lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
import open3d as o3d
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import numpy as np
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from scipy.spatial import KDTree
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import copy
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def compute_point_to_mesh_distance(point_cloud, mesh):
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"""
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计算点云到 Mesh 的距离
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Args:
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point_cloud: Open3D 点云对象
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mesh: Open3D 网格对象
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Returns:
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distances: 每个点到最近三角形面的距离
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"""
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# 方法1：使用 Open3D 内置函数（新版本）
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try:
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distances = mesh.compute_point_cloud_distance(point_cloud)
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return np.asarray(distances)
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except AttributeError:
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pass
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# 方法2：使用 mesh 顶点最近邻（近似）
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mesh_points = np.asarray(mesh.vertices)
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points = np.asarray(point_cloud.points)
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tree = KDTree(mesh_points)
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distances, indices = tree.query(points)
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return distances
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def compute_statistics(distances):
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"""计算距离统计信息"""
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stats = {
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'mean': np.mean(distances),
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'rmse': np.sqrt(np.mean(distances ** 2)),
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'max': np.max(distances),
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'min': np.min(distances),
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'std': np.std(distances),
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'median': np.median(distances),
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}
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# 分位数
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percentiles = [50, 75, 90, 95, 99]
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for p in percentiles:
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stats[f'percentile_{p}'] = np.percentile(distances, p)
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return stats
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def visualize_distance(point_cloud, mesh, distances, max_dist=None):
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"""可视化距离（按距离着色）"""
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pcd_colored = copy.deepcopy(point_cloud)
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if max_dist is None:
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max_dist = np.percentile(distances, 95)
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# 生成颜色（红：远，蓝：近）
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colors = np.zeros((len(distances), 3))
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norm_dist = np.clip(distances / (max_dist + 1e-8), 0, 1)
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colors[:, 0] = norm_dist # 红色通道（远）
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colors[:, 2] = 1 - norm_dist # 蓝色通道（近）
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pcd_colored.colors = o3d.utility.Vector3dVector(colors)
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# Mesh 着色为灰色
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mesh_colored = copy.deepcopy(mesh)
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mesh_colored.paint_uniform_color([0.5, 0.5, 0.5])
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# 可视化
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o3d.visualization.draw_geometries(
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[mesh_colored, pcd_colored],
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window_name="Distance to Mesh (Blue=Near, Red=Far)",
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width=1024,
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height=768
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)
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def print_statistics(stats):
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"""打印统计信息"""
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print("=" * 60)
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print("点云到 Mesh 距离统计")
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print("=" * 60)
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print(f"平均距离: {stats['mean']:.6f} m ({stats['mean'] * 1000:.3f} mm)")
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print(f"RMSE: {stats['rmse']:.6f} m ({stats['rmse'] * 1000:.3f} mm)")
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print(f"中位数: {stats['median']:.6f} m ({stats['median'] * 1000:.3f} mm)")
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print(f"标准差: {stats['std']:.6f} m ({stats['std'] * 1000:.3f} mm)")
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print(f"最大距离: {stats['max']:.6f} m ({stats['max'] * 1000:.3f} mm)")
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print(f"最小距离: {stats['min']:.6f} m ({stats['min'] * 1000:.3f} mm)")
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print("\n距离分位数:")
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print(f" 50%: {stats['percentile_50']:.6f} m ({stats['percentile_50'] * 1000:.3f} mm)")
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print(f" 75%: {stats['percentile_75']:.6f} m ({stats['percentile_75'] * 1000:.3f} mm)")
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print(f" 90%: {stats['percentile_90']:.6f} m ({stats['percentile_90'] * 1000:.3f} mm)")
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print(f" 95%: {stats['percentile_95']:.6f} m ({stats['percentile_95'] * 1000:.3f} mm)")
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print(f" 99%: {stats['percentile_99']:.6f} m ({stats['percentile_99'] * 1000:.3f} mm)")
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# ========== 主程序 ==========
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if __name__ == "__main__":
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# 读取文件（使用原始字符串避免转义问题）
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file_path = r"E:\point_distance\data\40_noisy.ply"
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mesh_path = r"E:\point_distance\data\40.stl"
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print("读取点云...")
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source_pcd = o3d.io.read_point_cloud(file_path)
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print("读取 Mesh...")
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mesh = o3d.io.read_triangle_mesh(mesh_path)
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# 检查读取是否成功
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if len(source_pcd.points) == 0:
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print("错误：点云读取失败，请检查文件路径")
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exit()
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if len(mesh.vertices) == 0:
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print("错误：Mesh 读取失败，请检查文件路径")
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exit()
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print(f"\n点云点数: {len(source_pcd.points)}")
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print(f"Mesh 顶点数: {len(mesh.vertices)}")
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print(f"Mesh 面数: {len(mesh.triangles)}")
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# 可选：Mesh 法线计算（用于更好的可视化）
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mesh.compute_vertex_normals()
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# 计算距离
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print("\n计算点云到 Mesh 的距离...")
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distances = compute_point_to_mesh_distance(source_pcd, mesh)
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# 计算统计信息
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stats = compute_statistics(distances)
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# 打印统计信息
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print_statistics(stats)
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# 可视化
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print("\n可视化...")
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visualize_distance(source_pcd, mesh, distances) |