labelme_coco.py

import os
import json
from PIL import Image

def labelme_to_coco(labelme_json_folder, output_json_file):
    # Create the basic structure for COCO
    coco_output = {
        "info": {},
        "licenses": [],
        "images": [],
        "annotations": [],
        "categories": []
    }

    # Initialize counters and maps
    annotation_id = 1
    category_id = 1
    category_map = {}

    # Read each LabelMe JSON file
    for json_file in os.listdir(labelme_json_folder):
        if not json_file.endswith('.json'):
            continue
        
        json_path = os.path.join(labelme_json_folder, json_file)
        with open(json_path) as f:
            labelme_data = json.load(f)
        
        # Process image info
        image_path = os.path.join(labelme_json_folder, labelme_data['imagePath'])
        image = Image.open(image_path)
        width, height = image.size
        image_info = {
            "id": len(coco_output["images"]) + 1,
            "file_name": labelme_data['imagePath'],
            "width": width,
            "height": height
        }
        coco_output["images"].append(image_info)
        
        # Process annotations
        for shape in labelme_data['shapes']:
            label = shape['label']
            if label not in category_map:
                category_map[label] = category_id
                coco_output["categories"].append({
                    "id": category_id,
                    "name": label,
                    "supercategory": "none"
                })
                category_id += 1
            
            polygon = shape['points']
            segmentation = [point for sublist in polygon for point in sublist]
            min_x = min([p[0] for p in polygon])
            max_x = max([p[0] for p in polygon])
            min_y = min([p[1] for p in polygon])
            max_y = max([p[1] for p in polygon])
            bbox = [min_x, min_y, max_x - min_x, max_y - min_y]
            
            annotation = {
                "id": annotation_id,
                "image_id": image_info["id"],
                "category_id": category_map[label],
                "segmentation": [segmentation],
                "area": bbox[2] * bbox[3],
                "bbox": bbox,
                "iscrowd": 0
            }
            coco_output["annotations"].append(annotation)
            annotation_id += 1

    # Save to COCO JSON file
    with open(output_json_file, 'w') as f:
        json.dump(coco_output, f, indent=4)

# Example usage:
labelme_json_folder = 'data/'
output_json_file = 'data/coco_annotations.json'
labelme_to_coco(labelme_json_folder, output_json_file)