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
380 lines
12 KiB
Python
380 lines
12 KiB
Python
# import numpy as np
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# import open3d as o3d
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# import random
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#
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#
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# def ransac_plane(points, threshold=0.01, max_iterations=1000, min_inliers=100):
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# """
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# RANSAC 平面拟合（单个）
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# """
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# best_inliers = []
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# best_plane = None
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# n_points = len(points)
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#
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# if n_points < 3:
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# return None, []
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#
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# for _ in range(max_iterations):
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# sample_idx = random.sample(range(n_points), 3)
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# p1, p2, p3 = points[sample_idx]
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#
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# v1 = p2 - p1
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# v2 = p3 - p1
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# normal = np.cross(v1, v2)
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# norm = np.linalg.norm(normal)
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# if norm < 1e-8:
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# continue
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# normal = normal / norm
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# a, b, c = normal
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# d = -np.dot(normal, p1)
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#
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# distances = np.abs(np.dot(points, normal) + d)
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# inliers = np.where(distances < threshold)[0]
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#
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# if len(inliers) > len(best_inliers) and len(inliers) >= min_inliers:
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# best_inliers = inliers
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# best_plane = (a, b, c, d)
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#
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# if len(best_inliers) > 0.9 * n_points:
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# break
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#
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# # 用内点重新拟合
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# if best_inliers is not None and len(best_inliers) > 3:
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# inlier_points = points[best_inliers]
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# centroid = np.mean(inlier_points, axis=0)
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# _, _, vh = np.linalg.svd(inlier_points - centroid)
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# normal = vh[2]
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# normal = normal / np.linalg.norm(normal)
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# a, b, c = normal
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# d = -np.dot(normal, centroid)
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# best_plane = (a, b, c, d)
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#
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# return best_plane, best_inliers
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#
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#
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# def sequential_ransac_planes(points, threshold=0.01, max_iterations=1000, min_inliers=100, max_planes=5):
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# """
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# 迭代 RANSAC 拟合多个平面
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# """
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# remaining_points = points.copy()
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# remaining_indices = np.arange(len(points))
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#
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# planes = []
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# inliers_list = []
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#
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# for i in range(max_planes):
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# if len(remaining_points) < min_inliers:
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# break
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#
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# plane, inliers = ransac_plane(remaining_points, threshold, max_iterations, min_inliers)
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#
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# if plane is None or len(inliers) < min_inliers:
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# break
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#
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# planes.append(plane)
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# original_indices = remaining_indices[inliers]
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# inliers_list.append(original_indices)
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#
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# # 移除内点
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# mask = np.ones(len(remaining_points), dtype=bool)
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# mask[inliers] = False
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# remaining_points = remaining_points[mask]
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# remaining_indices = remaining_indices[mask]
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#
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# print(f"平面 {len(planes)}: {len(inliers)} 个内点, 剩余 {len(remaining_points)} 个点")
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#
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# return planes, inliers_list
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#
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#
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# def create_plane_mesh(plane, bounds):
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# """
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# 创建平面网格用于可视化
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# """
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# a, b, c, d = plane
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# x_min, x_max = bounds[0]
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# y_min, y_max = bounds[1]
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#
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# x = np.linspace(x_min, x_max, 50)
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# y = np.linspace(y_min, y_max, 50)
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# X, Y = np.meshgrid(x, y)
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#
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# if abs(c) > 1e-8:
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# Z = -(a * X + b * Y + d) / c
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# else:
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# Z = np.zeros_like(X)
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#
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# vertices = []
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# triangles = []
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# rows, cols = X.shape
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#
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# for i in range(rows):
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# for j in range(cols):
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# vertices.append([X[i, j], Y[i, j], Z[i, j]])
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#
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# for i in range(rows - 1):
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# for j in range(cols - 1):
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# idx = i * cols + j
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# triangles.append([idx, idx + 1, idx + cols])
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# triangles.append([idx + 1, idx + cols + 1, idx + cols])
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#
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# mesh = o3d.geometry.TriangleMesh()
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# mesh.vertices = o3d.utility.Vector3dVector(np.array(vertices))
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# mesh.triangles = o3d.utility.Vector3iVector(np.array(triangles))
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# mesh.compute_vertex_normals()
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#
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# return mesh
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#
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#
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# def visualize_multiple_planes(points, planes, inliers_list):
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# """
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# 可视化多个平面（点云 + 平面网格）
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#
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# Args:
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# points: (N, 3) 点云坐标
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# planes: 平面方程列表 [(a,b,c,d), ...]
