moving_object_cluster.py

# import open3d as o3d
import numpy as np
# import matplotlib.pyplot as plt


def load_bin(file_path):
    pointcloud = np.fromfile(file_path, dtype=np.float32).reshape(-1, 4)
    return pointcloud

# # print("->正在加载点云... ")
pointcloud = load_bin("demo_pc/velodyne/000001.bin")
pointcloud = pointcloud[:, :3]
# pcd = o3d.geometry.PointCloud()
# pcd.points = o3d.utility.Vector3dVector(pointcloud)
# # o3d.visualization.draw_geometries([point_cloud_o3d])
# print(pcd)
# # print(pcd.compute_mahalanobis_distance())

# print("->正在DBSCAN聚类...")
# eps = 1           # 同一聚类中最大点间距
# min_points = 20     # 有效聚类的最小点数
# with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
#     labels = np.array(pcd.cluster_dbscan(eps, min_points, print_progress=True))
# max_label = labels.max()    # 获取聚类标签的最大值 [-1,0,1,2,...,max_label]，label = -1 为噪声，因此总聚类个数为 max_label + 1
# 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  # labels = -1 的簇为噪声，以黑色显示
# pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
# o3d.visualization.draw_geometries([pcd])


from pylab import *
from scipy.cluster.vq import *

features = pointcloud
centroids, variance = kmeans(features,5)
code, distance = vq(features, centroids)
figure()
ndx = where(code == 0)[0]
plot(features[ndx, 0], features[ndx, 1], '*')
ndx = where(code == 1)[0]
plot(features[ndx, 0], features[ndx, 1], 'r.')
plot(centroids[:, 0], centroids[:, 1], 'go')

title('test')
axis('off')
show()