PCL（点云库）：使用conda安装python封装版点云库

朱雀 2024-04-17 05:47 83阅读 0赞

最近要使用机器视觉完成点云三维重建，需要综合使用到机器视觉库opencv以及点云库 PCL

opencv之前已经使用了Python版，为了能够配合起来，pcl也使用python版。

由于conda配置环境非常方便，因此此处使用conda进行配置。

conda create -n pcl
     conda activate pcl
     conda config --add channels conda-forge
     conda install -c sirokujira python-pcl

测试样例：  
运行成功后，则证明环境安装完整

*  目前经证明，**ubuntu上通过conda安装有问题**。官方build的包要么是无法导入pcl，要么是无法导入显示模块。

import numpy as np
    import pcl
    import random
    
    import pcl.pcl_visualization
    
    
    def main():
        # pcl::PointCloud<pcl::PointXYZRGB> cloud;
        cloud = pcl.PointCloud_PointXYZRGB()
    
        # Fill in the cloud data
        # cloud.width = 15;
        # cloud.height = 10;
        # cloud.points.resize (cloud.width * cloud.height)
        # cloud.resize (np.array([15, 10], dtype=np.float))
        # points = np.zeros((10, 15, 4), dtype=np.float32)
        points = np.zeros((150, 4), dtype=np.float32)
        RAND_MAX = 1.0
        # Generate the data
        for i in range(0, 75):
            # set Point Plane
            points[i][0] = 1024 * random.random() / (RAND_MAX + 1.0)
            points[i][1] = 1024 * random.random() / (RAND_MAX + 1.0)
            points[i][2] = 0.1 * random.random() / (RAND_MAX + 1.0)
            points[i][3] = 255 << 16 | 255 << 8 | 255
    
        for i in range(75, 150):
            # set Point Randomize
            points[i][0] = 1024 * random.random() / (RAND_MAX + 1.0)
            points[i][1] = 1024 * random.random() / (RAND_MAX + 1.0)
            points[i][2] = 1024 * random.random() / (RAND_MAX + 1.0)
            points[i][3] = 255 << 16 | 255 << 8 | 255
    
        # Set a few outliers
        points[0][2] = 2.0
        points[3][2] = -2.0
        points[6][2] = 4.0
    
        print(cloud)
    
        for i in range(0, 150):
            print(points[i][0], points[i][1], points[i][2], points[i][3])
    
        cloud.from_array(points)
    
        # Create the segmentation object
        # pcl::SACSegmentation<pcl::PointXYZRGB> seg
        seg = cloud.make_segmenter()
        # Optional
        seg.set_optimize_coefficients(True)
        # Mandatory
        seg.set_model_type(pcl.SACMODEL_PLANE)
        seg.set_method_type(pcl.SAC_RANSAC)
        seg.set_distance_threshold(0.1)
    
        # pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients)
        # pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
        inliers, model = seg.segment()
    
        # if inliers.size
        # return
        # end
    
        print(model)
        # std::cerr << "Model coefficients: " << coefficients->values[0] << " "
        # << coefficients->values[1] << " "
        # << coefficients->values[2] << " "
        # << coefficients->values[3] << std::endl;
        #
        # std::cerr << "Model inliers: " << inliers->indices.size () << std::endl;
        # for (size_t i = 0; i < inliers->indices.size (); ++i)
        # { 
        # std::cerr << inliers->indices[i] << " " << cloud.points[inliers->indices[i]].x << " "
        # << cloud.points[inliers->indices[i]].y << " "
        # << cloud.points[inliers->indices[i]].z << std::endl;
        # cloud.points[inliers->indices[i]].r = 255;
        # cloud.points[inliers->indices[i]].g = 0;
        # cloud.points[inliers->indices[i]].b = 0;
        # }
        for i in inliers:
            points[i][3] = 255 << 16 | 255 << 8 | 0
    
        cloud.from_array(points)
    
        #
        # pcl::visualization::CloudViewer viewer("Cloud Viewer");
        # viewer.showCloud(cloud.makeShared());
        # while (!viewer.wasStopped ())
        visual = pcl.pcl_visualization.CloudViewing()
        visual.ShowColorCloud(cloud)
    
        v = True
        while v:
            v = not(visual.WasStopped())
    
    
    if __name__ == "__main__":
        # import cProfile
        # cProfile.run('main()', sort='time')
        main()