[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。