官网下载数据，文件夹列表
S1
├── MyPoseFeatures
│ ├── D2_Positions
│ ├── D3_Positions
│ └── D3_Positions_mono
└── Videos
预处理 video 得到 image folder 和 h36m_meta.mat
参考：https://github.com/mks0601/Integral-Human-Pose-Regression-for-3D-Human-Pose-Estimation
举例
joint world:
[[ -91.679 154.404 907.261 ]
[-223.23566 163.80551 890.5342 ]
[-188.4703 14.077106 475.1688 ]
[-261.84055 186.55286 61.438915]
[ 39.877888 145.00247 923.98785 ]
[ -11.675994 160.89919 484.39148 ]
[ -51.550297 220.14624 35.834396]
[-132.34781 215.73018 1128.8396 ]
[ -97.1674 202.34435 1383.1466 ]
[-112.97073 127.96946 1477.4457 ]
[-120.03289 190.96477 1573.4 ]
[ 25.895456 192.35947 1296.1571 ]
[ 107.10581 116.050285 1040.5062 ]
[ 129.8381 -48.024918 850.94806 ]
[-230.36955 203.17923 1311.9639 ]
[-315.40536 164.55284 1049.1747 ]
[-350.77136 43.442127 831.3473 ]
[-102.237045 197.76935 1304.0605 ]]
R，旋转矩阵
[[-0.91536173 0.40180837 0.02574754]
[ 0.05154812 0.18037357 -0.98224649]
[-0.39931903 -0.89778361 -0.18581953]]
t，平移向量
[1841.10702775 4955.28462345 1563.4453959 ]
1.世界坐标系 -> 相机坐标系
# [R|t] world coords -> camera coords
joint_cam = np.zeros((joint_num, 3)) # joint camera
for i in range(joint_num): # joint i
joint_cam[i] = np.dot(R, joint_world[i] - T) # R * (pt - T)
joint camera:
[[-176.73076784 -321.04861816 5203.88206303]
[ -52.96191047 -309.70448602 5251.08279041]
[-155.64154642 73.07174899 5448.80703224]
[ -29.83156629 506.78443787 5400.13835389]
[-300.49984891 -332.39280376 5156.68125879]
[-258.2404934 99.60903353 5244.68148685]
[-209.48436358 548.83381157 5290.76368503]
[-109.15762315 -529.72821331 5123.89062695]
[-140.19118527 -780.12134477 5074.60479308]
[-153.18189694 -886.97617334 5130.16531069]
[-118.93483841 -970.22834498 5058.59901809]
[-259.08998524 -690.13356164 5050.59206199]
[-370.67088701 -448.59930158 5134.17727889]
[-462.28660396 -290.82948094 5307.627479 ]
[ -19.76034608 -716.91807819 5140.27254657]
[ 35.79160009 -470.14493959 5257.73846566]
[ 13.89246202 -279.85297007 5421.06857876]
[-139.42516671 -703.52587972 5095.4322918 ]]
选择 Pelvis 所在位置 作为 相机中心，后面用之求 relative depth
center_cam = joint_cam[root_idx] # (x,y,z) mm
[-176.73076784 -321.04861816 5203.88206303]
2.相机坐标系 -> 像素坐标系，并 get relative depth
# Subtract center depth
joint_img = np.zeros((joint_num, 3))
joint_img[:, 0], joint_img[:, 1], joint_img[:, 2] = cam2pixel(joint_cam, f, c) # x,y
joint_img[:, 2] = joint_img[:, 2] - center_cam[2] # z
cam2pixel 等价于：(f/dx) * (X/Z) = f * (X/Z) / dx 三角变换，/dx, + center_x
def cam2pixel(cam_coord, f, c):
x = cam_coord[..., 0] / cam_coord[..., 2] * f[0] + c[0]
y = cam_coord[..., 1] / cam_coord[..., 2] * f[1] + c[1]
z = cam_coord[..., 2]
return x, y, z
f，焦距，f/dx, f/dy
[1145.04940459 1143.78109572]
c，principal point，主点，主轴与像平面的交点，即下图 O1
[512.54150496 515.45148698]
相机模型
joint image，像素坐标系，Depth 为相对深度 mm
[[ 473.65410166 444.88698613 0. ]
[ 500.99264799 447.99223133 47.20072737]
[ 479.8339308 530.790273 244.9249692 ]
[ 506.21599717 622.79141452 196.25629086]
[ 445.81502802 441.72488139 -47.20080424]
[ 456.16093657 537.17462304 40.79942382]
[ 467.20401493 634.10084948 86.881622 ]
[ 488.1477619 397.20284344 -79.99143608]
[ 480.90833552 339.61748947 -129.27726995]
[ 478.35140985 317.6982923 -73.71675234]
[ 485.61976923 296.07674793 -145.28304494]
[ 453.80169194 359.16055878 -153.29000105]
[ 429.87266525 415.51348857 -69.70478414]
[ 412.80937366 452.77842567 103.74541597]
[ 508.13968145 355.92738829 -63.60951646]
[ 520.33632979 413.17502388 53.85640262]
[ 515.47589974 456.40583351 217.18651573]
[ 481.20977587 357.52973363 -108.44977124]]