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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os | |
| import os.path as osp | |
| from typing import Dict, List, Optional | |
| import numpy as np | |
| from mmengine.fileio import dump, load | |
| from mmengine.logging import MMLogger | |
| from mmpose.registry import METRICS | |
| from .coco_metric import CocoMetric | |
| try: | |
| from poseval import eval_helpers | |
| from poseval.evaluateAP import evaluateAP | |
| has_poseval = True | |
| except (ImportError, ModuleNotFoundError): | |
| has_poseval = False | |
| class PoseTrack18Metric(CocoMetric): | |
| """PoseTrack18 evaluation metric. | |
| Evaluate AP, and mAP for keypoint detection tasks. | |
| Support PoseTrack18 (video) dataset. Please refer to | |
| `<https://github.com/leonid-pishchulin/poseval>`__ | |
| for more details. | |
| Args: | |
| ann_file (str, optional): Path to the coco format annotation file. | |
| If not specified, ground truth annotations from the dataset will | |
| be converted to coco format. Defaults to None | |
| score_mode (str): The mode to score the prediction results which | |
| should be one of the following options: | |
| - ``'bbox'``: Take the score of bbox as the score of the | |
| prediction results. | |
| - ``'bbox_keypoint'``: Use keypoint score to rescore the | |
| prediction results. | |
| Defaults to ``'bbox_keypoint'` | |
| keypoint_score_thr (float): The threshold of keypoint score. The | |
| keypoints with score lower than it will not be included to | |
| rescore the prediction results. Valid only when ``score_mode`` is | |
| ``bbox_keypoint``. Defaults to ``0.2`` | |
| nms_mode (str): The mode to perform Non-Maximum Suppression (NMS), | |
| which should be one of the following options: | |
| - ``'oks_nms'``: Use Object Keypoint Similarity (OKS) to | |
| perform NMS. | |
| - ``'soft_oks_nms'``: Use Object Keypoint Similarity (OKS) | |
| to perform soft NMS. | |
| - ``'none'``: Do not perform NMS. Typically for bottomup mode | |
| output. | |
| Defaults to ``'oks_nms'` | |
| nms_thr (float): The Object Keypoint Similarity (OKS) threshold | |
| used in NMS when ``nms_mode`` is ``'oks_nms'`` or | |
| ``'soft_oks_nms'``. Will retain the prediction results with OKS | |
| lower than ``nms_thr``. Defaults to ``0.9`` | |
| format_only (bool): Whether only format the output results without | |
| doing quantitative evaluation. This is designed for the need of | |
| test submission when the ground truth annotations are absent. If | |
| set to ``True``, ``outfile_prefix`` should specify the path to | |
| store the output results. Defaults to ``False`` | |
| outfile_prefix (str | None): The prefix of json files. It includes | |
| the file path and the prefix of filename, e.g., ``'a/b/prefix'``. | |
| If not specified, a temp file will be created. Defaults to ``None`` | |
| **kwargs: Keyword parameters passed to :class:`mmeval.BaseMetric` | |
| """ | |
| default_prefix: Optional[str] = 'posetrack18' | |
| def __init__(self, | |
| ann_file: Optional[str] = None, | |
| score_mode: str = 'bbox_keypoint', | |
| keypoint_score_thr: float = 0.2, | |
| nms_mode: str = 'oks_nms', | |
| nms_thr: float = 0.9, | |
| format_only: bool = False, | |
| outfile_prefix: Optional[str] = None, | |
| collect_device: str = 'cpu', | |
| prefix: Optional[str] = None) -> None: | |
| # raise an error to avoid long time running without getting results | |
| if not has_poseval: | |
| raise ImportError('Please install ``poseval`` package for ' | |
| 'evaluation on PoseTrack dataset ' | |
| '(see `requirements/optional.txt`)') | |
| super().__init__( | |
| ann_file=ann_file, | |
| score_mode=score_mode, | |
| keypoint_score_thr=keypoint_score_thr, | |
| nms_mode=nms_mode, | |
| nms_thr=nms_thr, | |
| format_only=format_only, | |
| outfile_prefix=outfile_prefix, | |
| collect_device=collect_device, | |
| prefix=prefix) | |
| def results2json(self, keypoints: Dict[int, list], | |
