mirror of
https://github.com/jiawanlong/Cesium-Examples.git
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113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
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import numpy as np
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from scipy.interpolate import griddata
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import json
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# 输入数据
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data = [
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{"x":117.224793, "y":31.826156, "z":146.9, "value":10},
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{"x":117.225075, "y":31.826087, "z":146.7, "value":20},
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{"x":117.224762, "y":31.826164, "z":126.4, "value":40},
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{"x":117.22503, "y":31.826097, "z":122.1, "value":10},
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{"x":117.224726, "y":31.826172, "z":76.4, "value":50},
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{"x":117.225057, "y":31.826091, "z":73.7, "value":30},
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{"x":117.225231, "y":31.826224, "z":139.5, "value":60},
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{"x":117.225226, "y":31.826208, "z":111.9, "value":70},
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{"x":117.225282, "y":31.826299, "z":52, "value":20},
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{"x":117.225176, "y":31.826403, "z":147.1, "value":10},
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{"x":117.22491, "y":31.826468, "z":137.1, "value":100},
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{"x":117.225166, "y":31.826406, "z":120.9, "value":30},
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{"x":117.224881, "y":31.826476, "z":108, "value":40},
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{"x":117.225204, "y":31.826396, "z":72.2, "value":60},
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{"x":117.224856, "y":31.826482, "z":67.6, "value":90}
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]
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# 提取坐标和值
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points = np.array([(d['x'], d['y'], d['z']) for d in data])
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values = np.array([d['value'] for d in data])
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# 创建网格
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x_min, x_max = points[:, 0].min(), points[:, 0].max()
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y_min, y_max = points[:, 1].min(), points[:, 1].max()
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z_min, z_max = points[:, 2].min(), points[:, 2].max()
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# 生成网格点 - 增加网格密度以获得更精细的点云
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grid_x, grid_y, grid_z = np.mgrid[
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x_min:x_max:30j,
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y_min:y_max:30j,
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z_min:z_max:30j
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]
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# 进行三维插值
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grid_values = griddata(
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points,
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values,
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(grid_x, grid_y, grid_z),
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method='linear',
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fill_value=np.nan # 对于超出凸包的区域填充NaN
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)
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# 提取所有有效点（非NaN值）
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valid_mask = ~np.isnan(grid_values)
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interpolated_points = {
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"x": grid_x[valid_mask].tolist(),
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"y": grid_y[valid_mask].tolist(),
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"z": grid_z[valid_mask].tolist(),
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"values": grid_values[valid_mask].tolist()
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}
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# 保存插值点云到JSON文件
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with open('interpolated_point_cloud.json', 'w') as f:
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json.dump(interpolated_points, f, indent=2)
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print("插值点云已保存到 interpolated_point_cloud.json")
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# 创建可视化图形（可选）
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection='3d')
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# 绘制原始点
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scatter1 = ax.scatter(points[:, 0], points[:, 1], points[:, 2],
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c=values, cmap=cm.viridis, s=50, label='Original Points')
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# 绘制插值点云（采样部分点以避免过于密集）
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sample_mask = np.random.choice(len(interpolated_points["x"]),
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min(1000, len(interpolated_points["x"])),
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replace=False)
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sampled_x = [interpolated_points["x"][i] for i in sample_mask]
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sampled_y = [interpolated_points["y"][i] for i in sample_mask]
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sampled_z = [interpolated_points["z"][i] for i in sample_mask]
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sampled_values = [interpolated_points["values"][i] for i in sample_mask]
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scatter2 = ax.scatter(sampled_x, sampled_y, sampled_z,
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c=sampled_values, cmap=cm.viridis, s=10, alpha=0.5,
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label='Interpolated Points')
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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ax.set_title('Original and Interpolated Points')
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ax.legend()
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plt.colorbar(scatter1, ax=ax, shrink=0.5, aspect=5)
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plt.savefig('point_cloud_visualization.png', dpi=300, bbox_inches='tight')
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plt.show()
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print("可视化结果已保存到 point_cloud_visualization.png")
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# 返回JSON数据
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print("\n插值点云JSON数据结构:")
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print(f"点数: {len(interpolated_points['x'])}")
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print(f"X范围: [{min(interpolated_points['x']):.6f}, {max(interpolated_points['x']):.6f}]")
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print(f"Y范围: [{min(interpolated_points['y']):.6f}, {max(interpolated_points['y']):.6f}]")
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print(f"Z范围: [{min(interpolated_points['z']):.6f}, {max(interpolated_points['z']):.6f}]")
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print(f"值范围: [{min(interpolated_points['values']):.2f}, {max(interpolated_points['values']):.2f}]")
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# 如果您需要直接使用这个JSON数据，可以这样访问:
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# interpolated_points["x"] - 所有点的X坐标列表
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# interpolated_points["y"] - 所有点的Y坐标列表
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# interpolated_points["z"] - 所有点的Z坐标列表
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# interpolated_points["values"] - 所有点的值列表
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