PCL Library e Visual Studio 2019

 Per installare ed usare PCL su Windows la cosa piu' comoda e' utilizzare il pacchetto PCL-1.11.1-AllInOne-msvc2019-win64 

Una volta installato il file exe per esempio in C:\Program Files\PCL 1.11.1

Per creare un progetto in QT con le PCL si devono aggiungere nella path OpenNI2 (stranamente l'installer non la ha aggiunta nella PATH)

Ho sostituito la versione di CMAKE da quella di default di QT con l'installer di CMake  (modificabile dai Kit di Qt) ed come compilatore ho selezionato MSVC2019

il file CMAKE e' il seguente

===============================================

cmake_minimum_required(VERSION 3.5)

project(t3 LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 11)

set(CMAKE_CXX_STANDARD_REQUIRED ON)

find_package(PCL 1.3 REQUIRED COMPONENTS common io features)

include_directories(${PCL_INCLUDE_DIRS})

link_directories(${PCL_LIBRARY_DIRS})

add_definitions(${PCL_DEFINITIONS})

add_executable(t3 main.cpp)

target_link_libraries(t3 ${PCL_LIBRARIES})

===============================================

mentre un programma di esempio e' 

===============================================

#include <iostream>

#include <pcl/io/pcd_io.h>

#include <pcl/point_types.h>



#include <pcl/features/normal_3d.h>



using namespace std;



int main()

{

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);



     if (pcl::io::loadPCDFile<pcl::PointXYZ> ("c://gabriele.pcd", *cloud) == -1) //* load the file

     {

       PCL_ERROR ("Couldn't read file test_pcd.pcd \n");

       return (-1);

     }

     //std::cout << "Loaded " << cloud->width * cloud->height << " data points from test_pcd.pcd with the following fields: " << std::endl;

     /*for (size_t i = 0; i < cloud->points.size (); ++i)

       std::cout << "    " << cloud->points[i].x << " "    << cloud->points[i].y << " "    << cloud->points[i].z << std::endl;

    */

     // Create the normal estimation class, and pass the input dataset to it



     pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;

     ne.setInputCloud (cloud);

    // Create an empty kdtree representation, and pass it to the normal estimation object.

       // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).

       pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());

       ne.setSearchMethod (tree);



       // Output datasets

       pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);



       // Use all neighbors in a sphere of radius 3cm

       ne.setRadiusSearch (0.03);



       // Compute the features

       ne.compute (*cloud_normals);



       //std::cout << endl << cloud_normals->size() << endl;





       // cloud_normals->size () should have the same size as the input cloud->size ()*



       int i = 0;

       for (pcl::Normal n : *cloud_normals) {

           //std::cerr << i << " n = " << n.normal_x << ", " << n.normal_y << ", " << n.normal_z << "\n";

           std::cout << n.normal_x << "," << n.normal_y << "," << n.normal_z << "\n";

           i++;

       }







    return 0;

}