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9b69558 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | import numpy as np
def convert_3dpose_to_2dpose_body(body_keypoints, face_keypoints):
"""
将20点的3D坐标映射到18点的2D坐标。
:param poses: 输入的20点坐标列表，每个点为 [x, y, z]
:return: 映射得到的18点坐标列表，每个点为 [x, y]
"""
# 映射关系：索引位置
body_mapping = {
0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 8, 9: 9, 10: 10, 11: 23, 13: 22, 12: 21,
14: 11, 15: 12, 16: 13, 17: 20, 18: 18, 19: 19
}
face_mapping = {
1: 16, 8: 14, 4: 0, 7: 15, 0: 17
}
# 初始化18点坐标列表，默认值为 [-1, -1]
result = [[-1, -1] for _ in range(24)]
# 遍历映射关系，将对应的20点坐标映射到18点坐标
for src_idx, dst_idx in body_mapping.items():
if src_idx < len(body_keypoints): # 确保索引不越界
result[dst_idx] = [body_keypoints[src_idx][1],body_keypoints[src_idx][0]] # 提取 x, y 坐标
for src_idx, dst_idx in face_mapping.items():
if src_idx < len(face_keypoints):
result[dst_idx] = [face_keypoints[src_idx][1], face_keypoints[src_idx][0]]
return result
def convert_3dpose_to_2dpose_hand(left_hand_keypoints, right_hand_keypoints, body_keypoints):
"""
将20点的3D坐标映射到18点的2D坐标。
:param poses: 输入的20点坐标列表，每个点为 [x, y, z]
:return: 映射得到的18点坐标列表，每个点为 [x, y]
"""
# 映射关系：索引位置
hand_mapping = {
0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10,
10: 11, 11: 12, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18,
18: 19, 19: 20
}
body_mapping_left = {3: 0}
body_mapping_right = {6: 0}
# 初始化18点坐标列表，默认值为 [-1, -1]
left_result = [[-1, -1] for _ in range(21)]
right_result = [[-1, -1] for _ in range(21)]
# 遍历映射关系，将对应的20点坐标映射到18点坐标
for src_idx, dst_idx in hand_mapping.items():
if src_idx < len(left_hand_keypoints): # 确保索引不越界
left_result[dst_idx] = [left_hand_keypoints[src_idx][1], left_hand_keypoints[src_idx][0]] # 提取 x, y 坐标
right_result[dst_idx] = [right_hand_keypoints[src_idx][1], right_hand_keypoints[src_idx][0]]
for src_idx, dst_idx in body_mapping_left.items():
if src_idx < len(body_keypoints):
left_result[dst_idx] = [body_keypoints[src_idx][1], body_keypoints[src_idx][0]]
for src_idx, dst_idx in body_mapping_right.items():
if src_idx < len(body_keypoints):
right_result[dst_idx] = [body_keypoints[src_idx][1], body_keypoints[src_idx][0]]
return [left_result, right_result]
def convert_3dpose_to_2dpose_face(face_keypoints):
result = [[-1, -1] if i in [0, 1, 4, 5, 6, 7, 8] else [pt[1], pt[0]] for i, pt in enumerate(face_keypoints)]
return result
def correct_lift_end_kpt_by_phmr(start, end, dwpose_kpts, lift_start, lift_end, phmr_start, phmr_end):
'''
检查另一端是否符合要求, 符合要求则返回lift后结果，不然返回phmr结果
'''
if dwpose_kpts[start][0] == -1:
return
lift_vec = np.array(lift_end) - np.array(lift_start)
phmr_vec = np.array(phmr_end) - np.array(phmr_start)
start_distance = np.linalg.norm(np.array(lift_start) - np.array(phmr_start))
end_distance = np.linalg.norm(np.array(lift_end) - np.array(phmr_end))
lift_vec_len = np.linalg.norm(lift_vec)
phmr_vec_len = np.linalg.norm(phmr_vec)
if start_distance + end_distance > phmr_vec_len:
dwpose_kpts[end] = [-1, -1]
theta = np.arccos(np.dot(lift_vec, phmr_vec) / (lift_vec_len * phmr_vec_len))
if lift_vec_len > phmr_vec_len * 1.65 or lift_vec_len < phmr_vec_len * 0.4 or theta > np.pi / 4:
dwpose_kpts[end] = [-1, -1]
return
def mix_3d_poses(poses_dwpose, poses_3dpose):
'''
组合两种pose，用3dPose的身体，DWPose的face和hand
'''
poses = []
for pose_dwpose, pose_3dpose in zip(poses_dwpose, poses_3dpose):
pose = {
"bodies": {
"candidate": pose_3dpose["bodies"]["candidate"],
"subset": pose_dwpose["bodies"]["subset"]
},
"faces": pose_dwpose["faces"],
"hands": pose_dwpose["hands"]
}
poses.append(pose)
return poses
def correct_hand_from_3d(hand_keypoints_dwpose, hand_keypoints_3dpose):
'''
如果dwpose的手部关节点和3dpose的手部关节点相差过大，则去掉最远的那一端
'''
edges_palm = [
[1, 2], [2, 3], [3, 4],
[5, 6], [6, 7], [7, 8],
[9, 10], [10, 11], [11, 12],
[13, 14], [14, 15], [15, 16],
[17, 18], [18, 19], [19, 20],
]
edges_finger = [[0, 1], [0, 5], [0, 9], [0, 13], [0, 17]]
max_length_palm = 0
max_length_finger = 0
for edge in edges_palm:
