi__err(x,y) stbi__err(y) +#else + #define stbi__err(x,y) stbi__err(x) +#endif + +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) + +STBIDEF void stbi_image_free(void *retval_from_stbi_load) +{ + STBI_FREE(retval_from_stbi_load); +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); +#endif + +#ifndef STBI_NO_HDR +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); +#endif + +static int stbi__vertically_flip_on_load = 0; + +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load = flag_true_if_should_flip; +} + +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #endif + #ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } + #endif + + #ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s,x,y,comp,req_comp, ri); + #endif + + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); +} + +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi_uc *reduced; + + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } +} + +#ifndef STBI_NO_GIF +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} +#endif + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; +} + +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) +static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +{ + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } +} +#endif + +#ifndef STBI_NO_STDIO + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); +#endif + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbi__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + + +STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + unsigned char *result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; +} +#endif + +#ifndef STBI_NO_LINEAR +static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *data; + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data,x,y,comp,req_comp); + return hdr_data; + } + #endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +} + +STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_STDIO +STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + float *result; + FILE *f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s,f); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} +#endif // !STBI_NO_STDIO + +#endif // !STBI_NO_LINEAR + +// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is +// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always +// reports false! + +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; + #endif +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result=0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; +} + +STBIDEF int stbi_is_hdr_from_file(FILE *f) +{ + #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s,f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; + #else + STBI_NOTUSED(f); + return 0; + #endif +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; + #endif +} + +#ifndef STBI_NO_LINEAR +static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; + +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +#endif + +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } + + +////////////////////////////////////////////////////////////////////////////// +// +// Common code used by all image loaders +// + +enum +{ + STBI__SCAN_load=0, + STBI__SCAN_type, + STBI__SCAN_header +}; + +static void stbi__refill_buffer(stbi__context *s) +{ + int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start+1; + *s->img_buffer = 0; + } else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } +} + +stbi_inline static stbi_uc stbi__get8(stbi__context *s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; +} + +stbi_inline static int stbi__at_eof(stbi__context *s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } + + return s->img_buffer >= s->img_buffer_end; +} + +static void stbi__skip(stbi__context *s, int n) +{ + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; +} + +static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +{ + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; + + memcpy(buffer, s->img_buffer, blen); + + count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); + res = (count == (n-blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } + + if (s->img_buffer+n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } else + return 0; +} + +static int stbi__get16be(stbi__context *s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); +} + +static stbi__uint32 stbi__get32be(stbi__context *s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); +} + +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else +static int stbi__get16le(stbi__context *s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); +} +#endif + +#ifndef STBI_NO_BMP +static stbi__uint32 stbi__get32le(stbi__context *s) +{ + stbi__uint32 z = stbi__get16le(s); + return z + (stbi__get16le(s) << 16); +} +#endif + +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings + + +////////////////////////////////////////////////////////////////////////////// +// +// generic converter from built-in img_n to req_comp +// individual types do this automatically as much as possible (e.g. jpeg +// does all cases internally since it needs to colorspace convert anyway, +// and it never has alpha, so very few cases ). png can automatically +// interleave an alpha=255 channel, but falls back to this for other cases +// +// assume data buffer is malloced, so malloc a new one and free that one +// only failure mode is malloc failing + +static stbi_uc stbi__compute_y(int r, int g, int b) +{ + return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + unsigned char *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + unsigned char *src = data + j * x * img_n ; + unsigned char *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) +{ + int i,k,n; + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + } + if (n < comp) { + for (i=0; i < x*y; ++i) { + output[i*comp + n] = data[i*comp + n]/255.0f; + } + } + STBI_FREE(data); + return output; +} +#endif + +#ifndef STBI_NO_HDR +#define stbi__float2int(x) ((int) (x)) +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) +{ + int i,k,n; + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + if (k < comp) { + float z = data[i*comp+k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + } + STBI_FREE(data); + return output; +} +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// "baseline" JPEG/JFIF decoder +// +// simple implementation +// - doesn't support delayed output of y-dimension +// - simple interface (only one output format: 8-bit interleaved RGB) +// - doesn't try to recover corrupt jpegs +// - doesn't allow partial loading, loading multiple at once +// - still fast on x86 (copying globals into locals doesn't help x86) +// - allocates lots of intermediate memory (full size of all components) +// - non-interleaved case requires this anyway +// - allows good upsampling (see next) +// high-quality +// - upsampled channels are bilinearly interpolated, even across blocks +// - quality integer IDCT derived from IJG's 'slow' +// performance +// - fast huffman; reasonable integer IDCT +// - some SIMD kernels for common paths on targets with SSE2/NEON +// - uses a lot of intermediate memory, could cache poorly + +#ifndef STBI_NO_JPEG + +// huffman decoding acceleration +#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache + +typedef struct +{ + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' +} stbi__huffman; + +typedef struct +{ + stbi__context *s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; + +// sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; + +// definition of jpeg image component + struct + { + int id; + int h,v; + int tq; + int hd,ha; + int dc_pred; + + int x,y,w2,h2; + stbi_uc *data; + void *raw_data, *raw_coeff; + stbi_uc *linebuf; + short *coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; + + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop + + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; + + int scan_n, order[4]; + int restart_interval, todo; + +// kernels + void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); + stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); +} stbi__jpeg; + +static int stbi__build_huffman(stbi__huffman *h, int *count) +{ + int i,j,k=0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i=0; i < 16; ++i) + for (j=0; j < count[i]; ++j) + h->size[k++] = (stbi_uc) (i+1); + h->size[k] = 0; + + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for(j=1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16) (code++); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16-j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; + + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i=0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS-s); + int m = 1 << (FAST_BITS-s); + for (j=0; j < m; ++j) { + h->fast[c+j] = (stbi_uc) i; + } + } + } + return 1; +} + +// build a table that decodes both magnitude and value of small ACs in +// one go. +static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) +{ + int i; + for (i=0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; + + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); + } + } + } +} + +static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +{ + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char) c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); +} + +// (1 << n) - 1 +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; + +// decode a jpeg huffman value from the bitstream +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) +{ + unsigned int temp; + int c,k; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } + + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k=FAST_BITS+1 ; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } + + if (k > j->code_bits) + return -1; + + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; +} + +// bias[n] = (-1<code_bits < n) stbi__grow_buffer_unsafe(j); + + sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB + k = stbi_lrot(j->code_buffer, n); + STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & ~sgn); +} + +// get some unsigned bits +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +{ + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; +} + +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +{ + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; +} + +// given a value that's at position X in the zigzag stream, +// where does it appear in the 8x8 matrix coded as row-major? +static const stbi_uc stbi__jpeg_dezigzag[64+15] = +{ + 0, 1, 8, 16, 9, 2, 3, 10, + 17, 24, 32, 25, 18, 11, 4, 5, + 12, 19, 26, 33, 40, 48, 41, 34, + 27, 20, 13, 6, 7, 14, 21, 28, + 35, 42, 49, 56, 57, 50, 43, 36, + 29, 22, 15, 23, 30, 37, 44, 51, + 58, 59, 52, 45, 38, 31, 39, 46, + 53, 60, 61, 54, 47, 55, 62, 63, + // let corrupt input sample past end + 63, 63, 63, 63, 63, 63, 63, 63, + 63, 63, 63, 63, 63, 63, 63 +}; + +// decode one 64-entry block-- +static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant) +{ + int diff,dc,k; + int t; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + + // 0 all the ac values now so we can do it 32-bits at a time + memset(data,0,64*sizeof(data[0])); + + diff = t ? stbi__extend_receive(j, t) : 0; + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc * dequant[0]); + + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * dequant[zig]); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } else { + k += r; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); + } + } + } while (k < 64); + return 1; +} + +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +{ + int diff,dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data,0,64*sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + diff = t ? stbi__extend_receive(j, t) : 0; + + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc << j->succ_low); + } else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short) (1 << j->succ_low); + } + return 1; +} + +// @OPTIMIZE: store non-zigzagged during the decode passes, +// and only de-zigzag when dequantizing +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +{ + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->succ_high == 0) { + int shift = j->succ_low; + + if (j->eob_run) { + --j->eob_run; + return 1; + } + + k = j->spec_start; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) << shift); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; + } else { + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) << shift); + } + } + } while (k <= j->spec_end); + } else { + // refinement scan for these AC coefficients + + short bit = (short) (1 << j->succ_low); + + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short *p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + } else { + k = j->spec_start; + do { + int r,s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; + } + + // advance by r + while (k <= j->spec_end) { + short *p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } else { + if (r == 0) { + *p = (short) s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; +} + +// take a -128..127 value and stbi__clamp it and convert to 0..255 +stbi_inline static stbi_uc stbi__clamp(int x) +{ + // trick to use a single test to catch both cases + if ((unsigned int) x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc) x; +} + +#define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) +#define stbi__fsh(x) ((x) * 4096) + +// derived from jidctint -- DCT_ISLOW +#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ + int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \ + p2 = s2; \ + p3 = s6; \ + p1 = (p2+p3) * stbi__f2f(0.5411961f); \ + t2 = p1 + p3*stbi__f2f(-1.847759065f); \ + t3 = p1 + p2*stbi__f2f( 0.765366865f); \ + p2 = s0; \ + p3 = s4; \ + t0 = stbi__fsh(p2+p3); \ + t1 = stbi__fsh(p2-p3); \ + x0 = t0+t3; \ + x3 = t0-t3; \ + x1 = t1+t2; \ + x2 = t1-t2; \ + t0 = s7; \ + t1 = s5; \ + t2 = s3; \ + t3 = s1; \ + p3 = t0+t2; \ + p4 = t1+t3; \ + p1 = t0+t3; \ + p2 = t1+t2; \ + p5 = (p3+p4)*stbi__f2f( 1.175875602f); \ + t0 = t0*stbi__f2f( 0.298631336f); \ + t1 = t1*stbi__f2f( 2.053119869f); \ + t2 = t2*stbi__f2f( 3.072711026f); \ + t3 = t3*stbi__f2f( 1.501321110f); \ + p1 = p5 + p1*stbi__f2f(-0.899976223f); \ + p2 = p5 + p2*stbi__f2f(-2.562915447f); \ + p3 = p3*stbi__f2f(-1.961570560f); \ + p4 = p4*stbi__f2f(-0.390180644f); \ + t3 += p1+p4; \ + t2 += p2+p3; \ + t1 += p2+p4; \ + t0 += p1+p3; + +static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) +{ + int i,val[64],*v=val; + stbi_uc *o; + short *d = data; + + // columns + for (i=0; i < 8; ++i,++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 + && d[40]==0 && d[48]==0 && d[56]==0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0]*4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } else { + STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[ 0] = (x0+t3) >> 10; + v[56] = (x0-t3) >> 10; + v[ 8] = (x1+t2) >> 10; + v[48] = (x1-t2) >> 10; + v[16] = (x2+t1) >> 10; + v[40] = (x2-t1) >> 10; + v[24] = (x3+t0) >> 10; + v[32] = (x3-t0) >> 10; + } + } + + for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128<<17); + x1 += 65536 + (128<<17); + x2 += 65536 + (128<<17); + x3 += 65536 + (128<<17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0+t3) >> 17); + o[7] = stbi__clamp((x0-t3) >> 17); + o[1] = stbi__clamp((x1+t2) >> 17); + o[6] = stbi__clamp((x1-t2) >> 17); + o[2] = stbi__clamp((x2+t1) >> 17); + o[5] = stbi__clamp((x2-t1) >> 17); + o[3] = stbi__clamp((x3+t0) >> 17); + o[4] = stbi__clamp((x3-t0) >> 17); + } +} + +#ifdef STBI_SSE2 +// sse2 integer IDCT. not the fastest possible implementation but it +// produces bit-identical results to the generic C version so it's +// fully "transparent". +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; + + // dot product constant: even elems=x, odd elems=y + #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) + + // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) + // out(1) = c1[even]*x + c1[odd]*y + #define dct_rot(out0,out1, x,y,c0,c1) \ + __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ + __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ + __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ + __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ + __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ + __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) + + // out = in << 12 (in 16-bit, out 32-bit) + #define dct_widen(out, in) \ + __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ + __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) + + // wide add + #define dct_wadd(out, a, b) \ + __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_add_epi32(a##_h, b##_h) + + // wide sub + #define dct_wsub(out, a, b) \ + __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) + + // butterfly a/b, add bias, then shift by "s" and pack + #define dct_bfly32o(out0, out1, a,b,bias,s) \ + { \ + __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ + __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ + dct_wadd(sum, abiased, b); \ + dct_wsub(dif, abiased, b); \ + out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ + out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ + } + + // 8-bit interleave step (for transposes) + #define dct_interleave8(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi8(a, b); \ + b = _mm_unpackhi_epi8(tmp, b) + + // 16-bit interleave step (for transposes) + #define dct_interleave16(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi16(a, b); \ + b = _mm_unpackhi_epi16(tmp, b) + + #define dct_pass(bias,shift) \ + { \ + /* even part */ \ + dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ + __m128i sum04 = _mm_add_epi16(row0, row4); \ + __m128i dif04 = _mm_sub_epi16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \ + dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \ + __m128i sum17 = _mm_add_epi16(row1, row7); \ + __m128i sum35 = _mm_add_epi16(row3, row5); \ + dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \ + dct_wadd(x4, y0o, y4o); \ + dct_wadd(x5, y1o, y5o); \ + dct_wadd(x6, y2o, y5o); \ + dct_wadd(x7, y3o, y4o); \ + dct_bfly32o(row0,row7, x0,x7,bias,shift); \ + dct_bfly32o(row1,row6, x1,x6,bias,shift); \ + dct_bfly32o(row2,row5, x2,x5,bias,shift); \ + dct_bfly32o(row3,row4, x3,x4,bias,shift); \ + } + + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); + + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); + + // load + row0 = _mm_load_si128((const __m128i *) (data + 0*8)); + row1 = _mm_load_si128((const __m128i *) (data + 1*8)); + row2 = _mm_load_si128((const __m128i *) (data + 2*8)); + row3 = _mm_load_si128((const __m128i *) (data + 3*8)); + row4 = _mm_load_si128((const __m128i *) (data + 4*8)); + row5 = _mm_load_si128((const __m128i *) (data + 5*8)); + row6 = _mm_load_si128((const __m128i *) (data + 6*8)); + row7 = _mm_load_si128((const __m128i *) (data + 7*8)); + + // column pass + dct_pass(bias_0, 10); + + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); + + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); + + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } + + // row pass + dct_pass(bias_1, 17); + + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); + + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... + + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... + + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... + + // store + _mm_storel_epi64((__m128i *) out, p0); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p2); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p1); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p3); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); + } + +#undef dct_const +#undef dct_rot +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_interleave8 +#undef dct_interleave16 +#undef dct_pass +} + +#endif // STBI_SSE2 + +#ifdef STBI_NEON + +// NEON integer IDCT. should produce bit-identical +// results to the generic C version. +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; + + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); + +#define dct_long_mul(out, inq, coeff) \ + int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) + +#define dct_long_mac(out, acc, inq, coeff) \ + int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) + +#define dct_widen(out, inq) \ + int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ + int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) + +// wide add +#define dct_wadd(out, a, b) \ + int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vaddq_s32(a##_h, b##_h) + +// wide sub +#define dct_wsub(out, a, b) \ + int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vsubq_s32(a##_h, b##_h) + +// butterfly a/b, then shift using "shiftop" by "s" and pack +#define dct_bfly32o(out0,out1, a,b,shiftop,s) \ + { \ + dct_wadd(sum, a, b); \ + dct_wsub(dif, a, b); \ + out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ + out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ + } + +#define dct_pass(shiftop, shift) \ + { \ + /* even part */ \ + int16x8_t sum26 = vaddq_s16(row2, row6); \ + dct_long_mul(p1e, sum26, rot0_0); \ + dct_long_mac(t2e, p1e, row6, rot0_1); \ + dct_long_mac(t3e, p1e, row2, rot0_2); \ + int16x8_t sum04 = vaddq_s16(row0, row4); \ + int16x8_t dif04 = vsubq_s16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + int16x8_t sum15 = vaddq_s16(row1, row5); \ + int16x8_t sum17 = vaddq_s16(row1, row7); \ + int16x8_t sum35 = vaddq_s16(row3, row5); \ + int16x8_t sum37 = vaddq_s16(row3, row7); \ + int16x8_t sumodd = vaddq_s16(sum17, sum35); \ + dct_long_mul(p5o, sumodd, rot1_0); \ + dct_long_mac(p1o, p5o, sum17, rot1_1); \ + dct_long_mac(p2o, p5o, sum35, rot1_2); \ + dct_long_mul(p3o, sum37, rot2_0); \ + dct_long_mul(p4o, sum15, rot2_1); \ + dct_wadd(sump13o, p1o, p3o); \ + dct_wadd(sump24o, p2o, p4o); \ + dct_wadd(sump23o, p2o, p3o); \ + dct_wadd(sump14o, p1o, p4o); \ + dct_long_mac(x4, sump13o, row7, rot3_0); \ + dct_long_mac(x5, sump24o, row5, rot3_1); \ + dct_long_mac(x6, sump23o, row3, rot3_2); \ + dct_long_mac(x7, sump14o, row1, rot3_3); \ + dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \ + dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \ + dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \ + dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \ + } + + // load + row0 = vld1q_s16(data + 0*8); + row1 = vld1q_s16(data + 1*8); + row2 = vld1q_s16(data + 2*8); + row3 = vld1q_s16(data + 3*8); + row4 = vld1q_s16(data + 4*8); + row5 = vld1q_s16(data + 5*8); + row6 = vld1q_s16(data + 6*8); + row7 = vld1q_s16(data + 7*8); + + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + + // column pass + dct_pass(vrshrn_n_s32, 10); + + // 16bit 8x8 transpose + { +// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. +// whether compilers actually get this is another story, sadly. +#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } +#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } + + // pass 1 + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); + + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); + + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); + +#undef dct_trn16 +#undef dct_trn32 +#undef dct_trn64 + } + + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); + + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + + // again, these can translate into one instruction, but often don't. +#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } +#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } + + // sadly can't use interleaved stores here since we only write + // 8 bytes to each scan line! + + // 8x8 8-bit transpose pass 1 + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); + + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); + + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); + + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); + +#undef dct_trn8_8 +#undef dct_trn8_16 +#undef dct_trn8_32 + } + +#undef dct_long_mul +#undef dct_long_mac +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_pass +} + +#endif // STBI_NEON + +#define STBI__MARKER_none 0xff +// if there's a pending marker from the entropy stream, return that +// otherwise, fetch from the stream and get a marker. if there's no +// marker, return 0xff, which is never a valid marker value +static stbi_uc stbi__get_marker(stbi__jpeg *j) +{ + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; +} + +// in each scan, we'll have scan_n components, and the order +// of the components is specified by order[] +#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) + +// after a restart interval, stbi__jpeg_reset the entropy decoder and +// the dc prediction +static void stbi__jpeg_reset(stbi__jpeg *j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels +} + +static int stbi__parse_entropy_coded_data(stbi__jpeg *z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i,j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + STBI_SIMD_ALIGN(short, data[64]); + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x)*8; + int y2 = (j*z->img_comp[n].v + y)*8; + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } else { + if (z->scan_n == 1) { + int i,j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x); + int y2 = (j*z->img_comp[n].v + y); + short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } +} + +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) +{ + int i; + for (i=0; i < 64; ++i) + data[i] *= dequant[i]; +} + +static void stbi__jpeg_finish(stbi__jpeg *z) +{ + if (z->progressive) { + // dequantize and idct the data + int i,j,n; + for (n=0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + } + } + } + } +} + +static int stbi__process_marker(stbi__jpeg *z, int m) +{ + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker","Corrupt JPEG"); + + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; + + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s)-2; + while (L > 0) { + int q = stbi__get8(z->s); + int p = q >> 4, sixteen = (p != 0); + int t = q & 15,i; + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + + for (i=0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); + } + return L==0; + + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s)-2; + while (L > 0) { + stbi_uc *v; + int sizes[16],i,n=0; + int q = stbi__get8(z->s); + int tc = q >> 4; + int th = q & 15; + if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); + for (i=0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; + } + L -= 17; + if (tc == 0) { + if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; + v = z->huff_dc[th].values; + } else { + if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; + v = z->huff_ac[th].values; + } + for (i=0; i < n; ++i) + v[i] = stbi__get8(z->s); + if (tc != 0) + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + L -= n; + } + return L==0; + } + + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); + return 1; + } + + return stbi__err("unknown marker","Corrupt JPEG"); +} + +// after we see SOS +static int stbi__process_scan_header(stbi__jpeg *z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); + if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); + for (i=0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); + z->order[i] = which; + } + + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } else { + if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); + z->spec_end = 63; + } + } + + return 1; +} + +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + +static int stbi__process_frame_header(stbi__jpeg *z, int scan) +{ + stbi__context *s = z->s; + int Lf,p,i,q, h_max=1,v_max=1,c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); + s->img_n = c; + for (i=0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } + + if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + + z->rgb = 0; + for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); + } + + if (scan != STBI__SCAN_load) return 1; + + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); + + for (i=0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } + + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; + + for (i=0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); + } + } + + return 1; +} + +// use comparisons since in some cases we handle more than one case (e.g. SOF) +#define stbi__DNL(x) ((x) == 0xdc) +#define stbi__SOI(x) ((x) == 0xd8) +#define stbi__EOI(x) ((x) == 0xd9) +#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) +#define stbi__SOS(x) ((x) == 0xda) + +#define stbi__SOF_progressive(x) ((x) == 0xc2) + +static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z,m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; +} + +// decode image to YCbCr format +static int stbi__decode_jpeg_image(stbi__jpeg *j) +{ + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none ) { + // handle 0s at the end of image data from IP Kamera 9060 + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + if (x == 255) { + j->marker = stbi__get8(j->s); + break; + } + } + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 + } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + } else { + if (!stbi__process_marker(j, m)) return 0; + } + m = stbi__get_marker(j); + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; +} + +// static jfif-centered resampling (across block boundaries) + +typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, + int w, int hs); + +#define stbi__div4(x) ((stbi_uc) ((x) >> 2)) + +static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; +} + +static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i=0; i < w; ++i) + out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); + return out; +} + +static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples horizontally for every one in input + int i; + stbi_uc *input = in_near; + + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } + + out[0] = input[0]; + out[1] = stbi__div4(input[0]*3 + input[1] + 2); + for (i=1; i < w-1; ++i) { + int n = 3*input[i]+2; + out[i*2+0] = stbi__div4(n+input[i-1]); + out[i*2+1] = stbi__div4(n+input[i+1]); + } + out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); + out[i*2+1] = input[w-1]; + + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); + + return out; +} + +#define stbi__div16(x) ((stbi_uc) ((x) >> 4)) + +static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i,t0,t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + out[0] = stbi__div4(t1+2); + for (i=1; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i=0,t0,t1; + + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w-1) & ~7); i += 8) { +#if defined(STBI_SSE2) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); + + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); + + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i *) (out + i*2), outv); +#elif defined(STBI_NEON) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); + + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i*2, o); +#endif + + // "previous" value for next iter + t1 = 3*in_near[i+7] + in_far[i+7]; + } + + t0 = t1; + t1 = 3*in_near[i] + in_far[i]; + out[i*2] = stbi__div16(3*t1 + t0 + 8); + + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} +#endif + +static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i,j; + STBI_NOTUSED(in_far); + for (i=0; i < w; ++i) + for (j=0; j < hs; ++j) + out[i*hs+j] = in_near[i]; + return out; +} + +// this is a reduced-precision calculation of YCbCr-to-RGB introduced +// to make sure the code produces the same results in both SIMD and scalar +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) +{ + int i; + for (i=0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +{ + int i = 0; + +#ifdef STBI_SSE2 + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); + __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); + __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); + __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); + __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); + __m128i xw = _mm_set1_epi16(255); // alpha channel + + for (; i+7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); + + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); + + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); + + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); + + // store + _mm_storeu_si128((__m128i *) (out + 0), o0); + _mm_storeu_si128((__m128i *) (out + 16), o1); + out += 32; + } + } +#endif + +#ifdef STBI_NEON + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); + int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); + int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); + int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); + + for (; i+7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); + + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); + + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); + + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8*4; + } + } +#endif + + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} +#endif + +// set up the kernels +static void stbi__setup_jpeg(stbi__jpeg *j) +{ + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; + +#ifdef STBI_SSE2 + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } +#endif + +#ifdef STBI_NEON + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; +#endif +} + +// clean up the temporary component buffers +static void stbi__cleanup_jpeg(stbi__jpeg *j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); +} + +typedef struct +{ + resample_row_func resample; + stbi_uc *line0,*line1; + int hs,vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on +} stbi__resample; + +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + +static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe + + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } + + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; + + // resample and color-convert + { + int k; + unsigned int i,j; + stbi_uc *output; + stbi_uc *coutput[4] = { NULL, NULL, NULL, NULL }; + + stbi__resample res_comp[4]; + + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs-1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j=0; j < z->s->img_y; ++j) { + stbi_uc *out = output + n * z->s->img_x * j; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; + } + } + if (n >= 3) { + stbi_uc *y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else + for (i=0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } else { + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } + } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } +} + +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; +} + +static int stbi__jpeg_test(stbi__context *s) +{ + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; +} + +static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +{ + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind( j->s ); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; +} + +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +{ + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; +} +#endif + +// public domain zlib decode v0.2 Sean Barrett 2006-11-18 +// simple implementation +// - all input must be provided in an upfront buffer +// - all output is written to a single output buffer (can malloc/realloc) +// performance +// - fast huffman + +#ifndef STBI_NO_ZLIB + +// fast-way is faster to check than jpeg huffman, but slow way is slower +#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables +#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) + +// zlib-style huffman encoding +// (jpegs packs from left, zlib from right, so can't share code) +typedef struct +{ + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[288]; + stbi__uint16 value[288]; +} stbi__zhuffman; + +stbi_inline static int stbi__bitreverse16(int n) +{ + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; +} + +stbi_inline static int stbi__bit_reverse(int v, int bits) +{ + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16-bits); +} + +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) +{ + int i,k=0; + int code, next_code[16], sizes[17]; + + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i=0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i=1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i=1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16) code; + z->firstsymbol[i] = (stbi__uint16) k; + code = (code + sizes[i]); + if (sizes[i]) + if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); + z->maxcode[i] = code << (16-i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i=0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); + z->size [c] = (stbi_uc ) s; + z->value[c] = (stbi__uint16) i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); + } + } + ++next_code[s]; + } + } + return 1; +} + +// zlib-from-memory implementation for PNG reading +// because PNG allows splitting the zlib stream arbitrarily, +// and it's annoying structurally to have PNG call ZLIB call PNG, +// we require PNG read all the IDATs and combine them into a single +// memory buffer + +typedef struct +{ + stbi_uc *zbuffer, *zbuffer_end; + int num_bits; + stbi__uint32 code_buffer; + + char *zout; + char *zout_start; + char *zout_end; + int z_expandable; + + stbi__zhuffman z_length, z_distance; +} stbi__zbuf; + +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +{ + if (z->zbuffer >= z->zbuffer_end) return 0; + return *z->zbuffer++; +} + +static void stbi__fill_bits(stbi__zbuf *z) +{ + do { + STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); +} + +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +{ + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; +} + +static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s,k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s=STBI__ZFAST_BITS+1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s == 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; + STBI_ASSERT(z->size[b] == s); + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} + +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s; + if (a->num_bits < 16) stbi__fill_bits(a); + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); +} + +static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +{ + char *q; + int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); + cur = (int) (z->zout - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); + while (cur + n > limit) + limit *= 2; + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; +} + +static const int stbi__zlength_base[31] = { + 3,4,5,6,7,8,9,10,11,13, + 15,17,19,23,27,31,35,43,51,59, + 67,83,99,115,131,163,195,227,258,0,0 }; + +static const int stbi__zlength_extra[31]= +{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; + +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; + +static int stbi__parse_huffman_block(stbi__zbuf *a) +{ + char *zout = a->zout; + for(;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char) z; + } else { + stbi_uc *p; + int len,dist; + if (z == 256) { + a->zout = zout; + return 1; + } + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); + if (zout + len > a->zout_end) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc *) (zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } +} + +static int stbi__compute_huffman_codes(stbi__zbuf *a) +{ + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286+32+137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i,n; + + int hlit = stbi__zreceive(a,5) + 257; + int hdist = stbi__zreceive(a,5) + 1; + int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; + + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i=0; i < hclen; ++i) { + int s = stbi__zreceive(a,3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc) c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; + return 1; +} + +static int stbi__parse_uncompressed_block(stbi__zbuf *a) +{ + stbi_uc header[4]; + int len,nlen,k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + STBI_ASSERT(a->num_bits == 0); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; +} + +static int stbi__parse_zlib_header(stbi__zbuf *a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; +} + +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: +{ + int i; // use <= to match clearly with spec + for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; + for ( ; i <= 255; ++i) stbi__zdefault_length[i] = 9; + for ( ; i <= 279; ++i) stbi__zdefault_length[i] = 7; + for ( ; i <= 287; ++i) stbi__zdefault_length[i] = 8; + + for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; +} +*/ + +static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + do { + final = stbi__zreceive(a,1); + type = stbi__zreceive(a,2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } else if (type == 3) { + return 0; + } else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; +} + +static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +{ + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; + + return stbi__parse_zlib(a, parse_header); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +{ + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int) (a.zout - a.zout_start); + else + return -1; +} + +STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer+len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int) (a.zout - a.zout_start); + else + return -1; +} +#endif + +// public domain "baseline" PNG decoder v0.10 Sean Barrett 2006-11-18 +// simple implementation +// - only 8-bit samples +// - no CRC checking +// - allocates lots of intermediate memory +// - avoids problem of streaming data between subsystems +// - avoids explicit window management +// performance +// - uses stb_zlib, a PD zlib implementation with fast huffman decoding + +#ifndef STBI_NO_PNG +typedef struct +{ + stbi__uint32 length; + stbi__uint32 type; +} stbi__pngchunk; + +static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +{ + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; +} + +static int stbi__check_png_header(stbi__context *s) +{ + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i=0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); + return 1; +} + +typedef struct +{ + stbi__context *s; + stbi_uc *idata, *expanded, *out; + int depth; +} stbi__png; + + +enum { + STBI__F_none=0, + STBI__F_sub=1, + STBI__F_up=2, + STBI__F_avg=3, + STBI__F_paeth=4, + // synthetic filters used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static stbi_uc first_row_filter[5] = +{ + STBI__F_none, + STBI__F_sub, + STBI__F_none, + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static int stbi__paeth(int a, int b, int c) +{ + int p = a + b - c; + int pa = abs(p-a); + int pb = abs(p-b); + int pc = abs(p-c); + if (pa <= pb && pa <= pc) return a; + if (pb <= pc) return b; + return c; +} + +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; + +// create the png data from post-deflated data +static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16? 