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| import numpy as np | |
| import os | |
| # Pose数据列表 | |
| pose_data = [ | |
| np.array([2.3752, 0.0658, -1.7781, -0.1833, 0.0058, -0.1768, -0.4969, 0.3412, | |
| 0.1805, 0.2078, -0.0801, 0.0113, 0.7961, -0.0638, -0.0320, 1.1230, | |
| 0.1120, 0.0431, -0.0053, -0.0622, -0.1404, -0.0399, 0.1543, -0.0574, | |
| 0.1719, -0.1349, 0.1215, -0.0742, 0.0198, -0.2016, -0.1918, 0.0664, | |
| 0.1843, -0.1893, -0.0836, -0.2172, 0.3527, -0.0895, 0.0831, 0.1069, | |
| -0.6679, -0.3409, -0.4127, 0.2049, -0.5758, 0.4160, 0.2027, 0.0371, | |
| -0.0056, -0.8203, -0.7385, -0.5755, 0.6557, 0.1240, 0.3627, -1.5995, | |
| 0.2493, -0.1409, 1.3971, -0.2052, -0.0726, -0.5489, 0.4632, 0.0383, | |
| 0.4377, -0.3150, 0.0867, -0.0317, -0.0694, 0.0872, 0.0519, 0.1608]), | |
| np.array([3.0614e+00, 6.2280e-02, -7.6650e-03, -1.4160e-01, -5.1097e-02, | |
| 5.0161e-02, -1.3916e-01, -7.7943e-02, -1.4492e-03, 1.0476e-01, | |
| 1.9706e-02, -5.5241e-02, 4.3114e-01, -2.3592e-03, 1.0349e-01, | |
| 3.8765e-01, -5.6183e-01, -2.5906e-01, -1.0401e-01, -3.5997e-02, | |
| 3.2958e-02, 1.1873e-01, 2.8326e-01, 9.6266e-02, -1.7527e-02, | |
| -4.6820e-01, 4.8134e-03, -3.4153e-03, 3.1733e-02, 7.2645e-02, | |
| -1.3809e-01, -2.2420e-02, 3.7563e-01, -1.1704e-01, 3.8316e-02, | |
| -3.5116e-01, 2.1393e-01, -9.0961e-02, 2.1819e-02, -2.2145e-01, | |
| -8.2792e-02, 4.7571e-01, -3.4885e-01, 1.5275e-01, -3.7524e-01, | |
| 1.9898e-01, -5.6398e-02, 1.5147e-01, -3.6606e-01, -5.0230e-01, | |
| 3.5257e-01, -5.7167e-01, 3.5682e-01, -1.2872e-01, 5.8685e-02, | |
| -2.1642e+00, 6.4898e-01, -2.4495e-01, 2.1219e+00, -7.5819e-01, | |
| -4.4861e-01, -4.6142e-01, -1.9626e-01, -3.5913e-01, 4.2019e-01, | |
| 2.7658e-01, 1.7639e-01, -5.2321e-02, -1.1038e-01, 1.2812e-01, | |
| 6.4935e-02, 2.2037e-01]), | |
| np.array([2.9954, 0.0478, -0.0210, -0.0661, -0.4360, 0.0570, -0.2681, 0.2788, | |
| -0.0715, 0.2030, 0.0093, -0.1145, 0.2436, -0.0254, -0.1626, 0.5336, | |
| -0.0321, 0.2793, 0.0192, 0.0449, 0.0101, -0.0073, 0.1966, -0.0735, | |
| 0.0612, -0.1001, 0.1643, 0.1277, 0.0090, 0.0318, -0.1977, 0.0466, | |
| 0.2682, -0.1723, -0.0298, -0.2414, -0.0103, -0.0858, 0.0773, 0.0530, | |
| -0.0082, 0.0819, 0.0522, 0.1311, 0.1491, 0.1614, -0.0862, 0.0777, | |
| 0.3365, -0.4108, -1.1308, 0.3167, 0.3025, 0.9944, 0.5398, -1.5167, | |
| -0.0472, 0.3905, 1.4840, -0.1251, -0.2937, -0.1276, 0.0920, -0.2136, | |
| 0.1464, 0.0138, -0.2083, -0.0330, -0.1633, -0.1662, 0.1062, 0.1335]), | |
