6年前：

GitHub - YuliangXiu/PoseFlow: PoseFlow: Efficient Online Pose Tracking (BMVC'18)

报错：

Clarification on min_keypoints in tracking · Issue #1411 · open-mmlab/mmpose · GitHub

https://github.com/open-mmlab/mmpose/blob/c8e91ff456d82c7f985bf938cabfb68b2aa51d27/mmpose/apis/inference_tracking.py#L227

# Copyright (c) OpenMMLab. All rights reserved.
import warningsimport numpy as npfrom mmpose.core import OneEuroFilter, oks_ioudef _compute_iou(bboxA, bboxB):"""Compute the Intersection over Union (IoU) between two boxes .Args:bboxA (list): The first bbox info (left, top, right, bottom, score).bboxB (list): The second bbox info (left, top, right, bottom, score).Returns:float: The IoU value."""x1 = max(bboxA[0], bboxB[0])y1 = max(bboxA[1], bboxB[1])x2 = min(bboxA[2], bboxB[2])y2 = min(bboxA[3], bboxB[3])inter_area = max(0, x2 - x1) * max(0, y2 - y1)bboxA_area = (bboxA[2] - bboxA[0]) * (bboxA[3] - bboxA[1])bboxB_area = (bboxB[2] - bboxB[0]) * (bboxB[3] - bboxB[1])union_area = float(bboxA_area + bboxB_area - inter_area)if union_area == 0:union_area = 1e-5warnings.warn('union_area=0 is unexpected')iou = inter_area / union_areareturn ioudef _track_by_iou(res, results_last, thr):"""Get track id using IoU tracking greedily.Args:res (dict): The bbox & pose results of the person instance.results_last (list[dict]): The bbox & pose & track_id info of thelast frame (bbox_result, pose_result, track_id).thr (float): The threshold for iou tracking.Returns:int: The track id for the new person instance.list[dict]: The bbox & pose & track_id info of the personsthat have not been matched on the last frame.dict: The matched person instance on the last frame."""bbox = list(res['bbox'])max_iou_score = -1max_index = -1match_result = {}for index, res_last in enumerate(results_last):bbox_last = list(res_last['bbox'])iou_score = _compute_iou(bbox, bbox_last)if iou_score > max_iou_score:max_iou_score = iou_scoremax_index = indexif max_iou_score > thr:track_id = results_last[max_index]['track_id']match_result = results_last[max_index]del results_last[max_index]else:track_id = -1return track_id, results_last, match_resultdef _track_by_oks(res, results_last, thr):"""Get track id using OKS tracking greedily.Args:res (dict): The pose results of the person instance.results_last (list[dict]): The pose & track_id info of thelast frame (pose_result, track_id).thr (float): The threshold for oks tracking.Returns:int: The track id for the new person instance.list[dict]: The pose & track_id info of the personsthat have not been matched on the last frame.dict: The matched person instance on the last frame."""pose = res['keypoints'].reshape((-1))area = res['area']max_index = -1match_result = {}if len(results_last) == 0:return -1, results_last, match_resultpose_last = np.array([res_last['keypoints'].reshape((-1)) for res_last in results_last])area_last = np.array([res_last['area'] for res_last in results_last])oks_score = oks_iou(pose, pose_last, area, area_last)max_index = np.argmax(oks_score)if oks_score[max_index] > thr:track_id = results_last[max_index]['track_id']match_result = results_last[max_index]del results_last[max_index]else:track_id = -1return track_id, results_last, match_resultdef _get_area(results):"""Get bbox for each person instance on the current frame.Args:results (list[dict]): The pose results of the current frame(pose_result).Returns:list[dict]: The bbox & pose info of the current frame(bbox_result, pose_result, area)."""for result in results:if 'bbox' in result:result['area'] = ((result['bbox'][2] - result['bbox'][0]) *(result['bbox'][3] - result['bbox'][1]))else:xmin = np.min(result['keypoints'][:, 0][result['keypoints'][:, 0] > 0],initial=1e10)xmax = np.max(result['keypoints'][:, 0])ymin = np.min(result['keypoints'][:, 1][result['keypoints'][:, 1] > 0],initial=1e10)ymax = np.max(result['keypoints'][:, 1])result['area'] = (xmax - xmin) * (ymax - ymin)result['bbox'] = np.array([xmin, ymin, xmax, ymax])return resultsdef _temporal_refine(result, match_result, fps=None):"""Refine koypoints using tracked person instance on last frame.Args:results (dict): The pose results of the current frame(pose_result).match_result (dict): The pose results of the last frame(match_result)Returns:(array): The person keypoints after refine."""if 'one_euro' in match_result:result['keypoints'][:, :2] = match_result['one_euro'](result['keypoints'][:, :2])result['one_euro'] = match_result['one_euro']else:result['one_euro'] = OneEuroFilter(result['keypoints'][:, :2], fps=fps)return result['keypoints']def get_track_id(results,results_last,next_id,min_keypoints=3,use_oks=False,tracking_thr=0.3,use_one_euro=False,fps=None):"""Get track id for each person instance on the current frame.Args:results (list[dict]): The bbox & pose results of the current frame(bbox_result, pose_result).results_last (list[dict], optional): The bbox & pose & track_id infoof the last frame (bbox_result, pose_result, track_id). None isequivalent to an empty result list. Default: Nonenext_id (int): The track id for the new person instance.min_keypoints (int): Minimum number of keypoints recognized as person.0 means no minimum threshold required. Default: 3.use_oks (bool): Flag to using oks tracking. default: False.tracking_thr (float): The threshold for tracking.use_one_euro (bool): Option to use one-euro-filter. default: False.fps (optional): Parameters that d_cutoffwhen one-euro-filter is used as a video inputReturns:tuple:- results (list[dict]): The bbox & pose & track_id info of the \current frame (bbox_result, pose_result, track_id).