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import argparse
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from collections import defaultdict
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from mmengine import dump, load
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'stad_anno', help='spatiotemporal action detection anno path')
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parser.add_argument('det_path', help='output detection anno path')
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args = parser.parse_args()
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return args
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def generate_mmdet_coco_anno(args):
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ori_anno = load(args.stad_anno)
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train_videos = ori_anno['train_videos']
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val_videos = ori_anno['test_videos']
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videos = {'train': train_videos, 'val': val_videos}
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for split in ['train', 'val']:
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img_id = 0
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bbox_id = 0
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img_list = []
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anno_list = []
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for vid in videos[split][0]:
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vid_tubes = ori_anno['gttubes'][vid]
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height, width = ori_anno['resolution'][vid]
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frm2bbox = defaultdict(list)
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for label_idx, tube_list in vid_tubes.items():
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for tube in tube_list:
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for frm_anno in tube:
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frm_idx, bbox = frm_anno[0], frm_anno[1:]
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frm2bbox[frm_idx].append({'label': 0, 'bbox': bbox})
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for frm_idx, frm_bboxes in frm2bbox.items():
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img_path = f'{vid}/{int(frm_idx):05d}.jpg'
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img_instance = {
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'file_name': img_path,
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'height': height,
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'width': width,
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'id': img_id
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}
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img_list.append(img_instance)
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for bbox_info in frm_bboxes:
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label = bbox_info['label']
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x1, y1, x2, y2 = bbox_info['bbox']
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bbox = [x1, y1, x2 - x1, y2 - y1]
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anno_instance = {
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'area': bbox[2] * bbox[3],
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'image_id': img_id,
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'bbox': bbox,
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'category_id': label,
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'iscrowd': 0,
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'id': bbox_id
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}
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anno_list.append(anno_instance)
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bbox_id += 1
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img_id += 1
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total_anno = {
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'images': img_list,
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'annotations': anno_list,
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'categories': [{
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'id': 0,
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'name': 'person'
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}],
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}
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dump(total_anno, args.det_path[:-5] + f'_{split}' + args.det_path[-5:])
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if __name__ == '__main__':
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args = parse_args()
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generate_mmdet_coco_anno(args)
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