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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+catmus-medieval/language_model.binary filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +0,0 @@
----
-title: Yolov11 Pylaia Catmus
-emoji: 🏆
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.23.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,279 @@
+import warnings
+warnings.simplefilter("ignore", UserWarning)
+
+from uuid import uuid4
+from laia.scripts.htr.decode_ctc import run as decode
+from laia.common.arguments import CommonArgs, DataArgs, TrainerArgs, DecodeArgs
+import sys
+from tempfile import NamedTemporaryFile, mkdtemp
+from pathlib import Path
+from contextlib import redirect_stdout
+import re
+from PIL import Image
+from bidi.algorithm import get_display
+import multiprocessing
+from ultralytics import YOLO
+import cv2
+import numpy as np
+import pandas as pd
+import logging
+from typing import List, Optional, Tuple, Dict
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import supervision as sv
+import os
+import spaces
+import torch
+
+# Define models
+MODEL_OPTIONS = {
+    "YOLOv11-Nano": "medieval-yolov11n.pt",
+    "YOLOv11-Small": "medieval-yolov11s.pt",
+    "YOLOv11-Medium": "medieval-yolov11m.pt",
+    "YOLOv11-Large": "medieval-yolov11l.pt",
+    "YOLOv11-XLarge": "medieval-yolov11x.pt"
+}
+
+# Dictionary to store loaded models
+models: Dict[str, YOLO] = {}
+
+# Load all models
+for name, model_file in MODEL_OPTIONS.items():
+    model_path = hf_hub_download(
+        repo_id="biglam/medieval-manuscript-yolov11",
+        filename=model_file
+    )
+    models[name] = YOLO(model_path)
+
+# Configure logging
+logging.getLogger("lightning.pytorch").setLevel(logging.ERROR)
+
+# Load YOLOv8 model
+model = YOLO(model_path)
+images = Path(mkdtemp())
+DEFAULT_HEIGHT = 128
+TEXT_DIRECTION = "LTR"
+NUM_WORKERS = multiprocessing.cpu_count()
+
+# Regex pattern for extracting results
+IMAGE_ID_PATTERN = r"(?P<image_id>[-a-z0-9]{36})"
+CONFIDENCE_PATTERN = r"(?P<confidence>[0-9.]+)"  # For line
+TEXT_PATTERN = r"\s*(?P<text>.*)\s*"
+LINE_PREDICTION = re.compile(rf"{IMAGE_ID_PATTERN} {CONFIDENCE_PATTERN} {TEXT_PATTERN}")
+
+# Create annotators
+LABEL_ANNOTATOR = sv.LabelAnnotator(text_color=sv.Color.BLACK)
+BOX_ANNOTATOR = sv.BoxAnnotator()
+
+# Select device
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def get_width(image, height=DEFAULT_HEIGHT):
+    aspect_ratio = image.width / image.height
+    return height * aspect_ratio
+
+def simplify_polygons(polygons: List[np.ndarray], approx_level: float = 0.01) -> List[Optional[np.ndarray]]:
+    """Simplify polygon contours using Douglas-Peucker algorithm.
+    
+    Args:
+        polygons: List of polygon contours
+        approx_level: Approximation level (0-1), lower values mean more simplification
+        
+    Returns:
+        List of simplified polygons (or None for invalid polygons)
+    """
+    result = []
+    for polygon in polygons:
+        if len(polygon) < 4:
+            result.append(None)
+            continue
+
+        perimeter = cv2.arcLength(polygon, True)
+        approx = cv2.approxPolyDP(polygon, approx_level * perimeter, True)
+        if len(approx) < 4:
+            result.append(None)
+            continue
+
+        result.append(approx.squeeze())
+    return result
+
+def predict_text(input_img):
+    """PyLaia text recognition function"""
+    model_dir = 'catmus-medieval'
+    temperature = 2.0
+    batch_size = 1
+
+    weights_path = f"{model_dir}/weights.ckpt"
+    syms_path = f"{model_dir}/syms.txt"
+    language_model_params = {"language_model_weight": 1.0}
+    use_language_model = True
+    if use_language_model:
+        language_model_params.update({
+            "language_model_path": f"{model_dir}/language_model.binary",
+            "lexicon_path": f"{model_dir}/lexicon.txt",
+            "tokens_path": f"{model_dir}/tokens.txt",
+        })
+
+    common_args = CommonArgs(
+        checkpoint="weights.ckpt",
+        train_path=f"{model_dir}",
+        experiment_dirname="",
+    )
+
+    data_args = DataArgs(batch_size=batch_size, color_mode="L")
+    trainer_args = TrainerArgs(progress_bar_refresh_rate=0)
+    decode_args = DecodeArgs(
+        include_img_ids=True,
+        join_string="",
+        convert_spaces=True,
+        print_line_confidence_scores=True,
+        print_word_confidence_scores=False,
+        temperature=temperature,
+        use_language_model=use_language_model,
+        **language_model_params,
+    )
+
+    with NamedTemporaryFile() as pred_stdout, NamedTemporaryFile() as img_list:
+        image_id = uuid4()
+        input_img = input_img.resize((int(get_width(input_img)), DEFAULT_HEIGHT))
+        input_img.save(f"{images}/{image_id}.jpg")
+        Path(img_list.name).write_text("\n".join([str(image_id)]))
+
+        with redirect_stdout(open(pred_stdout.name, mode="w")):
+            decode(
+                syms=str(syms_path),
+                img_list=img_list.name,
+                img_dirs=[str(images)],
+                common=common_args,
+                data=data_args,
+                trainer=trainer_args,
+                decode=decode_args,
+                num_workers=1,
+            )
+            sys.stdout.flush()
+        predictions = Path(pred_stdout.name).read_text().strip().splitlines()
+
+    _, score, text = LINE_PREDICTION.match(predictions[0]).groups()
+    return text, float(score)
+
+@spaces.GPU
+def detect_and_recognize(image, model_name, conf_threshold, iou_threshold):
+    if image is None:
+        return None, ""
+    
+    # Get model path
+    model_path = hf_hub_download(
+        repo_id="biglam/medieval-manuscript-yolov11",
+        filename=MODEL_OPTIONS[model_name]
+    )
+    
+    # Load model
+    model = YOLO(model_path)
+    
+    # Perform inference
+    results = model.predict(
+        image,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        classes=0,
+        device=device
+    )[0]
+    
+    # Convert results to supervision Detections
+    boxes = results.boxes.xyxy.cpu().numpy()
+    confidence = results.boxes.conf.cpu().numpy()
+    class_ids = results.boxes.cls.cpu().numpy().astype(int)
+    
+    # Sort boxes by y-coordinate
+    sorted_indices = np.argsort(boxes[:, 1])
+    boxes = boxes[sorted_indices]
+    confidence = confidence[sorted_indices]
+    
+    # Create Detections object
+    detections = sv.Detections(
+        xyxy=boxes,
+        confidence=confidence,
+        class_id=class_ids
+    )
+    
+    # Create labels
+    labels = [
+        f"Line {i+1} ({conf:.2f})"
+        for i, conf in enumerate(confidence)
+    ]
+
+    # Annotate image
+    annotated_image = image.copy()
+    annotated_image = BOX_ANNOTATOR.annotate(scene=annotated_image, detections=detections)
+    annotated_image = LABEL_ANNOTATOR.annotate(scene=annotated_image, detections=detections, labels=labels)
+    
+    # Create text summary
+    text_summary = "\n".join([f"Line {i+1}: Confidence {conf:.2f}" for i, conf in enumerate(confidence)])
+    
+    return annotated_image, text_summary
+
+def gradio_reset():
+    return None, None, ""
+
+if __name__ == "__main__":
+    print(f"Using device: {device}")
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("# Medieval Manuscript Text Detection")
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(
+                    label="Input Image",
+                    type="numpy"
+                )
+                with gr.Accordion("Detection Settings", open=True):
+                    model_selector = gr.Dropdown(
+                        choices=list(MODEL_OPTIONS.keys()),
+                        value=list(MODEL_OPTIONS.keys())[0],
+                        label="Model",
+                        info="Select YOLO model variant"
+                    )
+                    with gr.Row():
+                        conf_threshold = gr.Slider(
+                            label="Confidence Threshold",
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.05,
+                            value=0.25,
+                        )
+                        iou_threshold = gr.Slider(
+                            label="IoU Threshold",
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.05,
+                            value=0.45,
+                        )
+                with gr.Row():
+                    clear_btn = gr.Button("Clear")
+                    detect_btn = gr.Button("Detect", variant="primary")
+                    
+            with gr.Column():
+                output_image = gr.Image(
+                    label="Detection Result",
+                    type="numpy"
+                )
+                text_output = gr.Textbox(
+                    label="Detection Summary",
+                    lines=10
+                )
+
+        # Connect buttons to functions
+        detect_btn.click(
+            detect_and_recognize,
+            inputs=[input_image, model_selector, conf_threshold, iou_threshold],
+            outputs=[output_image, text_output]
+        )
+        clear_btn.click(
+            gradio_reset,
+            inputs=None,
+            outputs=[input_image, output_image, text_output]
+        )
+
+    demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+

