| 5 | |
| 6 | |
| 7 | def _create_coco_gt_results(dataset): |
| 8 | from mmtrack.core import outs2results |
| 9 | |
| 10 | results = defaultdict(list) |
| 11 | for img_info in dataset.data_infos: |
| 12 | ann = dataset.get_ann_info(img_info) |
| 13 | scores = np.ones((ann['bboxes'].shape[0], 1), dtype=np.float) |
| 14 | bboxes = np.concatenate((ann['bboxes'], scores), axis=1) |
| 15 | det_results = outs2results( |
| 16 | bboxes=bboxes, |
| 17 | labels=ann['labels'], |
| 18 | num_classes=len(dataset.CLASSES)) |
| 19 | track_results = outs2results( |
| 20 | bboxes=bboxes, |
| 21 | labels=ann['labels'], |
| 22 | ids=ann['instance_ids'].astype(np.int), |
| 23 | num_classes=len(dataset.CLASSES)) |
| 24 | results['det_bboxes'].append(det_results['bbox_results']) |
| 25 | results['track_bboxes'].append(track_results['bbox_results']) |
| 26 | return results |