| np.array([3.0080, 0.0306, -0.2129, -0.0872, 0.0768, 0.0630, -0.7311, -0.2889, | |
| -0.4064, 0.2019, 0.0288, -0.0278, 0.4179, -0.1456, -0.0833, 1.8403, | |
| -0.1916, 0.4140, -0.0385, 0.0567, 0.0509, -0.2573, 0.3238, 0.0203, | |
| -0.1047, -0.2087, 0.3207, -0.0567, -0.0078, 0.0251, -0.2247, 0.0581, | |
| 0.3587, -0.2693, 0.0096, -0.3148, 0.3628, 0.0998, 0.0767, -0.3403, | |
| 0.1187, 0.3583, -0.3395, -0.0055, -0.2204, 0.2906, 0.0092, 0.0590, | |
| -0.2578, -0.2382, 0.3031, -0.2698, 0.3195, -0.2413, -0.0659, -2.2600, | |
| 0.8711, -0.1026, 2.1879, -0.8472, -0.2476, -0.3128, 0.0327, -0.0614, | |
| 0.2795, 0.1144, -0.1259, -0.0362, -0.2552, -0.0544, 0.1021, 0.0793]), | |
| np.array([-4.4739e-02, -2.5408e-01, 3.0967e+00, -3.2824e-03, 2.9921e-02, | |
| 1.7044e-02, -6.2180e-02, 2.2239e-02, -2.2721e-02, 1.7281e-01, | |
| -4.6111e-02, 7.4796e-02, 2.1117e-01, -1.6837e-01, 2.9683e-02, | |
| 5.8315e-01, 7.6520e-02, 5.6352e-02, -1.1508e-01, -2.0811e-02, | |
| 3.2421e-02, 3.9826e-01, 3.4520e-01, -2.0537e-01, 3.8679e-01, | |
| -3.0132e-01, 1.6660e-01, 3.5923e-02, 3.8529e-04, -5.1066e-02, | |
| -1.0309e-01, -1.7867e-02, 2.7689e-01, -9.9123e-02, 4.2380e-02, | |
| -2.7752e-01, -3.2211e-01, -2.2977e-02, -4.1069e-04, -2.9728e-01, | |
| -2.5900e-01, 3.1511e-01, -2.7042e-01, 2.6003e-01, -3.3097e-01, | |
| -2.2513e-01, -3.0654e-03, 6.8447e-02, -3.8446e-01, -2.3619e-01, | |
| -3.3112e-02, -3.7500e-01, 1.8820e-01, 1.4296e-02, -3.6162e-01, | |
| -1.2229e+00, 4.3001e-01, -3.5856e-01, 1.2067e+00, -4.0485e-01, | |
| 1.6419e-01, -3.9610e-01, 2.3677e-01, 5.5694e-02, 3.8777e-01, | |
| -2.1233e-01, 2.2479e-01, -2.9042e-02, -8.7811e-02, 2.0511e-01, | |
| 9.6519e-03, -4.0550e-02]), | |
| np.array([1.7333, -0.0567, 2.4417, -0.0180, -0.0489, 0.0498, -1.3883, 0.3772, | |
| 0.1170, 0.4974, -0.2696, 0.0821, -0.0471, -0.1734, -0.1726, 2.0454, | |
| -0.1127, 0.3053, -0.0921, -0.0844, 0.1480, 0.1261, 0.0869, -0.0254, | |
| 0.3634, 0.0286, 0.2676, -0.0111, -0.1716, 0.0840, -0.1960, 0.0720, | |
| 0.2174, -0.2858, -0.0343, -0.2553, 0.2707, 0.2487, -0.2674, -0.1070, | |
| -0.8237, 0.1282, -0.0901, 0.8288, 0.1630, 0.2839, -0.0728, 0.0767, | |
| -0.4713, -1.1815, -0.7286, 0.1370, 0.6182, 1.1314, 0.0833, -1.2036, | |
| 0.1424, 0.3215, 0.4865, -0.0045, 0.1883, -0.3926, -0.0261, -0.0714, | |
| 0.3457, 0.4706, -0.1923, -0.1117, -0.2896, 0.0486, 0.1790, 0.2757]), | |
| np.array([1.4261e+00, -3.0460e-01, 2.5091e+00, -1.2012e-01, -2.9618e-01, | |
| -1.8254e-02, -1.3069e+00, 1.9453e-01, -3.7499e-02, 4.6489e-01, | |