catmus-medieval/language_model.arpa.gz

@@ -0,0 +1,3 @@
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+oid sha256:754aca0dfb5b8c96b922e7ae96ad515a4bdd131760a8b59a3648b2c33d41329f
+size 21600345

catmus-medieval/language_model.binary

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+oid sha256:8d825caa68d8a1d7db10196c9de6591550fc04dec020ddb2c502786330d11c91
+size 50512864

catmus-medieval/lexicon.txt

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+ 
+ 
+ 
+ 
+ 
+𐞥 𐞥
+<unk> <unk>
+<space> <space>

catmus-medieval/syms.txt

@@ -0,0 +1,254 @@
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+# 1
+& 2
+' 3
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+<unk> 252
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catmus-medieval/weights.ckpt

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+oid sha256:c6946999eedc1a7dbf9263e2161a9c7c24518d92bfe89383074b302785c2baa6
+size 43382489

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73323fbadaac87df634073e34de1b553b04b501e7c8ad2cfff0eee2725bc4f78
+size 121881322

requirements.txt

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+imagesize==1.4.1
+jsonargparse[signatures]==4.7
+matplotlib==3.9.2
+natsort==8.4.0
+pytorch-lightning==1.4.2
+scipy==1.11.3
+textdistance==4.6.3
+torch>=1.13,<1.14
+torchaudio>=0.13,<0.14
+torchmetrics<0.8.0
+torchvision>=0.14,<0.15
+mdutils==1.6.0
+prettytable==3.11.0
+python-bidi==0.6.0
+pylaia==1.1.2
+numpy==1.26.4
+opencv-python==4.11.0.86
+pillow==11.2.0
+python-bidi==0.6.0
+transformers==4.50.3
+ultralytics==8.3.99
+gradio
+supervision

runtime.txt

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+python-3.10