lightweight的group_keypoints解释

def group_keypoints(all_keypoints_by_type, pafs, pose_entry_size=20, min_paf_score=0.05):     pose_entries = []     all_keypoints = np.array([item for sublist in all_keypoints_by_type for item in sublist])     points_per_limb = 10     grid = np.arange(points_per_limb, dtype=np.float32).reshape(1, -1, 1)     all_keypoints_by_type = [np.array(keypoints, np.float32) for keypoints in all_keypoints_by_type]     for part_id in range(len(BODY_PARTS_PAF_IDS)):         part_pafs = pafs[:, :, BODY_PARTS_PAF_IDS[part_id]]         kpts_a = all_keypoints_by_type[BODY_PARTS_KPT_IDS[part_id][0]]         kpts_b = all_keypoints_by_type[BODY_PARTS_KPT_IDS[part_id][1]]         n = len(kpts_a)         m = len(kpts_b)         if n == 0 or m == 0:             continue           # Get vectors between all pairs of keypoints, i.e. candidate limb vectors.         a = kpts_a[:, :2]#某个关键点的坐标         a = np.broadcast_to(a[None], (m, n, 2))#广播机制用b中的坐标减去a中的所有坐标会生成m*n维度的向量。         b = kpts_b[:, :2]#关键点坐标         vec_raw = (b[:, None, :] - a).reshape(-1, 1, 2)           # Sample points along every candidate limb vector.         steps = (1 / (points_per_limb - 1) * vec_raw)         points = steps * grid + a.reshape(-1, 1, 2)         points = points.round().astype(dtype=np.int32)         x = points[..., 0].ravel()         y = points[..., 1].ravel()           # Compute affinity score between candidate limb vectors and part affinity field.         field = part_pafs[y, x].reshape(-1, points_per_limb, 2)         vec_norm = np.linalg.norm(vec_raw, ord=2, axis=-1, keepdims=True)         vec = vec_raw / (vec_norm + 1e-6)         affinity_scores = (field * vec).sum(-1).reshape(-1, points_per_limb)         valid_affinity_scores = affinity_scores > min_paf_score         valid_num = valid_affinity_scores.sum(1)         affinity_scores = (affinity_scores * valid_affinity_scores).sum(1) / (valid_num + 1e-6)         success_ratio = valid_num / points_per_limb           # Get a list of limbs according to the obtained affinity score.         valid_limbs = np.where(np.logical_and(affinity_scores > 0, success_ratio > 0.8))[0]         if len(valid_limbs) == 0:             continue         b_idx, a_idx = np.divmod(valid_limbs, n)         affinity_scores = affinity_scores[valid_limbs]           # Suppress incompatible connections.         a_idx, b_idx, affinity_scores = connections_nms(a_idx, b_idx, affinity_scores)         connections = list(zip(kpts_a[a_idx, 3].astype(np.int32),                                kpts_b[b_idx, 3].astype(np.int32),                                affinity_scores))         if len(connections) == 0:             continue           if part_id == 0:             pose_entries = [np.ones(pose_entry_size) * -1 for _ in range(len(connections))]             for i in range(len(connections)):                 pose_entries[i][BODY_PARTS_KPT_IDS[0][0]] = connections[i][0]                 pose_entries[i][BODY_PARTS_KPT_IDS[0][1]] = connections[i][1]                 pose_entries[i][-1] = 2                 pose_entries[i][-2] = np.sum(all_keypoints[connections[i][0:2], 2]) + connections[i][2]         elif part_id == 17 or part_id == 18:             kpt_a_id = BODY_PARTS_KPT_IDS[part_id][0]             kpt_b_id = BODY_PARTS_KPT_IDS[part_id][1]             for i in range(len(connections)):                 for j in range(len(pose_entries)):                     if pose_entries[j][kpt_a_id] == connections[i][0] and pose_entries[j][kpt_b_id] == -1:                         pose_entries[j][kpt_b_id] = connections[i][1]                     elif pose_entries[j][kpt_b_id] == connections[i][1] and pose_entries[j][kpt_a_id] == -1:                         pose_entries[j][kpt_a_id] = connections[i][0]             continue         else:             kpt_a_id = BODY_PARTS_KPT_IDS[part_id][0]             kpt_b_id = BODY_PARTS_KPT_IDS[part_id][1]             for i in range(len(connections)):                 num = 0                 for j in range(len(pose_entries)):                     if pose_entries[j][kpt_a_id] == connections[i][0]:                         pose_entries[j][kpt_b_id] = connections[i][1]                         num += 1                         pose_entries[j][-1] += 1                         pose_entries[j][-2] += all_keypoints[connections[i][1], 2] + connections[i][2]                 if num == 0:                     pose_entry = np.ones(pose_entry_size) * -1                     pose_entry[kpt_a_id] = connections[i][0]                     pose_entry[kpt_b_id] = connections[i][1]                     pose_entry[-1] = 2                     pose_entry[-2] = np.sum(all_keypoints[connections[i][0:2], 2]) + connections[i][2]                     pose_entries.append(pose_entry)       filtered_entries = []     for i in range(len(pose_entries)):         if pose_entries[i][-1] < 3 or (pose_entries[i][-2] / pose_entries[i][-1] < 0.2):             continue         filtered_entries.append(pose_entries[i])     pose_entries = np.asarray(filtered_entries)     return pose_entries, all_keypoints