3dReconst.cpp
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#include "3dReconst.h"
#include "Autocalib.h"
//rectifier epipolar Lines and image
vector<cv::Mat> Reconst::RectificaEpipolarLines(vector<cv::Mat> image, vector<vector<cv::Point2d> > MeasureVector, vector<cv::Mat> fundMatrix)
{
vector<cv::Mat> outImage;
vector<cv::Mat> tempImage=image;
vector<vector<cv::Point2d> > tempMeasureVector=MeasureVector;
cv::Mat Frect = (cv::Mat_<double>(3,3) << 0,0,0,0,0,-1.0,0,1.0,0);
cout << Frect << endl;
Autocalib calib;
for (int i=0;i<tempImage.size()-1;i++)
{
RectifierAffine *rec = new RectifierAffine();
rec->init(&tempImage[i],&tempImage[i+1],&fundMatrix[i],&MeasureVector[i],&MeasureVector[i+1]);
rec->rectify();
cv::Mat input1, input2;
rec->getResult(input1, input2,&MeasureVector[i],&MeasureVector[i+1]);
outImage.push_back(input1);
outImage.push_back(input2);
cv::Mat retifImg;
retifImg=calib.plotStereoWithEpilines(outImage[2*i],outImage[2*i+1],Frect,MeasureVector[i],MeasureVector[i+1]);
cv::namedWindow( "Epipolar lines: rectified", cv::WINDOW_NORMAL );
cv::imshow("Epipolar lines: rectified", retifImg);
tempMeasureVector[i+1].swap(MeasureVector[i+1]);
cv::waitKey(0);
}
return outImage;
}
//get disparity map and the dense point
void Reconst::disparity(vector<cv::Mat> image)
{
DenseMatcher DM;
for (int i=0;i<image.size()/2;i++)
{
DM.init(&image[2*i],&image[2*i+1]);
DM.calculateDisparityMap();
DM.plotDisparityMap();
cv::Mat densePt=DM.getDensePoint();
_densePoint.push_back(densePt);
}
}
//do the triangulation and get 3D point cloud
void Reconst::GetPointCloud(vector<cv::Mat> P, cv::Mat image )
{
vector<cv::Mat> rectifiedMeseureMatrice=_densePoint;
vector<cv::Mat> cameraMatrix=GetCameraMatrix(P);
//cout<<rectifiedMeseureMatrice[1].cols<< endl;
pcl::PointCloud<pcl::PointXYZ> allPointCloud;
Triangulator triang;
for (int i=0;i<cameraMatrix.size();i++)
{
pcl::PointCloud<pcl::PointXYZ> ptCloud;
ptCloud=triang.triangulatePoints_LinearLS(_densePoint[i], cameraMatrix[i]);
//ptCloud.is_dense = true;
int nbr=i;
//save 3D point cloud in a file
string name = "pointCloud"+to_string(nbr)+".pcd";
pcl::io::savePCDFileASCII (name, ptCloud);
cerr << "Saved " << ptCloud.points.size () << " data points to pointCloud" <<i<<".pcd." << endl;
}
}
vector<cv::Mat> Reconst::GetCameraMatrix(vector<cv::Mat> P)
{
vector<cv::Mat> CameraMatrix;
CameraMatrix.resize(P.size()-1);
for(int i=0;i<P.size()-1;i++)
{
CameraMatrix[i].push_back(P[i]);
CameraMatrix[i].push_back(P[i+1]);
}
return CameraMatrix;
}
void Reconst::filterPointCloud(int taille)
{
for(int i=0;i<taille;i++ )
{
std::stringstream ss1;
ss1 << "pointCloud" <<std::to_string(i)<<".pcd" ;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PCDWriter writer;
// Fill in the cloud data
pcl::PCDReader reader;
// Replace the path below with the path where you saved your file
reader.read<pcl::PointXYZ> (ss1.str (), *cloud);
std::cerr << "Cloud before filtering: " << std::endl;
std::cerr << *cloud << std::endl;
// Creating the KdTree object for the search method of the extraction
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (cloud);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (8); // 2cm
ec.setMinClusterSize (30);
ec.setMaxClusterSize (1000000);
ec.setSearchMethod (tree);
ec.setInputCloud (cloud);
ec.extract (cluster_indices);
if(cluster_indices.size()==0){ std::cout<<"no data"<<std::endl; }
int cloudSize=0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
cloud_cluster->points.push_back (cloud->points[*pit]); //*
cloud_cluster->width = cloud_cluster->points.size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
if(cloud_cluster->points.size ()>cloudSize)
{
std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;
std::stringstream ss2;
ss2 << "pointCloud_filtered" <<std::to_string(i)<< ".pcd";
writer.write<pcl::PointXYZ> (ss2.str (), *cloud_cluster, false);
cloudSize=cloud_cluster->points.size ();
} //*
}
}
}