limb_length_3dpose = np.linalg.norm(np.array(hand_keypoints_3dpose[edge[0]]) - np.array(hand_keypoints_3dpose[edge[1]]))
if limb_length_3dpose > max_length_palm:
max_length_palm = limb_length_3dpose
for edge in edges_finger:
limb_length_3dpose = np.linalg.norm(np.array(hand_keypoints_3dpose[edge[0]]) - np.array(hand_keypoints_3dpose[edge[1]]))
if limb_length_3dpose > max_length_finger:
max_length_finger = limb_length_3dpose
for edge in edges_palm:
limb_length_dwpose = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[0]]) - np.array(hand_keypoints_dwpose[edge[1]]))
if limb_length_dwpose > max_length_palm * 1.5:
if -1 in hand_keypoints_dwpose[edge[0]] or -1 in hand_keypoints_dwpose[edge[1]] or -1 in hand_keypoints_3dpose[edge[0]] or -1 in hand_keypoints_3dpose[edge[1]]:
continue
distance_point_0 = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[0]]) - np.array(hand_keypoints_3dpose[edge[0]]))
distance_point_1 = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[1]]) - np.array(hand_keypoints_3dpose[edge[1]]))
if distance_point_0 > distance_point_1:
hand_keypoints_dwpose[edge[1]] = [-1, -1]
else:
hand_keypoints_dwpose[edge[0]] = [-1, -1]
for edge in edges_finger:
limb_length_dwpose = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[0]]) - np.array(hand_keypoints_dwpose[edge[1]]))
if limb_length_dwpose > max_length_finger * 1.5:
if -1 in hand_keypoints_dwpose[edge[0]] or -1 in hand_keypoints_dwpose[edge[1]] or -1 in hand_keypoints_3dpose[edge[0]] or -1 in hand_keypoints_3dpose[edge[1]]:
continue
distance_point_0 = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[0]]) - np.array(hand_keypoints_3dpose[edge[0]]))
distance_point_1 = np.linalg.norm(np.array(hand_keypoints_dwpose[edge[1]]) - np.array(hand_keypoints_3dpose[edge[1]]))
if distance_point_0 > distance_point_1:
hand_keypoints_dwpose[edge[1]] = [-1, -1]
else:
hand_keypoints_dwpose[edge[0]] = [-1, -1]
return hand_keypoints_dwpose
def correct_body_from_3d(body_keypoints_dwpose, body_keypoints_3dpose, subset_dwpose, subset_3dpose):
'''
如果dwpose的骨骼长度和3dpose的骨骼长度相差过大，则去掉最远的那一端
'''
limbSeq = [
[2, 3],
[2, 6],
[3, 4],
[4, 5],
[6, 7],
[7, 8],
[2, 9],
[9, 10],
[10, 11],
[2, 12],
[12, 13],
[13, 14],
[2, 1],
[1, 15],
[15, 17],
[1, 16],
[16, 18],
[3, 17],
[6, 18],
]
for ori_limb in limbSeq:
limb = [ori_limb[0] - 1, ori_limb[1] - 1]
limb_length_dwpose = np.linalg.norm(np.array(body_keypoints_dwpose[limb[0]]) - np.array(body_keypoints_dwpose[limb[1]]))
limb_length_3dpose = np.linalg.norm(np.array(body_keypoints_3dpose[limb[0]]) - np.array(body_keypoints_3dpose[limb[1]]))
if subset_dwpose[0][limb[0]] == -1 or subset_dwpose[0][limb[1]] == -1 or subset_3dpose[0][limb[0]] == -1 or subset_3dpose[0][limb[1]] == -1:
continue
if limb_length_dwpose > limb_length_3dpose * 2:
# 判断较远端
distance_point_0 = np.linalg.norm(np.array(body_keypoints_dwpose[limb[0]]) - np.array(body_keypoints_3dpose[limb[0]]))
distance_point_1 = np.linalg.norm(np.array(body_keypoints_dwpose[limb[1]]) - np.array(body_keypoints_3dpose[limb[1]]))
if distance_point_0 > distance_point_1:
if limb[1] == 1: # 核心
continue
body_keypoints_dwpose[limb[1]] = [-1, -1]
subset_dwpose[0][limb[1]] = -1
else:
if limb[0] == 1: # 核心
continue
body_keypoints_dwpose[limb[0]] = [-1, -1]
subset_dwpose[0][limb[0]] = -1
return body_keypoints_dwpose, subset_dwpose
def correct_full_pose_from_3d(poses_dwpose, poses_3dpose):
'''
如果dwpose的骨骼长度和3dpose的骨骼长度相差过大，则去掉离3d pose最远的那一端
'''
poses = []
for pose_dwpose, pose_3dpose in zip(poses_dwpose, poses_3dpose):
new_candidate, new_subset = correct_body_from_3d(pose_dwpose["bodies"]["candidate"], pose_3dpose["bodies"]["candidate"], pose_dwpose["bodies"]["subset"], pose_3dpose["bodies"]["subset"])
new_hands_0 = correct_hand_from_3d(pose_dwpose["hands"][0], pose_3dpose["hands"][0])
new_hands_1 = correct_hand_from_3d(pose_dwpose["hands"][1], pose_3dpose["hands"][1])
pose = {
"bodies": {
"candidate": new_candidate,
"subset": new_subset
},
"faces": pose_dwpose["faces"],
"hands": [new_hands_0, new_hands_1]
}
poses.append(pose)
return poses |