pyviewercloud

pyviewercloud是用于在Python中直接使用viewercloud的绑定。

Viewercloud 是一个库，也是一个命令行工具，用于读取和显示点云。它最初用于显示 KITTI 点云。但它也测试了 Lyft Level 5 数据集 点云。

它还将能够显示您最喜欢的算法计算出的3D注释和3D BoundingBox。

Viewercloud将打开一个OpenGL窗口来显示点云。按 q 关闭。

它还可以捕获当前视图的屏幕截图并将其保存为png。按 s 捕获屏幕截图。

Python使用方法

您可以使用 Pyo3 和 Pyo3-numpy 安装 pyviewercloud，它是 viewercloud 的Python绑定。

poetry add pyviewercloud

pip install pyviewercloud

import numpy as np
import pyviewercloud as pyviewer

# Create a new viewer with a window size 1200x1800.
viewer = pyviewer.PointcloudViewer(1200, 1800, 15000)

# Load some pointcloud from the lyft perception dataset
# Currently only support pointcloud as numpy.ndarray Nx3 in np.float32
lyft_point_cloud_1 = np.fromfile("tests/data/lyft/host-a101_lidar0_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]
lyft_point_cloud_2= np.fromfile("tests/data/lyft/host-a101_lidar1_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]
lyft_point_cloud_3= np.fromfile("tests/data/lyft/host-a101_lidar2_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]

# Add them one by one to the viewer to have different color
viewer.add_pointcloud(lyft_point_cloud_1, [255, 0, 0])
viewer.add_pointcloud(lyft_point_cloud_2, [0, 0, 255])
viewer.add_pointcloud(lyft_point_cloud_3, [0, 255, 0])

# You can now display the window
viewer.show()

import numpy as np
import pyviewercloud as pyviewer

# Create a new viewer with a window size 1200x1800.
viewer = pyviewer.PointcloudViewer(1200, 1800, 15000)

# Load some pointcloud from the kitti dataset
kitti_point_cloud = np.fromfile("tests/data/kitti/velodyne/000001.bin", dtype=np.float32).reshape((-1, 4))[:,:3]
viewer.add_pointcloud(kitti_point_cloud, [255, 255, 255])

# Add some centroids to have the same color.
# Currently only support centroids as numpy.ndarray Nx3 in np.float32
centroids = np.array([[-11.5,0,-0.8]]).astype(np.float32)
viewer.add_centroid(centroids, [255, 0, 0])
viewer.show()