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46 lines
1.5 KiB
46 lines
1.5 KiB
2 years ago
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"""
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This code converts all RefCOCO(+/g) detections from Mask R-CNN
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(https://github.com/lichengunc/MAttNet)
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to image_id -> [box], where each box is {box, category_id, category_name, score}
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"""
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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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dets_dir = 'datasets/refer/detections'
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image_set = set()
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dataset_names = ['refcoco_unc', 'refcoco+_unc', 'refcocog_umd']
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Detections = {}
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for dataset_name in dataset_names:
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dets_file = osp.join(dets_dir, dataset_name,
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'res101_coco_minus_refer_notime_dets.json')
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detections = json.load(open(dets_file, 'r'))
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for det in detections:
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image_set.add(det['image_id'])
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Detections[dataset_name] = detections
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num_images = len(image_set)
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iid_to_dets = {}
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for dataset_name in dataset_names:
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detections = Detections[dataset_name]
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for det in detections:
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image_id = det['image_id']
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if image_id in image_set:
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box = {'box': det['box'],
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'category_id': det['category_id'],
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'category_name': det['category_name'],
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'score': det['score']}
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iid_to_dets[image_id] = iid_to_dets.get(image_id, []) + [box]
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for det in detections:
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image_id = det['image_id']
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if image_id in image_set:
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image_set.remove(image_id)
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num_dets = sum([len(dets) for dets in iid_to_dets.values()])
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print(f'{num_dets} detections in {num_images} images for {dataset_names}.')
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# save
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with open('index/iid_to_dets.json', 'w') as f:
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json.dump(iid_to_dets, f)
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