bool transformCloud(double theta, char axis, double x, double y, double z, pcl::PointCloud<pcl::PointXYZRGB>::Ptr source_cloud, pcl::PointCloud<pcl::PointXYZRGB>::Ptr transformed_cloud)
{
Eigen::Affine3f transform = Eigen::Affine3f::Identity();
// Define the translation
transform.translation() << x, y, z;
switch( axis ) {
case 'x':
// The same rotation matrix as before; tetha radians arround X axis
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitX()));
break;
case 'y':
// The same rotation matrix as before; tetha radians arround Y axis
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitY()));
break;
case 'z':
// The same rotation matrix as before; tetha radians arround Z axis
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitZ()));
break;
}
// Print the transformation
//printf ("\nTransformation matrix:\n");
//std::cout << transform.matrix() << std::endl;
// Executing the transformation
pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform);
return true;
}
StereoProperties VelStereoMatcher::vecToStereo(const gsl_vector* vec)
{
float x = gsl_vector_get(vec, 0);
float y = gsl_vector_get(vec, 1);
float z = gsl_vector_get(vec, 2);
float ax = gsl_vector_get(vec, 3);
float ay = gsl_vector_get(vec, 4);
float az = gsl_vector_get(vec, 5);
float r = std::sqrt(ax * ax + ay * ay + az * az);
float fx = gsl_vector_get(vec, 6);
float fy = gsl_vector_get(vec, 7);
float cx = gsl_vector_get(vec, 8);
float cy = gsl_vector_get(vec, 9);
float baseline = gsl_vector_get(vec, 10);
//normalize axis
ax /= r;
ay /= r;
az /= r;
//create tform
Eigen::Affine3f tform = Eigen::Affine3f::Identity();
tform.translation() << x, y, z;
tform.rotate(Eigen::AngleAxisf(r, Eigen::Vector3f(ax, ay, az)));
//create stereo
return StereoProperties(fx,fy,cx,cy,baseline,tform);
}
void fuzzyAffines()
{
std::vector<Eigen::Matrix4f> trans;
trans.reserve(count/10);
for( size_t i=0; i<count/10; i++ )
{
Eigen::Vector3f x = Eigen::Vector3f::Random();
Eigen::Vector3f y = Eigen::Vector3f::Random();
x.normalize();
y.normalize();
Eigen::Vector3f z = x.cross(y);
z.normalize();
y = z.cross(x);
y.normalize();
Eigen::Affine3f t = Eigen::Affine3f::Identity();
Eigen::Matrix3f r = Eigen::Matrix3f::Identity();
r.col(0) = x;
r.col(1) = y;
r.col(2) = z;
t.rotate(r);
t.translate( 0.5f * Eigen::Vector3f::Random() + Eigen::Vector3f(0.5,0.5,0.5) );
trans.push_back( t.matrix() );
}
s_plot.setColor( Eigen::Vector4f(1,0,0,1) );
s_plot.setLineWidth( 3.0 );
s_plot( trans, nox::plot<float>::Pos | nox::plot<float>::CS );
}
bool targetViewpoint(const Eigen::Vector3f& rayo,const Eigen::Vector3f& target,const Eigen::Vector3f& down,
Eigen::Affine3f& transf)
{
// uz: versor pointing toward the destination
Eigen::Vector3f uz = target - rayo;
if (std::abs(uz.norm()) < 1e-3) {
std::cout << __FILE__ << "," << __LINE__ << ": target point on ray origin!" << std::endl;
return false;
}
uz.normalize();
//std::cout << "uz " << uz.transpose() << ", norm " << uz .norm() << std::endl;
// ux: versor pointing toward the ground
Eigen::Vector3f ux = down - down.dot(uz) * uz;
if (std::abs(ux.norm()) < 1e-3) {
std::cout << __FILE__ << "," << __LINE__ << ": ray to target toward ground direction!" << std::endl;
return false;
}
ux.normalize();
//std::cout << "ux " << ux.transpose() << ", norm " << ux.norm() << std::endl;
Eigen::Vector3f uy = uz.cross(ux);
//std::cout << "uy " << uy.transpose() << ", norm " << uy.norm() << std::endl;
Eigen::Matrix3f rot;
rot << ux.x(), uy.x(), uz.x(),
ux.y(), uy.y(), uz.y(),
ux.z(), uy.z(), uz.z();
transf.setIdentity();
transf.translate(rayo);
transf.rotate(rot);
//std::cout << __FILE__ << "\nrotation\n" << rot << "\ntranslation\n" << rayo << "\naffine\n" << transf.matrix() << std::endl;
return true;
}
void rotatePointCloud(pcl::PointCloud<PointT>::Ptr inputCloud, pcl::PointCloud<PointT>::Ptr outputCloud, double theta)
{
// Create rotation matrix.