2 : 1); + stbi__context *s = a->s; + stbi__uint32 i,j,stride = x*out_n*bytes; + stbi__uint32 img_len, img_width_bytes; + int k; + int img_n = s->img_n; // copy it into a local for later + + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); + + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + img_len = (img_width_bytes + 1) * y; + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); + + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *prior; + int filter = *raw++; + + if (filter > 4) + return stbi__err("invalid filter","Corrupt PNG"); + + if (depth < 8) { + STBI_ASSERT(img_width_bytes <= x); + cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place + filter_bytes = 1; + width = img_width_bytes; + } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // handle first byte explicitly + for (k=0; k < filter_bytes; ++k) { + switch (filter) { + case STBI__F_none : cur[k] = raw[k]; break; + case STBI__F_sub : cur[k] = raw[k]; break; + case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; + case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; + case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; + case STBI__F_avg_first : cur[k] = raw[k]; break; + case STBI__F_paeth_first: cur[k] = raw[k]; break; + } + } + + if (depth == 8) { + if (img_n != out_n) + cur[img_n] = 255; // first pixel + raw += img_n; + cur += out_n; + prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; + } else { + raw += 1; + cur += 1; + prior += 1; + } + + // this is a little gross, so that we don't switch per-pixel or per-component + if (depth < 8 || img_n == out_n) { + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ + case f: \ + for (k=0; k < nk; ++k) + switch (filter) { + // "none" filter turns into a memcpy here; make that explicit. + case STBI__F_none: memcpy(cur, raw, nk); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; + } + #undef STBI__CASE + raw += nk; + } else { + STBI_ASSERT(img_n+1 == out_n); + #define STBI__CASE(f) \ + case f: \ + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) + switch (filter) { + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } + } + } + } + + // we make a separate pass to expand bits to pixels; for performance, + // this could run two scanlines behind the above code, so it won't + // intefere with filtering but will still be in the cache. + if (depth < 8) { + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; + // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit + // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + + // note that the final byte might overshoot and write more data than desired. + // we can allocate enough data that this never writes out of memory, but it + // could also overwrite the next scanline. can it overwrite non-empty data + // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. + // so we need to explicitly clamp the final ones + + if (depth == 4) { + for (k=x*img_n; k >= 2; k-=2, ++in) { + *cur++ = scale * ((*in >> 4) ); + *cur++ = scale * ((*in ) & 0x0f); + } + if (k > 0) *cur++ = scale * ((*in >> 4) ); + } else if (depth == 2) { + for (k=x*img_n; k >= 4; k-=4, ++in) { + *cur++ = scale * ((*in >> 6) ); + *cur++ = scale * ((*in >> 4) & 0x03); + *cur++ = scale * ((*in >> 2) & 0x03); + *cur++ = scale * ((*in ) & 0x03); + } + if (k > 0) *cur++ = scale * ((*in >> 6) ); + if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); + if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); + } else if (depth == 1) { + for (k=x*img_n; k >= 8; k-=8, ++in) { + *cur++ = scale * ((*in >> 7) ); + *cur++ = scale * ((*in >> 6) & 0x01); + *cur++ = scale * ((*in >> 5) & 0x01); + *cur++ = scale * ((*in >> 4) & 0x01); + *cur++ = scale * ((*in >> 3) & 0x01); + *cur++ = scale * ((*in >> 2) & 0x01); + *cur++ = scale * ((*in >> 1) & 0x01); + *cur++ = scale * ((*in ) & 0x01); + } + if (k > 0) *cur++ = scale * ((*in >> 7) ); + if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); + if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); + if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); + if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); + if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); + if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); + } + if (img_n != out_n) { + int q; + // insert alpha = 255 + cur = a->out + stride*j; + if (img_n == 1) { + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; + } + } else { + STBI_ASSERT(img_n == 3); + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; + } + } + } + } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } + } + + return 1; +} + +static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc *final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); + + // de-interlacing + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + for (p=0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i,j,x,y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; + } + for (j=0; j < y; ++j) { + for (i=0; i < x; ++i) { + int out_y = j*yspc[p]+yorig[p]; + int out_x = i*xspc[p]+xorig[p]; + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); + } + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; + + return 1; +} + +static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i=0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } else { + for (i=0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +{ + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc *p, *temp_out, *orig = a->out; + + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); + + // between here and free(out) below, exitting would leak + temp_out = p; + + if (pal_img_n == 3) { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p += 3; + } + } else { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p[3] = palette[n+3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; + + STBI_NOTUSED(len); + + return 1; +} + +static int stbi__unpremultiply_on_load = 0; +static int stbi__de_iphone_flag = 0; + +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; +} + +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag = flag_true_if_should_convert; +} + +static void stbi__de_iphone(stbi__png *z) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + if (s->img_out_n == 3) { // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 3; + } + } else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i=0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; + } else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } else { + // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 4; + } + } + } +} + +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) + +static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +{ + stbi_uc palette[1024], pal_img_n=0; + stbi_uc has_trans=0, tc[3]={0}; + stbi__uint16 tc16[3]; + stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; + int first=1,k,interlace=0, color=0, is_iphone=0; + stbi__context *s = z->s; + + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; + + if (!stbi__check_png_header(s)) return 0; + + if (scan == STBI__SCAN_type) return 1; + + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C','g','B','I'): + is_iphone = 1; + stbi__skip(s, c.length); + break; + case STBI__PNG_TYPE('I','H','D','R'): { + int comp,filter; + if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); + first = 0; + if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); + s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); + filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); + interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); + if (!pal_img_n) { + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (scan == STBI__SCAN_header) return 1; + } else { + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); + // if SCAN_header, have to scan to see if we have a tRNS + } + break; + } + + case STBI__PNG_TYPE('P','L','T','E'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); + pal_len = c.length / 3; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); + for (i=0; i < pal_len; ++i) { + palette[i*4+0] = stbi__get8(s); + palette[i*4+1] = stbi__get8(s); + palette[i*4+2] = stbi__get8(s); + palette[i*4+3] = 255; + } + break; + } + + case STBI__PNG_TYPE('t','R','N','S'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); + if (pal_img_n) { + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); + pal_img_n = 4; + for (i=0; i < c.length; ++i) + palette[i*4+3] = stbi__get8(s); + } else { + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); + if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); + has_trans = 1; + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } + } + break; + } + + case STBI__PNG_TYPE('I','D','A','T'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); + if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if ((int)(ioff + c.length) < (int)ioff) return 0; + if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc *p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; + } + if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); + ioff += c.length; + break; + } + + case STBI__PNG_TYPE('I','E','N','D'): { + stbi__uint32 raw_len, bpl; + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (scan != STBI__SCAN_load) return 1; + if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); + // initial guess for decoded data size to avoid unnecessary reallocs + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component + raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; + z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + if (z->expanded == NULL) return 0; // zlib should set error + STBI_FREE(z->idata); z->idata = NULL; + if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n+1; + else + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } + if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) + stbi__de_iphone(z); + if (pal_img_n) { + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; + } + STBI_FREE(z->expanded); z->expanded = NULL; + return 1; + } + + default: + // if critical, fail + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if ((c.type & (1 << 29)) == 0) { + #ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); + #endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); + } + stbi__skip(s, c.length); + break; + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } +} + +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) +{ + void *result=NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; + + return result; +} + +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi__png p; + p.s = s; + return stbi__do_png(&p, x,y,comp,req_comp, ri); +} + +static int stbi__png_test(stbi__context *s) +{ + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; +} + +static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +{ + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind( p->s ); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; +} + +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); +} + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} +#endif + +// Microsoft/Windows BMP image + +#ifndef STBI_NO_BMP +static int stbi__bmp_test_raw(stbi__context *s) +{ + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; +} + +static int stbi__bmp_test(stbi__context *s) +{ + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; +} + + +// returns 0..31 for the highest set bit +static int stbi__high_bit(unsigned int z) +{ + int n=0; + if (z == 0) return -1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1; z >>= 1; } + return n; +} + +static int stbi__bitcount(unsigned int a) +{ + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; +} + +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; +} + +typedef struct +{ + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + } + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } + } else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else { + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + stbi__get32le(s); // discard color space + for (i=0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved + } + } + } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z=0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i=0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width)&3; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } + } + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); + } + } + } else { + int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; + int z = 0; + int easy=0; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; + else /* bpp = 32 and pad = 0 */ width=0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + } + for (j=0; j < (int) s->img_y; ++j) { + if (easy) { + for (i=0; i < (int) s->img_x; ++i) { + unsigned char a; + out[z+2] = stbi__get8(s); + out[z+1] = stbi__get8(s); + out[z+0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } else { + int bpp = info.bpp; + for (i=0; i < (int) s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + + if (flip_vertically) { + stbi_uc t; + for (j=0; j < (int) s->img_y>>1; ++j) { + stbi_uc *p1 = out + j *s->img_x*target; + stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; + for (i=0; i < (int) s->img_x*target; ++i) { + t = p1[i]; p1[i] = p2[i]; p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; +} +#endif + +// Targa Truevision - TGA +// by Jonathan Dummer +#ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +{ + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; + stbi__get8(s); // discard Offset + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { + stbi__rewind(s); + return 0; // only RGB or indexed allowed + } + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } + tga_w = stbi__get16le(s); + if( tga_w < 1 ) { + stbi__rewind(s); + return 0; // test width + } + tga_h = stbi__get16le(s); + if( tga_h < 1 ) { + stbi__rewind(s); + return 0; // test height + } + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; + } + if (x) *x = tga_w; + if (y) *y = tga_h; + if (comp) *comp = tga_comp; + return 1; // seems to have passed everything +} + +static int stbi__tga_test(stbi__context *s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: + stbi__rewind(s); + return res; +} + +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16=0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char *tga_data; + unsigned char *tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = {0}; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + + // do a tiny bit of precessing + if ( tga_image_type >= 8 ) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); + + // If I'm paletted, then I'll use the number of bits from the palette + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); + + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; + + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset ); + + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { + for (i=0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } else { + // do I need to load a palette? + if ( tga_indexed) + { + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start ); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i=0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if ( tga_is_RLE ) + { + if ( RLE_count == 0 ) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } else if ( !RLE_repeating ) + { + read_next_pixel = 1; + } + } else + { + read_next_pixel = 1; + } + // OK, if I need to read a pixel, do it now + if ( read_next_pixel ) + { + // load however much data we did have + if ( tga_indexed ) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx+j]; + } + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } + } + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel + + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i*tga_comp+j] = raw_data[j]; + + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if ( tga_inverted ) + { + for (j = 0; j*2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; + } + } + } + // clear my palette, if I had one + if ( tga_palette != NULL ) + { + STBI_FREE( tga_palette ); + } + } + + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i=0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } + + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + // OK, done + return tga_data; +} +#endif + +// ************************************************************************************************* +// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; +} + +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) +{ + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w,h; + stbi_uc *out; + STBI_NOTUSED(ri); + + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); + + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + + // Skip 6 reserved bytes. + stbi__skip(s, 6 ); + + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); + + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s,stbi__get32be(s) ); + + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s) ); + + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s) ); + + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + + // Create the destination image. + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w*h; + + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); + + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop + + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2 ); + + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc *p; + + p = out+channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } + + } else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } + } else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } + } + } + } + + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + if (comp) *comp = 4; + *y = h; + *x = w; + + return out; +} +#endif + +// ************************************************************************************************* +// Softimage PIC loader +// by Tom Seddon +// +// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format +// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ + +#ifndef STBI_NO_PIC +static int stbi__pic_is4(stbi__context *s,const char *str) +{ + int i; + for (i=0; i<4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; + + return 1; +} + +static int stbi__pic_test_core(stbi__context *s) +{ + int i; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) + return 0; + + for(i=0;i<84;++i) + stbi__get8(s); + + if (!stbi__pic_is4(s,"PICT")) + return 0; + + return 1; +} + +typedef struct +{ + stbi_uc size,type,channel; +} stbi__pic_packet; + +static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +{ + int mask=0x80, i; + + for (i=0; i<4; ++i, mask>>=1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); + dest[i]=stbi__get8(s); + } + } + + return dest; +} + +static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +{ + int mask=0x80,i; + + for (i=0;i<4; ++i, mask>>=1) + if (channel&mask) + dest[i]=src[i]; +} + +static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +{ + int act_comp=0,num_packets=0,y,chained; + stbi__pic_packet packets[10]; + + // this will (should...) cater for even some bizarre stuff like having data + // for the same channel in multiple packets. + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return stbi__errpuc("bad format","too many packets"); + + packet = &packets[num_packets++]; + + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + + act_comp |= packet->channel; + + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + + for(y=0; ytype) { + default: + return stbi__errpuc("bad format","packet has bad compression type"); + + case 0: {//uncompressed + int x; + + for(x=0;xchannel,dest)) + return 0; + break; + } + + case 1://Pure RLE + { + int left=width, i; + + while (left>0) { + stbi_uc count,value[4]; + + count=stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); + + if (count > left) + count = (stbi_uc) left; + + if (!stbi__readval(s,packet->channel,value)) return 0; + + for(i=0; ichannel,dest,value); + left -= count; + } + } + break; + + case 2: {//Mixed RLE + int left=width; + while (left>0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; + + if (count==128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file","scanline overrun"); + + if (!stbi__readval(s,packet->channel,value)) + return 0; + + for(i=0;ichannel,dest,value); + } else { // Raw + ++count; + if (count>left) return stbi__errpuc("bad file","scanline overrun"); + + for(i=0;ichannel,dest)) + return 0; + } + left-=count; + } + break; + } + } + } + } + + return result; +} + +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) +{ + stbi_uc *result; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; + + for (i=0; i<92; ++i) + stbi__get8(s); + + x = stbi__get16be(s); + y = stbi__get16be(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); + + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' + + // intermediate buffer is RGBA + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + memset(result, 0xff, x*y*4); + + if (!stbi__pic_load_core(s,x,y,comp, result)) { + STBI_FREE(result); + result=0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result=stbi__convert_format(result,4,req_comp,x,y); + + return result; +} + +static int stbi__pic_test(stbi__context *s) +{ + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; +} +#endif + +// ************************************************************************************************* +// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb + +#ifndef STBI_NO_GIF +typedef struct +{ + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; +} stbi__gif_lzw; + +typedef struct +{ + int w,h; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc *color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; +} stbi__gif; + +static int stbi__gif_test_raw(stbi__context *s) +{ + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; +} + +static int stbi__gif_test(stbi__context *s) +{ + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; +} + +static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +{ + int i; + for (i=0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } +} + +static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +{ + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); + + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); + + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; + + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + + if (is_info) return 1; + + if (g->flags & 0x80) + stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); + + return 1; +} + +static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind( s ); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; +} + +static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +{ + stbi_uc *p, *c; + int idx; + + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); + + if (g->cur_y >= g->max_y) return; + + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; + + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; + + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; + + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } +} + +static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw *p; + + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; + } + + // support no starting clear code + avail = clear+2; + oldcode = -1; + + len = 0; + for(;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; + } + --len; + bits |= (stbi__int32) stbi__get8(s) << valid_bits; + valid_bits += 8; + } else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s,len); + return g->out; + } else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } + + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + + p->prefix = (stbi__int16) oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + + stbi__out_gif_code(g, (stbi__uint16) code); + + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; + } + + oldcode = code; + } else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } +} + +// this function is designed to support animated gifs, although stb_image doesn't support it +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); + + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc *) stbi__malloc(4 * pcount); + g->background = (stbi_uc *) stbi__malloc(4 * pcount); + g->history = (stbi_uc *) stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); + } + + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { + stbi__int32 x, y, w, h; + stbi_uc *o; + + x = stbi__get16le(s); + y = stbi__get16le(s); + w = stbi__get16le(s); + h = stbi__get16le(s); + if (((x + w) > (g->w)) || ((y + h) > (g->h))) + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + + g->line_size = g->w * 4; + g->start_x = x * 4; + g->start_y = y * g->line_size; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; + + // if the width of the specified rectangle is 0, that means + // we may not see *any* pixels or the image is malformed; + // to make sure this is caught, move the current y down to + // max_y (which is what out_gif_code checks). + if (w == 0) + g->cur_y = g->max_y; + + g->lflags = stbi__get8(s); + + if (g->lflags & 0x40) { + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; + } else { + g->step = g->line_size; + g->parse = 0; + } + + if (g->lflags & 0x80) { + stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc *) g->lpal; + } else if (g->flags & 0x80) { + g->color_table = (stbi_uc *) g->pal; + } else + return stbi__errpuc("missing color table", "Corrupt GIF"); + + o = stbi__process_gif_raster(s, g); + if (!o) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + + return o; + } + + case 0x21: // Comment Extension. + { + int len; + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } else { + stbi__skip(s, len); + break; + } + } + while ((len = stbi__get8(s)) != 0) { + stbi__skip(s, len); + } + break; + } + + case 0x3B: // gif stream termination code + return (stbi_uc *) s; // using '1' causes warning on some compilers + + default: + return stbi__errpuc("unknown code", "Corrupt GIF"); + } + } +} + +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + STBI_NOTUSED(ri); + + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); + } + + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + + return u; +} + +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +{ + return stbi__gif_info_raw(s,x,y,comp); +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR loader +// originally by Nicolas Schulz +#ifndef STBI_NO_HDR +static int stbi__hdr_test_core(stbi__context *s, const char *signature) +{ + int i; + for (i=0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; +} + +static int stbi__hdr_test(stbi__context* s) +{ + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; +} + +#define STBI__HDR_BUFLEN 1024 +static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +{ + int len=0; + char c = '\0'; + + c = (char) stbi__get8(z); + + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN-1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char) stbi__get8(z); + } + + buffer[len] = 0; + return buffer; +} + +static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) +{ + if ( input[3] != 0 ) { + float f1; + // Exponent + f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } +} + +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int width, height; + stbi_uc *scanline; + float *hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1,c2, z; + const char *headerToken; + STBI_NOTUSED(ri); + + // Check identifier + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); + + // Parse header + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); + + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int) strtol(token, NULL, 10); + + *x = width; + *y = height; + + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; + + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + + // Read data + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); + + // Load image data + // image data is stored as some number of sca + if ( width < 8 || width >= 32768) { + // Read flat data + for (j=0; j < height; ++j) { + for (i=0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } else { + // Read RLE-encoded data + scanline = NULL; + + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc) c1; + rgbe[1] = (stbi_uc) c2; + rgbe[2] = (stbi_uc) len; + rgbe[3] = (stbi_uc) stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; + STBI_FREE(scanline); + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } + + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } else { + // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } + } + for (i=0; i < width; ++i) + stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } + + return hdr_data; +} + +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (stbi__hdr_test(s) == 0) { + stbi__rewind( s ); + return 0; + } + + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) { + stbi__rewind( s ); + return 0; + } + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *y = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *x = (int) strtol(token, NULL, 10); + *comp = 3; + return 1; +} +#endif // STBI_NO_HDR + +#ifndef STBI_NO_BMP +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +{ + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) *comp = info.ma ? 