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# inliers_list: 每个平面对应的内点索引列表
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# """
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# # 颜色列表（每个平面不同颜色）
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# colors = [
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# [1, 0, 0], # 红色
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# [0, 1, 0], # 绿色
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# [0, 0, 1], # 蓝色
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# [1, 1, 0], # 黄色
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# [1, 0, 1], # 品红
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# [0, 1, 1], # 青色
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# [1, 0.5, 0], # 橙色
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# [0.5, 0, 1], # 紫色
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# ]
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#
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# geometries = []
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#
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# # 1. 为每个平面的内点着色
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# point_colors = np.zeros((len(points), 3))
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# for i, inliers in enumerate(inliers_list):
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# color = colors[i % len(colors)]
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# point_colors[inliers] = color
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#
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# # 未分配的点（剩余点）设为灰色
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# unassigned = np.where(np.sum(point_colors, axis=1) == 0)[0]
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# point_colors[unassigned] = [0.5, 0.5, 0.5]
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#
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# colored_pcd = o3d.geometry.PointCloud()
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# colored_pcd.points = o3d.utility.Vector3dVector(points)
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# colored_pcd.colors = o3d.utility.Vector3dVector(point_colors)
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# geometries.append(colored_pcd)
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#
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# # 2. 为每个平面创建网格
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# for i, (plane, inliers) in enumerate(zip(planes, inliers_list)):
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# # 计算该平面对应点的范围
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# inlier_points = points[inliers]
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# if len(inlier_points) > 0:
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# x_min, x_max = inlier_points[:, 0].min(), inlier_points[:, 0].max()
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# y_min, y_max = inlier_points[:, 1].min(), inlier_points[:, 1].max()
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#
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# # 稍微扩展边界
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# x_range = x_max - x_min
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# y_range = y_max - y_min
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# x_min -= x_range * 0.1
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# x_max += x_range * 0.1
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# y_min -= y_range * 0.1
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# y_max += y_range * 0.1
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#
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# mesh = create_plane_mesh(plane, [[x_min, x_max], [y_min, y_max]])
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# mesh.paint_uniform_color(colors[i % len(colors)])
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# # 设置半透明
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# mesh.paint_uniform_color([colors[i % len(colors)][0],
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# colors[i % len(colors)][1],
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# colors[i % len(colors)][2]])
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# geometries.append(mesh)
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#
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# # 可视化
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# o3d.visualization.draw_geometries(
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# geometries,
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# window_name="Multiple Planes - RANSAC",
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# width=1024,
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# height=768
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# )
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#
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#
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# def visualize_single_plane(points, plane, inliers):
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# """
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# 可视化单个平面（兼容单平面情况）
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# """
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# visualize_multiple_planes(points, [plane], [inliers])
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#
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#
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# # ========== 主程序 ==========
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# if __name__ == "__main__":
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# # 读取点云
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# file_path = r"E:\point_distance\data\part_car_head.ply"
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# pcd = o3d.io.read_point_cloud(file_path)
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#
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# if len(pcd.points) == 0:
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# print("读取失败，请检查文件路径")
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# exit()
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#
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# points = np.asarray(pcd.points)
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# print(f"原始点云点数: {len(points)}")
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#
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# # 计算点云范围
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# points_range = points.max(axis=0) - points.min(axis=0)
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# print(f"点云范围: X[{points_range[0]:.3f}], Y[{points_range[1]:.3f}], Z[{points_range[2]:.3f}]")
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#
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# # 设置阈值
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# threshold = points_range[2] * 0.02
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# print(f"使用阈值: {threshold:.4f}")
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#
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# # ========== 单平面拟合 ==========
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# # print("\n" + "=" * 50)
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# # print("单平面拟合")
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# # print("=" * 50)
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# #
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# # plane, inliers = ransac_plane(points, threshold=threshold, max_iterations=1, min_inliers=100)
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# #
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# # if plane is not None:
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# # a, b, c, d = plane
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# # print(f"拟合平面: {a:.4f}x + {b:.4f}y + {c:.4f}z + {d:.4f} = 0")
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# # print(f"内点数量: {len(inliers)} / {len(points)} ({len(inliers) / len(points) * 100:.1f}%)")
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# # visualize_single_plane(points, plane, inliers)
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#
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# # ========== 多平面拟合 ==========
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# print("\n" + "=" * 50)
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# print("多平面拟合")
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# print("=" * 50)
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#
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# planes, inliers_list = sequential_ransac_planes(
