Function pose_pck_accuracy

PATH/core/data/transforms/post_transforms.py:441–481

Calculate the pose accuracy of PCK for each individual keypoint and the averaged accuracy across all keypoints from heatmaps. Note: PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones

(output, target, mask, thr=0.05, normalize=None)

def pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None):
    """Calculate the pose accuracy of PCK for each individual keypoint and the
    averaged accuracy across all keypoints from heatmaps.

    Note:
        PCK metric measures accuracy of the localization of the body joints.
        The distances between predicted positions and the ground-truth ones
        are typically normalized by the bounding box size.
        The threshold (thr) of the normalized distance is commonly set
        as 0.05, 0.1 or 0.2 etc.

        batch_size: N
        num_keypoints: K
        heatmap height: H
        heatmap width: W

    Args:
        output (np.ndarray[N, K, H, W]): Model output heatmaps.
        target (np.ndarray[N, K, H, W]): Groundtruth heatmaps.
        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
            joints, and True for visible. Invisible joints will be ignored for
            accuracy calculation.
        thr (float): Threshold of PCK calculation. Default 0.05.
        normalize (np.ndarray[N, 2]): Normalization factor for H&W.

    Returns:
        tuple: A tuple containing keypoint accuracy.

        - np.ndarray[K]: Accuracy of each keypoint.
        - float: Averaged accuracy across all keypoints.
        - int: Number of valid keypoints.
    """
    N, K, H, W = output.shape
    if K == 0:
        return None, 0, 0
    if normalize is None:
        normalize = np.tile(np.array([[H, W]]), (N, 1))

    pred, _ = _get_max_preds(output)
    gt, _ = _get_max_preds(target)
    return keypoint_pck_accuracy(pred, gt, mask, thr, normalize)


def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):