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# import torchvision.transforms as transforms
# import torch.nn.functional as F
# import cv2
# import os
# import logging
# from pathlib import Path
import numpy as np
# import os
import torch
import matplotlib
# import cv2
# import random
# from PIL import Image
# import imageio
def prob_to_mask(prob):
"""
Transforms a probability map of stopping points (shape: (n_layer+1, H, W))
into a binary mask (shape: (H, W, n_layer, 1)) where for each pixel, layers
with index ≤ stopping index (as given by argmax) are marked valid.
"""
num_layer_plus1, H, W = prob.shape
# Get stopping index for each pixel; values are in {0, 1, ..., n_layer}
stopping_indices = torch.argmax(prob, dim=0) # (H, W)
# Create a tensor with layer indices [1, 2, ..., n_layer]
layer_indices = torch.arange(1, num_layer_plus1, device=prob.device).view(-1, 1, 1)
# Compare: a layer is valid if its index is <= the stopping index.
pred_mask = (layer_indices <= stopping_indices.unsqueeze(0))
# Permute and unsqueeze to get shape (H, W, n_layer, 1)
pred_mask = pred_mask.permute(1, 2, 0).unsqueeze(-1)
return pred_mask
def colorize(value, vmin=None, vmax=None, cmap='rainbow', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
"""Converts a depth map to a color image.
Args:
value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
Returns:
numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
"""
if isinstance(value, torch.Tensor):
value = value.detach().cpu().numpy()
value = value.squeeze()
if invalid_mask is None:
invalid_mask = value == invalid_val
mask = np.logical_not(invalid_mask)
# normalize
vmin = np.percentile(value[mask],2) if vmin is None else vmin
vmax = np.percentile(value[mask],85) if vmax is None else vmax
if vmin != vmax:
value = (value - vmin) / (vmax - vmin) # vmin..vmax
else:
# Avoid 0-division
value = value * 0.
value[invalid_mask] = np.nan
cmapper = matplotlib.cm.get_cmap(cmap)
if value_transform:
value = value_transform(value)
# value = value / value.max()
value = cmapper(value, bytes=True) # (nxmx4)
# img = value[:, :, :]
img = value[...]
img[invalid_mask] = background_color
if gamma_corrected:
# gamma correction
img = img / 255
img = np.power(img, 2.2)
img = img * 255
img = img.astype(np.uint8)
return img
def denormalize(x):
"""Reverses the imagenet normalization applied to the input.
Args:
x (torch.Tensor - shape(N,3,H,W)): input tensor
Returns:
torch.Tensor - shape(N,3,H,W): Denormalized input
"""
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device)
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device)
return x * std + mean |