4 : 3; + return 1; +} +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +{ + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind( s ); + return 0; + } + *comp = 4; + return 1; +} + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +{ + int act_comp=0,num_packets=0,chained,dummy; + stbi__pic_packet packets[10]; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); + + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } + if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind( s ); + return 0; + } + + stbi__skip(s, 8); + + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return 0; + + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; + + if (stbi__at_eof(s)) { + stbi__rewind( s ); + return 0; + } + if (packet->size != 8) { + stbi__rewind( s ); + return 0; + } + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); + + return 1; +} +#endif + +// ************************************************************************************************* +// Portable Gray Map and Portable Pixel Map loader +// by Ken Miller +// +// PGM: http://netpbm.sourceforge.net/doc/pgm.html +// PPM: http://netpbm.sourceforge.net/doc/ppm.html +// +// Known limitations: +// Does not support comments in the header section +// Does not support ASCII image data (formats P2 and P3) +// Does not support 16-bit-per-channel + +#ifndef STBI_NO_PNM + +static int stbi__pnm_test(stbi__context *s) +{ + char p, t; + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind( s ); + return 0; + } + return 1; +} + +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + STBI_NOTUSED(ri); + + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) + return 0; + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); + + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + + if (req_comp && req_comp != s->img_n) { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; +} + +static int stbi__pnm_isspace(char c) +{ + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; +} + +static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +{ + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } +} + +static int stbi__pnm_isdigit(char c) +{ + return c >= '0' && c <= '9'; +} + +static int stbi__pnm_getinteger(stbi__context *s, char *c) +{ + int value = 0; + + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value*10 + (*c - '0'); + *c = (char) stbi__get8(s); + } + + return value; +} + +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +{ + int maxv, dummy; + char c, p, t; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); + + // Get identifier + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + + c = (char) stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); + + *x = stbi__pnm_getinteger(s, &c); // read width + stbi__pnm_skip_whitespace(s, &c); + + *y = stbi__pnm_getinteger(s, &c); // read height + stbi__pnm_skip_whitespace(s, &c); + + maxv = stbi__pnm_getinteger(s, &c); // read max value + + if (maxv > 255) + return stbi__err("max value > 255", "PPM image not 8-bit"); + else + return 1; +} +#endif + +static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +{ + #ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; + #endif + + // test tga last because it's a crappy test! + #ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; + #endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +} + +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_info_from_file(f, x, y, comp); + fclose(f); + return result; +} + +STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s,x,y,comp); + fseek(f,pos,SEEK_SET); + return r; +} + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + +#endif // STB_IMAGE_IMPLEMENTATION + +/* + revision history: + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA + 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value + 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning + 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit + 2.03 (2015-04-12) extra corruption checking (mmozeiko) + stbi_set_flip_vertically_on_load (nguillemot) + fix NEON support; fix mingw support + 2.02 (2015-01-19) fix incorrect assert, fix warning + 2.01 (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2 + 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG + 2.00 (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg) + progressive JPEG (stb) + PGM/PPM support (Ken Miller) + STBI_MALLOC,STBI_REALLOC,STBI_FREE + GIF bugfix -- seemingly never worked + STBI_NO_*, STBI_ONLY_* + 1.48 (2014-12-14) fix incorrectly-named assert() + 1.47 (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb) + optimize PNG (ryg) + fix bug in interlaced PNG with user-specified channel count (stb) + 1.46 (2014-08-26) + fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG + 1.45 (2014-08-16) + fix MSVC-ARM internal compiler error by wrapping malloc + 1.44 (2014-08-07) + various warning fixes from Ronny Chevalier + 1.43 (2014-07-15) + fix MSVC-only compiler problem in code changed in 1.42 + 1.42 (2014-07-09) + don't define _CRT_SECURE_NO_WARNINGS (affects user code) + fixes to stbi__cleanup_jpeg path + added STBI_ASSERT to avoid requiring assert.h + 1.41 (2014-06-25) + fix search&replace from 1.36 that messed up comments/error messages + 1.40 (2014-06-22) + fix gcc struct-initialization warning + 1.39 (2014-06-15) + fix to TGA optimization when req_comp != number of components in TGA; + fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite) + add support for BMP version 5 (more ignored fields) + 1.38 (2014-06-06) + suppress MSVC warnings on integer casts truncating values + fix accidental rename of 'skip' field of I/O + 1.37 (2014-06-04) + remove duplicate typedef + 1.36 (2014-06-03) + convert to header file single-file library + if de-iphone isn't set, load iphone images color-swapped instead of returning NULL + 1.35 (2014-05-27) + various warnings + fix broken STBI_SIMD path + fix bug where stbi_load_from_file no longer left file pointer in correct place + fix broken non-easy path for 32-bit BMP (possibly never used) + TGA optimization by Arseny Kapoulkine + 1.34 (unknown) + use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case + 1.33 (2011-07-14) + make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements + 1.32 (2011-07-13) + support for "info" function for all supported filetypes (SpartanJ) + 1.31 (2011-06-20) + a few more leak fixes, bug in PNG handling (SpartanJ) + 1.30 (2011-06-11) + added ability to load files via callbacks to accomidate custom input streams (Ben Wenger) + removed deprecated format-specific test/load functions + removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway + error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) + fix inefficiency in decoding 32-bit BMP (David Woo) + 1.29 (2010-08-16) + various warning fixes from Aurelien Pocheville + 1.28 (2010-08-01) + fix bug in GIF palette transparency (SpartanJ) + 1.27 (2010-08-01) + cast-to-stbi_uc to fix warnings + 1.26 (2010-07-24) + fix bug in file buffering for PNG reported by SpartanJ + 1.25 (2010-07-17) + refix trans_data warning (Won Chun) + 1.24 (2010-07-12) + perf improvements reading from files on platforms with lock-heavy fgetc() + minor perf improvements for jpeg + deprecated type-specific functions so we'll get feedback if they're needed + attempt to fix trans_data warning (Won Chun) + 1.23 fixed bug in iPhone support + 1.22 (2010-07-10) + removed image *writing* support + stbi_info support from Jetro Lauha + GIF support from Jean-Marc Lienher + iPhone PNG-extensions from James Brown + warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva) + 1.21 fix use of 'stbi_uc' in header (reported by jon blow) + 1.20 added support for Softimage PIC, by Tom Seddon + 1.19 bug in interlaced PNG corruption check (found by ryg) + 1.18 (2008-08-02) + fix a threading bug (local mutable static) + 1.17 support interlaced PNG + 1.16 major bugfix - stbi__convert_format converted one too many pixels + 1.15 initialize some fields for thread safety + 1.14 fix threadsafe conversion bug + header-file-only version (#define STBI_HEADER_FILE_ONLY before including) + 1.13 threadsafe + 1.12 const qualifiers in the API + 1.11 Support installable IDCT, colorspace conversion routines + 1.10 Fixes for 64-bit (don't use "unsigned long") + optimized upsampling by Fabian "ryg" Giesen + 1.09 Fix format-conversion for PSD code (bad global variables!) + 1.08 Thatcher Ulrich's PSD code integrated by Nicolas Schulz + 1.07 attempt to fix C++ warning/errors again + 1.06 attempt to fix C++ warning/errors again + 1.05 fix TGA loading to return correct *comp and use good luminance calc + 1.04 default float alpha is 1, not 255; use 'void *' for stbi_image_free + 1.03 bugfixes to STBI_NO_STDIO, STBI_NO_HDR + 1.02 support for (subset of) HDR files, float interface for preferred access to them + 1.01 fix bug: possible bug in handling right-side up bmps... not sure + fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all + 1.00 interface to zlib that skips zlib header + 0.99 correct handling of alpha in palette + 0.98 TGA loader by lonesock; dynamically add loaders (untested) + 0.97 jpeg errors on too large a file; also catch another malloc failure + 0.96 fix detection of invalid v value - particleman@mollyrocket forum + 0.95 during header scan, seek to markers in case of padding + 0.94 STBI_NO_STDIO to disable stdio usage; rename all #defines the same + 0.93 handle jpegtran output; verbose errors + 0.92 read 4,8,16,24,32-bit BMP files of several formats + 0.91 output 24-bit Windows 3.0 BMP files + 0.90 fix a few more warnings; bump version number to approach 1.0 + 0.61 bugfixes due to Marc LeBlanc, Christopher Lloyd + 0.60 fix compiling as c++ + 0.59 fix warnings: merge Dave Moore's -Wall fixes + 0.58 fix bug: zlib uncompressed mode len/nlen was wrong endian + 0.57 fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available + 0.56 fix bug: zlib uncompressed mode len vs. nlen + 0.55 fix bug: restart_interval not initialized to 0 + 0.54 allow NULL for 'int *comp' + 0.53 fix bug in png 3->4; speedup png decoding + 0.52 png handles req_comp=3,4 directly; minor cleanup; jpeg comments + 0.51 obey req_comp requests, 1-component jpegs return as 1-component, + on 'test' only check type, not whether we support this variant + 0.50 (2006-11-19) + first released version +*/ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/models/deeplab/inference/tflite/stb_image_resize.h b/models/deeplab/inference/tflite/stb_image_resize.h new file mode 100644 index 0000000000000000000000000000000000000000..4f6ad35e1cb51d5e48d79aa9eb73284a3fba58b7 --- /dev/null +++ b/models/deeplab/inference/tflite/stb_image_resize.h @@ -0,0 +1,2630 @@ +/* stb_image_resize - v0.96 - public domain image resizing + by Jorge L Rodriguez (@VinoBS) - 2014 + http://github.com/nothings/stb + + Written with emphasis on usability, portability, and efficiency. (No + SIMD or threads, so it be easily outperformed by libs that use those.) + Only scaling and translation is supported, no rotations or shears. + Easy API downsamples w/Mitchell filter, upsamples w/cubic interpolation. + + COMPILING & LINKING + In one C/C++ file that #includes this file, do this: + #define STB_IMAGE_RESIZE_IMPLEMENTATION + before the #include. That will create the implementation in that file. + + QUICKSTART + stbir_resize_uint8( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, num_channels) + stbir_resize_float(...) + stbir_resize_uint8_srgb( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0) + stbir_resize_uint8_srgb_edgemode( + input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0, STBIR_EDGE_CLAMP) + // WRAP/REFLECT/ZERO + + FULL API + See the "header file" section of the source for API documentation. + + ADDITIONAL DOCUMENTATION + + SRGB & FLOATING POINT REPRESENTATION + The sRGB functions presume IEEE floating point. If you do not have + IEEE floating point, define STBIR_NON_IEEE_FLOAT. This will use + a slower implementation. + + MEMORY ALLOCATION + The resize functions here perform a single memory allocation using + malloc. To control the memory allocation, before the #include that + triggers the implementation, do: + + #define STBIR_MALLOC(size,context) ... + #define STBIR_FREE(ptr,context) ... + + Each resize function makes exactly one call to malloc/free, so to use + temp memory, store the temp memory in the context and return that. + + ASSERT + Define STBIR_ASSERT(boolval) to override assert() and not use assert.h + + OPTIMIZATION + Define STBIR_SATURATE_INT to compute clamp values in-range using + integer operations instead of float operations. This may be faster + on some platforms. + + DEFAULT FILTERS + For functions which don't provide explicit control over what filters + to use, you can change the compile-time defaults with + + #define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_something + #define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_something + + See stbir_filter in the header-file section for the list of filters. + + NEW FILTERS + A number of 1D filter kernels are used. For a list of + supported filters see the stbir_filter enum. To add a new filter, + write a filter function and add it to stbir__filter_info_table. + + PROGRESS + For interactive use with slow resize operations, you can install + a progress-report callback: + + #define STBIR_PROGRESS_REPORT(val) some_func(val) + + The parameter val is a float which goes from 0 to 1 as progress is made. + + For example: + + static void my_progress_report(float progress); + #define STBIR_PROGRESS_REPORT(val) my_progress_report(val) + + #define STB_IMAGE_RESIZE_IMPLEMENTATION + #include "stb_image_resize.h" + + static void my_progress_report(float progress) + { + printf("Progress: %f%%\n", progress*100); + } + + MAX CHANNELS + If your image has more than 64 channels, define STBIR_MAX_CHANNELS + to the max you'll have. + + ALPHA CHANNEL + Most of the resizing functions provide the ability to control how + the alpha channel of an image is processed. The important things + to know about this: + + 1. The best mathematically-behaved version of alpha to use is + called "premultiplied alpha", in which the other color channels + have had the alpha value multiplied in. If you use premultiplied + alpha, linear filtering (such as image resampling done by this + library, or performed in texture units on GPUs) does the "right + thing". While premultiplied alpha is standard in the movie CGI + industry, it is still uncommon in the videogame/real-time world. + + If you linearly filter non-premultiplied alpha, strange effects + occur. (For example, the 50/50 average of 99% transparent bright green + and 1% transparent black produces 50% transparent dark green when + non-premultiplied, whereas premultiplied it produces 50% + transparent near-black. The former introduces green energy + that doesn't exist in the source image.) + + 2. Artists should not edit premultiplied-alpha images; artists + want non-premultiplied alpha images. Thus, art tools generally output + non-premultiplied alpha images. + + 3. You will get best results in most cases by converting images + to premultiplied alpha before processing them mathematically. + + 4. If you pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, the + resizer does not do anything special for the alpha channel; + it is resampled identically to other channels. This produces + the correct results for premultiplied-alpha images, but produces + less-than-ideal results for non-premultiplied-alpha images. + + 5. If you do not pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, + then the resizer weights the contribution of input pixels + based on their alpha values, or, equivalently, it multiplies + the alpha value into the color channels, resamples, then divides + by the resultant alpha value. Input pixels which have alpha=0 do + not contribute at all to output pixels unless _all_ of the input + pixels affecting that output pixel have alpha=0, in which case + the result for that pixel is the same as it would be without + STBIR_FLAG_ALPHA_PREMULTIPLIED. However, this is only true for + input images in integer formats. For input images in float format, + input pixels with alpha=0 have no effect, and output pixels + which have alpha=0 will be 0 in all channels. (For float images, + you can manually achieve the same result by adding a tiny epsilon + value to the alpha channel of every image, and then subtracting + or clamping it at the end.) + + 6. You can suppress the behavior described in #5 and make + all-0-alpha pixels have 0 in all channels by #defining + STBIR_NO_ALPHA_EPSILON. + + 7. You can separately control whether the alpha channel is + interpreted as linear or affected by the colorspace. By default + it is linear; you almost never want to apply the colorspace. + (For example, graphics hardware does not apply sRGB conversion + to the alpha channel.) + + CONTRIBUTORS + Jorge L Rodriguez: Implementation + Sean Barrett: API design, optimizations + Aras Pranckevicius: bugfix + Nathan Reed: warning fixes + + REVISIONS + 0.96 (2019-03-04) fixed warnings + 0.95 (2017-07-23) fixed warnings + 0.94 (2017-03-18) fixed warnings + 0.93 (2017-03-03) fixed bug with certain combinations of heights + 0.92 (2017-01-02) fix integer overflow on large (>2GB) images + 0.91 (2016-04-02) fix warnings; fix handling of subpixel regions + 0.90 (2014-09-17) first released version + + LICENSE + See end of file for license information. + + TODO + Don't decode all of the image data when only processing a partial tile + Don't use full-width decode buffers when only processing a partial tile + When processing wide images, break processing into tiles so data fits in L1 cache + Installable filters? + Resize that respects alpha test coverage + (Reference code: FloatImage::alphaTestCoverage and FloatImage::scaleAlphaToCoverage: + https://code.google.com/p/nvidia-texture-tools/source/browse/trunk/src/nvimage/FloatImage.cpp ) +*/ + +#ifndef STBIR_INCLUDE_STB_IMAGE_RESIZE_H +#define STBIR_INCLUDE_STB_IMAGE_RESIZE_H + +#ifdef _MSC_VER +typedef unsigned char stbir_uint8; +typedef unsigned short stbir_uint16; +typedef unsigned int stbir_uint32; +#else +#include +typedef uint8_t stbir_uint8; +typedef uint16_t stbir_uint16; +typedef uint32_t stbir_uint32; +#endif + +#ifndef STBIRDEF +#ifdef STB_IMAGE_RESIZE_STATIC +#define STBIRDEF static +#else +#ifdef __cplusplus +#define STBIRDEF extern "C" +#else +#define STBIRDEF extern +#endif +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// Easy-to-use API: +// +// * "input pixels" points to an array of image data with 'num_channels' channels (e.g. RGB=3, RGBA=4) +// * input_w is input image width (x-axis), input_h is input image height (y-axis) +// * stride is the offset between successive rows of image data in memory, in bytes. you can +// specify 0 to mean packed continuously in memory +// * alpha channel is treated identically to other channels. +// * colorspace is linear or sRGB as specified by function name +// * returned result is 1 for success or 0 in case of an error. +// #define STBIR_ASSERT() to trigger an assert on parameter validation errors. +// * Memory required grows approximately linearly with input and output size, but with +// discontinuities at input_w == output_w and input_h == output_h. +// * These functions use a "default" resampling filter defined at compile time. To change the filter, +// you can change the compile-time defaults by #defining STBIR_DEFAULT_FILTER_UPSAMPLE +// and STBIR_DEFAULT_FILTER_DOWNSAMPLE, or you can use the medium-complexity API. + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + + +// The following functions interpret image data as gamma-corrected sRGB. +// Specify STBIR_ALPHA_CHANNEL_NONE if you have no alpha channel, +// or otherwise provide the index of the alpha channel. Flags value +// of 0 will probably do the right thing if you're not sure what +// the flags mean. + +#define STBIR_ALPHA_CHANNEL_NONE -1 + +// Set this flag if your texture has premultiplied alpha. Otherwise, stbir will +// use alpha-weighted resampling (effectively premultiplying, resampling, +// then unpremultiplying). +#define STBIR_FLAG_ALPHA_PREMULTIPLIED (1 << 0) +// The specified alpha channel should be handled as gamma-corrected value even +// when doing sRGB operations. +#define STBIR_FLAG_ALPHA_USES_COLORSPACE (1 << 1) + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags); + + +typedef enum +{ + STBIR_EDGE_CLAMP = 1, + STBIR_EDGE_REFLECT = 2, + STBIR_EDGE_WRAP = 3, + STBIR_EDGE_ZERO = 4, +} stbir_edge; + +// This function adds the ability to specify how requests to sample off the edge of the image are handled. +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode); + +////////////////////////////////////////////////////////////////////////////// +// +// Medium-complexity API +// +// This extends the easy-to-use API as follows: +// +// * Alpha-channel can be processed separately +// * If alpha_channel is not STBIR_ALPHA_CHANNEL_NONE +// * Alpha channel will not be gamma corrected (unless flags&STBIR_FLAG_GAMMA_CORRECT) +// * Filters will be weighted by alpha channel (unless flags&STBIR_FLAG_ALPHA_PREMULTIPLIED) +// * Filter can be selected explicitly +// * uint16 image type +// * sRGB colorspace available for all types +// * context parameter for passing to STBIR_MALLOC + +typedef enum +{ + STBIR_FILTER_DEFAULT = 0, // use same filter type that easy-to-use API chooses + STBIR_FILTER_BOX = 1, // A trapezoid w/1-pixel wide ramps, same result as box for integer scale ratios + STBIR_FILTER_TRIANGLE = 2, // On upsampling, produces same results as bilinear texture filtering + STBIR_FILTER_CUBICBSPLINE = 3, // The cubic b-spline (aka Mitchell-Netrevalli with B=1,C=0), gaussian-esque + STBIR_FILTER_CATMULLROM = 4, // An interpolating cubic spline + STBIR_FILTER_MITCHELL = 5, // Mitchell-Netrevalli filter with B=1/3, C=1/3 +} stbir_filter; + +typedef enum +{ + STBIR_COLORSPACE_LINEAR, + STBIR_COLORSPACE_SRGB, + + STBIR_MAX_COLORSPACES, +} stbir_colorspace; + +// The following functions are all identical except for the type of the image data + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + + + +////////////////////////////////////////////////////////////////////////////// +// +// Full-complexity API +// +// This extends the medium API as follows: +// +// * uint32 image type +// * not typesafe +// * separate filter types for each axis +// * separate edge modes for each axis +// * can specify scale explicitly for subpixel correctness +// * can specify image source tile using texture coordinates + +typedef enum +{ + STBIR_TYPE_UINT8 , + STBIR_TYPE_UINT16, + STBIR_TYPE_UINT32, + STBIR_TYPE_FLOAT , + + STBIR_MAX_TYPES +} stbir_datatype; + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context); + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset); + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1); +// (s0, t0) & (s1, t1) are the top-left and bottom right corner (uv addressing style: [0, 1]x[0, 1]) of a region of the input image to use. + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBIR_INCLUDE_STB_IMAGE_RESIZE_H + + + + + +#ifdef STB_IMAGE_RESIZE_IMPLEMENTATION + +#ifndef STBIR_ASSERT +#include +#define STBIR_ASSERT(x) assert(x) +#endif + +// For memset +#include + +#include + +#ifndef STBIR_MALLOC +#include +// use comma operator to evaluate c, to avoid "unused parameter" warnings +#define STBIR_MALLOC(size,c) ((void)(c), malloc(size)) +#define STBIR_FREE(ptr,c) ((void)(c), free(ptr)) +#endif + +#ifndef _MSC_VER +#ifdef __cplusplus +#define stbir__inline inline +#else +#define stbir__inline +#endif +#else +#define stbir__inline __forceinline +#endif + + +// should produce compiler error if size is wrong +typedef unsigned char stbir__validate_uint32[sizeof(stbir_uint32) == 4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBIR__NOTUSED(v) (void)(v) +#else +#define STBIR__NOTUSED(v) (void)sizeof(v) +#endif + +#define STBIR__ARRAY_SIZE(a) (sizeof((a))/sizeof((a)[0])) + +#ifndef STBIR_DEFAULT_FILTER_UPSAMPLE +#define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_CATMULLROM +#endif + +#ifndef STBIR_DEFAULT_FILTER_DOWNSAMPLE +#define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_MITCHELL +#endif + +#ifndef STBIR_PROGRESS_REPORT +#define STBIR_PROGRESS_REPORT(float_0_to_1) +#endif + +#ifndef STBIR_MAX_CHANNELS +#define STBIR_MAX_CHANNELS 64 +#endif + +#if STBIR_MAX_CHANNELS > 65536 +#error "Too many channels; STBIR_MAX_CHANNELS must be no more than 65536." +// because we store the indices in 16-bit variables +#endif + +// This value is added to alpha just before premultiplication to avoid +// zeroing out color values. It is equivalent to 2^-80. If you don't want +// that behavior (it may interfere if you have floating point images with +// very small alpha values) then you can define STBIR_NO_ALPHA_EPSILON to +// disable it. +#ifndef STBIR_ALPHA_EPSILON +#define STBIR_ALPHA_EPSILON ((float)1 / (1 << 20) / (1 << 20) / (1 << 20) / (1 << 20)) +#endif + + + +#ifdef _MSC_VER +#define STBIR__UNUSED_PARAM(v) (void)(v) +#else +#define STBIR__UNUSED_PARAM(v) (void)sizeof(v) +#endif + +// must match stbir_datatype +static unsigned char stbir__type_size[] = { + 1, // STBIR_TYPE_UINT8 + 2, // STBIR_TYPE_UINT16 + 4, // STBIR_TYPE_UINT32 + 4, // STBIR_TYPE_FLOAT +}; + +// Kernel function centered at 0 +typedef float (stbir__kernel_fn)(float x, float scale); +typedef float (stbir__support_fn)(float scale); + +typedef struct +{ + stbir__kernel_fn* kernel; + stbir__support_fn* support; +} stbir__filter_info; + +// When upsampling, the contributors are which source pixels contribute. +// When downsampling, the contributors are which destination pixels are contributed to. +typedef struct +{ + int n0; // First contributing pixel + int n1; // Last contributing pixel +} stbir__contributors; + +typedef struct +{ + const void* input_data; + int input_w; + int input_h; + int input_stride_bytes; + + void* output_data; + int output_w; + int output_h; + int output_stride_bytes; + + float s0, t0, s1, t1; + + float horizontal_shift; // Units: output pixels + float vertical_shift; // Units: output pixels + float horizontal_scale; + float vertical_scale; + + int channels; + int alpha_channel; + stbir_uint32 flags; + stbir_datatype type; + stbir_filter horizontal_filter; + stbir_filter vertical_filter; + stbir_edge edge_horizontal; + stbir_edge edge_vertical; + stbir_colorspace colorspace; + + stbir__contributors* horizontal_contributors; + float* horizontal_coefficients; + + stbir__contributors* vertical_contributors; + float* vertical_coefficients; + + int decode_buffer_pixels; + float* decode_buffer; + + float* horizontal_buffer; + + // cache these because ceil/floor are inexplicably showing up in profile + int horizontal_coefficient_width; + int vertical_coefficient_width; + int horizontal_filter_pixel_width; + int vertical_filter_pixel_width; + int horizontal_filter_pixel_margin; + int vertical_filter_pixel_margin; + int horizontal_num_contributors; + int vertical_num_contributors; + + int ring_buffer_length_bytes; // The length of an individual entry in the ring buffer. The total number of ring buffers is stbir__get_filter_pixel_width(filter) + int ring_buffer_num_entries; // Total number of entries in the ring buffer. + int ring_buffer_first_scanline; + int ring_buffer_last_scanline; + int ring_buffer_begin_index; // first_scanline is at this index in the ring buffer + float* ring_buffer; + + float* encode_buffer; // A temporary buffer to store floats so we don't lose precision while we do multiply-adds. + + int horizontal_contributors_size; + int horizontal_coefficients_size; + int vertical_contributors_size; + int vertical_coefficients_size; + int decode_buffer_size; + int horizontal_buffer_size; + int ring_buffer_size; + int encode_buffer_size; +} stbir__info; + + +static const float stbir__max_uint8_as_float = 255.0f; +static const float stbir__max_uint16_as_float = 65535.0f; +static const double stbir__max_uint32_as_float = 4294967295.0; + + +static stbir__inline int stbir__min(int a, int b) +{ + return a < b ? a : b; +} + +static stbir__inline float stbir__saturate(float x) +{ + if (x < 0) + return 0; + + if (x > 1) + return 1; + + return x; +} + +#ifdef STBIR_SATURATE_INT +static stbir__inline stbir_uint8 stbir__saturate8(int x) +{ + if ((unsigned int) x <= 255) + return x; + + if (x < 0) + return 0; + + return 255; +} + +static stbir__inline stbir_uint16 stbir__saturate16(int x) +{ + if ((unsigned int) x <= 65535) + return x; + + if (x < 0) + return 0; + + return 65535; +} +#endif + +static float stbir__srgb_uchar_to_linear_float[256] = { + 0.000000f, 0.000304f, 0.000607f, 0.000911f, 0.001214f, 0.001518f, 0.001821f, 0.002125f, 0.002428f, 0.002732f, 0.003035f, + 0.003347f, 0.003677f, 0.004025f, 0.004391f, 0.004777f, 0.005182f, 0.005605f, 0.006049f, 0.006512f, 0.006995f, 0.007499f, + 0.008023f, 0.008568f, 0.009134f, 0.009721f, 0.010330f, 0.010960f, 0.011612f, 0.012286f, 0.012983f, 0.013702f, 0.014444f, + 0.015209f, 0.015996f, 0.016807f, 0.017642f, 0.018500f, 0.019382f, 0.020289f, 0.021219f, 0.022174f, 0.023153f, 0.024158f, + 0.025187f, 0.026241f, 0.027321f, 0.028426f, 0.029557f, 0.030713f, 0.031896f, 0.033105f, 0.034340f, 0.035601f, 0.036889f, + 0.038204f, 0.039546f, 0.040915f, 0.042311f, 0.043735f, 0.045186f, 0.046665f, 0.048172f, 0.049707f, 0.051269f, 0.052861f, + 0.054480f, 0.056128f, 0.057805f, 0.059511f, 0.061246f, 0.063010f, 0.064803f, 0.066626f, 0.068478f, 0.070360f, 0.072272f, + 0.074214f, 0.076185f, 0.078187f, 0.080220f, 0.082283f, 0.084376f, 0.086500f, 0.088656f, 0.090842f, 0.093059f, 0.095307f, + 0.097587f, 0.099899f, 0.102242f, 0.104616f, 0.107023f, 0.109462f, 0.111932f, 0.114435f, 0.116971f, 0.119538f, 0.122139f, + 0.124772f, 0.127438f, 0.130136f, 0.132868f, 0.135633f, 0.138432f, 0.141263f, 0.144128f, 0.147027f, 0.149960f, 0.152926f, + 0.155926f, 0.158961f, 0.162029f, 0.165132f, 0.168269f, 0.171441f, 0.174647f, 0.177888f, 0.181164f, 0.184475f, 0.187821f, + 0.191202f, 0.194618f, 0.198069f, 0.201556f, 0.205079f, 0.208637f, 0.212231f, 0.215861f, 0.219526f, 0.223228f, 0.226966f, + 0.230740f, 0.234551f, 0.238398f, 0.242281f, 0.246201f, 0.250158f, 0.254152f, 0.258183f, 0.262251f, 0.266356f, 0.270498f, + 0.274677f, 0.278894f, 0.283149f, 0.287441f, 0.291771f, 0.296138f, 0.300544f, 0.304987f, 0.309469f, 0.313989f, 0.318547f, + 0.323143f, 0.327778f, 0.332452f, 0.337164f, 0.341914f, 0.346704f, 0.351533f, 0.356400f, 0.361307f, 0.366253f, 0.371238f, + 0.376262f, 0.381326f, 0.386430f, 0.391573f, 0.396755f, 0.401978f, 0.407240f, 0.412543f, 0.417885f, 0.423268f, 0.428691f, + 0.434154f, 0.439657f, 0.445201f, 0.450786f, 0.456411f, 0.462077f, 0.467784f, 0.473532f, 0.479320f, 0.485150f, 0.491021f, + 0.496933f, 0.502887f, 0.508881f, 0.514918f, 0.520996f, 0.527115f, 0.533276f, 0.539480f, 0.545725f, 0.552011f, 0.558340f, + 0.564712f, 0.571125f, 0.577581f, 0.584078f, 0.590619f, 0.597202f, 0.603827f, 0.610496f, 0.617207f, 0.623960f, 0.630757f, + 0.637597f, 0.644480f, 0.651406f, 0.658375f, 0.665387f, 0.672443f, 0.679543f, 0.686685f, 0.693872f, 0.701102f, 0.708376f, + 0.715694f, 0.723055f, 0.730461f, 0.737911f, 0.745404f, 0.752942f, 0.760525f, 0.768151f, 0.775822f, 0.783538f, 0.791298f, + 0.799103f, 0.806952f, 0.814847f, 0.822786f, 0.830770f, 0.838799f, 0.846873f, 0.854993f, 0.863157f, 0.871367f, 0.879622f, + 0.887923f, 0.896269f, 0.904661f, 0.913099f, 0.921582f, 0.930111f, 0.938686f, 0.947307f, 0.955974f, 0.964686f, 0.973445f, + 0.982251f, 0.991102f, 1.0f +}; + +static float stbir__srgb_to_linear(float f) +{ + if (f <= 0.04045f) + return f / 12.92f; + else + return (float)pow((f + 0.055f) / 1.055f, 2.4f); +} + +static float stbir__linear_to_srgb(float f) +{ + if (f <= 0.0031308f) + return f * 12.92f; + else + return 1.055f * (float)pow(f, 1 / 2.4f) - 0.055f; +} + +#ifndef STBIR_NON_IEEE_FLOAT +// From https://gist.github.com/rygorous/2203834 + +typedef union +{ + stbir_uint32 u; + float f; +} stbir__FP32; + +static const stbir_uint32 fp32_to_srgb8_tab4[104] = { + 0x0073000d, 0x007a000d, 0x0080000d, 0x0087000d, 0x008d000d, 0x0094000d, 0x009a000d, 0x00a1000d, + 0x00a7001a, 0x00b4001a, 0x00c1001a, 0x00ce001a, 0x00da001a, 0x00e7001a, 0x00f4001a, 0x0101001a, + 0x010e0033, 0x01280033, 0x01410033, 0x015b0033, 0x01750033, 0x018f0033, 0x01a80033, 0x01c20033, + 0x01dc0067, 0x020f0067, 0x02430067, 0x02760067, 0x02aa0067, 0x02dd0067, 0x03110067, 0x03440067, + 0x037800ce, 0x03df00ce, 0x044600ce, 0x04ad00ce, 0x051400ce, 0x057b00c5, 0x05dd00bc, 0x063b00b5, + 0x06970158, 0x07420142, 0x07e30130, 0x087b0120, 0x090b0112, 0x09940106, 0x0a1700fc, 0x0a9500f2, + 0x0b0f01cb, 0x0bf401ae, 0x0ccb0195, 0x0d950180, 0x0e56016e, 0x0f0d015e, 0x0fbc0150, 0x10630143, + 0x11070264, 0x1238023e, 0x1357021d, 0x14660201, 0x156601e9, 0x165a01d3, 0x174401c0, 0x182401af, + 0x18fe0331, 0x1a9602fe, 0x1c1502d2, 0x1d7e02ad, 0x1ed4028d, 0x201a0270, 0x21520256, 0x227d0240, + 0x239f0443, 0x25c003fe, 0x27bf03c4, 0x29a10392, 0x2b6a0367, 0x2d1d0341, 0x2ebe031f, 0x304d0300, + 0x31d105b0, 0x34a80555, 0x37520507, 0x39d504c5, 0x3c37048b, 0x3e7c0458, 0x40a8042a, 0x42bd0401, + 0x44c20798, 0x488e071e, 0x4c1c06b6, 0x4f76065d, 0x52a50610, 0x55ac05cc, 0x5892058f, 0x5b590559, + 0x5e0c0a23, 0x631c0980, 0x67db08f6, 0x6c55087f, 0x70940818, 0x74a007bd, 0x787d076c, 0x7c330723, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float in) +{ + static const stbir__FP32 almostone = { 0x3f7fffff }; // 1-eps + static const stbir__FP32 minval = { (127-13) << 23 }; + stbir_uint32 tab,bias,scale,t; + stbir__FP32 f; + + // Clamp to [2^(-13), 1-eps]; these two values map to 0 and 1, respectively. + // The tests are carefully written so that NaNs map to 0, same as in the reference + // implementation. + if (!(in > minval.f)) // written this way to catch NaNs + in = minval.f; + if (in > almostone.f) + in = almostone.f; + + // Do the table lookup and unpack bias, scale + f.f = in; + tab = fp32_to_srgb8_tab4[(f.u - minval.u) >> 20]; + bias = (tab >> 16) << 9; + scale = tab & 0xffff; + + // Grab next-highest mantissa bits and perform linear interpolation + t = (f.u >> 12) & 0xff; + return (unsigned char) ((bias + scale*t) >> 16); +} + +#else +// sRGB transition values, scaled by 1<<28 +static int stbir__srgb_offset_to_linear_scaled[256] = +{ + 0, 40738, 122216, 203693, 285170, 366648, 448125, 529603, + 611080, 692557, 774035, 855852, 942009, 1033024, 1128971, 1229926, + 1335959, 1447142, 1563542, 1685229, 1812268, 1944725, 2082664, 2226148, + 2375238, 2529996, 2690481, 2856753, 3028870, 3206888, 3390865, 3580856, + 3776916, 3979100, 4187460, 4402049, 4622919, 4850123, 5083710, 5323731, + 5570236, 5823273, 6082892, 6349140, 6622065, 6901714, 7188133, 7481369, + 7781466, 8088471, 8402427, 8723380, 9051372, 9386448, 9728650, 10078021, + 10434603, 10798439, 11169569, 11548036, 11933879, 12327139, 12727857, 13136073, + 13551826, 13975156, 14406100, 14844697, 15290987, 15745007, 16206795, 16676389, + 17153826, 17639142, 18132374, 18633560, 19142734, 19659934, 20185196, 20718552, + 21260042, 21809696, 22367554, 22933648, 23508010, 24090680, 24681686, 25281066, + 25888850, 26505076, 27129772, 27762974, 28404716, 29055026, 29713942, 30381490, + 31057708, 31742624, 32436272, 33138682, 33849884, 34569912, 35298800, 36036568, + 36783260, 37538896, 38303512, 39077136, 39859796, 40651528, 41452360, 42262316, + 43081432, 43909732, 44747252, 45594016, 46450052, 47315392, 48190064, 49074096, + 49967516, 50870356, 51782636, 52704392, 53635648, 54576432, 55526772, 56486700, + 57456236, 58435408, 59424248, 60422780, 61431036, 62449032, 63476804, 64514376, + 65561776, 66619028, 67686160, 68763192, 69850160, 70947088, 72053992, 73170912, + 74297864, 75434880, 76581976, 77739184, 78906536, 80084040, 81271736, 82469648, + 83677792, 84896192, 86124888, 87363888, 88613232, 89872928, 91143016, 92423512, + 93714432, 95015816, 96327688, 97650056, 98982952, 100326408, 101680440, 103045072, + 104420320, 105806224, 107202800, 108610064, 110028048, 111456776, 112896264, 114346544, + 115807632, 117279552, 118762328, 120255976, 121760536, 123276016, 124802440, 126339832, + 127888216, 129447616, 131018048, 132599544, 134192112, 135795792, 137410592, 139036528, + 140673648, 142321952, 143981456, 145652208, 147334208, 149027488, 150732064, 152447968, + 154175200, 155913792, 157663776, 159425168, 161197984, 162982240, 164777968, 166585184, + 168403904, 170234160, 172075968, 173929344, 175794320, 177670896, 179559120, 181458992, + 183370528, 185293776, 187228736, 189175424, 191133888, 193104112, 195086128, 197079968, + 199085648, 201103184, 203132592, 205173888, 207227120, 209292272, 211369392, 213458480, + 215559568, 217672656, 219797792, 221934976, 224084240, 226245600, 228419056, 230604656, + 232802400, 235012320, 237234432, 239468736, 241715280, 243974080, 246245120, 248528464, + 250824112, 253132064, 255452368, 257785040, 260130080, 262487520, 264857376, 267239664, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float f) +{ + int x = (int) (f * (1 << 28)); // has headroom so you don't need to clamp + int v = 0; + int i; + + // Refine the guess with a short binary search. + i = v + 128; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 64; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 32; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 16; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 8; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 4; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 2; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 1; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + + return (stbir_uint8) v; +} +#endif + +static float stbir__filter_trapezoid(float x, float scale) +{ + float halfscale = scale / 2; + float t = 0.5f + halfscale; + STBIR_ASSERT(scale <= 1); + + x = (float)fabs(x); + + if (x >= t) + return 0; + else + { + float r = 0.5f - halfscale; + if (x <= r) + return 1; + else + return (t - x) / scale; + } +} + +static float stbir__support_trapezoid(float scale) +{ + STBIR_ASSERT(scale <= 1); + return 0.5f + scale / 2; +} + +static float stbir__filter_triangle(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x <= 1.0f) + return 1 - x; + else + return 0; +} + +static float stbir__filter_cubic(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (4 + x*x*(3*x - 6))/6; + else if (x < 2.0f) + return (8 + x*(-12 + x*(6 - x)))/6; + + return (0.0f); +} + +static float stbir__filter_catmullrom(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return 1 - x*x*(2.5f - 1.5f*x); + else if (x < 2.0f) + return 2 - x*(4 + x*(0.5f*x - 2.5f)); + + return (0.0f); +} + +static float stbir__filter_mitchell(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (16 + x*x*(21 * x - 36))/18; + else if (x < 2.0f) + return (32 + x*(-60 + x*(36 - 7*x)))/18; + + return (0.0f); +} + +static float stbir__support_zero(float s) +{ + STBIR__UNUSED_PARAM(s); + return 0; +} + +static float stbir__support_one(float s) +{ + STBIR__UNUSED_PARAM(s); + return 1; +} + +static float stbir__support_two(float s) +{ + STBIR__UNUSED_PARAM(s); + return 2; +} + +static stbir__filter_info stbir__filter_info_table[] = { + { NULL, stbir__support_zero }, + { stbir__filter_trapezoid, stbir__support_trapezoid }, + { stbir__filter_triangle, stbir__support_one }, + { stbir__filter_cubic, stbir__support_two }, + { stbir__filter_catmullrom, stbir__support_two }, + { stbir__filter_mitchell, stbir__support_two }, +}; + +stbir__inline static int stbir__use_upsampling(float ratio) +{ + return ratio > 1; +} + +stbir__inline static int stbir__use_width_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->horizontal_scale); +} + +stbir__inline static int stbir__use_height_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->vertical_scale); +} + +// This is the maximum number of input samples that can affect an output sample +// with the given filter +static int stbir__get_filter_pixel_width(stbir_filter filter, float scale) +{ + STBIR_ASSERT(filter != 0); + STBIR_ASSERT(filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2 / scale); +} + +// This is how much to expand buffers to account for filters seeking outside +// the image boundaries. +static int stbir__get_filter_pixel_margin(stbir_filter filter, float scale) +{ + return stbir__get_filter_pixel_width(filter, scale) / 2; +} + +static int stbir__get_coefficient_width(stbir_filter filter, float scale) +{ + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1 / scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2); +} + +static int stbir__get_contributors(float scale, stbir_filter filter, int input_size, int output_size) +{ + if (stbir__use_upsampling(scale)) + return output_size; + else + return (input_size + stbir__get_filter_pixel_margin(filter, scale) * 2); +} + +static int stbir__get_total_horizontal_coefficients(stbir__info* info) +{ + return info->horizontal_num_contributors + * stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); +} + +static int stbir__get_total_vertical_coefficients(stbir__info* info) +{ + return info->vertical_num_contributors + * stbir__get_coefficient_width (info->vertical_filter, info->vertical_scale); +} + +static stbir__contributors* stbir__get_contributor(stbir__contributors* contributors, int n) +{ + return &contributors[n]; +} + +// For perf reasons this code is duplicated in stbir__resample_horizontal_upsample/downsample, +// if you change it here change it there too. +static float* stbir__get_coefficient(float* coefficients, stbir_filter filter, float scale, int n, int c) +{ + int width = stbir__get_coefficient_width(filter, scale); + return &coefficients[width*n + c]; +} + +static int stbir__edge_wrap_slow(stbir_edge edge, int n, int max) +{ + switch (edge) + { + case STBIR_EDGE_ZERO: + return 0; // we'll decode the wrong pixel here, and then overwrite with 0s later + + case STBIR_EDGE_CLAMP: + if (n < 0) + return 0; + + if (n >= max) + return max - 1; + + return n; // NOTREACHED + + case STBIR_EDGE_REFLECT: + { + if (n < 0) + { + if (n < max) + return -n; + else + return max - 1; + } + + if (n >= max) + { + int max2 = max * 2; + if (n >= max2) + return 0; + else + return max2 - n - 1; + } + + return n; // NOTREACHED + } + + case STBIR_EDGE_WRAP: + if (n >= 0) + return (n % max); + else + { + int m = (-n) % max; + + if (m != 0) + m = max - m; + + return (m); + } + // NOTREACHED + + default: + STBIR_ASSERT(!"Unimplemented edge type"); + return 0; + } +} + +stbir__inline static int stbir__edge_wrap(stbir_edge edge, int n, int max) +{ + // avoid per-pixel switch + if (n >= 0 && n < max) + return n; + return stbir__edge_wrap_slow(edge, n, max); +} + +// What input pixels contribute to this output pixel? +static void stbir__calculate_sample_range_upsample(int n, float out_filter_radius, float scale_ratio, float out_shift, int* in_first_pixel, int* in_last_pixel, float* in_center_of_out) +{ + float out_pixel_center = (float)n + 0.5f; + float out_pixel_influence_lowerbound = out_pixel_center - out_filter_radius; + float out_pixel_influence_upperbound = out_pixel_center + out_filter_radius; + + float in_pixel_influence_lowerbound = (out_pixel_influence_lowerbound + out_shift) / scale_ratio; + float in_pixel_influence_upperbound = (out_pixel_influence_upperbound + out_shift) / scale_ratio; + + *in_center_of_out = (out_pixel_center + out_shift) / scale_ratio; + *in_first_pixel = (int)(floor(in_pixel_influence_lowerbound + 0.5)); + *in_last_pixel = (int)(floor(in_pixel_influence_upperbound - 0.5)); +} + +// What output pixels does this input pixel contribute to? +static void stbir__calculate_sample_range_downsample(int n, float in_pixels_radius, float scale_ratio, float out_shift, int* out_first_pixel, int* out_last_pixel, float* out_center_of_in) +{ + float in_pixel_center = (float)n + 0.5f; + float in_pixel_influence_lowerbound = in_pixel_center - in_pixels_radius; + float in_pixel_influence_upperbound = in_pixel_center + in_pixels_radius; + + float out_pixel_influence_lowerbound = in_pixel_influence_lowerbound * scale_ratio - out_shift; + float out_pixel_influence_upperbound = in_pixel_influence_upperbound * scale_ratio - out_shift; + + *out_center_of_in = in_pixel_center * scale_ratio - out_shift; + *out_first_pixel = (int)(floor(out_pixel_influence_lowerbound + 0.5)); + *out_last_pixel = (int)(floor(out_pixel_influence_upperbound - 0.5)); +} + +static void stbir__calculate_coefficients_upsample(stbir_filter filter, float scale, int in_first_pixel, int in_last_pixel, float in_center_of_out, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + float total_filter = 0; + float filter_scale; + + STBIR_ASSERT(in_last_pixel - in_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = in_first_pixel; + contributor->n1 = in_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + { + float in_pixel_center = (float)(i + in_first_pixel) + 0.5f; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(in_center_of_out - in_pixel_center, 1 / scale); + + // If the coefficient is zero, skip it. (Don't do the <0 check here, we want the influence of those outside pixels.) + if (i == 0 && !coefficient_group[i]) + { + contributor->n0 = ++in_first_pixel; + i--; + continue; + } + + total_filter += coefficient_group[i]; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(in_last_pixel + 1) + 0.5f - in_center_of_out, 1/scale) == 0); + + STBIR_ASSERT(total_filter > 0.9); + STBIR_ASSERT(total_filter < 1.1f); // Make sure it's not way off. + + // Make sure the sum of all coefficients is 1. + filter_scale = 1 / total_filter; + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + coefficient_group[i] *= filter_scale; + + for (i = in_last_pixel - in_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__calculate_coefficients_downsample(stbir_filter filter, float scale_ratio, int out_first_pixel, int out_last_pixel, float out_center_of_in, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + + STBIR_ASSERT(out_last_pixel - out_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(scale_ratio) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = out_first_pixel; + contributor->n1 = out_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= out_last_pixel - out_first_pixel; i++) + { + float out_pixel_center = (float)(i + out_first_pixel) + 0.5f; + float x = out_pixel_center - out_center_of_in; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(x, scale_ratio) * scale_ratio; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(out_last_pixel + 1) + 0.5f - out_center_of_in, scale_ratio) == 0); + + for (i = out_last_pixel - out_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__normalize_downsample_coefficients(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, int input_size, int output_size) +{ + int num_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + int num_coefficients = stbir__get_coefficient_width(filter, scale_ratio); + int i, j; + int skip; + + for (i = 0; i < output_size; i++) + { + float scale; + float total = 0; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + { + float coefficient = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0); + total += coefficient; + } + else if (i < contributors[j].n0) + break; + } + + STBIR_ASSERT(total > 0.9f); + STBIR_ASSERT(total < 1.1f); + + scale = 1 / total; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0) *= scale; + else if (i < contributors[j].n0) + break; + } + } + + // Optimize: Skip zero coefficients and contributions outside of image bounds. + // Do this after normalizing because normalization depends on the n0/n1 values. + for (j = 0; j < num_contributors; j++) + { + int range, max, width; + + skip = 0; + while (*stbir__get_coefficient(coefficients, filter, scale_ratio, j, skip) == 0) + skip++; + + contributors[j].n0 += skip; + + while (contributors[j].n0 < 0) + { + contributors[j].n0++; + skip++; + } + + range = contributors[j].n1 - contributors[j].n0 + 1; + max = stbir__min(num_coefficients, range); + + width = stbir__get_coefficient_width(filter, scale_ratio); + for (i = 0; i < max; i++) + { + if (i + skip >= width) + break; + + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i) = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i + skip); + } + + continue; + } + + // Using min to avoid writing into invalid pixels. + for (i = 0; i < num_contributors; i++) + contributors[i].n1 = stbir__min(contributors[i].n1, output_size - 1); +} + +// Each scan line uses the same kernel values so we should calculate the kernel +// values once and then we can use them for every scan line. +static void stbir__calculate_filters(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, float shift, int input_size, int output_size) +{ + int n; + int total_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + + if (stbir__use_upsampling(scale_ratio)) + { + float out_pixels_radius = stbir__filter_info_table[filter].support(1 / scale_ratio) * scale_ratio; + + // Looping through out pixels + for (n = 0; n < total_contributors; n++) + { + float in_center_of_out; // Center of the current out pixel in the in pixel space + int in_first_pixel, in_last_pixel; + + stbir__calculate_sample_range_upsample(n, out_pixels_radius, scale_ratio, shift, &in_first_pixel, &in_last_pixel, &in_center_of_out); + + stbir__calculate_coefficients_upsample(filter, scale_ratio, in_first_pixel, in_last_pixel, in_center_of_out, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + } + else + { + float in_pixels_radius = stbir__filter_info_table[filter].support(scale_ratio) / scale_ratio; + + // Looping through in pixels + for (n = 0; n < total_contributors; n++) + { + float out_center_of_in; // Center of the current out pixel in the in pixel space + int out_first_pixel, out_last_pixel; + int n_adjusted = n - stbir__get_filter_pixel_margin(filter, scale_ratio); + + stbir__calculate_sample_range_downsample(n_adjusted, in_pixels_radius, scale_ratio, shift, &out_first_pixel, &out_last_pixel, &out_center_of_in); + + stbir__calculate_coefficients_downsample(filter, scale_ratio, out_first_pixel, out_last_pixel, out_center_of_in, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + + stbir__normalize_downsample_coefficients(contributors, coefficients, filter, scale_ratio, input_size, output_size); + } +} + +static float* stbir__get_decode_buffer(stbir__info* stbir_info) +{ + // The 0 index of the decode buffer starts after the margin. This makes + // it okay to use negative indexes on the decode buffer. + return &stbir_info->decode_buffer[stbir_info->horizontal_filter_pixel_margin * stbir_info->channels]; +} + +#define STBIR__DECODE(type, colorspace) ((type) * (STBIR_MAX_COLORSPACES) + (colorspace)) + +static void stbir__decode_scanline(stbir__info* stbir_info, int n) +{ + int c; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int input_w = stbir_info->input_w; + size_t input_stride_bytes = stbir_info->input_stride_bytes; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir_edge edge_horizontal = stbir_info->edge_horizontal; + stbir_edge edge_vertical = stbir_info->edge_vertical; + size_t in_buffer_row_offset = stbir__edge_wrap(edge_vertical, n, stbir_info->input_h) * input_stride_bytes; + const void* input_data = (char *) stbir_info->input_data + in_buffer_row_offset; + int max_x = input_w + stbir_info->horizontal_filter_pixel_margin; + int decode = STBIR__DECODE(type, colorspace); + + int x = -stbir_info->horizontal_filter_pixel_margin; + + // special handling for STBIR_EDGE_ZERO because it needs to return an item that doesn't appear in the input, + // and we want to avoid paying overhead on every pixel if not STBIR_EDGE_ZERO + if (edge_vertical == STBIR_EDGE_ZERO && (n < 0 || n >= stbir_info->input_h)) + { + for (; x < max_x; x++) + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + return; + } + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned char*)input_data)[input_pixel_index + c]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_uchar_to_linear_float[((const unsigned char*)input_data)[input_pixel_index + c]]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned char*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned short*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear((float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float)); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((const float*)input_data)[input_pixel_index + c]; + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((const float*)input_data)[input_pixel_index + c]); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((const float*)input_data)[input_pixel_index + alpha_channel]; + } + + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < max_x; x++) + { + int decode_pixel_index = x * channels; + + // If the alpha value is 0 it will clobber the color values. Make sure it's not. + float alpha = decode_buffer[decode_pixel_index + alpha_channel]; +#ifndef STBIR_NO_ALPHA_EPSILON + if (stbir_info->type != STBIR_TYPE_FLOAT) { + alpha += STBIR_ALPHA_EPSILON; + decode_buffer[decode_pixel_index + alpha_channel] = alpha; + } +#endif + for (c = 0; c < channels; c++) + { + if (c == alpha_channel) + continue; + + decode_buffer[decode_pixel_index + c] *= alpha; + } + } + } + + if (edge_horizontal == STBIR_EDGE_ZERO) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < 0; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + for (x = input_w; x < max_x; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + } +} + +static float* stbir__get_ring_buffer_entry(float* ring_buffer, int index, int ring_buffer_length) +{ + return &ring_buffer[index * ring_buffer_length]; +} + +static float* stbir__add_empty_ring_buffer_entry(stbir__info* stbir_info, int n) +{ + int ring_buffer_index; + float* ring_buffer; + + stbir_info->ring_buffer_last_scanline = n; + + if (stbir_info->ring_buffer_begin_index < 0) + { + ring_buffer_index = stbir_info->ring_buffer_begin_index = 0; + stbir_info->ring_buffer_first_scanline = n; + } + else + { + ring_buffer_index = (stbir_info->ring_buffer_begin_index + (stbir_info->ring_buffer_last_scanline - stbir_info->ring_buffer_first_scanline)) % stbir_info->ring_buffer_num_entries; + STBIR_ASSERT(ring_buffer_index != stbir_info->ring_buffer_begin_index); + } + + ring_buffer = stbir__get_ring_buffer_entry(stbir_info->ring_buffer, ring_buffer_index, stbir_info->ring_buffer_length_bytes / sizeof(float)); + memset(ring_buffer, 0, stbir_info->ring_buffer_length_bytes); + + return ring_buffer; +} + + +static void stbir__resample_horizontal_upsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int output_w = stbir_info->output_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + + for (x = 0; x < output_w; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int out_pixel_index = x * channels; + int coefficient_group = coefficient_width * x; + int coefficient_counter = 0; + + STBIR_ASSERT(n1 >= n0); + STBIR_ASSERT(n0 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n0 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + int c; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__resample_horizontal_downsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int input_w = stbir_info->input_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + int filter_pixel_margin = stbir_info->horizontal_filter_pixel_margin; + int max_x = input_w + filter_pixel_margin * 2; + + STBIR_ASSERT(!stbir__use_width_upsampling(stbir_info)); + + switch (channels) { + case 1: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 1; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + } + break; + + case 2: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 2; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + } + break; + + case 3: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 3; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + } + break; + + case 4: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 4; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + } + break; + + default: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * channels; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int c; + int out_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + } + break; + } +} + +static void stbir__decode_and_resample_upsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + // Now resample it into the ring buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + else + stbir__resample_horizontal_downsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + + // Now it's sitting in the ring buffer ready to be used as source for the vertical sampling. +} + +static void stbir__decode_and_resample_downsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + memset(stbir_info->horizontal_buffer, 0, stbir_info->output_w * stbir_info->channels * sizeof(float)); + + // Now resample it into the horizontal buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir_info->horizontal_buffer); + else + stbir__resample_horizontal_downsample(stbir_info, stbir_info->horizontal_buffer); + + // Now it's sitting in the horizontal buffer ready to be distributed into the ring buffers. +} + +// Get the specified scan line from the ring buffer. +static float* stbir__get_ring_buffer_scanline(int get_scanline, float* ring_buffer, int begin_index, int first_scanline, int ring_buffer_num_entries, int ring_buffer_length) +{ + int ring_buffer_index = (begin_index + (get_scanline - first_scanline)) % ring_buffer_num_entries; + return stbir__get_ring_buffer_entry(ring_buffer, ring_buffer_index, ring_buffer_length); +} + + +static void stbir__encode_scanline(stbir__info* stbir_info, int num_pixels, void *output_buffer, float *encode_buffer, int channels, int alpha_channel, int decode) +{ + int x; + int n; + int num_nonalpha; + stbir_uint16 nonalpha[STBIR_MAX_CHANNELS]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + float alpha = encode_buffer[pixel_index + alpha_channel]; + float reciprocal_alpha = alpha ? 1.0f / alpha : 0; + + // unrolling this produced a 1% slowdown upscaling a large RGBA linear-space image on my machine - stb + for (n = 0; n < channels; n++) + if (n != alpha_channel) + encode_buffer[pixel_index + n] *= reciprocal_alpha; + + // We added in a small epsilon to prevent the color channel from being deleted with zero alpha. + // Because we only add it for integer types, it will automatically be discarded on integer + // conversion, so we don't need to subtract it back out (which would be problematic for + // numeric precision reasons). + } + } + + // build a table of all channels that need colorspace correction, so + // we don't perform colorspace correction on channels that don't need it. + for (x = 0, num_nonalpha = 0; x < channels; ++x) + { + if (x != alpha_channel || (stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + { + nonalpha[num_nonalpha++] = (stbir_uint16)x; + } + } + + #define STBIR__ROUND_INT(f) ((int) ((f)+0.5)) + #define STBIR__ROUND_UINT(f) ((stbir_uint32) ((f)+0.5)) + + #ifdef STBIR__SATURATE_INT + #define STBIR__ENCODE_LINEAR8(f) stbir__saturate8 (STBIR__ROUND_INT((f) * stbir__max_uint8_as_float )) + #define STBIR__ENCODE_LINEAR16(f) stbir__saturate16(STBIR__ROUND_INT((f) * stbir__max_uint16_as_float)) + #else + #define STBIR__ENCODE_LINEAR8(f) (unsigned char ) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint8_as_float ) + #define STBIR__ENCODE_LINEAR16(f) (unsigned short) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint16_as_float) + #endif + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned char*)output_buffer)[index] = STBIR__ENCODE_LINEAR8(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned char*)output_buffer)[index] = stbir__linear_to_srgb_uchar(encode_buffer[index]); + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned char *)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR8(encode_buffer[pixel_index+alpha_channel]); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned short*)output_buffer)[index] = STBIR__ENCODE_LINEAR16(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned short*)output_buffer)[index] = (unsigned short)STBIR__ROUND_INT(stbir__linear_to_srgb(stbir__saturate(encode_buffer[index])) * stbir__max_uint16_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned short*)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR16(encode_buffer[pixel_index + alpha_channel]); + } + + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__saturate(encode_buffer[index])) * stbir__max_uint32_as_float); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__linear_to_srgb(stbir__saturate(encode_buffer[index]))) * stbir__max_uint32_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned int*)output_buffer)[pixel_index + alpha_channel] = (unsigned int)STBIR__ROUND_INT(((double)stbir__saturate(encode_buffer[pixel_index + alpha_channel])) * stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((float*)output_buffer)[index] = encode_buffer[index]; + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((float*)output_buffer)[index] = stbir__linear_to_srgb(encode_buffer[index]); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((float*)output_buffer)[pixel_index + alpha_channel] = encode_buffer[pixel_index + alpha_channel]; + } + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } +} + +static void stbir__resample_vertical_upsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + void* output_data = stbir_info->output_data; + float* encode_buffer = stbir_info->encode_buffer; + int decode = STBIR__DECODE(type, colorspace); + int coefficient_width = stbir_info->vertical_coefficient_width; + int coefficient_counter; + int contributor = n; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + int n0,n1, output_row_start; + int coefficient_group = coefficient_width * contributor; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + output_row_start = n * stbir_info->output_stride_bytes; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + memset(encode_buffer, 0, output_w * sizeof(float) * channels); + + // I tried reblocking this for better cache usage of encode_buffer + // (using x_outer, k, x_inner), but it lost speed. -- stb + + coefficient_counter = 0; + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 1; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + } + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 2; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + } + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 3; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + } + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 4; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + encode_buffer[in_pixel_index + 3] += ring_buffer_entry[in_pixel_index + 3] * coefficient; + } + } + break; + default: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * channels; + int c; + for (c = 0; c < channels; c++) + encode_buffer[in_pixel_index + c] += ring_buffer_entry[in_pixel_index + c] * coefficient; + } + } + break; + } + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, encode_buffer, channels, alpha_channel, decode); +} + +static void stbir__resample_vertical_downsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + float* horizontal_buffer = stbir_info->horizontal_buffer; + int coefficient_width = stbir_info->vertical_coefficient_width; + int contributor = n + stbir_info->vertical_filter_pixel_margin; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + int n0,n1; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (k = n0; k <= n1; k++) + { + int coefficient_index = k - n0; + int coefficient_group = coefficient_width * contributor; + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + + switch (channels) { + case 1: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 1; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 2; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 3; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 4; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + ring_buffer_entry[in_pixel_index + 3] += horizontal_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * channels; + + int c; + for (c = 0; c < channels; c++) + ring_buffer_entry[in_pixel_index + c] += horizontal_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__buffer_loop_upsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + float out_scanlines_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(1/scale_ratio) * scale_ratio; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + for (y = 0; y < stbir_info->output_h; y++) + { + float in_center_of_out = 0; // Center of the current out scanline in the in scanline space + int in_first_scanline = 0, in_last_scanline = 0; + + stbir__calculate_sample_range_upsample(y, out_scanlines_radius, scale_ratio, stbir_info->vertical_shift, &in_first_scanline, &in_last_scanline, &in_center_of_out); + + STBIR_ASSERT(in_last_scanline - in_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (in_first_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__decode_and_resample_upsample(stbir_info, in_first_scanline); + + while (in_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__decode_and_resample_upsample(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now all buffers should be ready to write a row of vertical sampling. + stbir__resample_vertical_upsample(stbir_info, y); + + STBIR_PROGRESS_REPORT((float)y / stbir_info->output_h); + } +} + +static void stbir__empty_ring_buffer(stbir__info* stbir_info, int first_necessary_scanline) +{ + int output_stride_bytes = stbir_info->output_stride_bytes; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int output_w = stbir_info->output_w; + void* output_data = stbir_info->output_data; + int decode = STBIR__DECODE(type, colorspace); + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (first_necessary_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline >= 0 && stbir_info->ring_buffer_first_scanline < stbir_info->output_h) + { + int output_row_start = stbir_info->ring_buffer_first_scanline * output_stride_bytes; + float* ring_buffer_entry = stbir__get_ring_buffer_entry(ring_buffer, stbir_info->ring_buffer_begin_index, ring_buffer_length); + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, ring_buffer_entry, channels, alpha_channel, decode); + STBIR_PROGRESS_REPORT((float)stbir_info->ring_buffer_first_scanline / stbir_info->output_h); + } + + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } +} + +static void stbir__buffer_loop_downsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + int output_h = stbir_info->output_h; + float in_pixels_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(scale_ratio) / scale_ratio; + int pixel_margin = stbir_info->vertical_filter_pixel_margin; + int max_y = stbir_info->input_h + pixel_margin; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (y = -pixel_margin; y < max_y; y++) + { + float out_center_of_in; // Center of the current out scanline in the in scanline space + int out_first_scanline, out_last_scanline; + + stbir__calculate_sample_range_downsample(y, in_pixels_radius, scale_ratio, stbir_info->vertical_shift, &out_first_scanline, &out_last_scanline, &out_center_of_in); + + STBIR_ASSERT(out_last_scanline - out_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (out_last_scanline < 0 || out_first_scanline >= output_h) + continue; + + stbir__empty_ring_buffer(stbir_info, out_first_scanline); + + stbir__decode_and_resample_downsample(stbir_info, y); + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__add_empty_ring_buffer_entry(stbir_info, out_first_scanline); + + while (out_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__add_empty_ring_buffer_entry(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now the horizontal buffer is ready to write to all ring buffer rows. + stbir__resample_vertical_downsample(stbir_info, y); + } + + stbir__empty_ring_buffer(stbir_info, stbir_info->output_h); +} + +static void stbir__setup(stbir__info *info, int input_w, int input_h, int output_w, int output_h, int channels) +{ + info->input_w = input_w; + info->input_h = input_h; + info->output_w = output_w; + info->output_h = output_h; + info->channels = channels; +} + +static void stbir__calculate_transform(stbir__info *info, float s0, float t0, float s1, float t1, float *transform) +{ + info->s0 = s0; + info->t0 = t0; + info->s1 = s1; + info->t1 = t1; + + if (transform) + { + info->horizontal_scale = transform[0]; + info->vertical_scale = transform[1]; + info->horizontal_shift = transform[2]; + info->vertical_shift = transform[3]; + } + else + { + info->horizontal_scale = ((float)info->output_w / info->input_w) / (s1 - s0); + info->vertical_scale = ((float)info->output_h / info->input_h) / (t1 - t0); + + info->horizontal_shift = s0 * info->output_w / (s1 - s0); + info->vertical_shift = t0 * info->output_h / (t1 - t0); + } +} + +static void stbir__choose_filter(stbir__info *info, stbir_filter h_filter, stbir_filter v_filter) +{ + if (h_filter == 0) + h_filter = stbir__use_upsampling(info->horizontal_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + if (v_filter == 0) + v_filter = stbir__use_upsampling(info->vertical_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + info->horizontal_filter = h_filter; + info->vertical_filter = v_filter; +} + +static stbir_uint32 stbir__calculate_memory(stbir__info *info) +{ + int pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + int filter_height = stbir__get_filter_pixel_width(info->vertical_filter, info->vertical_scale); + + info->horizontal_num_contributors = stbir__get_contributors(info->horizontal_scale, info->horizontal_filter, info->input_w, info->output_w); + info->vertical_num_contributors = stbir__get_contributors(info->vertical_scale , info->vertical_filter , info->input_h, info->output_h); + + // One extra entry because floating point precision problems sometimes cause an extra to be necessary. + info->ring_buffer_num_entries = filter_height + 1; + + info->horizontal_contributors_size = info->horizontal_num_contributors * sizeof(stbir__contributors); + info->horizontal_coefficients_size = stbir__get_total_horizontal_coefficients(info) * sizeof(float); + info->vertical_contributors_size = info->vertical_num_contributors * sizeof(stbir__contributors); + info->vertical_coefficients_size = stbir__get_total_vertical_coefficients(info) * sizeof(float); + info->decode_buffer_size = (info->input_w + pixel_margin * 2) * info->channels * sizeof(float); + info->horizontal_buffer_size = info->output_w * info->channels * sizeof(float); + info->ring_buffer_size = info->output_w * info->channels * info->ring_buffer_num_entries * sizeof(float); + info->encode_buffer_size = info->output_w * info->channels * sizeof(float); + + STBIR_ASSERT(info->horizontal_filter != 0); + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + STBIR_ASSERT(info->vertical_filter != 0); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + + if (stbir__use_height_upsampling(info)) + // The horizontal buffer is for when we're downsampling the height and we + // can't output the result of sampling the decode buffer directly into the + // ring buffers. + info->horizontal_buffer_size = 0; + else + // The encode buffer is to retain precision in the height upsampling method + // and isn't used when height downsampling. + info->encode_buffer_size = 0; + + return info->horizontal_contributors_size + info->horizontal_coefficients_size + + info->vertical_contributors_size + info->vertical_coefficients_size + + info->decode_buffer_size + info->horizontal_buffer_size + + info->ring_buffer_size + info->encode_buffer_size; +} + +static int stbir__resize_allocated(stbir__info *info, + const void* input_data, int input_stride_in_bytes, + void* output_data, int output_stride_in_bytes, + int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace, + void* tempmem, size_t tempmem_size_in_bytes) +{ + size_t memory_required = stbir__calculate_memory(info); + + int width_stride_input = input_stride_in_bytes ? input_stride_in_bytes : info->channels * info->input_w * stbir__type_size[type]; + int width_stride_output = output_stride_in_bytes ? output_stride_in_bytes : info->channels * info->output_w * stbir__type_size[type]; + +#ifdef STBIR_DEBUG_OVERWRITE_TEST +#define OVERWRITE_ARRAY_SIZE 8 + unsigned char overwrite_output_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_output_after_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_after_pre[OVERWRITE_ARRAY_SIZE]; + + size_t begin_forbidden = width_stride_output * (info->output_h - 1) + info->output_w * info->channels * stbir__type_size[type]; + memcpy(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE); +#endif + + STBIR_ASSERT(info->channels >= 0); + STBIR_ASSERT(info->channels <= STBIR_MAX_CHANNELS); + + if (info->channels < 0 || info->channels > STBIR_MAX_CHANNELS) + return 0; + + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (info->horizontal_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + if (info->vertical_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + + if (alpha_channel < 0) + flags |= STBIR_FLAG_ALPHA_USES_COLORSPACE | STBIR_FLAG_ALPHA_PREMULTIPLIED; + + if (!(flags&STBIR_FLAG_ALPHA_USES_COLORSPACE) || !(flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) { + STBIR_ASSERT(alpha_channel >= 0 && alpha_channel < info->channels); + } + + if (alpha_channel >= info->channels) + return 0; + + STBIR_ASSERT(tempmem); + + if (!tempmem) + return 0; + + STBIR_ASSERT(tempmem_size_in_bytes >= memory_required); + + if (tempmem_size_in_bytes < memory_required) + return 0; + + memset(tempmem, 0, tempmem_size_in_bytes); + + info->input_data = input_data; + info->input_stride_bytes = width_stride_input; + + info->output_data = output_data; + info->output_stride_bytes = width_stride_output; + + info->alpha_channel = alpha_channel; + info->flags = flags; + info->type = type; + info->edge_horizontal = edge_horizontal; + info->edge_vertical = edge_vertical; + info->colorspace = colorspace; + + info->horizontal_coefficient_width = stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_coefficient_width = stbir__get_coefficient_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_width = stbir__get_filter_pixel_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_width = stbir__get_filter_pixel_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_margin = stbir__get_filter_pixel_margin(info->vertical_filter , info->vertical_scale ); + + info->ring_buffer_length_bytes = info->output_w * info->channels * sizeof(float); + info->decode_buffer_pixels = info->input_w + info->horizontal_filter_pixel_margin * 2; + +#define STBIR__NEXT_MEMPTR(current, newtype) (newtype*)(((unsigned char*)current) + current##_size) + + info->horizontal_contributors = (stbir__contributors *) tempmem; + info->horizontal_coefficients = STBIR__NEXT_MEMPTR(info->horizontal_contributors, float); + info->vertical_contributors = STBIR__NEXT_MEMPTR(info->horizontal_coefficients, stbir__contributors); + info->vertical_coefficients = STBIR__NEXT_MEMPTR(info->vertical_contributors, float); + info->decode_buffer = STBIR__NEXT_MEMPTR(info->vertical_coefficients, float); + + if (stbir__use_height_upsampling(info)) + { + info->horizontal_buffer = NULL; + info->ring_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->encode_buffer = STBIR__NEXT_MEMPTR(info->ring_buffer, float); + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->encode_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + else + { + info->horizontal_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->ring_buffer = STBIR__NEXT_MEMPTR(info->horizontal_buffer, float); + info->encode_buffer = NULL; + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->ring_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + +#undef STBIR__NEXT_MEMPTR + + // This signals that the ring buffer is empty + info->ring_buffer_begin_index = -1; + + stbir__calculate_filters(info->horizontal_contributors, info->horizontal_coefficients, info->horizontal_filter, info->horizontal_scale, info->horizontal_shift, info->input_w, info->output_w); + stbir__calculate_filters(info->vertical_contributors, info->vertical_coefficients, info->vertical_filter, info->vertical_scale, info->vertical_shift, info->input_h, info->output_h); + + STBIR_PROGRESS_REPORT(0); + + if (stbir__use_height_upsampling(info)) + stbir__buffer_loop_upsample(info); + else + stbir__buffer_loop_downsample(info); + + STBIR_PROGRESS_REPORT(1); + +#ifdef STBIR_DEBUG_OVERWRITE_TEST + STBIR_ASSERT(memcmp(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE) == 0); +#endif + + return 1; +} + + +static int stbir__resize_arbitrary( + void *alloc_context, + const void* input_data, int input_w, int input_h, int input_stride_in_bytes, + void* output_data, int output_w, int output_h, int output_stride_in_bytes, + float s0, float t0, float s1, float t1, float *transform, + int channels, int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_filter h_filter, stbir_filter v_filter, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace) +{ + stbir__info info; + int result; + size_t memory_required; + void* extra_memory; + + stbir__setup(&info, input_w, input_h, output_w, output_h, channels); + stbir__calculate_transform(&info, s0,t0,s1,t1,transform); + stbir__choose_filter(&info, h_filter, v_filter); + memory_required = stbir__calculate_memory(&info); + extra_memory = STBIR_MALLOC(memory_required, alloc_context); + + if (!extra_memory) + return 0; + + result = stbir__resize_allocated(&info, input_data, input_stride_in_bytes, + output_data, output_stride_in_bytes, + alpha_channel, flags, type, + edge_horizontal, edge_vertical, + colorspace, extra_memory, memory_required); + + STBIR_FREE(extra_memory, alloc_context); + + return result; +} + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_FLOAT, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + edge_wrap_mode, edge_wrap_mode, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT16, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_FLOAT, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset) +{ + float transform[4]; + transform[0] = x_scale; + transform[1] = y_scale; + transform[2] = x_offset; + transform[3] = y_offset; + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,transform,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + s0,t0,s1,t1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +#endif // STB_IMAGE_RESIZE_IMPLEMENTATION + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/models/deeplab/inference/tflite/stb_image_write.h b/models/deeplab/inference/tflite/stb_image_write.h new file mode 100644 index 0000000000000000000000000000000000000000..a19b548ae9a507038acddeffea6c31fc4964640f --- /dev/null +++ b/models/deeplab/inference/tflite/stb_image_write.h @@ -0,0 +1,1622 @@ +/* stb_image_write - v1.13 - public domain - http://nothings.org/stb/stb_image_write.h + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk + + Before #including, + + #define STB_IMAGE_WRITE_IMPLEMENTATION + + in the file that you want to have the implementation. + + Will probably not work correctly with strict-aliasing optimizations. + + If using a modern Microsoft Compiler, non-safe versions of CRT calls may cause + compilation warnings or even errors. To avoid this, also before #including, + + #define STBI_MSC_SECURE_CRT + +ABOUT: + + This header file is a library for writing images to C stdio or a callback. + + The PNG output is not optimal; it is 20-50% larger than the file + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. + +BUILDING: + + You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. + You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace + malloc,realloc,free. + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +UNICODE: + + If compiling for Windows and you wish to use Unicode filenames, compile + with + #define STBIW_WINDOWS_UTF8 + and pass utf8-encoded filenames. Call stbiw_convert_wchar_to_utf8 to convert + Windows wchar_t filenames to utf8. + +USAGE: + + There are five functions, one for each image file format: + + int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. + + Each function returns 0 on failure and non-0 on success. + + The functions create an image file defined by the parameters. The image + is a rectangle of pixels stored from left-to-right, top-to-bottom. + Each pixel contains 'comp' channels of data stored interleaved with 8-bits + per channel, in the following order: 1=Y, 2=YA, 3=RGB, 4=RGBA. (Y is + monochrome color.) The rectangle is 'w' pixels wide and 'h' pixels tall. + The *data pointer points to the first byte of the top-left-most pixel. + For PNG, "stride_in_bytes" is the distance in bytes from the first byte of + a row of pixels to the first byte of the next row of pixels. + + PNG creates output files with the same number of components as the input. + The BMP format expands Y to RGB in the file format and does not + output alpha. + + PNG supports writing rectangles of data even when the bytes storing rows of + data are not consecutive in memory (e.g. sub-rectangles of a larger image), + by supplying the stride between the beginning of adjacent rows. The other + formats do not. (Thus you cannot write a native-format BMP through the BMP + writer, both because it is in BGR order and because it may have padding + at the end of the line.) + + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + + HDR expects linear float data. Since the format is always 32-bit rgb(e) + data, alpha (if provided) is discarded, and for monochrome data it is + replicated across all three channels. + + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + +CREDITS: + + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + + bugfixes: + github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + +*/ + +#ifndef INCLUDE_STB_IMAGE_WRITE_H +#define INCLUDE_STB_IMAGE_WRITE_H + +#include + +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else +#ifdef __cplusplus +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif +#endif + +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); + +#ifdef STBI_WINDOWS_UTF8 +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif +#endif + +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + +#endif//INCLUDE_STB_IMAGE_WRITE_H + +#ifdef STB_IMAGE_WRITE_IMPLEMENTATION + +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + +#include +#include +#include +#include + +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) +// ok +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." +#endif + +#ifndef STBIW_MALLOC +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) +#endif + + +#ifndef STBIW_MEMMOVE +#define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) +#endif + + +#ifndef STBIW_ASSERT +#include +#define STBIW_ASSERT(x) assert(x) +#endif + +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#ifdef __cplusplus +#define STBIW_EXTERN extern "C" +#else +#define STBIW_EXTERN extern +#endif +STBIW_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); + +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbiw__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f = stbiw__fopen(filename, "wb"); + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + +typedef unsigned int stbiw_uint32; +typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; + +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) +{ + while (*fmt) { + switch (*fmt++) { + case ' ': break; + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } + default: + STBIW_ASSERT(0); + return; + } + } +} + +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) +{ + unsigned char arr[3]; + arr[0] = a; arr[1] = b; arr[2] = c; + s->func(s->context, arr, 3); +} + +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) +{ + unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ + stbiw_uint32 zero = 0; + int i,j, j_end; + + if (y <= 0) + return; + + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) { + j_end = -1; j = y-1; + } else { + j_end = y; j = 0; + } + + for (; j != j_end; j += vdir) { + for (i=0; i < x; ++i) { + unsigned char *d = (unsigned char *) data + (j*x+i)*comp; + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); + } + s->func(s->context, &zero, scanline_pad); + } +} + +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +{ + if (y < 0 || x < 0) { + return 0; + } else { + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; + } +} + +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) +{ + int pad = (-x*3) & 3; + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header + 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header +} + +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) +{ + int has_alpha = (comp == 2 || comp == 4); + int colorbytes = has_alpha ? comp-1 : comp; + int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR writer +// by Baldur Karlsson + +#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) + +static void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +{ + int exponent; + float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + + if (maxcomp < 1e-32f) { + rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; + } else { + float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; + + rgbe[0] = (unsigned char)(linear[0] * normalize); + rgbe[1] = (unsigned char)(linear[1] * normalize); + rgbe[2] = (unsigned char)(linear[2] * normalize); + rgbe[3] = (unsigned char)(exponent + 128); + } +} + +static void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) +{ + unsigned char lengthbyte = STBIW_UCHAR(length+128); + STBIW_ASSERT(length+128 <= 255); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); +} + +static void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) +{ + unsigned char lengthbyte = STBIW_UCHAR(length); + STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); +} + +static void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) +{ + unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; + unsigned char rgbe[4]; + float linear[3]; + int x; + + scanlineheader[2] = (width&0xff00)>>8; + scanlineheader[3] = (width&0x00ff); + + /* skip RLE for images too small or large */ + if (width < 8 || width >= 32768) { + for (x=0; x < width; x++) { + switch (ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + s->func(s->context, rgbe, 4); + } + } else { + int c,r; + /* encode into scratch buffer */ + for (x=0; x < width; x++) { + switch(ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + scratch[x + width*0] = rgbe[0]; + scratch[x + width*1] = rgbe[1]; + scratch[x + width*2] = rgbe[2]; + scratch[x + width*3] = rgbe[3]; + } + + s->func(s->context, scanlineheader, 4); + + /* RLE each component separately */ + for (c=0; c < 4; c++) { + unsigned char *comp = &scratch[width*c]; + + x = 0; + while (x < width) { + // find first run + r = x; + while (r+2 < width) { + if (comp[r] == comp[r+1] && comp[r] == comp[r+2]) + break; + ++r; + } + if (r+2 >= width) + r = width; + // dump up to first run + while (x < r) { + int len = r-x; + if (len > 128) len = 128; + stbiw__write_dump_data(s, len, &comp[x]); + x += len; + } + // if there's a run, output it + if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd + // find next byte after run + while (r < width && comp[r] == comp[x]) + ++r; + // output run up to r + while (x < r) { + int len = r-x; + if (len > 127) len = 127; + stbiw__write_run_data(s, len, comp[x]); + x += len; + } + } + } + } + } +} + +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) +{ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. + unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef __STDC_WANT_SECURE_LIB__ + len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + + for(i=0; i < y; i++) + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)); + STBIW_FREE(scratch); + return 1; + } +} + +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS +// stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() +#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbm(a) stbiw__sbraw(a)[0] +#define stbiw__sbn(a) stbiw__sbraw(a)[1] + +#define stbiw__sbneedgrow(a,n) ((a)==0 || stbiw__sbn(a)+n >= stbiw__sbm(a)) +#define stbiw__sbmaybegrow(a,n) (stbiw__sbneedgrow(a,(n)) ? stbiw__sbgrow(a,n) : 0) +#define stbiw__sbgrow(a,n) stbiw__sbgrowf((void **) &(a), (n), sizeof(*(a))) + +#define stbiw__sbpush(a, v) (stbiw__sbmaybegrow(a,1), (a)[stbiw__sbn(a)++] = (v)) +#define stbiw__sbcount(a) ((a) ? stbiw__sbn(a) : 0) +#define stbiw__sbfree(a) ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0) + +static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) +{ + int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); + STBIW_ASSERT(p); + if (p) { + if (!*arr) ((int *) p)[1] = 0; + *arr = (void *) ((int *) p + 2); + stbiw__sbm(*arr) = m; + } + return *arr; +} + +static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) +{ + while (*bitcount >= 8) { + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); + *bitbuffer >>= 8; + *bitcount -= 8; + } + return data; +} + +static int stbiw__zlib_bitrev(int code, int codebits) +{ + int res=0; + while (codebits--) { + res = (res << 1) | (code & 1); + code >>= 1; + } + return res; +} + +static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit) +{ + int i; + for (i=0; i < limit && i < 258; ++i) + if (a[i] != b[i]) break; + return i; +} + +static unsigned int stbiw__zhash(unsigned char *data) +{ + stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); + hash ^= hash << 3; + hash += hash >> 5; + hash ^= hash << 4; + hash += hash >> 17; + hash ^= hash << 25; + hash += hash >> 6; + return hash; +} + +#define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount)) +#define stbiw__zlib_add(code,codebits) \ + (bitbuf |= (code) << bitcount, bitcount += (codebits), stbiw__zlib_flush()) +#define stbiw__zlib_huffa(b,c) stbiw__zlib_add(stbiw__zlib_bitrev(b,c),c) +// default huffman tables +#define stbiw__zlib_huff1(n) stbiw__zlib_huffa(0x30 + (n), 8) +#define stbiw__zlib_huff2(n) stbiw__zlib_huffa(0x190 + (n)-144, 9) +#define stbiw__zlib_huff3(n) stbiw__zlib_huffa(0 + (n)-256,7) +#define stbiw__zlib_huff4(n) stbiw__zlib_huffa(0xc0 + (n)-280,8) +#define stbiw__zlib_huff(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : (n) <= 255 ? stbiw__zlib_huff2(n) : (n) <= 279 ? stbiw__zlib_huff3(n) : stbiw__zlib_huff4(n)) +#define stbiw__zlib_huffb(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : stbiw__zlib_huff2(n)) + +#define stbiw__ZHASH 16384 + +#endif // STBIW_ZLIB_COMPRESS + +STBIWDEF unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) +{ +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin + static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; + static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; + static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; + static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + unsigned int bitbuf=0; + int i,j, bitcount=0; + unsigned char *out = NULL; + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**)); + if (hash_table == NULL) + return NULL; + if (quality < 5) quality = 5; + + stbiw__sbpush(out, 0x78); // DEFLATE 32K window + stbiw__sbpush(out, 0x5e); // FLEVEL = 1 + stbiw__zlib_add(1,1); // BFINAL = 1 + stbiw__zlib_add(1,2); // BTYPE = 1 -- fixed huffman + + for (i=0; i < stbiw__ZHASH; ++i) + hash_table[i] = NULL; + + i=0; + while (i < data_len-3) { + // hash next 3 bytes of data to be compressed + int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3; + unsigned char *bestloc = 0; + unsigned char **hlist = hash_table[h]; + int n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32768) { // if entry lies within window + int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); + if (d >= best) { best=d; bestloc=hlist[j]; } + } + } + // when hash table entry is too long, delete half the entries + if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) { + STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality); + stbiw__sbn(hash_table[h]) = quality; + } + stbiw__sbpush(hash_table[h],data+i); + + if (bestloc) { + // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal + h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1); + hlist = hash_table[h]; + n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32767) { + int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1); + if (e > best) { // if next match is better, bail on current match + bestloc = NULL; + break; + } + } + } + } + + if (bestloc) { + int d = (int) (data+i - bestloc); // distance back + STBIW_ASSERT(d <= 32767 && best <= 258); + for (j=0; best > lengthc[j+1]-1; ++j); + stbiw__zlib_huff(j+257); + if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); + for (j=0; d > distc[j+1]-1; ++j); + stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5); + if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); + i += best; + } else { + stbiw__zlib_huffb(data[i]); + ++i; + } + } + // write out final bytes + for (;i < data_len; ++i) + stbiw__zlib_huffb(data[i]); + stbiw__zlib_huff(256); // end of block + // pad with 0 bits to byte boundary + while (bitcount) + stbiw__zlib_add(0,1); + + for (i=0; i < stbiw__ZHASH; ++i) + (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); + + { + // compute adler32 on input + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; + while (j < data_len) { + for (i=0; i < blocklen; ++i) { s1 += data[j+i]; s2 += s1; } + s1 %= 65521; s2 %= 65521; + j += blocklen; + blocklen = 5552; + } + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); + } + *out_len = stbiw__sbn(out); + // make returned pointer freeable + STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); + return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS +} + +static unsigned int stbiw__crc32(unsigned char *buffer, int len) +{ +#ifdef STBIW_CRC32 + return STBIW_CRC32(buffer, len); +#else + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + + unsigned int crc = ~0u; + int i; + for (i=0; i < len; ++i) + crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; + return ~crc; +#endif +} + +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) +#define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); +#define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) + +static void stbiw__wpcrc(unsigned char **data, int len) +{ + unsigned int crc = stbiw__crc32(*data - len - 4, len+4); + stbiw__wp32(*data, crc); +} + +static unsigned char stbiw__paeth(int a, int b, int c) +{ + int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + + if (type==0) { + memcpy(line_buffer, z, width*n); + return; + } + + // first loop isn't optimized since it's just one pixel + for (i = 0; i < n; ++i) { + switch (type) { + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + switch (type) { + case 1: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-n]; break; + case 2: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } +} + +STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +{ + int force_filter = stbi_write_force_png_filter; + int ctype[5] = { -1, 0, 4, 2, 6 }; + unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; + unsigned char *out,*o, *filt, *zlib; + signed char *line_buffer; + int j,zlen; + + if (stride_bytes == 0) + stride_bytes = x * n; + + if (force_filter >= 5) { + force_filter = -1; + } + + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; + line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } + for (j=0; j < y; ++j) { + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { + est += abs((signed char) line_buffer[i]); + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; + } + } + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; + STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); + } + STBIW_FREE(line_buffer); + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); + STBIW_FREE(filt); + if (!zlib) return 0; + + // each tag requires 12 bytes of overhead + out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12); + if (!out) return 0; + *out_len = 8 + 12+13 + 12+zlen + 12; + + o=out; + STBIW_MEMMOVE(o,sig,8); o+= 8; + stbiw__wp32(o, 13); // header length + stbiw__wptag(o, "IHDR"); + stbiw__wp32(o, x); + stbiw__wp32(o, y); + *o++ = 8; + *o++ = STBIW_UCHAR(ctype[n]); + *o++ = 0; + *o++ = 0; + *o++ = 0; + stbiw__wpcrc(&o,13); + + stbiw__wp32(o, zlen); + stbiw__wptag(o, "IDAT"); + STBIW_MEMMOVE(o, zlib, zlen); + o += zlen; + STBIW_FREE(zlib); + stbiw__wpcrc(&o, zlen); + + stbiw__wp32(o,0); + stbiw__wptag(o, "IEND"); + stbiw__wpcrc(&o,0); + + STBIW_ASSERT(o == out + *out_len); + + return out; +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +{ + FILE *f; + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + + f = stbiw__fopen(filename, "wb"); + if (!f) { STBIW_FREE(png); return 0; } + fwrite(png, 1, len, f); + fclose(f); + STBIW_FREE(png); + return 1; +} +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height-1-clamped_row) : clamped_row)*width*comp; + for(col = x; col < x+8; ++col, ++pos) { + float r, g, b; + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width-1))*comp; + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +#endif // STB_IMAGE_WRITE_IMPLEMENTATION + +/* Revision history + 1.10 (2019-02-07) + support utf8 filenames in Windows; fix warnings and platform ifdefs + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support + 0.98 (2015-04-08) + added STBIW_MALLOC, STBIW_ASSERT etc + 0.97 (2015-01-18) + fixed HDR asserts, rewrote HDR rle logic + 0.96 (2015-01-17) + add HDR output + fix monochrome BMP + 0.95 (2014-08-17) + add monochrome TGA output + 0.94 (2014-05-31) + rename private functions to avoid conflicts with stb_image.h + 0.93 (2014-05-27) + warning fixes + 0.92 (2010-08-01) + casts to unsigned char to fix warnings + 0.91 (2010-07-17) + first public release + 0.90 first internal release +*/ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/models/deeplab/tools/dataset_converter/ade20k/dataset_prepare.sh b/models/deeplab/tools/dataset_converter/ade20k/dataset_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..48e3ada6a71741c7d4aeb7ae09765b36637aff45 --- /dev/null +++ b/models/deeplab/tools/dataset_converter/ade20k/dataset_prepare.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +# ADE20K dataset for semantic segmentation, 923MB (train: 20210, val: 2000) +echo "Downloading ADE20K dataset..." +wget -O ADEChallengeData2016.zip http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip + +# extract data +echo "Extracting dataset..." +unzip -e ADEChallengeData2016.zip + + +# convert ADE20K annotation +echo "Merge images & converting ADE20K segmant annotation..." + +# merge jpg input images +cd ADEChallengeData2016/images/training +ls | cut -d . -f1 > ../../train.txt +mv *.jpg ../ +cd - + +cd ADEChallengeData2016/images/validation +ls | cut -d . -f1 > ../../val.txt +mv *.jpg ../ +cd - +rm -rf ADEChallengeData2016/images/training ADEChallengeData2016/images/validation + +# merge png annotation images +cd ADEChallengeData2016/annotations/training +mv *.png ../ +cd - + +cd ADEChallengeData2016/annotations/validation +mv *.png ../ +cd - +rm -rf ADEChallengeData2016/annotations/training ADEChallengeData2016/annotations/validation + +# convert grayscale png annotation image to PascalVOC style color png label image +python gray_label_convert.py --input_path=ADEChallengeData2016/annotations/ --output_path=ADEChallengeData2016/labels/ + + +# clean up +rm -rf ADEChallengeData2016/annotations ADEChallengeData2016.zip +mv ADEChallengeData2016 ../../../ +echo "Done" + diff --git a/models/deeplab/tools/dataset_converter/ade20k/gray_label_convert.py b/models/deeplab/tools/dataset_converter/ade20k/gray_label_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..b3208b62ae8dd8e998a6f3143b5e54b242599f2b --- /dev/null +++ b/models/deeplab/tools/dataset_converter/ade20k/gray_label_convert.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import glob +import numpy as np +from PIL import Image +#import scipy.io +from tqdm import tqdm +#from collections import OrderedDict +from labelme.utils import lblsave as label_save + + +def gray_label_convert(input_path, output_path): + if not os.path.isdir(input_path): + raise ValueError('Input path does not exist!\n') + os.makedirs(output_path, exist_ok=True) + + # count class item number + #class_count = OrderedDict([(item, 0) for item in PASCAL_VOC_CLASSES]) + + label_files = glob.glob(os.path.join(input_path, '*.png')) + pbar = tqdm(total=len(label_files), desc='Label image converting') + for label_file in label_files: + label_array = np.asarray(Image.open(label_file)) + + # count object class for statistic + #label_list = list(np.unique(label_array)) + #for label in label_list: + #class_name = PASCAL_VOC_CLASSES[label] + #class_count[class_name] = class_count[class_name] + 1 + + # save numpy label array as png label image, + # using labelme utils function + label_file_name = os.path.basename(label_file) + label_save(os.path.join(output_path, label_file_name), label_array) + pbar.update(1) + + pbar.close() + # show item number statistic + #print('Image number for each class:') + #for (class_name, number) in class_count.items(): + #if class_name == 'background': + #continue + #print('%s: %d' % (class_name, number)) + print('total number of converted images: ', len(label_files)) + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='convert grayscale .png label image to PascalVOC style color .png label image') + parser.add_argument('--input_path', required=True, type=str, help='path to grayscale png label images') + parser.add_argument('--output_path', required=True, type=str, help='path to color png label images') + + args = parser.parse_args() + gray_label_convert(args.input_path, args.output_path) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/dataset_converter/cityscapes/dataset_prepare.sh b/models/deeplab/tools/dataset_converter/cityscapes/dataset_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..4c7c702ce4c1d469be381ea3fd781709c3c954d3 --- /dev/null +++ b/models/deeplab/tools/dataset_converter/cityscapes/dataset_prepare.sh @@ -0,0 +1,126 @@ +#!/bin/bash +# +# Cityscapes class label definition: +# +# https://blog.csdn.net/zz2230633069/article/details/84591532 +# https://github.com/CoinCheung/BiSeNet/blob/master/lib/cityscapes_cv2.py +# +# +# name | id | trainId | category | categoryId | hasInstances | ignoreInEval | color +# --------------------------------------------------------------------------------------------------------------------- +# unlabeled | 0 | 255 | void | 0 | 0 | 1 | (0, 0, 0) +# ego vehicle | 1 | 255 | void | 0 | 0 | 1 | (0, 0, 0) +# rectification border | 2 | 255 | void | 0 | 0 | 1 | (0, 0, 0) +# out of roi | 3 | 255 | void | 0 | 0 | 1 | (0, 0, 0) +# static | 4 | 255 | void | 0 | 0 | 1 | (0, 0, 0) +# dynamic | 5 | 255 | void | 0 | 0 | 1 | (111, 74, 0) +# ground | 6 | 255 | void | 0 | 0 | 1 | (81, 0, 81) +# road | 7 | 0 | flat | 1 | 0 | 0 | (128, 64, 128) +# sidewalk | 8 | 1 | flat | 1 | 0 | 0 | (244, 35, 232) +# parking | 9 | 255 | flat | 1 | 0 | 1 | (250, 170, 160) +# rail track | 10 | 255 | flat | 1 | 0 | 1 | (230, 150, 140) +# building | 11 | 2 | construction | 2 | 0 | 0 | (70, 70, 70) +# wall | 12 | 3 | construction | 2 | 0 | 0 | (102, 102, 156) +# fence | 13 | 4 | construction | 2 | 0 | 0 | (190, 153, 153) +# guard rail | 14 | 255 | construction | 2 | 0 | 1 | (180, 165, 180) +# bridge | 15 | 255 | construction | 2 | 0 | 1 | (150, 100, 100) +# tunnel | 16 | 255 | construction | 2 | 0 | 1 | (150, 120, 90) +# pole | 17 | 5 | object | 3 | 0 | 0 | (153, 153, 153) +# polegroup | 18 | 255 | object | 3 | 0 | 1 | (153, 153, 153) +# traffic light | 19 | 6 | object | 3 | 0 | 0 | (250, 170, 30) +# traffic sign | 20 | 7 | object | 3 | 0 | 0 | (220, 220, 0) +# vegetation | 21 | 8 | nature | 4 | 0 | 0 | (107, 142, 35) +# terrain | 22 | 9 | nature | 4 | 0 | 0 | (152, 251, 152) +# sky | 23 | 10 | sky | 5 | 0 | 0 | (70, 130, 180) +# person | 24 | 11 | human | 6 | 1 | 0 | (220, 20, 60) +# rider | 25 | 12 | human | 6 | 1 | 0 | (255, 0, 0) +# car | 26 | 13 | vehicle | 7 | 1 | 0 | (0, 0, 142) +# truck | 27 | 14 | vehicle | 7 | 1 | 0 | (0, 0, 70) +# bus | 28 | 15 | vehicle | 7 | 1 | 0 | (0, 60, 100) +# caravan | 29 | 255 | vehicle | 7 | 1 | 1 | (0, 0, 90) +# trailer | 30 | 255 | vehicle | 7 | 1 | 1 | (0, 0, 110) +# train | 31 | 16 | vehicle | 7 | 1 | 0 | (0, 80, 100) +# motorcycle | 32 | 17 | vehicle | 7 | 1 | 0 | (0, 0, 230) +# bicycle | 33 | 18 | vehicle | 7 | 1 | 0 | (119, 11, 32) +# license plate | -1 | -1 | vehicle | 7 | 0 | 1 | (0, 0, 142) +# +# Example usages: +# ID of label 'car': 26 +# Category of label with ID '26': vehicle +# Name of label with trainID '0': road + + +# Cityscapes dataset, 12GB +#echo "Downloading ADE20K dataset..." +#wget -O Cityscapes.zip http://xxx.com/Cityscapes.zip + +# extract data +echo "Extracting dataset..." +unzip -e Cityscapes.zip + +# convert Cityscapes annotation +echo "Merge images & converting Cityscapes segmant annotation..." + +# merge png input images +mkdir -p Cityscapes/images/train/ +pushd Cityscapes/leftImg8bit_trainvaltest/leftImg8bit/train/ +find -name *.png | xargs -n1 -i mv {} ../../../images/train/ +popd +pushd Cityscapes/images/train/ +ls | cut -d . -f1 > ../../train.txt +popd + +mkdir -p Cityscapes/images/val/ +pushd Cityscapes/leftImg8bit_trainvaltest/leftImg8bit/val/ +find -name *.png | xargs -n1 -i mv {} ../../../images/val/ +popd +pushd Cityscapes/images/val/ +ls | cut -d . -f1 > ../../val.txt +popd + +#mkdir -p Cityscapes/images/test/ +#pushd Cityscapes/leftImg8bit_trainvaltest/leftImg8bit/test/ +#find -name *.png | xargs -n1 -i mv {} ../../../images/test/ +#popd +#pushd Cityscapes/images/test/ +#ls | cut -d . -f1 > ../../test.txt +#popd + +# convert png input images to jpg, then merge together +pushd Cityscapes/images/train/ +ls -1 *.png | xargs -n 1 bash -c 'convert "$0" "${0%.png}.jpg"' +popd +mv Cityscapes/images/train/*.jpg Cityscapes/images/ + +pushd Cityscapes/images/val/ +ls -1 *.png | xargs -n 1 bash -c 'convert "$0" "${0%.png}.jpg"' +popd +mv Cityscapes/images/val/*.jpg Cityscapes/images/ + +#pushd Cityscapes/images/test/ +#ls -1 *.png | xargs -n 1 bash -c 'convert "$0" "${0%.png}.jpg"' +#popd +#mv Cityscapes/images/test/*.jpg Cityscapes/images/ + +rm -rf Cityscapes/images/train Cityscapes/images/val Cityscapes/images/test + + +# merge train & val png annotation images, test annotations are INVALID +# xxx_xxx_xxx_xxx_labelIds.png is for semantic segmentation label +mkdir -p Cityscapes/gray_labels/ +pushd Cityscapes/gtFine_trainvaltest/gtFine/train/ +find -name *_labelIds.png | xargs -n1 -i mv {} ../../../gray_labels/ +popd +pushd Cityscapes/gtFine_trainvaltest/gtFine/val/ +find -name *_labelIds.png | xargs -n1 -i mv {} ../../../gray_labels/ +popd + +# convert grayscale png annotation image to PascalVOC style color png label image +python ../ade20k/gray_label_convert.py --input_path=Cityscapes/gray_labels/ --output_path=Cityscapes/labels/ + + +# clean up +rm -rf Cityscapes/gray_labels Cityscapes/leftImg8bit_trainvaltest Cityscapes/gtFine_trainvaltest +mv Cityscapes ../../../ +echo "Done" + diff --git a/models/deeplab/tools/dataset_converter/label_statistics.py b/models/deeplab/tools/dataset_converter/label_statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..9e278ede955a57322a39db721c0bb28da887e096 --- /dev/null +++ b/models/deeplab/tools/dataset_converter/label_statistics.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import glob +import numpy as np +from PIL import Image +from tqdm import tqdm +from collections import OrderedDict + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.utils import get_classes, get_data_list + +def label_stat(label_path, dataset_file, class_names): + if not os.path.isdir(label_path): + raise ValueError('Input path does not exist!\n') + + if dataset_file: + # get dataset sample list + dataset = get_data_list(dataset_file) + png_files = [os.path.join(label_path, image_id.strip()+'.png') for image_id in dataset] + else: + png_files = glob.glob(os.path.join(label_path, '*.png')) + + num_classes = len(class_names) + # count class item number + class_count = OrderedDict([(item, 0) for item in class_names]) + valid_number = 0 + + pbar = tqdm(total=len(png_files), desc='Labels checking') + for png_file in png_files: + label_array = np.array(Image.open(png_file)) + # treat all the invalid label value as background + label_array[label_array>(num_classes-1)] = 0 + + # count object class for statistic + label_list = list(np.unique(label_array)) + if sum(label_list) > 0: + valid_number += 1 + for label in label_list: + class_name = class_names[label] + class_count[class_name] = class_count[class_name] + 1 + pbar.update(1) + + pbar.close() + # show item number statistic + print('Image number for each class:') + for (class_name, number) in class_count.items(): + if class_name == 'background': + continue + print('%s: %d' % (class_name, number)) + print('total number of valid label image: ', valid_number) + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='label statistic info of dataset') + parser.add_argument('--label_path', required=True, type=str, help='path to png label images') + parser.add_argument('--classes_path', required=True, type=str, help='path to class definitions') + parser.add_argument('--dataset_file', required=False, type=str, default=None, help='dataset txt file') + + args = parser.parse_args() + # prepare class name list, add background class + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + + label_stat(args.label_path, args.dataset_file, class_names) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/dataset_converter/labelme/json_to_dataset.py b/models/deeplab/tools/dataset_converter/labelme/json_to_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f6f617c5949c026839f90662deef61a1c790fe16 --- /dev/null +++ b/models/deeplab/tools/dataset_converter/labelme/json_to_dataset.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script to convert labelme json label file to voc png label image, +which inherited from demo script in labelme package +""" +import os, sys, argparse +import glob +import json +import base64 +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from labelme import utils + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..')) +from common.utils import get_classes + + +def label_convert(json_file_path, png_label_path, class_names): + if not os.path.isdir(json_file_path): + raise ValueError('Input path does not exist!\n') + os.makedirs(png_label_path, exist_ok=True) + + # all the json annotation file list + json_files = glob.glob(os.path.join(json_file_path, '*.json')) + + # form a dict of class_name to label value + label_name_to_value = {} + for i, class_name in enumerate(class_names): + label_name_to_value[class_name] = i + + # count class item number + class_count = OrderedDict([(item, 0) for item in class_names]) + + pbar = tqdm(total=len(json_files), desc='Label converting') + for i, json_file in enumerate(json_files): + data = json.load(open(json_file)) + + # get image info + imageData = data.get("imageData") + if not imageData: + imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"].replace('\\', '/')) + with open(imagePath, "rb") as f: + imageData = f.read() + imageData = base64.b64encode(imageData).decode("utf-8") + img = utils.img_b64_to_arr(imageData) + + # convert json labels to numpy label array + # and save to png + label_array, _ = utils.shapes_to_label( + img.shape, data["shapes"], label_name_to_value + ) + + # count object class for statistic + label_list = list(np.unique(label_array)) + for label in label_list: + class_name = class_names[label] + class_count[class_name] = class_count[class_name] + 1 + + utils.lblsave(os.path.join(png_label_path, os.path.basename(json_file)+".png"), label_array) + pbar.update(1) + + pbar.close() + # show item number statistic + print('Image number for each class:') + for (class_name, number) in class_count.items(): + if class_name == 'background': + continue + print('%s: %d' % (class_name, number)) + print('total number of converted images: ', len(json_files)) + + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='convert labelme json label to voc png label') + + parser.add_argument('--json_file_path', required=True, type=str, help='path to labelme annotated json label files') + parser.add_argument('--classes_path', type=str, required=False, default='../../../configs/voc_classes.txt', help='path to class definitions, default=%(default)s') + parser.add_argument('--png_label_path', required=True, type=str, help='output path of converted png label images') + + args = parser.parse_args() + + # add background class to match model & GT + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + + label_convert(args.json_file_path, args.png_label_path, class_names) + + +if __name__ == "__main__": + main() diff --git a/models/deeplab/tools/dataset_converter/mscoco2017/coco_convert.py b/models/deeplab/tools/dataset_converter/mscoco2017/coco_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..001f708d5f5ec65231d49a211af0a2bd12bfe616 --- /dev/null +++ b/models/deeplab/tools/dataset_converter/mscoco2017/coco_convert.