- next_id (int): The track id for the new person instance."""if use_one_euro:warnings.warn('In the future, get_track_id() will no longer perform ''temporal refinement and the arguments `use_one_euro` and ''`fps` will be deprecated. This part of function has been ''migrated to Smoother (mmpose.core.Smoother). See ''demo/top_down_pose_trackign_demo_with_mmdet.py for an ''example.', DeprecationWarning)if results_last is None:results_last = []results = _get_area(results)if use_oks:_track = _track_by_okselse:_track = _track_by_ioufor result in results:track_id, results_last, match_result = _track(result, results_last,tracking_thr)if track_id == -1:if np.count_nonzero(result['keypoints'][:, 1]) >= min_keypoints:result['track_id'] = next_idnext_id += 1else:# If the number of keypoints detected is small,# delete that person instance.result['keypoints'][:, 1] = -10result['bbox'] *= 0result['track_id'] = -1else:result['track_id'] = track_idif use_one_euro:result['keypoints'] = _temporal_refine(result, match_result, fps=fps)del match_resultreturn results, next_iddef vis_pose_tracking_result(model,img,result,radius=4,thickness=1,kpt_score_thr=0.3,dataset='TopDownCocoDataset',dataset_info=None,show=False,out_file=None):"""Visualize the pose tracking results on the image.Args:model (nn.Module): The loaded detector.img (str | np.ndarray): Image filename or loaded image.result (list[dict]): The results to draw over `img`(bbox_result, pose_result).radius (int): Radius of circles.thickness (int): Thickness of lines.kpt_score_thr (float): The threshold to visualize the keypoints.skeleton (list[tuple]): Default None.show (bool):  Whether to show the image. Default True.out_file (str|None): The filename of the output visualization image."""if hasattr(model, 'module'):model = model.modulepalette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],[230, 230, 0], [255, 153, 255], [153, 204, 255],[255, 102, 255], [255, 51, 255], [102, 178, 255],[51, 153, 255], [255, 153, 153], [255, 102, 102],[255, 51, 51], [153, 255, 153], [102, 255, 102],[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],[255, 255, 255]])if dataset_info is None and dataset is not None:warnings.warn('dataset is deprecated.''Please set `dataset_info` in the config.''Check https://github.com/open-mmlab/mmpose/pull/663 for details.',DeprecationWarning)# TODO: These will be removed in the later versions.if dataset in ('TopDownCocoDataset', 'BottomUpCocoDataset','TopDownOCHumanDataset'):kpt_num = 17skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],[5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],[8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4],[3, 5], [4, 6]]elif dataset == 'TopDownCocoWholeBodyDataset':kpt_num = 133skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],[5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],[8, 10], [1, 2], [0, 1], [0, 2],[1, 3], [2, 4], [3, 5], [4, 6], [15, 17], [15, 18],[15, 19], [16, 20], [16, 21], [16, 22], [91, 92],[92, 93], [93, 94], [94, 95], [91, 96], [96, 97],[97, 98], [98, 99], [91, 100], [100, 101], [101, 102],[102, 103], [91, 104], [104, 105], [105, 106],[106, 107], [91, 108], [108, 109], [109, 110],[110, 111], [112, 113], [113, 114], [114, 115],[115, 116], [112, 117], [117, 118], [118, 119],[119, 120], [112, 121], [121, 122], [122, 123],[123, 124], [112, 125], [125, 126], [126, 127],[127, 128], [112, 129], [129, 130], [130, 131],[131, 132]]radius = 1elif dataset == 'TopDownAicDataset':kpt_num = 14skeleton = [[2, 1], [1, 0], [0, 13], [13, 3], [3, 4], [4, 5],[8, 7], [7, 6], [6, 9], [9, 10], [10, 11], [12, 13],[0, 6], [3, 9]]elif dataset == 'TopDownMpiiDataset':kpt_num = 16skeleton = [[0, 1], [1, 2], [2, 6], [6, 3], [3, 4], [4, 5], [6, 7],[7, 8], [8, 9], [8, 12], [12, 11], [11, 10], [8, 13],[13, 14], [14, 15]]elif dataset in ('OneHand10KDataset', 'FreiHandDataset','PanopticDataset'):kpt_num = 21skeleton = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7],[7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13],[13, 14], [14, 15], [15, 16], [0, 17], [17, 18],[18, 19], [19, 20]]elif dataset == 'InterHand2DDataset':kpt_num = 21skeleton = [[0, 1], [1, 2], [2, 3], [4, 5], [5, 6], [6, 7], [8, 9],[9, 10], [10, 11], [12, 13], [13, 14], [14, 15],[16, 17], [17, 18], [18, 19], [3, 20], [7, 20],[11, 20], [15, 20], [19, 20]]else:raise NotImplementedError()elif dataset_info is not None:kpt_num = dataset_info.keypoint_numskeleton = dataset_info.skeletonfor res in result:track_id = res['track_id']bbox_color = palette[track_id % len(palette)]pose_kpt_color = palette[[track_id % len(palette)] * kpt_num]pose_link_color = palette[[track_id % len(palette)] * len(skeleton)]img = model.show_result(img, [res],skeleton,radius=radius,thickness=thickness,pose_kpt_color=pose_kpt_color,pose_link_color=pose_link_color,bbox_color=tuple(bbox_color.tolist()),kpt_score_thr=kpt_score_thr,show=show,out_file=out_file)return img