lightweight openpose

    current_poses = sorted(current_poses, key=lambda pose: pose.confidence, reverse=True)  # match confident poses first

    mask = np.ones(len(previous_poses), dtype=np.int32)

    flag=0

    for current_pose in current_poses:

        best_matched_id = None

        best_matched_pose_id = None

        best_matched_iou = 0

        all_image=0

        for id, previous_pose in enumerate(previous_poses):

            if not mask[id]:

                continue

            iou = get_similarity(current_pose, previous_pose)

            if iou > best_matched_iou:

                best_matched_iou = iou

                best_matched_pose_id = previous_pose.id

                best_matched_id = id

                all_image=previous_pose.all_save_image

        if best_matched_iou >= threshold:

            mask[best_matched_id] = 0

        else:  # pose not similar to any previous

            best_matched_pose_id = None

            all_image=None

如果要去解决遮挡问题的话，直接用一个pose跟之前所有的进行相似度计算因此不用mask。