MPII姿态估计性能评价标准-PCK

Overview

PCK是mpii使用的人体关键点估计评价标准，在coco之前，PCK一直是比较主流的metric，包括deepfashion，fashionAI等，都是使用的此标准。

PCK

PCK（Percentage of Correct Keypoints）定义为正确估计出关键点的比例，计算检测的关键点与其对应的groundtruth间的归一化距离小于设定阈值的比例(the percentage of detections that fall within a normalized distance of the ground truth)。
于是就有了PCK@0.5，也就是设定的阈值是0.5。
归一化距离是关键点预测值与人工标注值的欧式距离，进行人体尺度因子的归一化，MPII数据集是以当前人的头部直径作为尺度因子，即头部矩形框的左上点与右下点的欧式距离，使用此尺度因子的姿态估计指标也称PCKh。
需要注意的是PCK是针对于一个人joints的predict和gt，也就是说不存在多么预测结果与gt之前对应的问题，或者说这个对应问题在PCK计算之前就应该解决了，而PCK解决多人姿态估计时使用的方式是在人的维度上进行平均。
从下面的代码也可以看出，距离的计算是一一对应的，而多人的PCK就是求平均值。

code

from mmpose

def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):"""Calculate the pose accuracy of PCK for each individual keypoint and theaveraged accuracy across all keypoints for coordinates.Note:PCK metric measures accuracy of the localization of the body joints.The distances between predicted positions and the ground-truth onesare typically normalized by the bounding box size.The threshold (thr) of the normalized distance is commonly setas 0.05, 0.1 or 0.2 etc.batch_size: Nnum_keypoints: KArgs:pred (np.ndarray[N, K, 2]): Predicted keypoint location.gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.mask (np.ndarray[N, K]): Visibility of the target. False for invisiblejoints, and True for visible. Invisible joints will be ignored foraccuracy calculation.thr (float): Threshold of PCK calculation.normalize (np.ndarray[N, 2]): Normalization factor for H&W.Returns:tuple: A tuple containing keypoint accuracy.- acc (np.ndarray[K]): Accuracy of each keypoint.- avg_acc (float): Averaged accuracy across all keypoints.- cnt (int): Number of valid keypoints."""distances = _calc_distances(pred, gt, mask, normalize)acc = np.array([_distance_acc(d, thr) for d in distances])valid_acc = acc[acc >= 0]cnt = len(valid_acc)avg_acc = valid_acc.mean() if cnt > 0 else 0return acc, avg_acc, cntdef _calc_distances(preds, targets, mask, normalize):"""Calculate the normalized distances between preds and target.Note:batch_size: Nnum_keypoints: Kdimension of keypoints: D (normally, D=2 or D=3)Args:preds (np.ndarray[N, K, D]): Predicted keypoint location.targets (np.ndarray[N, K, D]): Groundtruth keypoint location.mask (np.ndarray[N, K]): Visibility of the target. False for invisiblejoints, and True for visible. Invisible joints will be ignored foraccuracy calculation.normalize (np.ndarray[N, D]): Typical value is heatmap_sizeReturns:np.ndarray[K, N]: The normalized distances.If target keypoints are missing, the distance is -1."""N, K, _ = preds.shapedistances = np.full((N, K), -1, dtype=np.float32)# handle invalid valuesnormalize[np.where(normalize <= 0)] = 1e6distances[mask] = np.linalg.norm(((preds - targets) / normalize[:, None, :])[mask], axis=-1)return distances.Tdef _distance_acc(distances, thr=0.5):"""Return the percentage below the distance threshold, while ignoringdistances values with -1.Note:batch_size: NArgs:distances (np.ndarray[N, ]): The normalized distances.thr (float): Threshold of the distances.Returns:float: Percentage of distances below the threshold.If all target keypoints are missing, return -1."""distance_valid = distances != -1num_distance_valid = distance_valid.sum()if num_distance_valid > 0:return (distances[distance_valid] < thr).sum() / num_distance_validreturn -1