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from pycocotools.coco import COCO |
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from os.path import join |
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import json |
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dataDir = '.' |
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for data_subset in ['val2017', 'train2017']: |
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dataset = dict() |
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annFile = '{}/annotations/instances_{}.json'.format(dataDir, data_subset) |
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coco = COCO(annFile) |
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n_imgs = len(coco.imgs) |
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for n, img_id in enumerate(coco.imgs): |
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print('subset: {} image id: {:04d} / {:04d}'.format(data_subset, n, n_imgs)) |
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img = coco.loadImgs(img_id)[0] |
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annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None) |
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anns = coco.loadAnns(annIds) |
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crop_base_path = join(data_subset, img['file_name'].split('/')[-1].split('.')[0]) |
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if len(anns) > 0: |
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dataset[crop_base_path] = dict() |
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for track_id, ann in enumerate(anns): |
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rect = ann['bbox'] |
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if rect[2] <= 0 or rect[3] <= 0: |
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continue |
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bbox = [rect[0], rect[1], rect[0]+rect[2]-1, rect[1]+rect[3]-1] |
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dataset[crop_base_path]['{:02d}'.format(track_id)] = {'000000': bbox} |
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print('save json (dataset), please wait 20 seconds~') |
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json.dump(dataset, open('{}.json'.format(data_subset), 'w'), indent=4, sort_keys=True) |
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print('done!') |
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