Datasets:
ArXiv:
License:
| import random | |
| import os | |
| import numpy as np | |
| import glob | |
| import matplotlib.pyplot as plt | |
| from collections import Counter | |
| from PIL import Image, ImageDraw, ImageFont | |
| from tqdm import tqdm | |
| from ultralytics.utils.plotting import Annotator, colors | |
| images_path = "./images" | |
| labels_path = "./labels" | |
| images = glob.glob(os.path.join(images_path, "*.jpg")) + \ | |
| glob.glob(os.path.join(images_path, "*.JPG")) | |
| label_map = { | |
| 0: "Illustration", | |
| 1: "Initial", | |
| 2: "Ornament", | |
| 3: "Stamp", | |
| 4: "Table", | |
| } | |
| output_dir = "./generated_html" | |
| if os.path.isdir(output_dir): | |
| print(f"{output_dir} existe déjà") | |
| else: | |
| os.mkdir(output_dir) | |
| annotations_dir = "./annotations" | |
| if os.path.isdir(annotations_dir): | |
| print(f"{annotations_dir} existe déjà") | |
| else: | |
| os.mkdir(annotations_dir) | |
| def classes_visualisation(labels_path: str, label_map: dict, output_dir: str): | |
| total_files = 0 | |
| total_labels = [] | |
| for filename in os.listdir(labels_path): | |
| if not filename.endswith(".txt"): | |
| continue | |
| total_files += 1 | |
| input_path = os.path.join(labels_path, filename) | |
| with open(input_path, "r") as f: | |
| lines = f.readlines() | |
| for line in lines: | |
| parts = line.strip().split() | |
| if not parts: | |
| continue | |
| label = int(parts[0]) | |
| total_labels.append(label) | |
| counts = Counter(total_labels) | |
| labels = [label_map[k] for k in counts.keys()] | |
| values = list(counts.values()) | |
| total_count = sum(values) | |
| # Fonction pour afficher pourcentage + valeur absolue dans chaque part | |
| def make_autopct(values): | |
| def my_autopct(pct): | |
| absolute = int(round(pct / 100.0 * sum(values))) | |
| return f"{pct:.1f}%\n({absolute})" | |
| return my_autopct | |
| plt.figure(figsize=(7, 7)) | |
| plt.pie( | |
| values, | |
| labels=labels, | |
| autopct=make_autopct(values), | |
| textprops={"fontsize": 9}, | |
| ) | |
| plt.title( | |
| f"GenHisDoc classes distribution\n" | |
| f"Total files: {total_files} | Total labels: {total_count}" | |
| ) | |
| # Légende avec le détail des effectifs par classe | |
| legend_labels = [f"{lab} (n={val})" for lab, val in zip(labels, values)] | |
| plt.legend( | |
| legend_labels, | |
| title="Classes", | |
| loc="center left", | |
| bbox_to_anchor=(1, 0, 0.5, 1), | |
| ) | |
| plt.tight_layout() | |
| plt.savefig( | |
| os.path.join(output_dir, "GenHisDoc_class_distribution.png"), | |
| bbox_inches="tight", | |
| ) | |
| plt.close() | |
| with open(f"{output_dir}/index.html", "w") as f: | |
| f.write( | |
| """<!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <link type="text/css" rel="stylesheet" href="style.css"> | |
| </head> | |
| <body> | |
| <header> | |
| </header> | |
| <img src="./GenHisDoc_class_distribution.png"> | |
| </body> | |
| </html> | |
| """ | |
| ) | |
| def draw_yolo_annotations(image_path: str, label_path: str, label_map: dict) -> Image.Image | None: | |
| """Dessine les bounding boxes YOLO sur l'image et retourne une PIL Image.""" | |
| if not os.path.exists(image_path): | |
| print(f"Image introuvable : {image_path}") | |
| return None | |
| if not os.path.exists(label_path): | |
| print(f"Label introuvable : {label_path}") | |
| return None | |
| img = np.array(Image.open(image_path).convert("RGB")) | |
| h, w = img.shape[:2] | |
| annotator = Annotator(img, line_width=2) | |
| with open(label_path, "r") as f: | |
| for line in f: | |
| parts = line.strip().split() | |
| if len(parts) < 5: | |
| continue | |
| cls_id = int(parts[0]) | |
| cx, cy, bw, bh = map(float, parts[1:5]) | |
| # Conversion YOLO (normalisé) → pixels (x1, y1, x2, y2) | |
| x1 = int((cx - bw / 2) * w) | |
| y1 = int((cy - bh / 2) * h) | |
| x2 = int((cx + bw / 2) * w) | |
| y2 = int((cy + bh / 2) * h) | |
| label = label_map.get(cls_id, str(cls_id)) | |
| annotator.box_label([x1, y1, x2, y2], label=label, color=colors(cls_id, True)) | |
| result = annotator.result() | |
| return Image.fromarray(result) | |
| def controle(label_map: dict): | |
| images_dir = images_path | |
| labels_dir = labels_path | |
| identifier_list = [] | |
| annotations_crées = 0 | |
| annotations_ignorées = 0 | |
| print("génération des annotations") | |
| for filename in tqdm(os.listdir(labels_dir)): | |
| if not filename.endswith(".txt"): | |
| continue | |
| identifier = filename.replace(".txt", "") | |
| identifier_list.append(identifier) | |
| output_path = os.path.join(annotations_dir, f"{identifier}.jpg") | |
| image = draw_yolo_annotations( | |
| os.path.join(images_dir, f"{identifier}.jpg"), | |
| os.path.join(labels_dir, f"{identifier}.txt"), | |
| label_map, | |
| ) | |
| if image is None: | |
| annotations_ignorées += 1 | |
| continue | |
| if not os.path.isfile(output_path): | |
| image.save(output_path) | |
| annotations_crées += 1 | |
| else: | |
| annotations_ignorées += 1 | |
| print(f"Annotations créées : {annotations_crées}") | |
| print(f"Annotations ignorées : {annotations_ignorées}") | |
| classes_visualisation(labels_path, label_map, output_dir) | |
| controle(label_map) | |