54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
# import numpy as np
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# import matplotlib.pyplot as plt
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# from mpl_toolkits.mplot3d import Axes3D
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# # 假设 points_and_normals 是你的 Nx6 矩阵
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# # 前三列是点坐标，后三列是法线
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# points_and_normals = np.loadtxt("/Users/hofee/Downloads/temp_output/cad_model_world/points_and_normals.txt") # 这里用随机点代替你的数据
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# points = points_and_normals[:100, :3]
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# normals = points_and_normals[:100, 3:]
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# # 创建3D图形
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# fig = plt.figure()
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# ax = fig.add_subplot(111, projection='3d')
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# # 绘制点云
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# ax.scatter(points[:, 0], points[:, 1], points[:, 2], color='b', marker='o')
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# # 绘制法线 (从每个点出发的一小段箭头)
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# ax.quiver(points[:, 0], points[:, 1], points[:, 2],
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# normals[:, 0], normals[:, 1], normals[:, 2], length=0.1, color='r')
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# plt.show()
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import numpy as np
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# 假设 points_and_normals 是你的 Nx6 矩阵
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# points_and_normals[:,:3] 是点的坐标
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# points_and_normals[:,3:] 是法线
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points_and_normals = np.loadtxt("/Users/hofee/Downloads/temp_output/cad_model_world/points_and_normals.txt") # 这里用随机点代替你的数据
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print(points_and_normals.shape)
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points = points_and_normals[300:400, :3]
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normals = points_and_normals[300:400, 3:]
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# 设置你想在法线方向上采样的距离范围和点数
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num_samples_per_point = 20 # 每个法线方向采样的点数
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sampling_distances = np.linspace(0, 0.5, num_samples_per_point) # 采样距离范围
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# 创建一个空列表来保存采样点
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sampled_points = []
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# 对每个点进行法线方向的采样
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for point, normal in zip(points, normals):
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for dist in sampling_distances:
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# 在法线方向上偏移点
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sampled_point = point + dist * normal
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sampled_points.append(sampled_point)
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# 转换为 numpy 数组
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sampled_points = np.array(sampled_points)
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# 保存为点云文件 (例如 .txt 或 .xyz 格式)
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np.savetxt('sampled_points.txt', sampled_points)
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print("采样点云已保存为 'sampled_points.xyz'") |