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import numpy as np +from tqdm import trange +from collections import OrderedDict +from labelme.utils import lblsave as label_save +from pycocotools.coco import COCO +from pycocotools import mask as coco_mask + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..')) +from common.utils import get_classes + +#VOC_CATEGORY_LIST = [0, 5, 2, 16, 9, 44, 6, 3, 17, 62, 21, 67, 18, 19, 4, + #1, 64, 20, 63, 7, 72] +coco_category_list = ['background', + 'person', + 'bicycle', + 'car', + 'motorbike', + 'aeroplane', + 'bus', + 'train', + 'truck', + 'boat', + 'traffic light', + 'fire hydrant', + 'street sign', + 'stop sign', + 'parking meter', + 'bench', + 'bird', + 'cat', + 'dog', + 'horse', + 'sheep', + 'cow', + 'elephant', + 'bear', + 'zebra', + 'giraffe', + 'hat', + 'backpack', + 'umbrella', + 'shoe', + 'eye glasses', + 'handbag', + 'tie', + 'suitcase', + 'frisbee', + 'skis', + 'snowboard', + 'sports ball', + 'kite', + 'baseball bat', + 'baseball glove', + 'skateboard', + 'surfboard', + 'tennis racket', + 'bottle', + 'plate', + 'wine glass', + 'cup', + 'fork', + 'knife', + 'spoon', + 'bowl', + 'banana', + 'apple', + 'sandwich', + 'orange', + 'broccoli', + 'carrot', + 'hot dog', + 'pizza', + 'donut', + 'cake', + 'chair', + 'sofa', + 'pottedplant', + 'bed', + 'mirror', + 'diningtable', + 'window', + 'desk', + 'toilet', + 'door', + 'tvmonitor', + 'laptop', + 'mouse', + 'remote', + 'keyboard', + 'cell phone', + 'microwave', + 'oven', + 'toaster', + 'sink', + 'refrigerator', + 'blender', + 'book', + 'clock', + 'vase', + 'scissors', + 'teddy bear', + 'hair drier', + 'toothbrush', + 'hair brush', + 'banner', + 'blanket', + 'branch', + 'bridge', + 'building-other', + 'bush', + 'cabinet', + 'cage', + 'cardboard', + 'carpet', + 'ceiling-other', + 'ceiling-tile', + 'cloth', + 'clothes', + 'clouds', + 'counter', + 'cupboard', + 'curtain', + 'desk-stuff', + 'dirt', + 'door-stuff', + 'fence', + 'floor-marble', + 'floor-other', + 'floor-stone', + 'floor-tile', + 'floor-wood', + 'flower', + 'fog', + 'food-other', + 'fruit', + 'furniture-other', + 'grass', + 'gravel', + 'ground-other', + 'hill', + 'house', + 'leaves', + 'light', + 'mat', + 'metal', + 'mirror-stuff', + 'moss', + 'mountain', + 'mud', + 'napkin', + 'net', + 'paper', + 'pavement', + 'pillow', + 'plant-other', + 'plastic', + 'platform', + 'playingfield', + 'railing', + 'railroad', + 'river', + 'road', + 'rock', + 'roof', + 'rug', + 'salad', + 'sand', + 'sea', + 'shelf', + 'sky-other', + 'skyscraper', + 'snow', + 'solid-other', + 'stairs', + 'stone', + 'straw', + 'structural-other', + 'table', + 'tent', + 'textile-other', + 'towel', + 'tree', + 'vegetable', + 'wall-brick', + 'wall-concrete', + 'wall-other', + 'wall-panel', + 'wall-stone', + 'wall-tile', + 'wall-wood', + 'water-other', + 'waterdrops', + 'window-blind', + 'window-other', + 'wood'] + +def get_label_array(annotations, height, width, class_names): + # alloc label array for whole image + label_array = np.zeros((height, width), dtype=np.uint8) + + for annotation in annotations: + # decode mask & category_id for each annotated instance + rle = coco_mask.frPyObjects(annotation['segmentation'], height, width) + mask = coco_mask.decode(rle) + category_id = annotation['category_id'] + + # search category_id from COCO full list to get its name, + # and match to target class list + category_name = coco_category_list[category_id] + if category_name in class_names: + class_id = class_names.index(category_name) + else: + continue + # paste instance mask to label array + if len(mask.shape) < 3: + label_array[:, :] += (label_array == 0) * (mask * class_id) + else: + label_array[:, :] += (label_array == 0) * (((np.sum(mask, axis=2)) > 0) * class_id).astype(np.uint8) + return label_array + + +def coco_label_convert(annotation_path, datasets, class_names, output_path): + if not os.path.isdir(annotation_path): + raise ValueError('Input path does not exist!\n') + + png_label_path = os.path.join(output_path, 'labels') + os.makedirs(png_label_path, exist_ok=True) + + for dataset in datasets: + # load COCO annotation + coco_annotation_file = os.path.join(annotation_path, 'instances_{}{}.json'.format(dataset, '2017')) + coco = COCO(coco_annotation_file) + + # list of all annotated images + image_ids = list(coco.imgs.keys()) + + # count class item number + class_count = OrderedDict([(item, 0) for item in class_names]) + # record picked image id + qualify_image_ids = [] + + tbar = trange(len(image_ids)) + for i in tbar: + image_id = image_ids[i] + # get all instances annotation & metadata of one image + image_annotations = coco.loadAnns(coco.getAnnIds(imgIds=image_id)) + image_metadata = coco.loadImgs(image_id)[0] + + # form a segmentation label array with instances annotation info + label_array = get_label_array(image_annotations, image_metadata['height'], + image_metadata['width'], class_names) + + # label array filter. only more than 1k valid label pixels + # would be used + if (label_array > 0).sum() > 1000: + qualify_image_ids.append(image_id) + + # count object class for statistic + label_list = list(np.unique(label_array)) + for label in label_list: + class_name = class_names[label] + class_count[class_name] = class_count[class_name] + 1 + + # save numpy label array as png label image, + # using labelme utils function + png_file_name = '%012d.png'%(image_id) + label_save(os.path.join(png_label_path, png_file_name), label_array) + + tbar.set_description('Doing: {}/{}, got {} qualified images'. \ + format(i, len(image_ids), len(qualify_image_ids))) + + qualify_image_ids.sort() + # save image list to dataset txt file + dataset_file = open(os.path.join(output_path,'%s.txt'%(dataset)), 'w') + for image_id in qualify_image_ids: + dataset_file.write('%012d'%(image_id)) + dataset_file.write('\n') + dataset_file.close() + + # show item number statistic + print('Image number for each class:') + for (class_name, number) in class_count.items(): + if class_name == 'background': + continue + print('%s: %d' % (class_name, number)) + print('total number of qualified images: ', len(qualify_image_ids)) + + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='convert MSCOCO 2017 segment annotation to .png label images') + parser.add_argument('--annotation_path', type=str, required=True, help='path to MSCOCO 2017 annotation file') + parser.add_argument('--set', required=False, type=str, default='all', choices=['all', 'train', 'val'], help='convert dataset, default=%(default)s') + parser.add_argument('--classes_path', type=str, required=False, default='../../../configs/coco_classes.txt', help='path to selected class definitions, default=%(default)s') + parser.add_argument('--output_path', required=True, type=str, help='output path containing converted png label image and dataset txt') + + args = parser.parse_args() + + # prepare class name list, add background class + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + + # dataset list + if args.set == 'all': + datasets = ['train', 'val'] + else: + datasets = [args.set] + + coco_label_convert(args.annotation_path, datasets, class_names, args.output_path) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/dataset_converter/mscoco2017/dataset_prepare.sh b/models/deeplab/tools/dataset_converter/mscoco2017/dataset_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..0f7d4e36fa6384fa8554628adc8bc831c6836ace --- /dev/null +++ b/models/deeplab/tools/dataset_converter/mscoco2017/dataset_prepare.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +# MSCOCO 2017 train set, 19GB +echo "Downloading MSCOCO 2017 train set..." +wget http://images.cocodataset.org/zips/train2017.zip + +# MSCOCO 2017 val set, 778MB +echo "Downloading MSCOCO 2017 val set..." +wget http://images.cocodataset.org/zips/val2017.zip + +# MSCOCO 2017 train&val annotation, 242MB +echo "Downloading MSCOCO 2017 annotation..." +wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip + +# extract data +echo "Extracting dataset & annotation..." +unzip -e train2017.zip +unzip -e val2017.zip +unzip -e annotations_trainval2017.zip + +# convert COCO annotation +echo "Converting MSCOCO 2017 segmant annotation..." +python coco_convert.py --dataset_path=./annotations/ --set=all --classes_path=../../../configs/coco_classes.txt --output_path=mscoco2017/ + +# move image and convert COCO annotation +echo "Final step..." +mv train2017/ mscoco2017/images/ +mv val2017/* mscoco2017/images/ +# merge a trainval dataset +cp mscoco2017/train.txt mscoco2017/trainval.txt && cat mscoco2017/val.txt >> mscoco2017/trainval.txt + +# clean up +rm -rf train2017 val2017 annotations train2017.zip val2017.zip annotations_trainval2017.zip +mv mscoco2017 ../../../ +echo "Done" + diff --git a/models/deeplab/tools/dataset_converter/voc_augment/dataset_prepare.sh b/models/deeplab/tools/dataset_converter/voc_augment/dataset_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..573862a853bb159b2537b905c4e70368da8212ca --- /dev/null +++ b/models/deeplab/tools/dataset_converter/voc_augment/dataset_prepare.sh @@ -0,0 +1,39 @@ +#!/bin/bash + +# original PASCAL VOC 2012, 2 GB +echo "Downloading PascalVOC 2012 dataset..." +wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar +tar -xvf VOCtrainval_11-May-2012.tar +mv VOCdevkit/VOC2012 VOC2012_orig && rm -r VOCdevkit + + +# augmented PASCAL VOC, 1.3 GB +echo "Downloading SBD dataset..." +wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz +tar -zxvf benchmark.tgz +mv benchmark_RELEASE VOC_aug + + +# convert SBD mat label file to PascalVOC png label file +echo "SBD label convert..." +python voc_aug_convert.py --mat_label_path=./VOC_aug/dataset/cls --png_label_path=./VOC_aug/dataset/cls_png --label_type=semantic + +# merge dataset txt files +echo "merge dataset..." +python imageset_merge.py --voc_set_file=./VOC2012_orig/ImageSets/Segmentation/train.txt --sbd_set_file=./VOC_aug/dataset/train.txt --output_file=./train.txt +python imageset_merge.py --voc_set_file=./VOC2012_orig/ImageSets/Segmentation/val.txt --sbd_set_file=./VOC_aug/dataset/val.txt --output_file=./val.txt +python imageset_merge.py --voc_set_file=./train.txt --sbd_set_file=./val.txt --output_file=./trainval.txt + +cp -rf ./VOC2012_orig/SegmentationClass/* ./VOC_aug/dataset/cls_png/ +cp -rf ./VOC_aug/dataset/cls_png/* ./VOC2012_orig/SegmentationClass/ +cp -rf ./train.txt ./VOC2012_orig/ImageSets/Segmentation/train.txt +cp -rf ./val.txt ./VOC2012_orig/ImageSets/Segmentation/val.txt +cp -rf ./trainval.txt ./VOC2012_orig/ImageSets/Segmentation/trainval.txt + +# create soft-link for train process access +cd VOC2012_orig && ln -s JPEGImages images && ln -s SegmentationClass labels && cd .. + +rm -rf train.txt val.txt trainval.txt VOC_aug +mv VOC2012_orig ../../../VOC2012 + +echo "Done" diff --git a/models/deeplab/tools/dataset_converter/voc_augment/imageset_merge.py b/models/deeplab/tools/dataset_converter/voc_augment/imageset_merge.py new file mode 100644 index 0000000000000000000000000000000000000000..e556f7ed409f33eaa891d239742c5e3bacaa2d3d --- /dev/null +++ b/models/deeplab/tools/dataset_converter/voc_augment/imageset_merge.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..')) +from common.utils import get_data_list + + +def merge_set(voc_set_file, sbd_set_file, output_file): + voc_set_list = get_data_list(voc_set_file, shuffle=False) + sbd_set_list = get_data_list(sbd_set_file, shuffle=False) + + # use set() struct to clean duplicate items + output_list = list(set(voc_set_list + sbd_set_list)) + output_list.sort() + + # save merged list + output_file = open(output_file, 'w') + for image_id in output_list: + output_file.write(image_id) + output_file.write('\n') + output_file.close() + + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='Merge PascalVOC & SBD (VOC Augmented) ImageSets') + + parser.add_argument('--voc_set_file', required=True, type=str, help='path to PascalVOC imageset txt file') + parser.add_argument('--sbd_set_file', required=True, type=str, help='path to SBD imageset txt file') + parser.add_argument('--output_file', required=True, type=str, help='output imageset txt file') + + args = parser.parse_args() + + merge_set(args.voc_set_file, args.sbd_set_file, args.output_file) + + +if __name__ == '__main__': + main() diff --git a/models/deeplab/tools/dataset_converter/voc_augment/voc_aug_convert.py b/models/deeplab/tools/dataset_converter/voc_augment/voc_aug_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..5de2635df7185a6e37a5ef7b774015e85d3aab8a --- /dev/null +++ b/models/deeplab/tools/dataset_converter/voc_augment/voc_aug_convert.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import glob +import numpy as np +from PIL import Image +import scipy.io +from tqdm import tqdm +from collections import OrderedDict +from labelme.utils import lblsave as label_save + +PASCAL_VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', + 'cow', 'diningtable', 'dog', 'horse', 'motorbike', + 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] + + +# extract numpy array from mat for http://www.cs.berkeley.edu/~bharath2/codes/SBD/download.html +# 'GTcls' key is for class segmentation +# 'GTinst' key is for instance segmentation +def get_array_from_mat(mat_file, label_type): + if label_type == 'semantic': + key = 'GTcls' + elif label_type == 'instance': + key = 'GTinst' + else: + raise ValueError('invalid label type') + + mat = scipy.io.loadmat(mat_file, mat_dtype=True, squeeze_me=True, struct_as_record=False) + return mat[key].Segmentation + + +#def get_pascal_palette(png_file): + #''' + #extract palette info from a sample PascalVOC + #segmentation label png file + #''' + #label_img = Image.open(png_file) + #palette = label_img.getpalette() + #return palette + + +#def convert_to_color_seg(label_array, palette): + #''' + #set palette to generate a colorful png label file + #''' + #label_img = Image.fromarray(label_array, mode='P') + #label_img.putpalette(palette) + #return label_img + + +def label_convert(mat_label_path, png_label_path, label_type): + if not os.path.isdir(mat_label_path): + raise ValueError('Input path does not exist!\n') + os.makedirs(png_label_path, exist_ok=True) + + # count class item number + class_count = OrderedDict([(item, 0) for item in PASCAL_VOC_CLASSES]) + + mat_files = glob.glob(os.path.join(mat_label_path, '*.mat')) + pbar = tqdm(total=len(mat_files), desc='Label converting') + for mat_file in mat_files: + label_array = get_array_from_mat(mat_file, label_type) + + # count object class for statistic + label_list = list(np.unique(label_array)) + for label in label_list: + class_name = PASCAL_VOC_CLASSES[label] + class_count[class_name] = class_count[class_name] + 1 + + # save numpy label array as png label image, + # using labelme utils function + png_file_name = os.path.basename(mat_file).split('.')[0]+'.png' + label_save(os.path.join(png_label_path, png_file_name), label_array) + pbar.update(1) + + pbar.close() + # show item number statistic + print('Image number for each class:') + for (class_name, number) in class_count.items(): + if class_name == 'background': + continue + print('%s: %d' % (class_name, number)) + print('total number of converted images: ', len(mat_files)) + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='convert Augmented PascalVOC .mat label to .png label') + parser.add_argument('--mat_label_path', required=True, type=str, help='path of mat label files in Augmented PascalVOC dataset') + parser.add_argument('--png_label_path', required=True, type=str, help='output path of converted png label files') + parser.add_argument('--label_type', required=False, type=str, default='semantic', choices=['semantic', 'instance'], help='label type: semantic/instance, default=%(default)s') + + args = parser.parse_args() + + label_convert(args.mat_label_path, args.png_label_path, args.label_type) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/evaluation/model_statistics.py b/models/deeplab/tools/evaluation/model_statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..e9cf885ff2e5fc98a552e8b238dd79040d8a119b --- /dev/null +++ b/models/deeplab/tools/evaluation/model_statistics.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script to calculate FLOPs & PARAMs of a tf.keras model. +Compatible with TF 1.x and TF 2.x +""" +import os, sys, argparse +import tensorflow as tf +import tensorflow.keras.backend as K +from tensorflow.keras.models import Model, load_model +from tensorflow.keras.layers import Input +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.utils import get_custom_objects + +# check tf version to be compatible with TF 2.x +if tf.__version__.startswith('2'): + import tensorflow.compat.v1 as tf + tf.disable_eager_execution() + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +def clever_format(value, format_string="%.2f"): + """ + Convert statistic value to clever format string + """ + if value <= 0: + raise ValueError('invalid statistic value {}'.format(value)) + + # get friendly statistic value string + if value > 0 and value <= 1e3: + value_string = format_string%(value) + elif value > 1e3 and value <= 1e6: + value_string = format_string%(value/1e3)+'K' + elif value > 1e6 and value <= 1e9: + value_string = format_string%(value/1e6)+'M' + elif value > 1e9 and value <= 1e12: + # here we can use either "GFLOPS" or "BFLOPS" + value_string = format_string%(value/1e9)+'G' + elif value > 1e12 and value <= 1e15: + value_string = format_string%(value/1e12)+'T' + elif value > 1e15 and value <= 1e18: + value_string = format_string%(value/1e15)+'P' + elif value > 1e18: + value_string = format_string%(value/1e18)+'E' + + return value_string + + +def get_flops(model): + run_meta = tf.RunMetadata() + graph = tf.get_default_graph() + + # We use the Keras session graph in the call to the profiler. + opts = tf.profiler.ProfileOptionBuilder.float_operation() + flops = tf.profiler.profile(graph=graph, run_meta=run_meta, cmd='op', options=opts) + + opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter() + params = tf.profiler.profile(graph=graph, run_meta=run_meta, cmd='op', options=opts) + + flops_value = flops.total_float_ops + param_value = params.total_parameters + + # get friendly FLOPs & PARAMs value string + flops_result_string = clever_format(flops_value, '%.4f') + param_result_string = clever_format(param_value, '%.4f') + + print('Total FLOPs: {} float_ops'.format(flops_result_string)) + print('Total PARAMs: {}'.format(param_result_string)) + + +def main(): + parser = argparse.ArgumentParser(description='tf.keras model FLOPs & PARAMs checking tool') + parser.add_argument('--model_path', type=str, required=True, help='model file to evaluate') + parser.add_argument('--model_image_size', type=str, required=False, default=None, help='model image input size as x, optional') + args = parser.parse_args() + + custom_object_dict = get_custom_objects() + model = load_model(args.model_path, compile=False, custom_objects=custom_object_dict) + + batch, height, width, channel = model.input.shape.as_list() + + if args.model_image_size: + height, width = args.model_image_size.split('x') + height, width = int(height), int(width) + + # to calculate FLOPs we need to use fixed input shape & batch size + assert height and width and channel, 'input shape should be specified' + + if not batch: + # if dynamic batch, rebuild model with batch_size=1 + input_tensor = Input(shape=(height, width, channel), batch_size=1) + output_tensor = model(input_tensor) + model = Model(input_tensor, output_tensor) + + K.set_learning_phase(0) + get_flops(model) + + +if __name__ == '__main__': + main() diff --git a/models/deeplab/tools/evaluation/onboard/onboard_png_convert.py b/models/deeplab/tools/evaluation/onboard/onboard_png_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..aac1500868b185e5c5225f0986279118c46b31ed --- /dev/null +++ b/models/deeplab/tools/evaluation/onboard/onboard_png_convert.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import glob +import numpy as np +from PIL import Image +from tqdm import tqdm +from collections import OrderedDict +from labelme.utils import lblsave as label_save + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..')) +from common.utils import get_classes + +def label_convert(input_label_path, output_label_path, class_names=None): + if not os.path.isdir(input_label_path): + raise ValueError('Input path does not exist!\n') + os.makedirs(output_label_path, exist_ok=True) + + if class_names: + # count class item number + class_count = OrderedDict([(item, 0) for item in class_names]) + + label_files = glob.glob(os.path.join(input_label_path, '*.png')) + pbar = tqdm(total=len(label_files), desc='Label converting') + for label_file in label_files: + label_array = np.array(Image.open(label_file)) + + if class_names: + # count object class for statistic + label_list = list(np.unique(label_array)) + for label in label_list: + if label >= len(class_names): + continue + class_name = class_names[label] + class_count[class_name] = class_count[class_name] + 1 + + # save numpy label array as png label image, + # using labelme utils function + png_file_name = os.path.basename(label_file).split('.')[0]+'.png' + label_save(os.path.join(output_label_path, png_file_name), label_array) + pbar.update(1) + pbar.close() + print('total number of converted images: ', len(label_files)) + + if class_names: + # show item number statistic + print('Image number for each class:') + for (class_name, number) in class_count.items(): + if class_name == 'background': + continue + print('%s: %d' % (class_name, number)) + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='convert onboard gray PNG label to PascalVOC PNG label') + parser.add_argument('--input_label_path', type=str, required=True, help='input path of gray label files') + parser.add_argument('--output_label_path', type=str, required=True, help='output path of converted png label files') + parser.add_argument('--classes_path', type=str, required=False, help='path to class definitions, optional', default=None) + + args = parser.parse_args() + + if args.classes_path: + # add background class to match model & GT + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + else: + class_names = None + + label_convert(args.input_label_path, args.output_label_path, class_names) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/evaluation/onboard/semantic_segment_eval.py b/models/deeplab/tools/evaluation/onboard/semantic_segment_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..09cbdcdcc639b1b2faa659d77d145a5c73b95bd6 --- /dev/null +++ b/models/deeplab/tools/evaluation/onboard/semantic_segment_eval.py @@ -0,0 +1,330 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Calculate mIOU and related metrics for semantic segmentation model on validation dataset +""" +import os, sys, argparse, time +import numpy as np +import cv2 +from PIL import Image +import matplotlib.pyplot as plt +import copy +import itertools +from tqdm import tqdm +from collections import OrderedDict +import operator + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', '..')) +from common.utils import get_data_list, get_classes + + +def plot_confusion_matrix(cm, classes, mIOU, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): + if normalize: + cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] + trained_classes = classes + plt.figure() + plt.imshow(cm, interpolation='nearest', cmap=cmap) + plt.title(title,fontsize=11) + tick_marks = np.arange(len(classes)) + plt.xticks(np.arange(len(trained_classes)), classes, rotation=90,fontsize=9) + plt.yticks(tick_marks, classes,fontsize=9) + thresh = cm.max() / 2. + for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): + plt.text(j, i, np.round(cm[i, j],2), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black", fontsize=7) + plt.tight_layout() + plt.ylabel('True label',fontsize=9) + plt.xlabel('Predicted label',fontsize=9) + + plt.title('Mean IOU: '+ str(np.round(mIOU*100, 2))) + output_path = os.path.join('result','confusion_matrix.png') + os.makedirs('result', exist_ok=True) + plt.savefig(output_path) + #plt.show() + return + + +def adjust_axes(r, t, fig, axes): + """ + Plot - adjust axes + """ + # get text width for re-scaling + bb = t.get_window_extent(renderer=r) + text_width_inches = bb.width / fig.dpi + # get axis width in inches + current_fig_width = fig.get_figwidth() + new_fig_width = current_fig_width + text_width_inches + propotion = new_fig_width / current_fig_width + # get axis limit + x_lim = axes.get_xlim() + axes.set_xlim([x_lim[0], x_lim[1]*propotion]) + + +def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar): + """ + Draw plot using Matplotlib + """ + # sort the dictionary by decreasing value, into a list of tuples + sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1)) + # unpacking the list of tuples into two lists + sorted_keys, sorted_values = zip(*sorted_dic_by_value) + # + if true_p_bar != "": + """ + Special case to draw in (green=true predictions) & (red=false predictions) + """ + fp_sorted = [] + tp_sorted = [] + for key in sorted_keys: + fp_sorted.append(dictionary[key] - true_p_bar[key]) + tp_sorted.append(true_p_bar[key]) + plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Predictions') + plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Predictions', left=fp_sorted) + # add legend + plt.legend(loc='lower right') + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + fp_val = fp_sorted[i] + tp_val = tp_sorted[i] + fp_str_val = " " + str(fp_val) + tp_str_val = fp_str_val + " " + str(tp_val) + # trick to paint multicolor with offset: + # first paint everything and then repaint the first number + t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold') + plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold') + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + else: + plt.barh(range(n_classes), sorted_values, color=plot_color) + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + str_val = " " + str(val) # add a space before + if val < 1.0: + str_val = " {0:.2f}".format(val) + t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold') + # re-set axes to show number inside the figure + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + # set window title + fig.canvas.set_window_title(window_title) + # write classes in y axis + tick_font_size = 12 + plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size) + """ + Re-scale height accordingly + """ + init_height = fig.get_figheight() + # comput the matrix height in points and inches + dpi = fig.dpi + height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing) + height_in = height_pt / dpi + # compute the required figure height + top_margin = 0.15 # in percentage of the figure height + bottom_margin = 0.05 # in percentage of the figure height + figure_height = height_in / (1 - top_margin - bottom_margin) + # set new height + if figure_height > init_height: + fig.set_figheight(figure_height) + + # set plot title + plt.title(plot_title, fontsize=14) + # set axis titles + # plt.xlabel('classes') + plt.xlabel(x_label, fontsize='large') + # adjust size of window + fig.tight_layout() + # save the plot + fig.savefig(output_path) + # show image + if to_show: + plt.show() + # close the plot + plt.close() + + +def plot_mIOU_result(IOUs, mIOU, num_classes): + ''' + Draw mIOU plot (Show IOU's of all classes in decreasing order) + ''' + window_title = "mIOU" + plot_title = "mIOU: {0:.3f}%".format(mIOU*100) + x_label = "Intersection Over Union" + output_path = os.path.join('result','mIOU.png') + os.makedirs('result', exist_ok=True) + draw_plot_func(IOUs, num_classes, window_title, plot_title, x_label, output_path, to_show=False, plot_color='royalblue', true_p_bar='') + + +def generate_matrix(gt_mask, pre_mask, num_classes): + valid = (gt_mask >= 0) & (gt_mask < num_classes) + label = num_classes * gt_mask[valid].astype('int') + pre_mask[valid] + count = np.bincount(label, minlength=num_classes**2) + confusion_matrix = count.reshape(num_classes, num_classes) + return confusion_matrix + + +def eval_mIOU(dataset, gt_label_path, pred_label_path, class_names, model_output_shape, show_background): + num_classes = len(class_names) + + # confusion matrix for all classes + confusion_matrix = np.zeros((num_classes, num_classes), dtype=float) + + # get model prediction result + pbar = tqdm(total=len(dataset), desc='Eval model') + for n, image_id in enumerate(dataset): + gt_label_filename = os.path.join(gt_label_path, image_id.strip()+'.png') + pred_label_filename = os.path.join(pred_label_path, image_id.strip()+'.png') + + # load groundtruth label mask + gt_mask = np.array(Image.open(gt_label_filename)) + # reset all the invalid label value as 255 + gt_mask[gt_mask>(num_classes-1)] = 255 + gt_mask = cv2.resize(gt_mask, model_output_shape, interpolation = cv2.INTER_NEAREST) + + # load model predict label mask + pred_mask = np.array(Image.open(pred_label_filename)) + + # check if mask shape matches + assert gt_mask.shape == pred_mask.shape, 'prediction mask shape mismatch with GT.' + + # update confusion matrix + pred_mask = pred_mask.astype('int') + gt_mask = gt_mask.astype('int') + confusion_matrix += generate_matrix(gt_mask, pred_mask, num_classes) + + # compare prediction result with label + # to update confusion matrix + #flat_pred = np.ravel(pred_mask).astype('int') + #flat_label = np.ravel(gt_mask).astype('int') + #for p, l in zip(flat_pred, flat_label): + #if l == num_classes or l == 255: + #continue + #if l < num_classes and p < num_classes: + #confusion_matrix[l, p] += 1 + #else: + #print('Invalid entry encountered, skipping! Label: ', l, + #' Prediction: ', p) + pbar.update(1) + pbar.close() + + # calculate Pixel accuracy + PixelAcc = np.diag(confusion_matrix).sum() / confusion_matrix.sum() + + # calculate Class accuracy + ClassAcc = np.diag(confusion_matrix) / confusion_matrix.sum(axis=1) + mClassAcc = np.nanmean(ClassAcc) + + # calculate mIoU + I = np.diag(confusion_matrix) + U = np.sum(confusion_matrix, axis=0) + np.sum(confusion_matrix, axis=1) - I + IoU = I/U + mIoU = np.nanmean(IoU) + + # calculate FW (Frequency Weighted) IoU + Freq = np.sum(confusion_matrix, axis=1) / np.sum(confusion_matrix) + FWIoU = (Freq[Freq > 0] * IoU[Freq > 0]).sum() + + # calculate Dice Coefficient + DiceCoef = 2*I / (U+I) + + # collect IOU/ClassAcc/Dice/Freq for every class + IOUs, CLASS_ACCs, DICEs, FREQs = {}, {}, {}, {} + for i,(class_name, iou, class_acc, dice, freq) in enumerate(zip(class_names, IoU, ClassAcc, DiceCoef, Freq)): + IOUs[class_name] = iou + CLASS_ACCs[class_name] = class_acc + DICEs[class_name] = dice + FREQs[class_name] = freq + + if not show_background: + #get ride of background class info + display_class_names = copy.deepcopy(class_names) + display_class_names.remove('background') + display_confusion_matrix = copy.deepcopy(confusion_matrix[1:, 1:]) + IOUs.pop('background') + num_classes = num_classes - 1 + else: + display_class_names = class_names + display_confusion_matrix = confusion_matrix + + #sort IoU result by value, in descending order + IOUs = OrderedDict(sorted(IOUs.items(), key=operator.itemgetter(1), reverse=True)) + + #calculate mIOU from final IOU dict + mIoU = np.nanmean(list(IOUs.values())) + + #show result + print('\nevaluation summary') + for class_name, iou in IOUs.items(): + print('%s: IoU %.4f, Freq %.4f, ClassAcc %.4f, Dice %.4f' % (class_name, iou, FREQs[class_name], CLASS_ACCs[class_name], DICEs[class_name])) + print('mIoU=%.3f' % (mIoU*100)) + print('FWIoU=%.3f' % (FWIoU*100)) + print('PixelAcc=%.3f' % (PixelAcc*100)) + print('mClassAcc=%.3f' % (mClassAcc*100)) + + # Plot mIOU & confusion matrix + plot_mIOU_result(IOUs, mIoU, num_classes) + plot_confusion_matrix(display_confusion_matrix, display_class_names, mIoU, normalize=True) + + return mIoU + + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='evaluate Deeplab model (h5/pb/tflite/mnn) with test dataset') + ''' + Command line options + ''' + parser.add_argument( + '--dataset_file', type=str, required=True, + help='eval samples txt file') + + parser.add_argument( + '--gt_label_path', type=str, required=True, + help='path containing groundtruth label png file') + + parser.add_argument( + '--pred_label_path', type=str, required=True, + help='path containing model predict label png file') + + parser.add_argument( + '--classes_path', type=str, required=False, default='configs/voc_classes.txt', + help='path to class definitions, default=%(default)s') + + parser.add_argument( + '--model_output_shape', type=str, + help='model mask output size as x, default=%(default)s', default='512x512') + + parser.add_argument( + '--show_background', default=False, action="store_true", + help='Show background evaluation info') + + args = parser.parse_args() + + # param parse + height, width = args.model_output_shape.split('x') + model_output_shape = (int(height), int(width)) + + # add background class + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + + # get dataset list + dataset = get_data_list(args.dataset_file) + + start = time.time() + eval_mIOU(dataset, args.gt_label_path, args.pred_label_path, class_names, model_output_shape, args.show_background) + end = time.time() + print("Evaluation time cost: {:.6f}s".format(end - start)) + + +if __name__ == '__main__': + main() diff --git a/models/deeplab/tools/evaluation/validate_deeplab.py b/models/deeplab/tools/evaluation/validate_deeplab.py new file mode 100644 index 0000000000000000000000000000000000000000..a5177b87d29bea9b2c707d3ff98bf1ef0167ee1e --- /dev/null +++ b/models/deeplab/tools/evaluation/validate_deeplab.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os, sys, argparse +import time +import numpy as np +#import cv2 +from PIL import Image +import matplotlib.pyplot as plt +from operator import mul +from functools import reduce +import MNN +import onnxruntime +from tensorflow.keras.models import load_model +import tensorflow.keras.backend as K +from tensorflow.lite.python import interpreter as interpreter_wrapper +import tensorflow as tf + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.data_utils import preprocess_image, mask_resize, mask_resize_fast +from common.utils import get_custom_objects, get_classes, visualize_segmentation +from deeplabv3p.metrics import mIOU +from deeplabv3p.postprocess_np import crf_postprocess + + +def validate_deeplab_model(model_path, image_file, class_names, model_image_size, do_crf, label_file, loop_count): + # load model + custom_object_dict = get_custom_objects() + model = load_model(model_path, compile=False, custom_objects=custom_object_dict) + K.set_learning_phase(0) + + num_classes = model.output.shape.as_list()[-1] + if class_names: + # check if classes number match with model prediction + assert num_classes == len(class_names), 'classes number mismatch with model.' + + # prepare input image + img = Image.open(image_file) + image_data = preprocess_image(img, model_image_size) + image = image_data[0].astype('uint8') + #origin image shape, in (width, height) format + origin_image_size = img.size + + # predict once first to bypass the model building time + model.predict([image_data]) + + # get predict output + start = time.time() + for i in range(loop_count): + prediction = model.predict([image_data]) + end = time.time() + print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count)) + + handle_prediction(prediction, image, np.array(img), num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file) + + +def validate_deeplab_model_onnx(model_path, image_file, class_names, do_crf, label_file, loop_count): + sess = onnxruntime.InferenceSession(model_path) + + input_tensors = [] + for i, input_tensor in enumerate(sess.get_inputs()): + input_tensors.append(input_tensor) + # assume only 1 input tensor for image + assert len(input_tensors) == 1, 'invalid input tensor number.' + + batch, height, width, channel = input_tensors[0].shape + model_image_size = (height, width) + + output_tensors = [] + for i, output_tensor in enumerate(sess.get_outputs()): + output_tensors.append(output_tensor) + # assume only 1 output tensor + assert len(output_tensors) == 1, 'invalid output tensor number.' + + num_classes = output_tensors[0].shape[-1] + if class_names: + # check if classes number match with model prediction + assert num_classes == len(class_names), 'classes number mismatch with model.' + + # prepare input image + img = Image.open(image_file) + image_data = preprocess_image(img, model_image_size) + image = image_data[0].astype('uint8') + #origin image shape, in (width, height) format + origin_image_size = img.size + + feed = {input_tensors[0].name: image_data} + + # predict once first to bypass the model building time + prediction = sess.run(None, feed) + + start = time.time() + for i in range(loop_count): + prediction = sess.run(None, feed) + + end = time.time() + print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count)) + + handle_prediction(prediction, image, np.array(img), num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file) + + +def validate_deeplab_model_mnn(model_path, image_file, class_names, do_crf, label_file, loop_count): + interpreter = MNN.Interpreter(model_path) + session = interpreter.createSession() + + # assume only 1 input tensor for image + input_tensor = interpreter.getSessionInput(session) + # get input shape + input_shape = input_tensor.getShape() + if input_tensor.getDimensionType() == MNN.Tensor_DimensionType_Tensorflow: + batch, height, width, channel = input_shape + elif input_tensor.getDimensionType() == MNN.Tensor_DimensionType_Caffe: + batch, channel, height, width = input_shape + else: + # should be MNN.Tensor_DimensionType_Caffe_C4, unsupported now + raise ValueError('unsupported input tensor dimension type') + + model_image_size = (height, width) + + # prepare input image + img = Image.open(image_file) + image_data = preprocess_image(img, model_image_size) + image = image_data[0].astype('uint8') + #origin image shape, in (width, height) format + origin_image_size = img.size + + # use a temp tensor to copy data + tmp_input = MNN.Tensor(input_shape, input_tensor.getDataType(),\ + image_data, input_tensor.getDimensionType()) + + # predict once first to bypass the model building time + input_tensor.copyFrom(tmp_input) + interpreter.runSession(session) + + start = time.time() + for i in range(loop_count): + input_tensor.copyFrom(tmp_input) + interpreter.runSession(session) + end = time.time() + print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count)) + + prediction = [] + output_tensor = interpreter.getSessionOutput(session) + output_shape = output_tensor.getShape() + output_elementsize = reduce(mul, output_shape) + + num_classes = output_shape[-1] + if class_names: + # check if classes number match with model prediction + assert num_classes == len(class_names), 'classes number mismatch with model.' + + assert output_tensor.getDataType() == MNN.Halide_Type_Float + + # copy output tensor to host, for further postprocess + tmp_output = MNN.Tensor(output_shape, output_tensor.getDataType(),\ + #np.zeros(output_shape, dtype=float), output_tensor.getDimensionType()) + tuple(np.zeros(output_shape, dtype=float).reshape(output_elementsize, -1)), output_tensor.getDimensionType()) + + output_tensor.copyToHostTensor(tmp_output) + + output_data = np.array(tmp_output.getData(), dtype=float).reshape(output_shape) + # our postprocess code based on TF channel last format, so if the output format + # doesn't match, we need to transpose + if output_tensor.getDimensionType() == MNN.Tensor_DimensionType_Caffe: + output_data = output_data.transpose((0,2,3,1)) + elif output_tensor.getDimensionType() == MNN.Tensor_DimensionType_Caffe_C4: + raise ValueError('unsupported output tensor dimension type') + + prediction.append(output_data) + handle_prediction(prediction, image, np.array(img), num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file) + + +def validate_deeplab_model_pb(model_path, image_file, class_names, do_crf, label_file, loop_count): + # check tf version to be compatible with TF 2.x + global tf + if tf.