[pcl][原创]python-pcl安装配置支持windows和ubuntu完美流程 2021版本

纵观全网pcl python版本配置存在各种各样的问题，基本没有一个文章可以完整搞定。直到今日，断断续续我也耗费数月终于打通整个流程，并且实现了windows和ubuntu2个系统安装和测试，先上一个结果图

其中windows测试成功环境为：

windows 10

VS2019专业版

PCL1.11.1

Anaconda3

python=3.6或者python3.7

安装流程：

第一步：安装VS2019和PCL1.11.1，由于安装过程简单我们省略

第二步：安装Anaconda3，我们仅仅需要anaconda3环境即可

第三步：打开我们的软件（注意必须要以管理员身份运行）

选择anaconda3的安装目录和PCL安装目录，点击开始配置，稍等4分钟左右，即可自动完成所有配置任务。

测试：我们打开anaconda3环境，并切换到pcl虚拟环境

conda activate pcl

然后我们一个读取pcd的代码测试一下，注意此代码是官网提供的代码

# -*- coding: utf-8 -*-
 # Point cloud library
 import pcl
 import pcl.pcl_visualization# Opencv
 # import opencv
 import cv2 def main():
     # These are track bar initial settings adjusted to the given pointcloud to make it completely visible.
     # Need to be adjusted depending on the pointcloud and its xyz limits if used with new pointclouds.
     # int a = 22;
     # int b = 12;
     # int c=  10;
     a = 22
     b = 12
     c = 10    # PCL Visualizer to view the pointcloud
     # pcl::visualization::PCLVisualizer viewer ("Simple visualizing window");
     viewer = pcl.pcl_visualization.PCLVisualizering()    # int main (int argc, char** argv)
     # {
     #   pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGBA>);
     #   pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZRGBA>);
     #   if (pcl::io::loadPLYFile<pcl::PointXYZRGBA> (argv[1], *cloud) == -1) //* load the ply file from command line
     #   {
     #       PCL_ERROR ("Couldn't load the file\n");
     #       return (-1);
     #   }
     cloud = pcl.load("rabbit.pcd")
     #cloud = pcl.load("Tile_173078_LD_010_017_L22.obj")    # pcl::copyPointCloud( *cloud,*cloud_filtered);
     # cloud_filtered = cloud.copyPointCloud()
     cloud_filtered = cloud    # float i
     # float j
     # float k    # cv::namedWindow("picture");
     # // Creating trackbars uisng opencv to control the pcl filter limits
     # cvCreateTrackbar("X_limit", "picture", &a, 30, NULL);
     # cvCreateTrackbar("Y_limit", "picture", &b, 30, NULL);
     # cvCreateTrackbar("Z_limit", "picture", &c, 30, NULL);
     # cv2.CreateTrackbar("X_limit", "picture", a, 30)
     # cv2.CreateTrackbar("Y_limit", "picture", b, 30)
     # cv2.CreateTrackbar("Z_limit", "picture", c, 30)    # // Starting the while loop where we continually filter with limits using trackbars and display pointcloud
     # char last_c = 0;
     last_c = 0    # while(true && (last_c != 27))
     while last_c != 27:        # pcl::copyPointCloud(*cloud_filtered, *cloud);
         # // i,j,k Need to be adjusted depending on the pointcloud and its xyz limits if used with new pointclouds.
         i = 0.1 * a
         j = 0.1 * b
         k = 0.1 * c        # Printing to ensure that the passthrough filter values are changing if we move trackbars.
         # cout << "i = " << i << " j = " << j << " k = " << k << endl;
         print("i = " + str(i) + " j = " + str(j) + " k = " + str(k))        # Applying passthrough filters with XYZ limits
         # pcl::PassThrough<pcl::PointXYZRGBA> pass;
         # pass.setInputCloud (cloud);
         # pass.setFilterFieldName ("y");
         # //  pass.setFilterLimits (-0.1, 0.1);
         # pass.setFilterLimits (-k, k);
         # pass.filter (*cloud);
         pass_th = cloud.make_passthrough_filter()
         pass_th.set_filter_field_name("y")
         pass_th.set_filter_limits(-k, k)
         cloud = pass_th.filter()        # pass.setInputCloud (cloud);
         # pass.setFilterFieldName ("x");
         # // pass.setFilterLimits (-0.1, 0.1);
         # pass.setFilterLimits (-j, j);
         # pass.filter (*cloud);
         # pass_th.setInputCloud(cloud)
         pass_th.set_filter_field_name("x")
         pass_th.set_filter_limits(-j, j)
         cloud = pass_th.filter()        # pass.setInputCloud (cloud);
         # pass.setFilterFieldName ("z");
         # //  pass.setFilterLimits (-10, 10);
         # pass.setFilterLimits (-i, i);
         # pass.filter (*cloud);
         # pass_th.setInputCloud(cloud)
         pass_th.set_filter_field_name("z")
         pass_th.set_filter_limits(-10, 10)
         cloud = pass_th.filter()        # // Visualizing pointcloud
         # viewer.addPointCloud (cloud, "scene_cloud");
         # viewer.spinOnce();
         # viewer.removePointCloud("scene_cloud");
         viewer.AddPointCloud(cloud, b'scene_cloud', 0)
         viewer.SpinOnce()
         # viewer.Spin()
         viewer.RemovePointCloud(b'scene_cloud', 0) if __name__ == "__main__":
     # import cProfile
     # cProfile.run('main()', sort='time')
     main()

以上代码我们保存test.py

执行python test.py即可正常显示结果，即就是上面的图片。

此外我们尝试了ubuntu18.04安装python-pcl和上面达到一样的效果。