[python][pcl]python-pcl案例之crophull

测试环境：

pcl==1.13.0

python-pcl==0.3.1

python==3.7

代码：

# -*- coding: utf-8 -*-
from __future__ import print_function

import numpy as np
import pcl


def main():
    # http://www.pcl-users.org/CropHull-filter-question-td4030345.html
    datacloud = pcl.load(
        '../examples/pcldata/tutorials/table_scene_mug_stereo_textured.pcd')

    print(datacloud)

    filterCloud = pcl.PointCloud()
    vt = pcl.Vertices()

    # // inside point
    # cloud->push_back(pcl::PointXYZ(M_PI * 0.3, M_PI * 0.3, 0));
    # // hull points
    # cloud->push_back(pcl::PointXYZ(0,0,0));
    # cloud->push_back(pcl::PointXYZ(M_PI,0,0));
    # cloud->push_back(pcl::PointXYZ(M_PI,M_PI*0.5,0));
    # cloud->push_back(pcl::PointXYZ(0,M_PI*0.5,0));
    # cloud->push_back(pcl::PointXYZ(0,0,0));
    # // outside point
    # cloud->push_back(pcl::PointXYZ(-M_PI * 0.3, -M_PI * 0.3, 0));

    points_2 = np.array([
        [1 * 0.3, 1 * 0.3, 0],
        [0, 0, 0],
        [1, 0, 0],
        [1, 1 * 0.5, 0],
        [0, 1 * 0.5, 0],
        [0, 0, 0],
        [-1 * 0.3, -1 * 0.3, 0]
    ], dtype=np.float32)
    filterCloud.from_array(points_2)
    print(filterCloud)

    vertices_point_1 = np.array([1, 2, 3, 4, 5], dtype=np.int)
    vt.from_array(vertices_point_1)

    # print(vt)
    # vt.vertices.push_back(1)
    # vt.vertices.push_back(2)
    # vt.vertices.push_back(3)
    # vt.vertices.push_back(4)
    # vt.vertices.push_back(5)
    # vertices = vector[pcl.Vertices]
    # vertices.push_back(vt)

    outputCloud = pcl.PointCloud()
    # crophull = pcl.CropHull()
    # crophull.setInputCloud(datacloud)
    crophull = datacloud.make_crophull()
    # crophull.setHullIndices(vertices)
    # crophull.setHullIndices(vt)
    # crophull.setHullCloud(filterCloud)
    # crophull.setDim(2)
    # crophull.setCropOutside(false)
    crophull.SetParameter(filterCloud, vt)

    # indices = vector[int]
    # cropHull.filter(indices);
    # outputCloud = cropHull.filter();
    # print("before: " + outputCloud)
    crophull.Filtering(outputCloud)
    print(outputCloud)

    # Viewer
    # // pcl::visualization::CloudViewer viewer ("Cluster viewer");
    # // viewer.showCloud(colored_cloud);

    # pcl.visualization.CloudViewer

    # Write Point
    # pcl::PCDWriter writer;
    # std::stringstream ss;
    # ss << "min_cut_seg" << ".pcd";
    # // writer.write<pcl::PointXYZRGB> (ss.str (), *cloud, false);
    # pcl::io::savePCDFile(ss.str(), *outputCloud, false);


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

输出：