| -1.7681e-01, 1.1480e-01, 1.7867e-01, -2.4253e-01, -1.0137e-01, | |
| 2.0227e+00, -5.9877e-02, 2.9890e-01, -5.5780e-02, -5.1556e-02, | |
| 1.4710e-01, 4.4349e-01, 3.9919e-02, -1.9014e-02, 1.3687e-01, | |
| 1.0056e-01, 2.7448e-01, 3.9938e-02, -1.2095e-02, 7.8835e-02, | |
| -2.3510e-01, 6.9572e-02, 2.4517e-01, -2.7999e-01, -8.5868e-02, | |
| -2.3118e-01, 1.7140e-01, 5.1235e-02, 8.8233e-03, -1.8278e-01, | |
| -8.4868e-01, 2.1059e-01, -8.8805e-02, 7.5416e-01, -1.2976e-01, | |
| 2.1373e-01, 9.2919e-03, 1.4747e-01, -5.4918e-02, -9.1706e-01, | |
| -8.6564e-01, -3.0029e-01, 1.0207e+00, 6.5611e-01, 6.2078e-01, | |
| -7.2618e-01, -1.5981e-01, -2.1578e-01, 1.5084e+00, -7.1146e-01, | |
| -1.7969e-01, -2.6289e-01, -1.9960e-01, 1.3575e-01, 2.0665e-01, | |
| 3.2092e-02, 4.1407e-02, 3.5012e-02, -4.4135e-02, 4.9747e-02, | |
| 1.9240e-03, 5.2838e-03]), | |
| np.array([5.1824e-01, -1.4906e-01, 2.9998e+00, -8.9804e-02, 7.9969e-02, | |
| 1.2960e-02, -6.9304e-01, -3.2315e-01, -4.9412e-01, 2.4757e-01, | |
| 2.3405e-03, 8.8145e-02, 2.9368e-01, -6.9512e-02, -1.0302e-01, | |
| 1.9027e+00, -2.2508e-01, 3.0125e-01, -6.5824e-02, 9.6872e-02, | |
| 9.2468e-03, 2.6954e-01, 7.0543e-02, -5.8890e-02, 3.6490e-01, | |
| -1.0701e-01, 1.7876e-01, 4.4869e-02, -1.8048e-02, 1.3204e-02, | |
| -1.5959e-01, 5.6190e-03, 2.4617e-01, -2.9041e-01, -6.4084e-02, | |
| -1.8760e-01, -1.5622e-02, -4.4083e-01, 6.6574e-02, -1.3525e-01, | |
| 1.0143e-01, -2.0383e-01, -2.1113e-01, 1.4702e-01, -4.7091e-01, | |
| -2.1075e-01, -1.5843e-01, 1.1590e-01, -1.5811e-01, -8.1192e-02, | |
| -1.1480e+00, -5.1663e-01, 4.5162e-01, 5.0580e-02, 2.2351e-01, | |
| -1.1500e-01, 2.9583e-01, -4.5928e-01, 1.2410e+00, -6.1835e-01, | |
| 5.3863e-01, 8.0154e-02, 8.2205e-02, 8.9368e-02, 6.1148e-01, | |
| -3.4663e-01, 1.4324e-01, -1.4439e-01, -1.4406e-01, 1.4954e-01, | |
| 1.9718e-02, 1.2337e-01]) | |
| ] | |
| # 保存pose数据 | |
| for i, pose in enumerate(pose_data): | |
| filename = f"poses/POSE_{i}.npz" | |
| np.savez(filename, pose=pose) | |
| print(f"Saved {filename} - shape: {pose.shape}") | |
| print(f"\n已保存 {len(pose_data)} 个pose文件到poses目录") | |
| # 验证文件保存是否正确 | |
| print("\n验证保存的文件:") | |
| for i in range(len(pose_data)): | |
| filename = f"poses/POSE_{i}.npz" | |
| if os.path.exists(filename): | |
| loaded = np.load(filename) | |
| pose = loaded['pose'] | |
| print(f"{filename}: shape={pose.shape}, dtype={pose.dtype}, first_value={pose[0]:.4f}") | |
| else: | |
| print(f"{filename}: 文件不存在") | |