matplotlib画3D人体关键点 |
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matplotlib画3D人体关键点
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做3D pose的同学经常需要可视化单帧的3D关键点结果来进行调试。
本文实验目的，用matplotlib把一组点画成3D火柴人。注意：我画的都是17关节点的，其他数量的关节点请对照着改。
输入： [[9.07790224, -72.22343977, -76.2384473, -14.76985357, 93.36553108, 37.00967529, 50.77555181, 23.629042, -8.17606904, -55.17824984, -20.64523825, 128.93648202, 161.99088735, 137.73662061, -105.13621915, -173.42035763, -139.20512344], # axis-x [17.67119202, 117.5121073, 80.80944741, 151.41362938, -82.1697299, -108.63658419, -62.51112022, 25.61594197, -0.93282285, -57.61404975, -33.30759321, -92.44999831, -192.53606268, -213.71209574, 110.08766607, 213.40229899, 135.20572051], # axis-y [-30.39547729, 26.18883494, -502.97420581, -927.51450811, 24.75648722, -501.73217965, -929.53198007, 196.18478224, 435.39843872, 489.66153962, 610.95493383, 412.93642629, 224.81752081, 97.52059155, 415.66381442, 258.698571, 97.77770797 # axis-z ]]输出：
代码： # author: muzhan import matplotlib import numpy as np matplotlib.use('Agg') import matplotlib.pyplot as plt ap1 = [[9.07790224, -72.22343977, -76.2384473, -14.76985357, 93.36553108, 37.00967529, 50.77555181, 23.629042, -8.17606904, -55.17824984, -20.64523825, 128.93648202, 161.99088735, 137.73662061, -105.13621915, -173.42035763, -139.20512344], [17.67119202, 117.5121073, 80.80944741, 151.41362938, -82.1697299, -108.63658419, -62.51112022, 25.61594197, -0.93282285, -57.61404975, -33.30759321, -92.44999831, -192.53606268, -213.71209574, 110.08766607, 213.40229899, 135.20572051], [-30.39547729, 26.18883494, -502.97420581, -927.51450811, 24.75648722, -501.73217965, -929.53198007, 196.18478224, 435.39843872, 489.66153962, 610.95493383, 412.93642629, 224.81752081, 97.52059155, 415.66381442, 258.698571, 97.77770797]] ap = np.array(ap1, dtype='float32').T / 1000.0 np_data = ap xp = np_data.T[0].T yp = np_data.T[1].T zp = np_data.T[2].T + 0.5 ax = plt.axes(projection='3d') radius = 1.7 ax.set_xlim3d([-radius / 2, radius / 2]) ax.set_zlim3d([0, radius]) ax.set_ylim3d([-radius / 2, radius / 2]) ax.view_init(elev=15., azim=70) ax.dist = 7.5 # 3D scatter ax.scatter3D(xp, yp, zp, cmap='Greens') # left leg, node [0, 1, 2, 3] ax.plot(xp[0:4], yp[0:4], zp[0:4], ls='-', color='red') # right leg ax.plot(np.hstack((xp[0], xp[4:7])), np.hstack((yp[0], yp[4:7])), np.hstack((zp[0], zp[4:7])), ls='-', color='blue') # spine, node [0, 7, 8, 9, 10] ax.plot(np.hstack((xp[0], xp[7:11])), np.hstack((yp[0], yp[7:11])), np.hstack((zp[0], zp[7:11])), ls='-', color='gray') # right arm, node [8, 11, 12, 13] ax.plot(np.hstack((xp[8], xp[11:14])), np.hstack((yp[8], yp[11:14])), np.hstack((zp[8], zp[11:14])), ls='-', color='blue') # left arm, node [8, 14, 15, 16] ax.plot(np.hstack((xp[8], xp[14:])), np.hstack((yp[8], yp[14:])), np.hstack((zp[8], zp[14:])), ls='-', color='red') plt.savefig('skeleton.jpg')上面那段代码就够画出骨架了~ 下面介绍另一种形式的输出关节点： 3D关节点通常有过一定的线性变换，所以有时候画出来图案比较奇怪，可能是需要调节scale以及平移。比如下面这个栗子，稍微需要做个转置以及往下平移： import matplotlib import numpy as np matplotlib.use('Agg') import matplotlib.pyplot as plt ap0 = [[-0.00001, -0.00002, 1.00830], [0.14475, -0.07537, 0.99674], [0.13747, -0.10477, 0.51222], [0.05090, -0.27371, 0.07246], [-0.14476, 0.07535, 1.01964], [-0.19476, -0.03135, 0.54859], [-0.33582, -0.27418, 0.14530], [0.03465, 0.07187, 1.27154], [0.08203, 0.09819, 1.55611], [0.13682, 0.16097, 1.71822], [0.09326, 0.02394, 1.77841], [-0.10811, 0.13847, 1.54135], [-0.40501, 0.27080, 1.54436], [-0.48869, 0.47729, 1.79511], [0.21069, -0.01737, 1.48483], [0.39367, -0.21920, 1.31686], [0.63089, -0.16669, 1.51139]] ap = np.array(ap0, dtype='float32') np_data = ap xp = np_data.T[0].T yp = np_data.T[1].T zp = np_data.T[2].T ax = plt.axes(projection='3d') radius = 1.7 ax.set_xlim3d([-radius / 2, radius / 2]) ax.set_zlim3d([0, radius]) ax.set_ylim3d([-radius / 2, radius / 2]) ax.view_init(elev=15., azim=70) ax.dist = 7.5 # 3D scatter ax.scatter3D(xp, yp, zp, cmap='Greens') # left leg, node [0, 1, 2, 3] ax.plot(xp[0:4], yp[0:4], zp[0:4], ls='-', color='red') # right leg ax.plot(np.hstack((xp[0], xp[4:7])), np.hstack((yp[0], yp[4:7])), np.hstack((zp[0], zp[4:7])), ls='-', color='blue') # spine, node [0, 7, 8, 9, 10] ax.plot(np.hstack((xp[0], xp[7:11])), np.hstack((yp[0], yp[7:11])), np.hstack((zp[0], zp[7:11])), ls='-', color='gray') # right arm, node [8, 11, 12, 13] ax.plot(np.hstack((xp[8], xp[11:14])), np.hstack((yp[8], yp[11:14])), np.hstack((zp[8], zp[11:14])), ls='-', color='blue') # left arm, node [8, 14, 15, 16] ax.plot(np.hstack((xp[8], xp[14:])), np.hstack((yp[8], yp[14:])), np.hstack((zp[8], zp[14:])), ls='-', color='red') plt.savefig('skeleton.jpg')输出为：
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