#!/usr/bin/env python3 """ Validate 100 frames with ball annotations from a COCO dataset. Generates HTML with toggleable bounding boxes. """ import json import base64 from pathlib import Path from typing import List, Dict, Tuple from PIL import Image import io def load_coco_annotations(annotation_path: str) -> Dict: """Load COCO format annotation file.""" with open(annotation_path, 'r') as f: return json.load(f) def get_images_with_balls(coco_data: Dict) -> List[Dict]: """Get images that have ball annotations.""" categories = {cat['id']: cat['name'] for cat in coco_data['categories']} # Find ball category ID ball_category_id = None for cat_id, cat_name in categories.items(): if cat_name.lower() == 'ball': ball_category_id = cat_id break if ball_category_id is None: raise ValueError("Ball category not found in annotations") # Group annotations by image image_annotations = {} for ann in coco_data['annotations']: if ann['category_id'] == ball_category_id: img_id = ann['image_id'] if img_id not in image_annotations: image_annotations[img_id] = [] image_annotations[img_id].append(ann['bbox']) # Get images with balls images = {img['id']: img for img in coco_data['images']} images_with_balls = [] for img_id in sorted(image_annotations.keys()): if img_id in images: images_with_balls.append({ 'image': images[img_id], 'bboxes': image_annotations[img_id] }) return images_with_balls def image_to_base64(image_path: Path) -> str: """Convert image to base64 string.""" try: with open(image_path, 'rb') as f: img_data = f.read() img = Image.open(io.BytesIO(img_data)) # Resize if too large (max 1920px width) max_width = 1920 if img.width > max_width: ratio = max_width / img.width new_height = int(img.height * ratio) img = img.resize((max_width, new_height), Image.Resampling.LANCZOS) # Convert to base64 buffer = io.BytesIO() img.save(buffer, format='PNG') img_str = base64.b64encode(buffer.getvalue()).decode() return f"data:image/png;base64,{img_str}" except Exception as e: print(f"Error loading image {image_path}: {e}") return "" def generate_html(images_data: List[Dict], annotation_file: str, output_path: Path): """Generate HTML with toggleable bounding boxes.""" html_content = f""" Ball Validation - {Path(annotation_file).name}

āš½ Ball Validation - 100 Samples

Dataset: {Path(annotation_file).name}

Total frames with balls: {len(images_data)}

Showing {len(images_data)} frames with ball annotations
""" for idx, img_data in enumerate(images_data): image_info = img_data['image'] bboxes = img_data['bboxes'] # Get image path annotation_dir = Path(annotation_file).parent image_path = annotation_dir / image_info['file_name'] # Try alternative paths if image not found if not image_path.exists(): # Try images subdirectory images_dir = annotation_dir / 'images' if images_dir.exists(): image_path = images_dir / image_info['file_name'] if not image_path.exists(): print(f"Warning: Image not found: {image_path}") continue # Convert image to base64 img_base64 = image_to_base64(image_path) if not img_base64: continue # Calculate bbox positions (relative to image) img_width = image_info['width'] img_height = image_info['height'] bbox_html = "" for bbox in bboxes: # COCO format: [x, y, width, height] x, y, w, h = bbox x_percent = (x / img_width) * 100 y_percent = (y / img_height) * 100 w_percent = (w / img_width) * 100 h_percent = (h / img_height) * 100 bbox_html += f"""
ball
""" html_content += f"""
Frame {idx + 1}
{bbox_html}
Frame {idx + 1}: {image_info['file_name']} | {len(bboxes)} ball(s) | Size: {img_width}x{img_height}
""" html_content += """
""" with open(output_path, 'w') as f: f.write(html_content) print(f"āœ… Generated HTML: {output_path}") def main(): """Main function to validate 100 frames.""" import sys if len(sys.argv) < 2: print("Usage: python 9_validate_100_frames_template.py ") sys.exit(1) annotation_file = sys.argv[1] annotation_path = Path(annotation_file) if not annotation_path.exists(): print(f"Error: Annotation file not found: {annotation_file}") sys.exit(1) print(f"šŸ“‹ Loading annotations from: {annotation_file}") coco_data = load_coco_annotations(annotation_file) print("šŸ” Finding images with ball annotations...") images_with_balls = get_images_with_balls(coco_data) print(f"šŸ“Š Found {len(images_with_balls)} images with ball annotations") # Select first 100 samples samples = images_with_balls[:100] print(f"āœ… Selected {len(samples)} samples for validation") # Generate output filename annotation_name = annotation_path.stem if annotation_name.startswith('_'): annotation_name = annotation_name[1:] output_html = annotation_path.parent / f"9_validate_100_frames_{annotation_name}.html" print(f"šŸŽØ Generating HTML visualization...") generate_html(samples, annotation_file, output_html) print(f"\nāœ… Done! Open {output_html} in your browser to view the validation.") if __name__ == "__main__": main()