处理点云数据(三)：三维点云可视化

三维可视化点云

使用Mayavi可视化

首先使用anaconda安装mayavi，打开命令行界面

conda install mayavi

如果python版本是py3则会出错，需要安装python2.7版本：

conda create -n py2 python=2

然后在py2的环境下安装mayavi：

conda install -n py2 mayavi

如果出现VTK版本不匹配，则更新VTK：

conda update -n py2 vtk

py2的目录在 D:\Anaconda\envs\py2里面，之后使用pycharm切换版本：

import numpy as np

def viz_mayavi(points, vals="distance"):
    x = points[:, 0]  # x position of point
    y = points[:, 1]  # y position of point
    z = points[:, 2]  # z position of point
    # r = lidar[:, 3]  # reflectance value of point
    d = np.sqrt(x ** 2 + y ** 2)  # Map Distance from sensor

    # Plot using mayavi -Much faster and smoother than matplotlib
    import mayavi.mlab

    if vals == "height":
        col = z
    else:
        col = d

    fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
    mayavi.mlab.points3d(x, y, z,
                         col,          # Values used for Color
                         mode="point",
                         colormap='spectral', # 'bone', 'copper', 'gnuplot'
                         # color=(0, 1, 0),   # Used a fixed (r,g,b) instead
                         figure=fig,
                         )
    mayavi.mlab.show()

points = np.loadtxt('0000000000.txt')
viz_mayavi(points)

使用Matplotlib可视化

pycharm切换回py3版本：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

points = np.loadtxt('0000000000.txt')
skip = 20   # Skip every n points

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
point_range = range(0, points.shape[0], skip) # skip points to prevent crash
ax.scatter(points[point_range, 0],   # x
           points[point_range, 1],   # y
           points[point_range, 2],   # z
           c=points[point_range, 2], # height data for color
           cmap='spectral',
           marker="x")
ax.axis('scaled')  # {equal, scaled}
plt.show()

特别说明：本文为本人学习所做笔记。
具体参考：http://ronny.rest/tutorials/module/pointclouds_01/point_cloud_birdseye/