__version__.startswith('2'): + import tensorflow.compat.v1 as tf + tf.disable_eager_execution() + + # NOTE: TF 1.x frozen pb graph need to specify input/output tensor name + # so we hardcode the input/output tensor names here to get them from model + input_tensor_name = 'graph/image_input:0' + output_tensor_name = 'graph/pred_mask/Softmax:0' + + #load frozen pb graph + def load_pb_graph(model_path): + # We parse the graph_def file + with tf.gfile.GFile(model_path, "rb") as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + + # We load the graph_def in the default graph + with tf.Graph().as_default() as graph: + tf.import_graph_def( + graph_def, + input_map=None, + return_elements=None, + name="graph", + op_dict=None, + producer_op_list=None + ) + return graph + + graph = load_pb_graph(model_path) + + # We can list operations, op.values() gives you a list of tensors it produces + # op.name gives you the name. These op also include input & output node + # print output like: + # prefix/Placeholder/inputs_placeholder + # ... + # prefix/Accuracy/predictions + # + # NOTE: prefix/Placeholder/inputs_placeholder is only op's name. + # tensor name should be like prefix/Placeholder/inputs_placeholder:0 + + #for op in graph.get_operations(): + #print(op.name, op.values()) + + image_input = graph.get_tensor_by_name(input_tensor_name) + output_tensor = graph.get_tensor_by_name(output_tensor_name) + + batch, height, width, channel = image_input.shape + model_image_size = (int(height), int(width)) + + num_classes = output_tensor.shape[-1] + if class_names: + # check if classes number match with model prediction + assert num_classes == len(class_names), 'classes number mismatch with model.' + + # prepare input image + img = Image.open(image_file) + image_data = preprocess_image(img, model_image_size) + image = image_data[0].astype('uint8') + #origin image shape, in (width, height) format + origin_image_size = img.size + + # predict once first to bypass the model building time + with tf.Session(graph=graph) as sess: + prediction = sess.run(output_tensor, feed_dict={ + image_input: image_data + }) + + start = time.time() + for i in range(loop_count): + with tf.Session(graph=graph) as sess: + prediction = sess.run(output_tensor, feed_dict={ + image_input: image_data + }) + end = time.time() + print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count)) + handle_prediction(prediction, image, np.array(img), num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file) + + +def validate_deeplab_model_tflite(model_path, image_file, class_names, do_crf, label_file, loop_count): + interpreter = interpreter_wrapper.Interpreter(model_path=model_path) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + output_details = interpreter.get_output_details() + + #print(input_details) + #print(output_details) + + # check the type of the input tensor + if input_details[0]['dtype'] == np.float32: + floating_model = True + + height = input_details[0]['shape'][1] + width = input_details[0]['shape'][2] + model_image_size = (height, width) + + num_classes = output_details[0]['shape'][-1] + if class_names: + # check if classes number match with model prediction + assert num_classes == len(class_names), 'classes number mismatch with model.' + + # prepare input image + img = Image.open(image_file) + image_data = preprocess_image(img, model_image_size) + image = image_data[0].astype('uint8') + #origin image shape, in (width, height) format + origin_image_size = img.size + + # predict once first to bypass the model building time + interpreter.set_tensor(input_details[0]['index'], image_data) + interpreter.invoke() + + start = time.time() + for i in range(loop_count): + interpreter.set_tensor(input_details[0]['index'], image_data) + interpreter.invoke() + end = time.time() + print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count)) + + prediction = [] + for output_detail in output_details: + output_data = interpreter.get_tensor(output_detail['index']) + prediction.append(output_data) + + handle_prediction(prediction, image, np.array(img), num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file) + return + + +def handle_prediction(prediction, image, origin_image, num_classes, class_names, model_image_size, origin_image_size, do_crf, label_file): + # generate prediction mask, + # add CRF postprocess if need + prediction = np.argmax(prediction, -1)[0].reshape(model_image_size) + if do_crf: + prediction = crf_postprocess(image, prediction, zero_unsure=False) + prediction = mask_resize_fast(prediction, origin_image_size) + + title_str = None + label = None + gt_title_str = None + # calculate mIOU if having label image + if label_file: + label = np.array(Image.open(label_file), dtype='int32') + # treat all the invalid label value as background + label[label>(num_classes-1)] = 0 + title_str = 'Predict Segmentation\nmIOU: '+str(mIOU(label, prediction)) + gt_title_str = 'GT Segmentation' + + image_array = visualize_segmentation(origin_image, prediction, label, class_names=class_names, title=title_str, gt_title=gt_title_str, ignore_count_threshold=500) + + # show result + Image.fromarray(image_array).show() + + +def main(): + parser = argparse.ArgumentParser(description='validate Deeplab model (h5/pb/onnx/tflite/mnn) with image') + parser.add_argument('--model_path', help='model file to predict', type=str, required=True) + + parser.add_argument('--image_file', help='image file to predict', type=str, required=True) + parser.add_argument('--model_image_size', help='model image input size as x, default=%(default)s', type=str, default='512x512') + parser.add_argument('--do_crf', action="store_true", help='whether to add CRF postprocess for model output', default=False) + parser.add_argument('--label_file', help='segmentation label image file', type=str, required=False, default=None) + parser.add_argument('--classes_path', help='path to class name definitions', type=str, required=False) + parser.add_argument('--loop_count', help='loop inference for certain times', type=int, default=1) + + args = parser.parse_args() + + class_names = None + if args.classes_path: + # add background class to match model & GT + class_names = get_classes(args.classes_path) + assert len(class_names) < 254, 'PNG image label only support less than 254 classes.' + class_names = ['background'] + class_names + + # param parse + height, width = args.model_image_size.split('x') + model_image_size = (int(height), int(width)) + + # support of tflite model + if args.model_path.endswith('.tflite'): + validate_deeplab_model_tflite(args.model_path, args.image_file, class_names, args.do_crf, args.label_file, args.loop_count) + # support of MNN model + elif args.model_path.endswith('.mnn'): + validate_deeplab_model_mnn(args.model_path, args.image_file, class_names, args.do_crf, args.label_file, args.loop_count) + # support of TF 1.x frozen pb model + elif args.model_path.endswith('.pb'): + validate_deeplab_model_pb(args.model_path, args.image_file, class_names, args.do_crf, args.label_file, args.loop_count) + # support of ONNX model + elif args.model_path.endswith('.onnx'): + validate_deeplab_model_onnx(args.model_path, args.image_file, class_names, args.do_crf, args.label_file, args.loop_count) + # normal keras h5 model + elif args.model_path.endswith('.h5'): + validate_deeplab_model(args.model_path, args.image_file, class_names, model_image_size, args.do_crf, args.label_file, args.loop_count) + else: + raise ValueError('invalid model file') + + +if __name__ == '__main__': + main() diff --git a/models/deeplab/tools/model_converter/custom_tflite_convert.py b/models/deeplab/tools/model_converter/custom_tflite_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..bcad92b6340dc3040f97952afa0243a310fb853d --- /dev/null +++ b/models/deeplab/tools/model_converter/custom_tflite_convert.py @@ -0,0 +1,525 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python command line interface for running TOCO.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import os +import sys + +from tensorflow.lite.python import lite +from tensorflow.lite.python import lite_constants +from tensorflow.lite.toco import toco_flags_pb2 as _toco_flags_pb2 +from tensorflow.python import keras +from tensorflow.python import tf2 +from tensorflow.python.platform import app + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.utils import get_custom_objects + +def _parse_array(values, type_fn=str): + if values is not None: + return [type_fn(val) for val in values.split(",") if val] + return None + + +def _parse_set(values): + if values is not None: + return set([item for item in values.split(",") if item]) + return None + + +def _parse_inference_type(value, flag): + """Converts the inference type to the value of the constant. + + Args: + value: str representing the inference type. + flag: str representing the flag name. + + Returns: + tf.dtype. + + Raises: + ValueError: Unsupported value. + """ + if value == "FLOAT": + return lite_constants.FLOAT + if value == "QUANTIZED_UINT8": + return lite_constants.QUANTIZED_UINT8 + raise ValueError("Unsupported value for --{0}. Only FLOAT and " + "QUANTIZED_UINT8 are supported.".format(flag)) + + +def _get_toco_converter(flags): + """Makes a TFLiteConverter object based on the flags provided. + + Args: + flags: argparse.Namespace object containing TFLite flags. + + Returns: + TFLiteConverter object. + + Raises: + ValueError: Invalid flags. + """ + # Parse input and output arrays. + input_arrays = _parse_array(flags.input_arrays) + input_shapes = None + if flags.input_shapes: + input_shapes_list = [ + _parse_array(shape, type_fn=int) + for shape in flags.input_shapes.split(":") + ] + input_shapes = dict(zip(input_arrays, input_shapes_list)) + output_arrays = _parse_array(flags.output_arrays) + + converter_kwargs = { + "input_arrays": input_arrays, + "input_shapes": input_shapes, + "output_arrays": output_arrays + } + + # Create TFLiteConverter. + if flags.graph_def_file: + converter_fn = lite.TFLiteConverter.from_frozen_graph + converter_kwargs["graph_def_file"] = flags.graph_def_file + elif flags.saved_model_dir: + converter_fn = lite.TFLiteConverter.from_saved_model + converter_kwargs["saved_model_dir"] = flags.saved_model_dir + converter_kwargs["tag_set"] = _parse_set(flags.saved_model_tag_set) + converter_kwargs["signature_key"] = flags.saved_model_signature_key + elif flags.keras_model_file: + converter_fn = lite.TFLiteConverter.from_keras_model_file + converter_kwargs["model_file"] = flags.keras_model_file + custom_object_dict = get_custom_objects() + converter_kwargs["custom_objects"] = custom_object_dict + else: + raise ValueError("--graph_def_file, --saved_model_dir, or " + "--keras_model_file must be specified.") + + return converter_fn(**converter_kwargs) + + +def _convert_tf1_model(flags): + """Calls function to convert the TensorFlow 1.X model into a TFLite model. + + Args: + flags: argparse.Namespace object. + + Raises: + ValueError: Invalid flags. + """ + # Create converter. + converter = _get_toco_converter(flags) + if flags.inference_type: + converter.inference_type = _parse_inference_type(flags.inference_type, + "inference_type") + if flags.inference_input_type: + converter.inference_input_type = _parse_inference_type( + flags.inference_input_type, "inference_input_type") + if flags.output_format: + converter.output_format = _toco_flags_pb2.FileFormat.Value( + flags.output_format) + + if flags.mean_values and flags.std_dev_values: + input_arrays = converter.get_input_arrays() + std_dev_values = _parse_array(flags.std_dev_values, type_fn=float) + + # In quantized inference, mean_value has to be integer so that the real + # value 0.0 is exactly representable. + if converter.inference_type == lite_constants.QUANTIZED_UINT8: + mean_values = _parse_array(flags.mean_values, type_fn=int) + else: + mean_values = _parse_array(flags.mean_values, type_fn=float) + quant_stats = list(zip(mean_values, std_dev_values)) + if ((not flags.input_arrays and len(input_arrays) > 1) or + (len(input_arrays) != len(quant_stats))): + raise ValueError("Mismatching --input_arrays, --std_dev_values, and " + "--mean_values. The flags must have the same number of " + "items. The current input arrays are '{0}'. " + "--input_arrays must be present when specifying " + "--std_dev_values and --mean_values with multiple input " + "tensors in order to map between names and " + "values.".format(",".join(input_arrays))) + converter.quantized_input_stats = dict(zip(input_arrays, quant_stats)) + if (flags.default_ranges_min is not None) and (flags.default_ranges_max is + not None): + converter.default_ranges_stats = (flags.default_ranges_min, + flags.default_ranges_max) + + if flags.drop_control_dependency: + converter.drop_control_dependency = flags.drop_control_dependency + if flags.reorder_across_fake_quant: + converter.reorder_across_fake_quant = flags.reorder_across_fake_quant + if flags.change_concat_input_ranges: + converter.change_concat_input_ranges = ( + flags.change_concat_input_ranges == "TRUE") + + if flags.allow_custom_ops: + converter.allow_custom_ops = flags.allow_custom_ops + if flags.target_ops: + ops_set_options = lite.OpsSet.get_options() + converter.target_ops = set() + for option in flags.target_ops.split(","): + if option not in ops_set_options: + raise ValueError("Invalid value for --target_ops. Options: " + "{0}".format(",".join(ops_set_options))) + converter.target_spec.supported_ops.add(lite.OpsSet(option)) + + if flags.post_training_quantize: + converter.optimizations = [lite.Optimize.DEFAULT] + if converter.inference_type == lite_constants.QUANTIZED_UINT8: + print("--post_training_quantize quantizes a graph of inference_type " + "FLOAT. Overriding inference type QUANTIZED_UINT8 to FLOAT.") + converter.inference_type = lite_constants.FLOAT + + if flags.quantize_to_float16: + converter.target_spec.supported_types = [lite.constants.FLOAT16] + if not flags.post_training_quantize: + print("--quantize_to_float16 will only take effect with the " + "--post_training_quantize flag enabled.") + + if flags.dump_graphviz_dir: + converter.dump_graphviz_dir = flags.dump_graphviz_dir + if flags.dump_graphviz_video: + converter.dump_graphviz_vode = flags.dump_graphviz_video + + # Convert model. + output_data = converter.convert() + with open(flags.output_file, "wb") as f: + f.write(output_data) + + +def _convert_tf2_model(flags): + """Calls function to convert the TensorFlow 2.0 model into a TFLite model. + + Args: + flags: argparse.Namespace object. + + Raises: + ValueError: Unsupported file format. + """ + # Load the model. + if flags.saved_model_dir: + converter = lite.TFLiteConverterV2.from_saved_model(flags.saved_model_dir) + elif flags.keras_model_file: + custom_object_dict = get_custom_objects() + model = keras.models.load_model(flags.keras_model_file, custom_objects = custom_object_dict) + converter = lite.TFLiteConverterV2.from_keras_model(model) + + # Convert the model. + tflite_model = converter.convert() + with open(flags.output_file, "wb") as f: + f.write(tflite_model) + + +def _check_tf1_flags(flags, unparsed): + """Checks the parsed and unparsed flags to ensure they are valid in 1.X. + + Raises an error if previously support unparsed flags are found. Raises an + error for parsed flags that don't meet the required conditions. + + Args: + flags: argparse.Namespace object containing TFLite flags. + unparsed: List of unparsed flags. + + Raises: + ValueError: Invalid flags. + """ + + # Check unparsed flags for common mistakes based on previous TOCO. + def _get_message_unparsed(flag, orig_flag, new_flag): + if flag.startswith(orig_flag): + return "\n Use {0} instead of {1}".format(new_flag, orig_flag) + return "" + + if unparsed: + output = "" + for flag in unparsed: + output += _get_message_unparsed(flag, "--input_file", "--graph_def_file") + output += _get_message_unparsed(flag, "--savedmodel_directory", + "--saved_model_dir") + output += _get_message_unparsed(flag, "--std_value", "--std_dev_values") + output += _get_message_unparsed(flag, "--batch_size", "--input_shapes") + output += _get_message_unparsed(flag, "--dump_graphviz", + "--dump_graphviz_dir") + if output: + raise ValueError(output) + + # Check that flags are valid. + if flags.graph_def_file and (not flags.input_arrays or + not flags.output_arrays): + raise ValueError("--input_arrays and --output_arrays are required with " + "--graph_def_file") + + if flags.input_shapes: + if not flags.input_arrays: + raise ValueError("--input_shapes must be used with --input_arrays") + if flags.input_shapes.count(":") != flags.input_arrays.count(","): + raise ValueError("--input_shapes and --input_arrays must have the same " + "number of items") + + if flags.std_dev_values or flags.mean_values: + if bool(flags.std_dev_values) != bool(flags.mean_values): + raise ValueError("--std_dev_values and --mean_values must be used " + "together") + if flags.std_dev_values.count(",") != flags.mean_values.count(","): + raise ValueError("--std_dev_values, --mean_values must have the same " + "number of items") + + if (flags.default_ranges_min is None) != (flags.default_ranges_max is None): + raise ValueError("--default_ranges_min and --default_ranges_max must be " + "used together") + + if flags.dump_graphviz_video and not flags.dump_graphviz_dir: + raise ValueError("--dump_graphviz_video must be used with " + "--dump_graphviz_dir") + + +def _get_tf1_parser(): + """Returns ArgumentParser for tflite_convert for TensorFlow 1.X.""" + parser = argparse.ArgumentParser( + description=("Command line tool to run TensorFlow Lite Converter.")) + + # Output file flag. + parser.add_argument( + "--output_file", + type=str, + help="Full filepath of the output file.", + required=True) + + # Input file flags. + input_file_group = parser.add_mutually_exclusive_group(required=True) + input_file_group.add_argument( + "--graph_def_file", + type=str, + help="Full filepath of file containing frozen TensorFlow GraphDef.") + input_file_group.add_argument( + "--saved_model_dir", + type=str, + help="Full filepath of directory containing the SavedModel.") + input_file_group.add_argument( + "--keras_model_file", + type=str, + help="Full filepath of HDF5 file containing tf.Keras model.") + + + # Model format flags. + parser.add_argument( + "--output_format", + type=str.upper, + choices=["TFLITE", "GRAPHVIZ_DOT"], + help="Output file format.") + parser.add_argument( + "--inference_type", + type=str.upper, + choices=["FLOAT", "QUANTIZED_UINT8"], + help="Target data type of real-number arrays in the output file.") + parser.add_argument( + "--inference_input_type", + type=str.upper, + choices=["FLOAT", "QUANTIZED_UINT8"], + help=("Target data type of real-number input arrays. Allows for a " + "different type for input arrays in the case of quantization.")) + + # Input and output arrays flags. + parser.add_argument( + "--input_arrays", + type=str, + help="Names of the input arrays, comma-separated.") + parser.add_argument( + "--input_shapes", + type=str, + help="Shapes corresponding to --input_arrays, colon-separated.") + parser.add_argument( + "--output_arrays", + type=str, + help="Names of the output arrays, comma-separated.") + + # SavedModel related flags. + parser.add_argument( + "--saved_model_tag_set", + type=str, + help=("Comma-separated set of tags identifying the MetaGraphDef within " + "the SavedModel to analyze. All tags must be present. In order to " + "pass in an empty tag set, pass in \"\". (default \"serve\")")) + parser.add_argument( + "--saved_model_signature_key", + type=str, + help=("Key identifying the SignatureDef containing inputs and outputs. " + "(default DEFAULT_SERVING_SIGNATURE_DEF_KEY)")) + + # Quantization flags. + parser.add_argument( + "--std_dev_values", + type=str, + help=("Standard deviation of training data for each input tensor, " + "comma-separated floats. Used for quantized input tensors. " + "(default None)")) + parser.add_argument( + "--mean_values", + type=str, + help=("Mean of training data for each input tensor, comma-separated " + "floats. Used for quantized input tensors. (default None)")) + parser.add_argument( + "--default_ranges_min", + type=float, + help=("Default value for min bound of min/max range values used for all " + "arrays without a specified range, Intended for experimenting with " + "quantization via \"dummy quantization\". (default None)")) + parser.add_argument( + "--default_ranges_max", + type=float, + help=("Default value for max bound of min/max range values used for all " + "arrays without a specified range, Intended for experimenting with " + "quantization via \"dummy quantization\". (default None)")) + # quantize_weights is DEPRECATED. + parser.add_argument( + "--quantize_weights", + dest="post_training_quantize", + action="store_true", + help=argparse.SUPPRESS) + parser.add_argument( + "--post_training_quantize", + dest="post_training_quantize", + action="store_true", + help=( + "Boolean indicating whether to quantize the weights of the " + "converted float model. Model size will be reduced and there will " + "be latency improvements (at the cost of accuracy). (default False)")) + parser.add_argument( + "--quantize_to_float16", + dest="quantize_to_float16", + action="store_true", + help=("Boolean indicating whether to quantize weights to fp16 instead of " + "the default int8 when post-training quantization " + "(--post_training_quantize) is enabled. (default False)")) + # Graph manipulation flags. + parser.add_argument( + "--drop_control_dependency", + action="store_true", + help=("Boolean indicating whether to drop control dependencies silently. " + "This is due to TensorFlow not supporting control dependencies. " + "(default True)")) + parser.add_argument( + "--reorder_across_fake_quant", + action="store_true", + help=("Boolean indicating whether to reorder FakeQuant nodes in " + "unexpected locations. Used when the location of the FakeQuant " + "nodes is preventing graph transformations necessary to convert " + "the graph. Results in a graph that differs from the quantized " + "training graph, potentially causing differing arithmetic " + "behavior. (default False)")) + # Usage for this flag is --change_concat_input_ranges=true or + # --change_concat_input_ranges=false in order to make it clear what the flag + # is set to. This keeps the usage consistent with other usages of the flag + # where the default is different. The default value here is False. + parser.add_argument( + "--change_concat_input_ranges", + type=str.upper, + choices=["TRUE", "FALSE"], + help=("Boolean to change behavior of min/max ranges for inputs and " + "outputs of the concat operator for quantized models. Changes the " + "ranges of concat operator overlap when true. (default False)")) + + # Permitted ops flags. + parser.add_argument( + "--allow_custom_ops", + action="store_true", + help=("Boolean indicating whether to allow custom operations. When false " + "any unknown operation is an error. When true, custom ops are " + "created for any op that is unknown. The developer will need to " + "provide these to the TensorFlow Lite runtime with a custom " + "resolver. (default False)")) + parser.add_argument( + "--target_ops", + type=str, + help=("Experimental flag, subject to change. Set of OpsSet options " + "indicating which converter to use. Options: {0}. One or more " + "option may be specified. (default set([OpsSet.TFLITE_BUILTINS]))" + "".format(",".join(lite.OpsSet.get_options())))) + + # Logging flags. + parser.add_argument( + "--dump_graphviz_dir", + type=str, + help=("Full filepath of folder to dump the graphs at various stages of " + "processing GraphViz .dot files. Preferred over --output_format=" + "GRAPHVIZ_DOT in order to keep the requirements of the output " + "file.")) + parser.add_argument( + "--dump_graphviz_video", + action="store_true", + help=("Boolean indicating whether to dump the graph after every graph " + "transformation")) + return parser + + +def _get_tf2_parser(): + """Returns ArgumentParser for tflite_convert for TensorFlow 2.0.""" + parser = argparse.ArgumentParser( + description=("Command line tool to run TensorFlow Lite Converter.")) + + # Output file flag. + parser.add_argument( + "--output_file", + type=str, + help="Full filepath of the output file.", + required=True) + + # Input file flags. + input_file_group = parser.add_mutually_exclusive_group(required=True) + input_file_group.add_argument( + "--saved_model_dir", + type=str, + help="Full path of the directory containing the SavedModel.") + input_file_group.add_argument( + "--keras_model_file", + type=str, + help="Full filepath of HDF5 file containing tf.Keras model.") + return parser + + +def run_main(_): + """Main in toco_convert.py.""" + if tf2.enabled(): + parser = _get_tf2_parser() + else: + parser = _get_tf1_parser() + + tflite_flags, unparsed = parser.parse_known_args(args=sys.argv[1:]) + + if tf2.enabled(): + _convert_tf2_model(tflite_flags) + else: + try: + _check_tf1_flags(tflite_flags, unparsed) + except ValueError as e: + parser.print_usage() + file_name = os.path.basename(sys.argv[0]) + sys.stderr.write("{0}: error: {1}\n".format(file_name, str(e))) + sys.exit(1) + _convert_tf1_model(tflite_flags) + + +def main(): + app.run(main=run_main, argv=sys.argv[:1]) + + +if __name__ == "__main__": + main() diff --git a/models/deeplab/tools/model_converter/keras_to_onnx.py b/models/deeplab/tools/model_converter/keras_to_onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..10a6eb08f35a22501aaa4c31fe57745a814e219a --- /dev/null +++ b/models/deeplab/tools/model_converter/keras_to_onnx.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script to convert Deeplab keras model to ONNX model +""" +import os, sys, argparse +import shutil +import subprocess +import tensorflow as tf +from tensorflow.keras.models import load_model +import keras2onnx +import onnx + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.utils import get_custom_objects + +os.environ['TF_KERAS'] = '1' + +def onnx_convert(keras_model_file, output_file, op_set): + custom_object_dict = get_custom_objects() + model = load_model(keras_model_file, custom_objects=custom_object_dict) + + # convert to onnx model + onnx_model = keras2onnx.convert_keras(model, model.name, custom_op_conversions=custom_object_dict, target_opset=op_set) + + # save converted onnx model + onnx.save_model(onnx_model, output_file) + + +def onnx_convert_with_savedmodel(keras_model_file, output_file, op_set): + # only available for TF 2.x + if not tf.__version__.startswith('2'): + raise ValueError('savedmodel convert only support in TF 2.x env') + + custom_object_dict = get_custom_objects() + model = load_model(keras_model_file, custom_objects=custom_object_dict) + + # export to saved model + model.save('tmp_savedmodel', save_format='tf') + + # use tf2onnx to convert to onnx model + cmd = 'python -m tf2onnx.convert --saved-model tmp_savedmodel --output {} --opset {}'.format(output_file, op_set) + process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE) + output, error = process.communicate() + + # clean saved model + shutil.rmtree('tmp_savedmodel') + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='Convert Deeplab tf.keras model to ONNX model') + parser.add_argument('--keras_model_file', required=True, type=str, help='path to keras model file') + parser.add_argument('--output_file', required=True, type=str, help='output onnx model file') + parser.add_argument('--op_set', required=False, type=int, help='onnx op set, default=%(default)s', default=10) + parser.add_argument('--with_savedmodel', default=False, action="store_true", help='use a temp savedmodel for convert') + + args = parser.parse_args() + + if args.with_savedmodel: + onnx_convert_with_savedmodel(args.keras_model_file, args.output_file, args.op_set) + else: + onnx_convert(args.keras_model_file, args.output_file, args.op_set) + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/tools/model_converter/keras_to_tensorflow.py b/models/deeplab/tools/model_converter/keras_to_tensorflow.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb6e3c1c19cd9030bd143adca319c457f48a14c --- /dev/null +++ b/models/deeplab/tools/model_converter/keras_to_tensorflow.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +convert keras model files to frozen pb tensorflow weight file. The resultant TensorFlow model +holds both the model architecture and its associated weights. +""" +import os, sys, argparse, logging +from pathlib import Path +import tensorflow as tf +from tensorflow.python.framework import graph_util +from tensorflow.python.framework import graph_io +from tensorflow.keras import backend as K +from tensorflow.keras.models import model_from_json, model_from_yaml, load_model + +# compatible with TF 2.x +if tf.__version__.startswith('2'): + import tensorflow.compat.v1 as tf + from tensorflow.compat.v1.keras import backend as K + from tensorflow.compat.v1.keras.models import model_from_json, model_from_yaml, load_model + tf.disable_eager_execution() + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from common.utils import get_custom_objects + +K.set_learning_phase(0) + +LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s" +logging.basicConfig(level=logging.DEBUG, format=LOG_FORMAT) + + +def load_input_model(input_model_path, input_json_path=None, input_yaml_path=None, custom_objects=None): + if not Path(input_model_path).exists(): + raise FileNotFoundError( + 'Model file `{}` does not exist.'.format(input_model_path)) + try: + model = load_model(input_model_path, custom_objects=custom_objects) + return model + except FileNotFoundError as err: + logging.error('Input mode file (%s) does not exist.', input_model_path) + raise err + except ValueError as wrong_file_err: + if input_json_path: + if not Path(input_json_path).exists(): + raise FileNotFoundError( + 'Model description json file `{}` does not exist.'.format( + input_json_path)) + try: + model = model_from_json(open(str(input_json_path)).read()) + model.load_weights(input_model_path) + return model + except Exception as err: + logging.error("Couldn't load model from json.") + raise err + elif input_yaml_path: + if not Path(input_yaml_path).exists(): + raise FileNotFoundError( + 'Model description yaml file `{}` does not exist.'.format( + input_yaml_path)) + try: + model = model_from_yaml(open(str(input_yaml_path)).read()) + model.load_weights(input_model_path) + return model + except Exception as err: + logging.error("Couldn't load model from yaml.") + raise err + else: + logging.error( + 'Input file specified only holds the weights, and not ' + 'the model definition. Save the model using ' + 'model.save(filename.h5) which will contain the network ' + 'architecture as well as its weights. ' + 'If the model is saved using the ' + 'model.save_weights(filename) function, either ' + 'input_model_json or input_model_yaml flags should be set to ' + 'to import the network architecture prior to loading the ' + 'weights. \n' + 'Check the keras documentation for more details ' + '(https://keras.io/getting-started/faq/)') + raise wrong_file_err + + +def keras_to_tensorflow(args): + # If output_model path is relative and in cwd, make it absolute from root + output_model = args.output_model + if str(Path(output_model).parent) == '.': + output_model = str((Path.cwd() / output_model)) + + output_fld = Path(output_model).parent + output_model_name = Path(output_model).name + output_model_stem = Path(output_model).stem + output_model_pbtxt_name = output_model_stem + '.pbtxt' + + # Create output directory if it does not exist + Path(output_model).parent.mkdir(parents=True, exist_ok=True) + + if args.channels_first: + K.set_image_data_format('channels_first') + else: + K.set_image_data_format('channels_last') + + custom_object_dict = get_custom_objects() + + model = load_input_model(args.input_model, args.input_model_json, args.input_model_yaml, custom_objects=custom_object_dict) + + # TODO(amirabdi): Support networks with multiple inputs + orig_output_node_names = [node.name.split(':')[0] for node in model.outputs] + #orig_output_node_names = [node.op.name for node in model.outputs] + if args.output_nodes_prefix: + num_output = len(orig_output_node_names) + pred = [None] * num_output + converted_output_node_names = [None] * num_output + + # Create dummy tf nodes to rename output + for i in range(num_output): + converted_output_node_names[i] = '{}{}'.format( + args.output_nodes_prefix, i) + pred[i] = tf.identity(model.outputs[i], + name=converted_output_node_names[i]) + else: + converted_output_node_names = orig_output_node_names + logging.info('Converted output node names are: %s', + str(converted_output_node_names)) + + sess = K.get_session() + if args.output_meta_ckpt: + saver = tf.train.Saver() + saver.save(sess, str(output_fld / output_model_stem)) + + if args.save_graph_def: + tf.train.write_graph(sess.graph.as_graph_def(), str(output_fld), + output_model_pbtxt_name, as_text=True) + logging.info('Saved the graph definition in ascii format at %s', + str(Path(output_fld) / output_model_pbtxt_name)) + + if args.quantize: + from tensorflow.tools.graph_transforms import TransformGraph + transforms = ["quantize_weights", "quantize_nodes"] + transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [], + converted_output_node_names, + transforms) + constant_graph = graph_util.convert_variables_to_constants( + sess, + transformed_graph_def, + converted_output_node_names) + else: + constant_graph = graph_util.convert_variables_to_constants( + sess, + sess.graph.as_graph_def(), + converted_output_node_names) + + graph_io.write_graph(constant_graph, str(output_fld), output_model_name, + as_text=False) + logging.info('Saved the freezed graph at %s', + str(Path(output_fld) / output_model_name)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--input_model', required=True, type=str, help='Path to the input model.') + parser.add_argument('--input_model_json', required=False, type=str, help='Path to the input model architecture in json format.') + parser.add_argument('--input_model_yaml', required=False, type=str, help='Path to the input model architecture in yaml format.') + parser.add_argument('--output_model', required=True, type=str, help='Path where the converted model will be stored.') + + parser.add_argument('--save_graph_def', default=False, action="store_true", help='Whether to save the graphdef.pbtxt file which contains the graph definition in ASCII format. default=%(default)s') + parser.add_argument('--output_nodes_prefix', required=False, type=str, help='If set, the output nodes will be renamed to `output_nodes_prefix`+i, where `i` will numerate the number of of output nodes of the network.') + parser.add_argument('--quantize', default=False, action="store_true", help='If set, the resultant TensorFlow graph weights will be converted from float into eight-bit equivalents. See documentation here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms. default=%(default)s') + parser.add_argument('--channels_first', default=False, action="store_true", help='Whether channels are the first dimension of a tensor. The default is TensorFlow behaviour where channels are the last dimension. default=%(default)s') + parser.add_argument('--output_meta_ckpt', default=False, action="store_true", help='If set to True, exports the model as .meta, .index, and .data files, with a checkpoint file. These can be later loaded in TensorFlow to continue training. default=%(default)s') + + args = parser.parse_args() + + keras_to_tensorflow(args) + + +if __name__ == '__main__': + main() diff --git a/models/deeplab/tools/model_converter/post_train_quant_convert.py b/models/deeplab/tools/model_converter/post_train_quant_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..65e28cdba62e766e5e9d18b3b9386b574f9116d5 --- /dev/null +++ b/models/deeplab/tools/model_converter/post_train_quant_convert.