[python][pcl]python-pcl案例之为平面模型构造凹凸外壳多边形

测试环境：

pcl==1.12.1

python-pcl==0.3.1

python==3.7

代码：

# -*- coding: utf-8 -*-
# Construct a concave or convex hull polygon for a plane model
# http://pointclouds.org/documentation/tutorials/hull_2d.php#hull-2d

import numpy as np
import pcl
import random


def main():
    #  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>),
    #                                      cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>),
    #                                      cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);
    # cloud = pcl.PointCloud()
    # cloud_filtered = pcl.PointCloud()
    # cloud_projected = pcl.PointCloud()

    #  pcl::PCDReader reader;
    #  reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);
    cloud = pcl.load(
        r"C:\Users\Administrator\Desktop\python-pcl\examples\pcldata\tutorials\table_scene_mug_stereo_textured.pcd")

    # // Build a filter to remove spurious NaNs
    # pcl::PassThrough<pcl::PointXYZ> pass;
    # pass.setInputCloud (cloud);
    # pass.setFilterFieldName ("z");
    # pass.setFilterLimits (0, 1.1);
    # pass.filter (*cloud_filtered);
    # std::cerr << "PointCloud after filtering has: "
    #           << cloud_filtered->points.size () << " data points." << std::endl;
    passthrough = cloud.make_passthrough_filter()
    passthrough.set_filter_field_name("z")
    passthrough.set_filter_limits(0.0, 1.1)
    cloud_filtered = passthrough.filter()
    print('PointCloud after filtering has: ' +
          str(cloud_filtered.size) + ' data points.')

    # pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
    # pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
    # // Create the segmentation object
    # pcl::SACSegmentation<pcl::PointXYZ> seg;
    # // Optional
    # seg.setOptimizeCoefficients (true);
    # // Mandatory
    # seg.setModelType (pcl::SACMODEL_PLANE);
    # seg.setMethodType (pcl::SAC_RANSAC);
    # seg.setDistanceThreshold (0.01);
    # seg.setInputCloud (cloud_filtered);
    # seg.segment (*inliers, *coefficients);
    # std::cerr << "PointCloud after segmentation has: "
    #           << inliers->indices.size () << " inliers." << std::endl;
    seg = cloud_filtered.make_segmenter_normals(ksearch=50)
    seg.set_optimize_coefficients(True)
    seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE)
    seg.set_method_type(pcl.SAC_RANSAC)
    seg.set_distance_threshold(0.01)
    indices, model = seg.segment()

    print('PointCloud after segmentation has: ' +
          str(indices.count) + ' inliers.')

    #   // Project the model inliers
    #   pcl::ProjectInliers<pcl::PointXYZ> proj;
    #   proj.setModelType (pcl::SACMODEL_PLANE);
    #   proj.setIndices (inliers);
    #   proj.setInputCloud (cloud_filtered);
    #   proj.setModelCoefficients (coefficients);
    #   proj.filter (*cloud_projected);
    #   std::cerr << "PointCloud after projection has: "
    #             << cloud_projected->points.size () << " data points." << std::endl;
    proj = cloud_filtered.make_ProjectInliers()
    proj.set_model_type(pcl.SACMODEL_PLANE)
    #   proj.setIndices (inliers);
    #   proj.setModelCoefficients (coefficients)
    cloud_projected = proj.filter()

    print('PointCloud after projection has: ' +
          str(cloud_projected.size) + ' data points.')

    #   // Create a Concave Hull representation of the projected inliers
    #   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull (new pcl::PointCloud<pcl::PointXYZ>);
    #   pcl::ConcaveHull<pcl::PointXYZ> chull;
    #   chull.setInputCloud (cloud_projected);
    #   chull.setAlpha (0.1);
    #   chull.reconstruct (*cloud_hull);
    #   std::cerr << "Concave hull has: " << cloud_hull->points.size ()
    #             << " data points." << std::endl;
    # cloud_projected = pcl.PointCloud()
    chull = cloud_projected.make_ConcaveHull()
    chull.set_Alpha(0.1)
    cloud_hull = chull.reconstruct()
    print('Concave hull has: ' + str(cloud_hull.size) + ' data points.')

    #   pcl::PCDWriter writer;
    #   writer.write ("table_scene_mug_stereo_textured_hull.pcd", *cloud_hull, false);

    if cloud_hull.size != 0:
        pcl.save(cloud_hull, 'table_scene_mug_stereo_textured_hull.pcd')


if __name__ == "__main__":
    # import cProfile
    # cProfile.run('main()', sort='time')
    main()

运行结果：

PointCloud after filtering has: 139656 data points.
PointCloud after segmentation has: <built-in method count of list object at 0x0000028CA3429A48> inliers.
PointCloud after projection has: 139656 data points.
Concave hull has: 281 data points.

table_scene_mug_stereo_textured.pcd文件需要去这个地址下载：

https://github.com/strawlab/python-pcl/blob/master/examples/pcldata/tutorials/table_scene_mug_stereo_textured.pcd