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# points,
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# threshold=threshold,
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# max_iterations=1,
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# min_inliers=100,
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# max_planes=1
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# )
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#
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# print(f"共提取 {len(planes)} 个平面")
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# for i, (plane, inliers) in enumerate(zip(planes, inliers_list)):
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# a, b, c, d = plane
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# print(f"平面 {i + 1}: {a:.4f}x + {b:.4f}y + {c:.4f}z + {d:.4f} = 0, 内点数: {len(inliers)}")
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#
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# if len(planes) > 0:
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# visualize_multiple_planes(points, planes, inliers_list)
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import numpy as np
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import open3d as o3d
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import random
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def create_square_plane_with_noise(size_mm=1000, noise_std_mm=0.5, num_points=10000):
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"""创建正方形平面点云并添加高斯噪声"""
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size_m = size_mm / 1000.0
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noise_std_m = noise_std_mm / 1000.0
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half_size = size_m / 2
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x = np.random.uniform(-half_size, half_size, num_points)
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y = np.random.uniform(-half_size, half_size, num_points)
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z = np.zeros(num_points)
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points = np.column_stack([x, y, z])
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noise_z = np.random.normal(0, noise_std_m, num_points)
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points[:, 2] += noise_z
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return points
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def estimate_plane_ransac_open3d(points, threshold_mm=0.5, max_iterations=2000):
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"""使用 Open3D 内置 RANSAC 平面估计"""
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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threshold_m = threshold_mm / 1000.0
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plane_model, inliers = pcd.segment_plane(
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distance_threshold=threshold_m,
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ransac_n=3,
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num_iterations=max_iterations
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)
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a, b, c, d = plane_model
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# 计算误差
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ideal_normal = np.array([0, 0, 1])
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estimated_normal = np.array([a, b, c])
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cos_angle = np.dot(ideal_normal, estimated_normal)
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cos_angle = np.clip(cos_angle, -1, 1)
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angle_error = np.degrees(np.arccos(cos_angle))
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offset_error = abs(d)
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inlier_points = points[inliers]
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distances = np.abs(np.dot(inlier_points, estimated_normal) + d)
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rmse = np.sqrt(np.mean(distances ** 2))
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print(f"\nRANSAC 平面拟合结果 (Open3D):")
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print(f" 估计平面: {a:.6f}x + {b:.6f}y + {c:.6f}z + {d:.6f} = 0")
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print(f" 内点数量: {len(inliers)} / {len(points)} ({len(inliers) / len(points) * 100:.1f}%)")
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print(f" 法线角度误差: {angle_error:.4f} 度")
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print(f" 偏移误差: {offset_error:.6f} m ({offset_error * 1000:.3f} mm)")
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print(f" 平面拟合 RMSE: {rmse:.6f} m ({rmse * 1000:.3f} mm)")
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return plane_model, inliers
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def visualize_plane_fitting(points, plane):
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"""可视化原始点云和拟合平面"""
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# 原始点云
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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pcd.paint_uniform_color([0.6, 0.8, 1.0])
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# 创建平面点云
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a, b, c, d = plane
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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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x = np.linspace(x_min, x_max, 50)
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y = np.linspace(y_min, y_max, 50)
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X, Y = np.meshgrid(x, y)
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if abs(c) > 1e-8:
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Z = -(a * X + b * Y + d) / c
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else:
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Z = np.zeros_like(X)
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plane_points = np.column_stack([X.ravel(), Y.ravel(), Z.ravel()])
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pcd_plane = o3d.geometry.PointCloud()
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pcd_plane.points = o3d.utility.Vector3dVector(plane_points)
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pcd_plane.paint_uniform_color([1, 0, 0]) # 红色
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o3d.visualization.draw_geometries(
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[pcd, pcd_plane],
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window_name="Plane Fitting",
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width=1024,
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height=768
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)
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# ========== 主程序 ==========
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if __name__ == "__main__":
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# 参数设置
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size_mm = 1000
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noise_std_mm = 0.5
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num_points = 100000
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print(f"参数:")
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print(f" 正方形边长: {size_mm} mm")
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print(f" 噪声标准差: {noise_std_mm} mm")
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print(f" 点数: {num_points}")
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# 生成点云
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points = create_square_plane_with_noise(size_mm, noise_std_mm, num_points)
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# 检查数据范围
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print(f"\n数据范围:")
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print(f" X: [{points[:, 0].min():.3f}, {points[:, 0].max():.3f}]")
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print(f" Y: [{points[:, 1].min():.3f}, {points[:, 1].max():.3f}]")
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print(f" Z: [{points[:, 2].min():.6f}, {points[:, 2].max():.6f}]")
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# 使用 Open3D RANSAC
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plane, inliers = estimate_plane_ransac_open3d(points, threshold_mm=0.5, max_iterations=2000)
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# 可视化
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visualize_plane_fitting(points, plane) |