Open3d官网代码学习

DBSCAN clustering报错

源代码：
Given a point cloud from e.g. a depth sensor we want to group local point cloud clusters together. For this purpose, we can use clustering algorithms. Open3D implements DBSCAN [Ester1996] that is a density based clustering algorithm. The algorithm is implemented in cluster_dbscan and requires two parameters: eps defines the distance to neighbors in a cluster and min_points defines the minimum number of points required to form a cluster. The function returns labels, where the label -1 indicates noise.

ply_point_cloud = o3d.data.PLYPointCloud()
pcd = o3d.io.read_point_cloud(ply_point_cloud.path)

with o3d.utility.VerbosityContextManager(
        o3d.utility.VerbosityLevel.Debug) as cm:
    labels = np.array(
        pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))

max_label = labels.max()
print(f"point cloud has {
      max_label + 1} clusters")
colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
colors[labels < 0] = 0
pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
o3d.visualization.draw_geometries([pcd],
                                  zoom=0.455,
                                  front=[-0.4999, -0.1659, -0.8499],
                                  lookat=[2.1813, 2.0619, 2.0999],
                                  up=[0.1204, -0.9852, 0.1215])

报错：
module ‘matplotlib’ has no attribute ‘get_cmap’
解决：colors = plt.cm.get_cmap(“tab20”)(labels / (max_label if max_label > 0 else 1))

官网链接：http://www.open3d.org/docs/release/python_api/open3d.visualization.html

学习代码如下：

import open3d as o3d
import numpy as np
import matplotlib as plt

# print("Load a ply point cloud, print it, and render it")
ply_point_cloud = o3d.data.PLYPointCloud()
pcd = o3d.io.read_point_cloud(ply_point_cloud.path)
pcd = o3d.io.read_point_cloud("wall_001.pcd")
print(pcd)
print(np.asarray(pcd.points))
# o3d.visualization.draw_geometries([pcd],
# zoom=0.3412,
# front=[0.4257, -0.2125, -0.8795],
# lookat=[2.6172, 2.0475, 1.532],
# up=[-0.0694, -0.9768, 0.2024])
#
'''下采样'''
downpcd = pcd.voxel_down_sample(voxel_size=0.05)
o3d.visualization.draw_geometries([downpcd],
                                  zoom=0.3412,
                                  front=[0.4257, -0.2125, -0.8795],
                                  lookat=[2.6172, 2.0475, 1.532],
                                  up=[-0.0694, -0.9768, 0.2024])
'''法线估计'''
downpcd.estimate_normals(
    search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
o3d.visualization.draw_geometries([downpcd],
                                  zoom=0.3412,
                                  front=[0.4257, -0.2125, -0.8795],
                                  lookat=[2.6172, 2.0475, 1.532],
                                  up=[-0.0694, -0.9768, 0.2024],
                                  point_show_normal=True)

'''访问估计的顶点法线'''
print(downpcd.normals[0])  # 打印第0个点
print(np.asarray(downpcd.normals)[:10, :])  # 将法线向量转换为 numpy 数组

'''裁剪点云'''
demo_crop_data = o3d.data.DemoCropPointCloud()
pcd = o3d.io.read_point_cloud(demo_crop_data.point_cloud_path)
vol = o3d.visualization.read_selection_polygon_volume(demo_crop_data.cropped_json_path)
chair = vol.crop_point_cloud(pcd)
o3d.visualization.draw_geometries([chair],
                                  zoom=0.7,
                                  front=[0.5439, -0.2333, -0.8060],
                                  lookat=[2.4615, 2.1331, 1.338],
                                  up=[-0.1781, -0.9708, 0.1608])
'''将所有点绘制成统一颜色'''
chair.paint_uniform_color([1, 0.706, 0])
o3d.visualization.draw_geometries([chair],
                                  zoom=0.7,
                                  front=[0.5439, -0.2333, -0.8060],
                                  lookat=[2.4615, 2.1331, 1.338],
                                  up=[-0.1781, -0.9708, 0.1608])

'''计算从源点云到目标点云最近点的距离'''
# 加载点云
demo_crop_data = o3d.data.DemoCropPointCloud()
pcd = o3d.io.read_point_cloud(demo_crop_data.point_cloud_path)
vol = o3d.visualization.read_selection_polygon_volume(demo_crop_data.cropped_json_path)
chair = vol.crop_point_cloud(pcd)
# 计算距离
dists = pcd.compute_point_cloud_distance(chair)
dists = np.asarray(dists)
ind = np.where(dists > 0.01)[0]
pcd_without_chair = pcd.select_by_index(ind)
o3d.visualization.draw_geometries([pcd_without_chair],
                                  zoom=0.3412,
                                  front=[0.4257, -0.2125, -0.8795],
                                  lookat=[2.6172, 2.0475, 1.532],
                                  up=[-0.0694, -0.9768, 0.2024])

'''计算边界体积'''
aabb = chair.get_axis_aligned_bounding_box()
aabb.color = (1, 0, 0)
obb = chair.get_oriented_bounding_box()
obb.color = (0, 1, 0)
o3d.visualization.draw_geometries([chair, aabb, obb],
                                  zoom=0.7,
                                  front=[0.5439, -0.2333, -0.8060],
                                  lookat=[2.4615, 2.1331, 1.338],
                                  up=[-0.1781, -0.9708, 0.1608])

'''DBSCAN聚类'''
pcd = o3d.io.read_point_cloud("wall_001.pcd")
with o3d.utility.VerbosityContextManager(
        o3d.utility.VerbosityLevel.Debug) as cm:
    labels = np.array(
        pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))
# eps定义到集群中邻居的距离和min_points定义形成集群所需的最小点数

max_label = labels.max()
print(f"point cloud has {
      max_label + 1} clusters")
colors = plt.cm.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
colors[labels < 0] = 0
pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
o3d.visualization.draw_geometries([pcd],
                                  zoom=0.455,
                                  front=[-0.4999, -0.1659, -0.8499],
                                  lookat=[2.1813, 2.0619, 2.0999],
                                  up=[0.1204, -0.9852, 0.1215])

'''平面分割'''
pcd = o3d.io.read_point_cloud("wall_001.pcd")
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
                                         ransac_n=3,
                                         num_iterations=1000)
[a, b, c, d] = plane_model
print(f"Plane equation: {
      a:.2f}x + {
      b:.2f}y + {
      c:.2f}z + {
      d:.2f} = 0")
# distance_threshold定义一个点到估计平面的最大距离，以被认为是一个ransac_n内点，定义随机采样以估计一个平面的点的数量，以及num_iterations定义一个随机平面被采样和验证的频率。
inlier_cloud = pcd.select_by_index(inliers)
inlier_cloud.paint_uniform_color([1.0, 0, 0])
outlier_cloud = pcd.select_by_index(inliers, invert=True)
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
                                  zoom=0.8,
                                  front=[-0.4999, -0.1659, -0.8499],
                                  lookat=[2.1813, 2.0619, 2.0999],
                                  up=[0.1204, -0.9852, 0.1215])