| outfile_prefix: str) -> str: | |
| """Dump the keypoint detection results into a json file. | |
| Args: | |
| keypoints (Dict[int, list]): Keypoint detection results | |
| of the dataset. | |
| outfile_prefix (str): The filename prefix of the json files. | |
| If the prefix is "somepath/xxx", the json files will be named | |
| "somepath/xxx.keypoints.json". | |
| Returns: | |
| str: The json file name of keypoint results. | |
| """ | |
| categories = [] | |
| cat = {} | |
| cat['supercategory'] = 'person' | |
| cat['id'] = 1 | |
| cat['name'] = 'person' | |
| cat['keypoints'] = [ | |
| 'nose', 'head_bottom', 'head_top', 'left_ear', 'right_ear', | |
| 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', | |
| 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', | |
| 'right_knee', 'left_ankle', 'right_ankle' | |
| ] | |
| cat['skeleton'] = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], | |
| [6, 12], [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], | |
| [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], | |
| [4, 6], [5, 7]] | |
| categories.append(cat) | |
| # path of directory for official gt files | |
| gt_folder = osp.join( | |
| osp.dirname(self.ann_file), | |
| osp.splitext(self.ann_file.split('_')[-1])[0]) | |
| # the json file for each video sequence | |
| json_files = [ | |
| pos for pos in os.listdir(gt_folder) if pos.endswith('.json') | |
| ] | |
| for json_file in json_files: | |
| gt = load(osp.join(gt_folder, json_file)) | |
| annotations = [] | |
| images = [] | |
| for image in gt['images']: | |
| img = {} | |
| img['id'] = image['id'] | |
| img['file_name'] = image['file_name'] | |
| images.append(img) | |
| img_kpts = keypoints[img['id']] | |
| for track_id, img_kpt in enumerate(img_kpts): | |
| ann = {} | |
| ann['image_id'] = img_kpt['img_id'] | |
| ann['keypoints'] = np.array( | |
| img_kpt['keypoints']).reshape(-1).tolist() | |
| ann['scores'] = np.array(ann['keypoints']).reshape( | |
| [-1, 3])[:, 2].tolist() | |
| ann['score'] = float(img_kpt['score']) | |
| ann['track_id'] = track_id | |
| annotations.append(ann) | |
| pred_file = osp.join(osp.dirname(outfile_prefix), json_file) | |
| info = {} | |
| info['images'] = images | |
| info['categories'] = categories | |
| info['annotations'] = annotations | |
| dump(info, pred_file, sort_keys=True, indent=4) | |
| def _do_python_keypoint_eval(self, outfile_prefix: str) -> List[tuple]: | |
| """Do keypoint evaluation using `poseval` package. | |
| Args: | |
| outfile_prefix (str): The filename prefix of the json files. | |
| If the prefix is "somepath/xxx", the json files will be named | |
| "somepath/xxx.keypoints.json". | |
| Returns: | |
| list: a list of tuples. Each tuple contains the evaluation stats | |
| name and corresponding stats value. | |
| """ | |
| logger: MMLogger = MMLogger.get_current_instance() | |
| # path of directory for official gt files | |
| # 'xxx/posetrack18_train.json' -> 'xxx/train/' | |
| gt_folder = osp.join( | |
| osp.dirname(self.ann_file), | |
| osp.splitext(self.ann_file.split('_')[-1])[0]) | |
| pred_folder = osp.dirname(outfile_prefix) | |
| argv = ['', gt_folder + '/', pred_folder + '/'] | |
| logger.info('Loading data') | |
| gtFramesAll, prFramesAll = eval_helpers.load_data_dir(argv) | |
| logger.info(f'# gt frames : {len(gtFramesAll)}') | |
| logger.info(f'# pred frames: {len(prFramesAll)}') | |
| # evaluate per-frame multi-person pose estimation (AP) | |
| # compute AP | |
| logger.info('Evaluation of per-frame multi-person pose estimation') | |
| apAll, _, _ = evaluateAP(gtFramesAll, prFramesAll, None, False, False) | |
| # print AP | |
| logger.info('Average Precision (AP) metric:') | |
| eval_helpers.printTable(apAll) | |
| stats = eval_helpers.getCum(apAll) | |
| stats_names = [ | |
| 'Head AP', 'Shou AP', 'Elb AP', 'Wri AP', 'Hip AP', 'Knee AP', | |
| 'Ankl AP', 'AP' | |
| ] | |
| info_str = list(zip(stats_names, stats)) | |
| return info_str | |