| """
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| This code is used to convert instance segmentation datasets annotated with Labelme to COCO format.
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| The background is not included in the final JSON categories, and the first category starts from 1
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|
|
| The script was modified from https://blog.csdn.net/weixin_45656040/article/details/108488298
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| """
|
|
|
| import collections
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| import datetime
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| import glob
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| import json
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| import os
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| import os.path as osp
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| import sys
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| import numpy as np
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| import PIL.Image
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| import labelme
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| import shutil
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|
|
| try:
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| import pycocotools.mask
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| except ImportError:
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| print('Please install pycocotools:\n\n pip install pycocotools\n')
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| sys.exit(1)
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|
|
| def main():
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| sets = ['train2017','val2017','test2017']
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| output_dir = './annotations'
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| if osp.exists(output_dir):
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| print('Output directory already exists:', output_dir)
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| shutil.rmtree(output_dir)
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| os.makedirs(output_dir)
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| print('Creating dataset:', output_dir)
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| for set in sets:
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|
|
| input_dir = './data_annotated/%s' % (set)
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| filename = 'instances_%s' % (set)
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| now = datetime.datetime.now()
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| data = dict(
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| info=dict(
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| description=None,
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| version=None,
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| contributor=None,
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| date_created=now.strftime('%Y-%m-%d %H:%M:%S.%f'),
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| ),
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| licenses=[dict(
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| id=0,
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| name=None,
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| )],
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| images=[
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|
|
| ],
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| type='instances',
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| annotations=[
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|
|
| ],
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| categories=[
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|
|
| ],
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| )
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|
|
| class_name_to_id = {}
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| for i, line in enumerate(open('labels.txt').readlines()):
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| class_id = i
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| class_name = line.strip()
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| if class_id == 0:
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| assert class_name == '_background_'
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| continue
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| class_name_to_id[class_name] = class_id
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| data['categories'].append(dict(
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| supercategory=None,
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| id=class_id,
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| name=class_name,
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| ))
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| out_ann_file = osp.join(output_dir, filename +'.json')
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| label_files = glob.glob(osp.join(input_dir, '*.json'))
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| for image_id, label_file in enumerate(label_files):
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| with open(label_file) as f:
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| label_data = json.load(f)
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| path=label_data['imagePath'].split("/")
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| img_file = './data_annotated/%s/'%(set) + path[-1]
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| img = np.asarray(PIL.Image.open(img_file))
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|
|
| data['images'].append(dict(
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| license=0,
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| url=None,
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| file_name=label_file.split('/')[-1].split('.')[0] + '.jpg',
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| height=img.shape[0],
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| width=img.shape[1],
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| date_captured=None,
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| id=image_id+1,
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| ))
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|
|
| masks = {}
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| segmentations = collections.defaultdict(list)
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| for shape in label_data['shapes']:
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| points = shape['points']
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| label = shape['label']
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| shape_type = shape.get('shape_type', None)
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| mask = labelme.utils.shape_to_mask(
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| img.shape[:2], points, shape_type
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| )
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|
|
| if label in masks:
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| masks[label] = masks[label] | mask
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| else:
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| masks[label] = mask
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|
|
| points = np.asarray(points).flatten().tolist()
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| segmentations[label].append(points)
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|
|
| for label, mask in masks.items():
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| cls_name = label.split('-')[0]
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| if cls_name not in class_name_to_id:
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| continue
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| cls_id = class_name_to_id[cls_name]
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|
|
| mask = np.asfortranarray(mask.astype(np.uint8))
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| mask = pycocotools.mask.encode(mask)
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| area = float(pycocotools.mask.area(mask))
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| bbox = pycocotools.mask.toBbox(mask).flatten().tolist()
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|
|
| data['annotations'].append(dict(
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| id=len(data['annotations'])+1,
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| image_id=image_id+1,
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| category_id=cls_id,
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| segmentation=segmentations[label],
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| area=area,
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| bbox=bbox,
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| iscrowd=0,
|
| ))
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|
|
| with open(out_ann_file, 'w') as f:
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| json.dump(data, f,indent=4)
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| print(set + ' is done')
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|
|
| if __name__ == '__main__':
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| main()
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|
|
|
|
