Gather input features. Args: extracted_results (List[List[Dict]]): Multi-frame feature extraction results Returns: List[List[dict]]: Multi-frame feature extraction results stored in a nested list. Each element of the outer list is the fea
(extracted_results)
| 403 | |
| 404 | |
| 405 | def _gather_input_features(extracted_results): |
| 406 | """Gather input features. |
| 407 | |
| 408 | Args: |
| 409 | extracted_results (List[List[Dict]]): |
| 410 | Multi-frame feature extraction results |
| 411 | |
| 412 | Returns: |
| 413 | List[List[dict]]: Multi-frame feature extraction results |
| 414 | stored in a nested list. Each element of the outer list is the |
| 415 | feature extraction results of a single frame, and each element of |
| 416 | the inner list is the extracted results of one person, |
| 417 | which contains: |
| 418 | features (ndarray): extracted features |
| 419 | track_id (int): unique id of each person, required when |
| 420 | ``with_track_id==True``` |
| 421 | """ |
| 422 | sequence_inputs = [] |
| 423 | for frame in extracted_results: |
| 424 | frame_inputs = [] |
| 425 | for res in frame: |
| 426 | inputs = dict() |
| 427 | if 'features' in res: |
| 428 | inputs['features'] = res['features'] |
| 429 | if 'track_id' in res: |
| 430 | inputs['track_id'] = res['track_id'] |
| 431 | frame_inputs.append(inputs) |
| 432 | sequence_inputs.append(frame_inputs) |
| 433 | return sequence_inputs |
| 434 | |
| 435 | |
| 436 | def _collate_feature_sequence(extracted_features, |
no outgoing calls
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