tools/labelme2coco_instance.py

"""
This code is used to convert instance segmentation datasets annotated with Labelme to COCO format.
The background is not included in the final JSON categories, and the first category starts from 1

The script was modified from https://blog.csdn.net/weixin_45656040/article/details/108488298
"""
#!/usr/bin/env python
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import numpy as np
import PIL.Image
import labelme
import shutil

try:
    import pycocotools.mask
except ImportError:
    print('Please install pycocotools:\n\n    pip install pycocotools\n')
    sys.exit(1)

def main():
    sets = ['train2017','val2017','test2017']
    output_dir = './annotations'
    if osp.exists(output_dir):
        print('Output directory already exists:', output_dir)
        shutil.rmtree(output_dir)
    os.makedirs(output_dir)
    print('Creating dataset:', output_dir)
    for set in sets:
        # images and label should be placed in the same folder, such as 'data_annotated/train2017'
        input_dir = './data_annotated/%s' % (set)
        filename = 'instances_%s' % (set)
        now = datetime.datetime.now()
        data = dict(
            info=dict(
                description=None,
                version=None,
                contributor=None,
                date_created=now.strftime('%Y-%m-%d %H:%M:%S.%f'),
            ),
            licenses=[dict(
                id=0,
                name=None,
            )],
            images=[
                # license, url, file_name, height, width, date_captured, id
            ],
            type='instances',
            annotations=[
                # segmentation, area, iscrowd, image_id, bbox, category_id, id
            ],
            categories=[
                # supercategory, id, name
            ],
        )

        class_name_to_id = {}
        for i, line in enumerate(open('labels.txt').readlines()):
            class_id = i  # starts with -1 #class_id = i - 1
            class_name = line.strip()
            if class_id == 0: # class_id == -1
                assert class_name == '_background_' # assert class_name == '__ignore__'
                continue
            class_name_to_id[class_name] = class_id
            data['categories'].append(dict(
                supercategory=None,
                id=class_id,
                name=class_name,
            ))
        out_ann_file = osp.join(output_dir,  filename +'.json')
        label_files = glob.glob(osp.join(input_dir, '*.json'))
        for image_id, label_file in enumerate(label_files):
            with open(label_file) as f:
                label_data = json.load(f)
            path=label_data['imagePath'].split("/")
            img_file = './data_annotated/%s/'%(set) + path[-1]
            img = np.asarray(PIL.Image.open(img_file))

            data['images'].append(dict(
                license=0,
                url=None,
                file_name=label_file.split('/')[-1].split('.')[0] + '.jpg', #file_name=label_file.split('/')[-1].split('.')[0] + '.jpg',
                height=img.shape[0],
                width=img.shape[1],
                date_captured=None,
                id=image_id+1, # image_id should start at 1 rather than 0
            ))

            masks = {}                                     # for area
            segmentations = collections.defaultdict(list)  # for segmentation
            for shape in label_data['shapes']:
                points = shape['points']
                label = shape['label']
                shape_type = shape.get('shape_type', None)
                mask = labelme.utils.shape_to_mask(
                    img.shape[:2], points, shape_type
                )

                if label in masks:
                    masks[label] = masks[label] | mask
                else:
                    masks[label] = mask

                points = np.asarray(points).flatten().tolist()
                segmentations[label].append(points)

            for label, mask in masks.items():
                cls_name = label.split('-')[0]
                if cls_name not in class_name_to_id:
                    continue
                cls_id = class_name_to_id[cls_name]

                mask = np.asfortranarray(mask.astype(np.uint8))
                mask = pycocotools.mask.encode(mask)
                area = float(pycocotools.mask.area(mask))
                bbox = pycocotools.mask.toBbox(mask).flatten().tolist()

                data['annotations'].append(dict(
                    id=len(data['annotations'])+1, # object_id should start at 1 rather than 0
                    image_id=image_id+1, # image_id should start at 1 rather than 0
                    category_id=cls_id,
                    segmentation=segmentations[label],
                    area=area,
                    bbox=bbox,
                    iscrowd=0,
                ))

        with open(out_ann_file, 'w') as f:
            json.dump(data, f,indent=4)
        print(set + ' is done')

if __name__ == '__main__':
    main()