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script to convert Deeplab keras model to an integer quantized tflite model +using latest Post-Training Integer Quantization Toolkit released in +tensorflow 2.0.0 build +""" +import os, sys, argparse +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import load_model + +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..')) +from deeplabv3p.data import SegmentationGenerator +from common.utils import get_data_list, get_custom_objects + +#tf.enable_eager_execution() + + +def post_train_quant_convert(keras_model_file, dataset_path, dataset, sample_num, model_input_shape, output_file): + #get input_shapes for converter + input_shapes=list((1,)+model_input_shape+(3,)) + + #prepare quant data generator + data_gen = SegmentationGenerator(dataset_path, dataset, + 1, #batch_size + 1, #num_classes, here we don't really use it + target_size=model_input_shape[::-1], + weighted_type=None, + is_eval=False, + augment=True) + + + custom_object_dict = get_custom_objects() + model = load_model(keras_model_file, custom_objects=custom_object_dict) + converter = tf.lite.TFLiteConverter.from_keras_model(model) + + def data_generator(): + i = 0 + for n, (image_data, y_true) in enumerate(data_gen): + i += 1 + if i > sample_num: + break + + yield [image_data] + + + converter.optimizations = [tf.lite.Optimize.DEFAULT] + #converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] + #converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_LATENCY] + converter.representative_dataset = tf.lite.RepresentativeDataset(data_generator) + + #converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + + tflite_model = converter.convert() + with open(output_file, "wb") as f: + f.write(tflite_model) + + + +def main(): + parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='TF 2.x post training integer quantization converter') + + parser.add_argument('--keras_model_file', required=True, type=str, help='path to keras model file') + parser.add_argument('--dataset_path', required=True, type=str, help='dataset path containing images and label png file') + parser.add_argument('--dataset_file', required=True, type=str, help='data samples txt file') + parser.add_argument('--sample_num', type=int, help='annotation sample number to feed the converter,default=%(default)s', default=30) + parser.add_argument('--model_input_shape', type=str, help='model image input shape as x, default=%(default)s', default='512x512') + parser.add_argument('--output_file', required=True, type=str, help='output tflite model file') + + args = parser.parse_args() + height, width = args.model_input_shape.split('x') + model_input_shape = (int(height), int(width)) + + # get dataset list + dataset = get_data_list(args.dataset_file) + + post_train_quant_convert(args.keras_model_file, args.dataset_path, dataset, args.sample_num, model_input_shape, args.output_file) + + + +if __name__ == '__main__': + main() + diff --git a/models/deeplab/unet/model.py b/models/deeplab/unet/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1cee849b5d1677da257f3e116031afa8fccaee2d --- /dev/null +++ b/models/deeplab/unet/model.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +create unet models +""" +from functools import partial +from tensorflow.keras.layers import Reshape, Softmax, Input +from tensorflow.keras.models import Model + +from unet.models.unet import UNetStandard, UNetLite, UNetSimple + +# +# A map of model type to construction function for UNet +# +unet_model_map = { + 'unet_standard': UNetStandard, + 'unet_lite': UNetLite, + 'unet_simple': UNetSimple, +} + +def get_unet_model(model_type, num_classes, model_input_shape, freeze_level=0, weights_path=None, training=True): + # check if model type is valid + if model_type not in unet_model_map.keys(): + raise ValueError('This model type is not supported now') + + model_function = unet_model_map[model_type] + + input_tensor = Input(shape=model_input_shape + (3,), name='image_input') + base_model = model_function(num_classes, input_tensor=input_tensor, + input_shape=model_input_shape + (3,), + weights=None) + + #base_model = Model(model.input, model.layers[-5].output) + #print('backbone layers number: {}'.format(backbone_len)) + + + # for training model, we need to flatten mask to calculate loss + if training: + x = Reshape((model_input_shape[0]*model_input_shape[1], num_classes)) (base_model.output) + else: + x = base_model.output + + x = Softmax(name='pred_mask')(x) + model = Model(base_model.input, x, name=model_type) + + if weights_path: + model.load_weights(weights_path, by_name=False)#, skip_mismatch=True) + print('Load weights {}.'.format(weights_path)) + + #if freeze_level in [1, 2]: + ## Freeze the backbone part or freeze all but final feature map & input layers. + #num = (backbone_len, len(base_model.layers))[freeze_level-1] + #for i in range(num): model.layers[i].trainable = False + #print('Freeze the first {} layers of total {} layers.'.format(num, len(model.layers))) + #elif freeze_level == 0: + ## Unfreeze all layers. + #for i in range(len(model.layers)): + #model.layers[i].trainable= True + #print('Unfreeze all of the layers.') + + return model + diff --git a/models/deeplab/unet/models/layers.py b/models/deeplab/unet/models/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..16d4678d8309b644921df4f4d57b6c8f51e1fc0d --- /dev/null +++ b/models/deeplab/unet/models/layers.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from __future__ import division + +from tensorflow.keras.layers import BatchNormalization +#from tensorflow.keras.regularizers import l2 +import tensorflow as tf + + +def normalize(x): + return x/127.5 - 1 + + +def img_resize(x, size, mode='bilinear'): + if mode == 'bilinear': + return tf.image.resize(x, size=size, method='bilinear') + elif mode == 'nearest': + return tf.image.resize(x, size=size, method='nearest') + else: + raise ValueError('output model file is not specified') + + +def CustomBatchNormalization(*args, **kwargs): + if tf.__version__ >= '2.2': + from tensorflow.keras.layers.experimental import SyncBatchNormalization + BatchNorm = SyncBatchNormalization + else: + BatchNorm = BatchNormalization + + return BatchNorm(*args, **kwargs) + diff --git a/models/deeplab/unet/models/unet.py b/models/deeplab/unet/models/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..726e7a6c30beb2cc629239b43a871bc62bdff850 --- /dev/null +++ b/models/deeplab/unet/models/unet.py @@ -0,0 +1,134 @@ + +import tensorflow as tf +from tensorflow.keras import layers as L +from tensorflow.keras.models import Model + +def channel_attention(x, ratio=8): + channel = x.shape[-1] + + # Global average pooling + avg_pool = L.GlobalAveragePooling2D()(x) + avg_pool = L.Reshape((1, 1, channel))(avg_pool) + + # Global max pooling + max_pool = L.GlobalMaxPooling2D()(x) + max_pool = L.Reshape((1, 1, channel))(max_pool) + + # Shared MLP + dense1 = L.Dense(channel // ratio, activation='relu') + dense2 = L.Dense(channel) + + avg_pool = dense2(dense1(avg_pool)) + max_pool = dense2(dense1(max_pool)) + + attention = L.Add()([avg_pool, max_pool]) + attention = L.Activation('sigmoid')(attention) + + return L.Multiply()([x, attention]) + +def spatial_attention(x): + # Average pooling + avg_pool = L.Lambda(lambda x: tf.reduce_mean(x, axis=-1, keepdims=True))(x) + + # Max pooling + max_pool = L.Lambda(lambda x: tf.reduce_max(x, axis=-1, keepdims=True))(x) + + # Concatenate + concat = L.Concatenate()([avg_pool, max_pool]) + + # Conv to get attention map + attention = L.Conv2D(1, 7, padding='same', activation='sigmoid')(concat) + + return L.Multiply()([x, attention]) + +def build_model(x_shape, y_shape, config): + inp = L.Input(shape=x_shape) + x = inp + + n_stages = config.get('num_stages', 2) + n_conv = config.get('num_conv', 1) + n_filters = config.get('num_filters', 16) + grow_mult = config.get('grow_factor', 1) + up_activation = config.get('up_act', 'relu') + conv_type = config.get('conv_type', 'conv') + use_aspp = config.get('aspp', False) + + if up_activation == 'lrelu': + up_activation = L.LeakyReLU() + else: + up_activation = L.Activation(up_activation) + + use_bn = 'bn-' not in conv_type + + conv = L.SeparableConv2D if 'sep-' in conv_type else L.Conv2D + conv_common = dict(padding='same', use_bias=not use_bn) + + def conv_block(*args, **kwargs): + def layer(x): + if use_bn: + act = kwargs.pop('activation', None) + x = conv(*args, **kwargs)(x) + x = L.BatchNormalization()(x) + return L.Activation(act)(x) if act else x + return conv(*args, **kwargs)(x) + return layer + + intermediate = [] + + for _ in range(n_conv): + x = conv_block(n_filters, 3, activation='relu', **conv_common)(x) + x = channel_attention(x) + x = spatial_attention(x) + + # downsample path + for i in range(n_stages): + intermediate.append(x) + n = round(n_filters * (grow_mult ** i)) + x = conv_block(n, 3, 2, activation='relu', **conv_common)(x) + + for _ in range(n_conv - 1): + x = conv_block(n, 3, activation='relu', **conv_common)(x) + x = channel_attention(x) + x = spatial_attention(x) + + middle = L.GlobalAveragePooling2D()(x) + + if use_aspp: + n = round(n / 4) + x1 = conv_block(n, 1, dilation_rate=1, activation='relu', **conv_common)(x) + x2 = conv_block(n, 3, dilation_rate=2, activation='relu', **conv_common)(x) + x3 = conv_block(n, 3, dilation_rate=4, activation='relu', **conv_common)(x) + x4 = conv_block(n, 3, dilation_rate=6, activation='relu', **conv_common)(x) + + # global feature + xg = L.Reshape((1, 1, -1))(middle) + xg = conv_block(n, 1, activation='relu', **conv_common)(xg) + feature_tiling = tf.pad(tf.shape(x)[1:3], tf.constant([[1, 1]]), constant_values=1) + xg = tf.tile(xg, feature_tiling) + + x = tf.concat([x1, x2, x3, x4, xg], axis=-1) + x = channel_attention(x) + x = spatial_attention(x) + + # upsample path + for i in range(n_stages - 1, -1, -1): + x = L.UpSampling2D(size=2, interpolation='bilinear')(x) + x = L.Concatenate()([x, intermediate.pop()]) + n = round(n_filters * (grow_mult ** i)) + for _ in range(n_conv): + x = conv_block(n, 3, **conv_common)(x) + x = up_activation(x) + x = channel_attention(x) + x = spatial_attention(x) + + # segmentation mask + out_mask = conv(y_shape[-1], 3, activation='sigmoid', padding='same', name='mask')(x) + # metadata tags (is_eye and is_blink) + out_tags = L.Dense(2, activation='sigmoid', name='tags')(middle) + + return Model(inp, [out_mask, out_tags]) + +if __name__ == '__main__': + shape = (128, 128, 1) + model = build_model(shape, shape, {'aspp': True}) + model.summary() \ No newline at end of file diff --git a/models/deeplab/weights/.gitignore b/models/deeplab/weights/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/predict.py b/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..bf11d2ec3e311cb1792750a5435e6c454fbb765d --- /dev/null +++ b/predict.py @@ -0,0 +1,81 @@ +import argparse + +import imageio +import numpy as np + +from tensorflow.keras.models import load_model +from PIL import Image, ImageOps +from tqdm import tqdm +from utils import draw_predictions, compute_metrics + + +def main(args): + video = imageio.get_reader(args.video) + n_frames = video.count_frames() + fps = video.get_meta_data()['fps'] + frame_w, frame_h = video.get_meta_data()['size'] + + model = load_model(args.model, compile=False) + input_shape = model.input.shape[1:3] + + # default RoI + if None in (args.rl, args.rt, args.rr, args.rb): + side = min(frame_w, frame_h) + args.rl = (frame_w - side) / 2 + args.rt = (frame_h - side) / 2 + args.rr = (frame_w + side) / 2 + args.rb = (frame_h + side) / 2 + + crop = (args.rl, args.rt, args.rr, args.rb) + + def preprocess(frame): + frame = Image.fromarray(frame) + eye = frame.crop(crop) + eye = ImageOps.grayscale(eye) + eye = eye.resize(input_shape) + return eye + + def predict(eye): + eye = np.array(eye).astype(np.float32) / 255.0 + eye = eye[None, :, :, None] + return model.predict(eye) + + out_video = imageio.get_writer(args.output_video, fps=fps) + + cropped = map(preprocess, video) + frames_and_predictions = map(lambda x: (x, predict(x)), cropped) + + with open(args.output_csv, 'w') as out_csv: + print('frame,pupil-area,pupil-x,pupil-y,eye,blink', file=out_csv) + for idx, (frame, predictions) in enumerate(tqdm(frames_and_predictions, total=n_frames)): + pupil_map, tags = predictions + is_eye, is_blink = tags.squeeze() + (pupil_y, pupil_x), pupil_area = compute_metrics(pupil_map, thr=args.thr, nms=True) + + row = [idx, pupil_area, pupil_x, pupil_y, is_eye, is_blink] + row = ','.join(list(map(str, row))) + print(row, file=out_csv) + + img = draw_predictions(frame, predictions, thr=args.thr) + img = np.array(img) + out_video.append_data(img) + + out_video.close() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Predict on test video') + parser.add_argument('model', type=str, help='Path to model') + parser.add_argument('video', type=str, default='', help='Video file to process (use \'\' for webcam)') + + parser.add_argument('-t', '--thr', type=float, default=0.5, help='Map Threshold') + parser.add_argument('-rl', type=int, help='RoI X coordinate of top left corner') + parser.add_argument('-rt', type=int, help='RoI Y coordinate of top left corner') + parser.add_argument('-rr', type=int, help='RoI X coordinate of right bottom corner') + parser.add_argument('-rb', type=int, help='RoI Y coordinate of right bottom corner') + + parser.add_argument('-ov', '--output-video', default='predictions.mp4', help='Output video') + parser.add_argument('-oc', '--output-csv', default='pupillometry.csv', help='Output CSV') + + args = parser.parse_args() + main(args) diff --git a/pupillometry-offline-videos.ipynb b/pupillometry-offline-videos.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ea1ed9a9dca2dcfe11b5c84b2097aa002da0bed9 --- /dev/null +++ b/pupillometry-offline-videos.ipynb @@ -0,0 +1,2494 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "recreational-beijing", + "metadata": {}, + "source": [ + "# Offline pupillometry\n", + "This notebook provides an example of how to use mEYE models in the Python environment to get pupillometry data from a prerecorded video file.\n", + "\n", + "## Installation\n", + "Follow the requiremets described in the repo [homepage](https://github.com/fabiocarrara/meye). \n", + "\n", + "Download locally the NN model in a location in your computer. \n", + "\n", + "For **mEYE** you need Python 3 environment with the following packages installed:\n", + "- tensorflow >= 2.4\n", + "- imageio, imageio-ffmpeg\n", + "- scipy\n", + "- tqdm\n", + "\n", + "For running the rest of this **notebook** you will need:\n", + "- cv2\n", + "- numpy\n", + "- pandas\n", + "- matplotlib\n", + "- scikit-image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q opencv-python-headless scikit-image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "welcome-prescription", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required packages for this notebook\n", + "import os\n", + "import numpy as np \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as ptch\n", + "%matplotlib inline\n", + "import cv2 as cv\n", + "from skimage.measure import label, regionprops\n", + "\n", + "# Import the NN model\n", + "from tensorflow.keras.models import load_model\n", + "\n", + "### WINDOWS ONLY (Comment if on other OS)\n", + "# import winsound # makes a beep sound when whole video analysis is finished\n", + "BEEP_FREQ = 2500 # Set Frequency To 2500 Hertz\n", + "BEEP_DURATION_MS = 1000 # Set Duration To 1000 ms == 1 second\n", + "###" + ] + }, + { + "cell_type": "markdown", + "id": "impressive-youth", + "metadata": {}, + "source": [ + "### Specify the full paths\n", + " - **MODELPATH**: the NN model that you downloaded locally on your PC\n", + " - **VIDEOPATH**: the video file you want to analyze" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eleven-avatar", + "metadata": {}, + "outputs": [], + "source": [ + "MODELPATH = r\"C:\\Users\\Leonardo\\Documents\\Python\\pupilNN\\meye-segmentation_i128_s4_c1_f16_g1_a-relu.hdf5\"\n", + "VIDEOPATH = r\"D:\\PizzorussoLAB\\proj_Fasting\\exp_acuity-2P\\2P_data\\cage_gNex6\\20210107\\gNex6_postFast_2021-01-07-095936-0000.avi\"\n", + "\n", + "model = load_model(MODELPATH)" + ] + }, + { + "cell_type": "markdown", + "id": "offensive-freeware", + "metadata": {}, + "source": [ + "## Crop the movie\n", + "\n", + "The NN makes predition on images of a fixed size. The following section provide some code to interactively and iteratively perform a square region of interest (ROI) around the eye. \n", + "You need to specify and eventually adjust 2 parameters:\n", + "- **TOPLEFT**: tuple containing the XY (columns,rows) coordinates of the top-left corner of the ROI\n", + "- **ROIWIDTH**: size of the side of the square ROI in pixels" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "labeled-gibson", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# EDIT PARAMETERS\n", + "# Manually select a square ROI for the eye, by specifying the XY coordinates\n", + "# and the width of the square in pixels\n", + "# ---------------------------------------------------\n", + "TOPLEFT = (260,50) # X,Y coordinates\n", + "ROIWIDTH = 200 # width on the square ROI in pixels\n", + "# ---------------------------------------------------\n", + "\n", + "cap = cv.VideoCapture(VIDEOPATH)\n", + "try:\n", + " numOfFrames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))\n", + " chosenFrame = np.random.randint(1, high=numOfFrames)# Choose randomly a frame\n", + " \n", + " cap.set(cv.CAP_PROP_POS_FRAMES,chosenFrame)\n", + " _, frame = cap.read() # Read the frame\n", + " frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)# Convert to grayscale\n", + "except:\n", + " print('An error occurred while getting a frame for the video.')\n", + "finally:\n", + " cap.release()\n", + "\n", + "fHeight, fWidth = frame.shape\n", + "cropL = TOPLEFT[0]\n", + "cropR = TOPLEFT[0]+ROIWIDTH\n", + "cropT = TOPLEFT[1]\n", + "cropB = TOPLEFT[1]+ROIWIDTH\n", + "\n", + "# Default crop is the selected crop is out of the image boundaries\n", + "if (any(np.array([cropT,cropB])>fHeight)) or (any(np.array([cropL,cropR])>fWidth)):\n", + " print('Crop limits exceed image size. Performing a default crop instead.')\n", + " cropL = round((fWidth-ROIWIDTH) / 2)\n", + " cropR = round((fWidth+ROIWIDTH) / 2)\n", + " cropT = round((fHeight-ROIWIDTH) / 2)\n", + " cropB = round((fHeight+ROIWIDTH) / 2) \n", + " \n", + "# Crop and resize \n", + "requiredFrameSize = model.input.shape[1:3]\n", + "crop = frame[cropT:cropB, cropL:cropR]\n", + "crop = cv.resize(crop,tuple(requiredFrameSize))\n", + "\n", + "# Create a Rectangle patch\n", + "rect = ptch.Rectangle((cropL,cropT),ROIWIDTH,ROIWIDTH,\n", + " linewidth=4,edgecolor=[.2,1,.2],facecolor='none')\n", + "\n", + "# Visual plotting\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,4))\n", + "ax1.imshow(frame, cmap='gray')\n", + "ax1.add_patch(rect)\n", + "ax1.set_title('Raw frame')\n", + "ax2.imshow(crop, cmap='gray')\n", + "ax2.set_title('Cropped, resized')\n", + "ax1.grid(color=[1,.4,.4], linewidth=1, linestyle='-.')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "favorite-bearing", + "metadata": {}, + "source": [ + "## Load and predict a single frame of the video\n", + "This section allows to make a prediciton on a single frame of the video.\n", + "By default the prediction returns: \n", + "- an **output image** of the same size as the input image, containing a **probability map** (i.e. each pixel value represent the probability for that pizel to be part of a pupil).\n", + "- the **eye-probability** (i.e., the probability for that frame to be an eye)\n", + "- the **blink-probability** (i.e., the probability for that frame to be a blink)\n", + "\n", + "The output image can be binarized based on a probability threshold to get a binay mask of the pupil.\n", + "\n", + "----\n", + "You need to specify and eventually adjust 2 parameters:\n", + "- **FRAMENUMBER**: The frame to analyze. If FRAMENUMBER=[], then a random frame will be taken\n", + "- **THRESHOLD**: The probability threshold to binarize the output image\n", + "- **IMCLOSING**: Size in pixel for morphological closing (fill pupil areas not detected). Increase to fill more.\n", + "- **INVERTIMAGE**: bool. Inverts the image (can be useful for tricky 2p images)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "permanent-fifth", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analyzed image # 20400 / 23236\n", + "Eye Probability: 99.82%\n", + "Blink Probability: 2.64%\n" + ] + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# EDIT PARAMETERS\n", + "# ---------------------------------------------------\n", + "FRAMENUMBER = 20400 # frame number to analyze. If [] a random frame will be taken \n", + "THRESHOLD = 0.1 # probability threshold for image binarization\n", + "IMCLOSING = 13 # pixel radius of circular kernel for morphological closing\n", + "INVERTIMAGE = True\n", + "# ---------------------------------------------------\n", + "\n", + "cap = cv.VideoCapture(VIDEOPATH)\n", + "try:\n", + " numOfFrames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))\n", + " if not FRAMENUMBER:\n", + " FRAMENUMBER = np.random.randint(1, high=numOfFrames)\n", + " if FRAMENUMBER>numOfFrames:\n", + " print('requested a frame (#{}) beyond video length ({}).'.format(FRAMENUMBER,numOfFrames),end='')\n", + " print(' Analyzing last frame instead')\n", + " FRAMENUMBER=numOfFrames\n", + " cap.set(cv.CAP_PROP_POS_FRAMES,FRAMENUMBER-1) \n", + " # -1 because CAP_PROP_FRAME_COUNT is 1-based while CAP_PROP_POS_FRAMES is 0-based\n", + " \n", + " # Read a frame from the \n", + " _, frame = cap.read()\n", + " # Convert to grayscale\n", + " frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n", + "except:\n", + " print('An error occurred while getting a frame for the video.')\n", + "finally:\n", + " cap.release()\n", + " \n", + "def cropResize(sourceImg):\n", + " cropped = sourceImg[cropT:cropB, cropL:cropR]\n", + " cropped = cv.resize(cropped,tuple(requiredFrameSize))\n", + " return cropped\n", + "\n", + "def morphProcessing(sourceImg):\n", + " # Binarize \n", + " binarized = sourceImg > THRESHOLD\n", + " # Divide in regions and keep only the biggest\n", + " label_img = label(binarized)\n", + " regions = regionprops(label_img)\n", + " if len(regions)==0:\n", + " morph = np.zeros(sourceImg.shape, dtype='uint8')\n", + " centroid = (np.nan, np.nan)\n", + " return (morph, centroid)\n", + " regions.sort(key=lambda x: x.area, reverse=True)\n", + " centroid = regions[0].centroid # centroid coordinates of the biggest object\n", + " if len(regions) > 1:\n", + " for rg in regions[1:]:\n", + " label_img[rg.coords[:,0], rg.coords[:,1]] = 0\n", + " label_img[label_img!=0] = 1\n", + " biggestRegion = (label_img*255).astype(np.uint8)\n", + " # Morphological\n", + " kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE,(IMCLOSING,IMCLOSING))\n", + " morph = cv.morphologyEx(biggestRegion, cv.MORPH_CLOSE, kernel)\n", + " return (morph, centroid)\n", + "\n", + "croppedFrame = cropResize(frame)\n", + "\n", + "if INVERTIMAGE:\n", + " croppedFrame = cv.bitwise_not(croppedFrame)\n", + "\n", + "networkInput = croppedFrame.astype(np.float32) / 255.0 # convert from uint8 [0, 255] to float32 [0, 1]\n", + "networkInput = networkInput[None, :, :, None] # add batch and channel dimensions\n", + "mask, info = model(networkInput)\n", + "\n", + "# Extract infos\n", + "prediction = mask[0,:,:,0]\n", + "eyeProbability = info[0,0]\n", + "blinkProbability = info[0,1]\n", + "\n", + "morphedMask, centroid = morphProcessing(prediction)\n", + "\n", + "# Generate falsecolor image\n", + "falseColor = np.dstack((morphedMask, croppedFrame, croppedFrame))\n", + "\n", + "# OUTPUT\n", + "print('Analyzed image # {} / {}'.format(FRAMENUMBER, numOfFrames))\n", + "print('Eye Probability: {:4.2%}'.format(eyeProbability))\n", + "print('Blink Probability: {:4.2%}'.format(blinkProbability))\n", + "\n", + "fig, ax = plt.subplots(nrows=1, ncols=5, figsize=(15,5))\n", + "ax[0].imshow(croppedFrame, cmap='gray')\n", + "ax[0].set_title('Cropped Frame')\n", + "\n", + "ax[1].imshow(prediction, vmin=0, vmax=1)\n", + "ax[1].set_title('Probability map')\n", + "\n", + "ax[2].imshow(prediction > THRESHOLD)\n", + "ax[2].set_title('Map Binarized')\n", + "\n", + "ax[3].imshow(morphedMask)\n", + "ax[3].set_title('After Morphology')\n", + "\n", + "ax[4].imshow(falseColor)\n", + "ax[4].plot(centroid[1], centroid[0], marker='+', markersize=12, mew=2, mec='g')\n", + "ax[4].set_title('Binary pupil')\n", + "\n", + "for a in ax.reshape(-1):\n", + " a.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "european-bruce", + "metadata": {}, + "source": [ + "## Predict pupil size for the whole video\n", + "\n", + "After adjusting all the parameters on single images in the previous section you can use this section to measure pupilsize on the whole video. \n", + "**This process can be long** (minutes), but having a GPU that tensorflow can use, might shorthen processing time significantly. " + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "protective-locking", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing frames... (400/23236)\n", + "Processing frames... (800/23236)\n", + "Processing frames... (1200/23236)\n", + "Processing frames... (1600/23236)\n", + "Processing frames... (2000/23236)\n", + "Processing frames... (2400/23236)\n", + "Processing frames... (2800/23236)\n", + "Processing frames... (3200/23236)\n", + "Processing frames... (3600/23236)\n", + "Processing frames... (4000/23236)\n", + "Processing frames... (4400/23236)\n", + "Processing frames... (4800/23236)\n", + "Processing frames... (5200/23236)\n", + "Processing frames... (5600/23236)\n", + "Processing frames... (6000/23236)\n", + "Processing frames... (6400/23236)\n", + "Processing frames... (6800/23236)\n", + "Processing frames... (7200/23236)\n", + "Processing frames... (7600/23236)\n", + "Processing frames... 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(23200/23236)\n" + ] + } + ], + "source": [ + "cap = cv.VideoCapture(VIDEOPATH)\n", + "numFrames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))\n", + "\n", + "df = pd.DataFrame(columns=['frameN','pupilSize','pupCntr_x','pupCntr_y','eyeProb','blinkProb'])\n", + "\n", + "try:\n", + " for i in range(numFrames):\n", + " _, frame = cap.read()\n", + " frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n", + " \n", + " croppedFrame = cropResize(frame)\n", + " if INVERTIMAGE:\n", + " croppedFrame = cv.bitwise_not(croppedFrame)\n", + " \n", + " networkInput = croppedFrame.astype(np.float32) / 255.0\n", + " networkInput = networkInput[None, :, :, None]\n", + " mask, info = model(networkInput)\n", + " prediction = mask[0,:,:,0]\n", + " morphedMask, centroid = morphProcessing(prediction)\n", + " \n", + " # Extract infos\n", + " eyeProbability = info[0,0]\n", + " blinkProbability = info[0,1]\n", + " \n", + " #Save values for the current frame\n", + " df = df.append({'frameN':int(i+1),\n", + " 'pupilSize':np.sum(morphedMask)/255,\n", + " 'pupCntr_x':centroid[1],\n", + " 'pupCntr_y':centroid[0],\n", + " 'eyeProb':eyeProbability,\n", + " 'blinkProb':blinkProbability},\n", + " ignore_index=True)\n", + " \n", + " if (i!=0) & (i%400==0) :\n", + " print('Processing frames... ({}/{})'.format(i,numFrames))\n", + "finally:\n", + " cap.release()\n", + " winsound.Beep(BEEP_FREQ, BEEP_DURATION_MS)" + ] + }, + { + "cell_type": "markdown", + "id": "quarterly-cocktail", + "metadata": {}, + "source": [ + "## Preprocessing of pupil size\n", + "In the following section the estimated pupil size for the whole video will undergo two preprocessing steps:\n", + "1. **Blink removal**: Blink events can generate artifacts in the pupil size prediction. We can correct for this by looking at the blink probability for each frame. Frames with a blink probability higher that a threshold (*BLINK_THRESH*) will be rejected and pupil size will be linearly interpolated between the points.\n", + "2. **Curve smoothing**: Ofthen the video FrameRate is much higher than the typical timecourse of pupil dilations. The resulting pupil size signal can thus be filtered to increase the saliency of biological fluctuations and remove noise. We implemented a simple *moving median filter*. Users can select an appropiate window size (in frames).\n", + "----\n", + "You need to specify and eventually adjust 2 parameters:\n", + "- **BLINK_THRESH**: Probability threshold for each frame to be classified as a blink\n", + "- **MEDIAN_WINDOW**: Window size (in frames) for median filtering. (Usually between 300-900ms works)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "activated-father", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. 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button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# EDIT PARAMETERS\n", + "# ---------------------------------------------------\n", + "BLINK_THRESH = 0.01\n", + "MEDIAN_WINDOW = 7\n", + "# ---------------------------------------------------\n", + "\n", + "%matplotlib notebook\n", + "# Remove blinks and interpolate the curve\n", + "isBlinking = df['blinkProb'] > BLINK_THRESH\n", + "processedPupil = df['pupilSize']\n", + "processedPupil[isBlinking] = np.nan\n", + "processedPupil.interpolate(inplace=True)\n", + "# Moving median filter\n", + "processedPupil = processedPupil.rolling(MEDIAN_WINDOW,min_periods=1, center=True).median()\n", + "df['processedPupil'] = processedPupil\n", + "\n", + "# Plotting\n", + "f, ax = plt.subplots(figsize=(14,4))\n", + "ax.plot(df['pupilSize'],label='Raw pupil')\n", + "ax.plot(processedPupil, label='Preprocessed Pupil')\n", + "ax.fill_between(df['frameN'], ax.get_ylim()[0], ax.get_ylim()[1],\n", + " where=isBlinking, interpolate=True,\n", + " facecolor='red', alpha=1,label='Blinking')\n", + "ax.set_title('Pupil Area Preprocessing')\n", + "ax.set_xlabel('Frame Number')\n", + "ax.set_ylabel('Pupil Area (px)')\n", + "ax.autoscale(enable=True, axis='x', tight=True)\n", + "ax.legend(loc=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "owned-pierce", + "metadata": {}, + "source": [ + "## Export data as a csv file\n", + "The following section will export pupillometry data in a .CSV file. \n", + "By default the file will be saved in the same folder as the movie, with the same filename and `_pupil` appended at the end. If you wish to give a custom name to the output file, you can edit the parameter `CUSTOM_NAME`. Doing this will use this name as the output filename instead." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "light-clinic", + "metadata": {}, + "outputs": [], + "source": [ + "# EDIT PARAMETERS\n", + "# ---------------------------------------------------\n", + "CUSTOM_NAME = []\n", + "# ---------------------------------------------------\n", + "\n", + "path, fN = os.path.split(VIDEOPATH)\n", + "fN, ext = os.path.splitext(fN)\n", + "fN = fN + '_pupil'\n", + "\n", + "if CUSTOM_NAME:\n", + " fN = CUSTOM_NAME\n", + "\n", + "newFileName = path + os.path.sep + fN + '.csv'\n", + "df.to_csv(newFileName, index=False) # Export to csv file" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/show.py b/show.py new file mode 100644 index 0000000000000000000000000000000000000000..b7a71f5c3edbcef302c1db6c2babfb7ea33db633 --- /dev/null +++ b/show.py @@ -0,0 +1,183 @@ +import argparse +import math +import os +os.sys.path += ['expman'] + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +from matplotlib.image import imread +from matplotlib.backends.backend_pdf import PdfPages + +import numpy as np +import pandas as pd +import seaborn as sns + +from glob import glob + +import expman + + +def ee(args): + sns.set_theme(context='notebook', style='whitegrid') + + exps = expman.gather(args.run).filter(args.filter) + mask_metrics = exps.collect('test_pred/mask_metrics.csv').groupby('exp_id')[['dice', 'iou']].max() + flops_nparams = exps.collect('flops_nparams.csv') + data = pd.merge(mask_metrics, flops_nparams, on='exp_id') + data['dice'] *= 100 + + named_data = data.rename({ + 'nparams': '# Params', + 'dice': 'mean Dice Coeff. (%)', + 'conv_type': '$t$ (Conv. Type)', + 'grow_factor': r'$\gamma$', + 'num_filters': '$k$ (# Filters)', + 'flops': 'FLOPs', + 'num_stages': '$s$ (# Stages)', + }, axis=1).replace({ + 'bn-conv': 'conv-bn', + 'sep-bn-conv': 'sep-conv-bn' + }) + + g = sns.relplot(data=named_data, + x='FLOPs', y='mean Dice Coeff. (%)', + hue='$t$ (Conv. Type)', + hue_order=['conv', 'conv-bn', 'sep-conv', 'sep-conv-bn'], + col='$s$ (# Stages)', style='$k$ (# Filters)', markers=True, markersize=9, + kind='line', dashes=True, facet_kws=dict(despine=False, legend_out=False), legend=True, + height=3.8, aspect=1.3, markeredgecolor='white') + + b_formatter = ticker.FuncFormatter(lambda x, pos: '{:.2f}'.format(x / 10 ** 9) + 'B') + + h, l = g.axes.flatten()[0].get_legend_handles_labels() + for hi in h: + hi.set_markeredgecolor('white') + g.axes.flatten()[0].legend_.remove() + g.fig.legend(h, l, ncol=2, bbox_to_anchor=(0.53 ,0.53), + fancybox=False, columnspacing=0, framealpha=1, handlelength=1.2) + + for ax in g.axes.flatten(): + ax.yaxis.set_minor_locator(ticker.AutoMinorLocator()) + ax.set_ylim(bottom=40, top=90) + ax.set_xscale('symlog') + ax.set_xlim(left=0.04 * 10 ** 9, right=2 * 10 ** 9) + + ax.xaxis.set_minor_locator(ticker.SymmetricalLogLocator(base=10, linthresh=2, subs=[1.5, 2,3,4,5,6,8])) + ax.xaxis.set_minor_formatter(b_formatter) + ax.grid(which='minor', linestyle='--', color='#eeeeee') + + ax.xaxis.set_major_formatter(b_formatter) + ax.tick_params(axis="x", which="both", rotation=90) + + plt.savefig(args.output, bbox_inches='tight') + + +def bd(args): + exps = expman.gather(args.run).filter(args.filter) + blink_metrics = exps.collect('test_pred/all_blink_roc_metrics.csv') + blink_metrics = blink_metrics.iloc[3::4].rename({'0': 'auc'}, axis=1) + aucs = blink_metrics.auc.values + print(f'{aucs.mean()} +- {aucs.std()}') + + +def dice_fps(args): + exps = expman.gather(args.run).filter(args.filter) + + mask_metrics = exps.collect('test_pred/mask_metrics.csv') + mask_metrics = mask_metrics.groupby('exp_name').dice.max() + + time_metrics = exps.collect('timings.csv') + time_metrics = time_metrics.rename({'Unnamed: 0': 'metrics', '0':'value'}, axis=1) + time_metrics = time_metrics.pivot_table(index='exp_name', columns='metrics', values='value') + + flops_nparams = exps.collect('flops_nparams.csv') + flops_nparams = flops_nparams.set_index('exp_name')[['flops','nparams']] + + table = pd.concat((time_metrics, mask_metrics, flops_nparams), axis=1)[['dice', 'fps', 'throughput', 'flops', 'nparams']] + table['dice'] = table.dice.map('{:.1%}'.format) + table['fps'] = table.fps.map('{:.1f}'.format) + table['throughput'] = (table.throughput*1000).map('{:.1f}ms'.format) + table['flops'] = (table.flops / 10**9).map('{:.1f}G'.format) + table['nparams'] = (table.nparams / 10**6).map('{:.2f}M'.format) + print(table) + + +def metrics(args): + exps = expman.gather(args.run).filter(args.filter) + mask_metrics = exps.collect('test_pred/mask_metrics.csv') + sns.lineplot(data=mask_metrics, x='thr', y='dice', hue='conv_type', size='grow_factor', style='num_filters') + plt.savefig(args.output) + + +def log(args): + exps = expman.gather(args.run).filter(args.filter) + with PdfPages(args.output) as pdf: + for exp_name, exp in sorted(exps.items()): + print(exp_name) + log = pd.read_csv(exp.path_to('log.csv'), index_col='epoch') + train_cols = [c for c in log.columns if 'val' not in c] + val_cols = [c for c in log.columns if 'val' in c] + + test_images = glob(os.path.join(exp.path_to('test_pred'), '*_samples.png')) + + fig = plt.figure(figsize=(14, 10)) + fig_shape = (2, 2) if test_images else (2, 1) + ax1 = plt.subplot2grid(fig_shape, (0, 0)) + ax2 = plt.subplot2grid(fig_shape, (1, 0)) + + log[train_cols].plot(ax=ax1) + log[val_cols].plot(ax=ax2) + ax1.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5)) + ax2.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5)) + ax2.set_ylim((0, 1)) + + if test_images: + test_images = sorted(test_images) + test_images = list(map(imread, test_images)) + max_w = max(i.shape[1] for i in test_images) + pads = [((0,0), (0, max_w - i.shape[1]), (0, 0)) for i in test_images] + test_images = np.concatenate([np.pad(i, pad) for i, pad in zip(test_images, pads)], axis=0) + + ax3 = plt.subplot2grid(fig_shape, (0, 1), rowspan=2) + ax3.imshow(test_images) + ax3.set_axis_off() + + log_plot_file = exp.path_to('log_plot.pdf') + plt.suptitle(exp_name) + plt.savefig(log_plot_file, bbox_inches='tight') + pdf.savefig(fig, bbox_inches='tight') + plt.close() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Show stuff') + parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) + subparsers = parser.add_subparsers() + + parser_log = subparsers.add_parser('log') + parser_log.add_argument('run', default='runs/') + parser_log.add_argument('-o', '--output', default='log_summary.pdf') + parser_log.set_defaults(func=log) + + parser_metrics = subparsers.add_parser('metrics') + parser_metrics.add_argument('run', default='runs/') + parser_metrics.add_argument('-o', '--output', default='mask_metrics_summary.pdf') + parser_metrics.set_defaults(func=metrics) + + parser_ee = subparsers.add_parser('ee') + parser_ee.add_argument('run', default='runs/') + parser_ee.add_argument('-o', '--output', default='ee_summary.pdf') + parser_ee.set_defaults(func=ee) + + parser_bd = subparsers.add_parser('bd') + parser_bd.add_argument('run', default='runs/') + parser_bd.set_defaults(func=bd) + + parser_dice_fps = subparsers.add_parser('dice-fps') + parser_dice_fps.add_argument('run', default='runs/') + parser_dice_fps.set_defaults(func=dice_fps) + + args = parser.parse_args() + args.func(args)