Eigen::Affine3f transform = Eigen::Affine3f::Identity();
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitX()));
// Transform the cloud and return it
pcl::transformPointCloud (*inputCloud, *outputCloud, transform);;
}
PointCloudViewer::PointCloudViewer(QWidget* parent, Qt::WindowFlags f): QVTKWidget(parent, f)
{
mImpl = new PointCloudViewer::Impl;
mImpl->Vis.addPointCloud(common::KinectPointCloud::Ptr(new common::KinectPointCloud));
Eigen::Affine3f trans;
trans.setIdentity();
trans.rotate(Eigen::AngleAxisf(3.14159265, Eigen::Vector3f(0, 0, 1)));
mImpl->Vis.addCoordinateSystem(1.0, trans);
mImpl->Vis.setBackgroundColor(0, 0, 0);
SetRenderWindow(mImpl->Vis.getRenderWindow().GetPointer());
}
/* rotate a single pcl::PointXYZ point */
pcl::PointCloud<pcl::PointXYZ>::Ptr rotateCloud(pcl::PointCloud<pcl::PointXYZ>::Ptr cloudToRotate,
float angleToRotateTo){
//create the rotation transformation matrix
Eigen::Affine3f rotationMatrix = Eigen::Affine3f::Identity();
rotationMatrix.rotate (Eigen::AngleAxisf (angleToRotateTo, Eigen::Vector3f::UnitZ()));
//Apply rotation
pcl::PointCloud<pcl::PointXYZ>::Ptr rotatedCloud (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::transformPointCloud (*cloudToRotate, *rotatedCloud, rotationMatrix);
return rotatedCloud;
}
void alignedAffines()
{
s_plot.setColor( Eigen::Vector4f(0,0,0,1) );
s_plot.setLineWidth( 1.0 );
for( size_t i=0; i<count; i++ )
{
Eigen::Affine3f t = Eigen::Affine3f::Identity();
t.rotate(Eigen::Matrix3f::Identity());
t.translate( 0.5f * Eigen::Vector3f::Random() + Eigen::Vector3f(0.5,0.5,0.5) );
s_plot( t.matrix(), nox::plot<float>::Pos | nox::plot<float>::CS );
}
}
pcl::PointCloud<pcl::PointXYZ>::Ptr ObjectDetector::transformCloud(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_transformed (new pcl::PointCloud<pcl::PointXYZ>);
// Create rotation + translation matrix
Eigen::Affine3f transform = Eigen::Affine3f::Identity();
transform.translation() << 0.0, -y_translation, 0.0;
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitX()));
// Perform transformation on point cloud
pcl::transformPointCloud(*cloud, *cloud_transformed, transform);
return cloud_transformed;
}
int main( int argc, char* argv[] )
{
QGuiApplication app( argc, argv );
RenderWindow window;
window.show();
Defaults::initialize();
Eigen::Affine3f mat = Eigen::Affine3f::Identity();
FileMesh* mesh = new FileMesh( GluonCore::DirectoryProvider::instance()->dataDirectory() + "/gluon/examples/graphics/duck.dae" );
GluonCore::ResourceManager::instance()->addResource< Mesh >( "duck.dae", mesh );
mesh->initialize();
Texture* texture = GluonCore::ResourceManager::instance()->createResource< Texture >( "duck.tga" );
texture->load( GluonCore::DirectoryProvider::instance()->dataDirectory() + "/gluon/examples/graphics/duck.tga" );
Material* material = GluonCore::ResourceManager::instance()->createResource< Material>( "duck" );
material->load( GluonCore::DirectoryProvider::instance()->dataDirectory() + "/gluon/examples/graphics/duck.gluonmaterial" );
material->build();
World* world = GluonCore::ResourceManager::instance()->resource< World >( Defaults::World );
Entity* ent = world->createEntity< Entity >();
mat.rotate( Eigen::AngleAxis<float>( -M_PI_4 /* pi/4 */, Eigen::Vector3f(0.f, 1.f, 0.f) ) );
ent->setTransform( mat );
ent->setMesh( mesh );
ent->setMaterialInstance( material->createInstance() );
ent->materialInstance()->setProperty( "texture0", QVariant::fromValue( texture ) );
Camera* cam = world->createEntity< Camera >();
mat = Eigen::Affine3f::Identity();
mat.translate( Eigen::Vector3f(0.f, 75.f, 100.f) );
cam->setTransform( mat );
cam->setVisibleArea( QSizeF( 200.f, 200.f ) );
cam->setNearPlane( 0.f );
cam->setFarPlane( 1000.f );
GluonCore::ResourceManager::instance()->resource< RenderTarget >( Defaults::RenderTarget )->addChild( cam );
//app.exec();
return app.exec();
}
/**
* @brief Renders the trackball representation.
* @todo setTrackballOrthographicMatrix should be set during viewport resize
*/
void render (void)
{
if(drawTrackball)
{
float ratio = (viewport[2] - viewport[0]) / (viewport[3] - viewport[1]);
setTrackballOrthographicMatrix(-ratio, ratio, -1.0, 1.0, 0.1, 100.0);
trackball_shader.bind();
//Using unique viewMatrix for the trackball, considering only the rotation to be visualized.
Eigen::Affine3f trackballViewMatrix = Eigen::Affine3f::Identity();
trackballViewMatrix.translate(defaultTranslation);
trackballViewMatrix.rotate(quaternion);
trackball_shader.setUniform("viewMatrix", trackballViewMatrix);
trackball_shader.setUniform("projectionMatrix", trackballProjectionMatrix);
trackball_shader.setUniform("nearPlane", near_plane);
trackball_shader.setUniform("farPlane", far_plane);
bindBuffers();
//X:
Eigen::Vector4f colorVector(1.0, 0.0, 0.0, 1.0);
trackball_shader.setUniform("modelMatrix", Eigen::Affine3f::Identity()*Eigen::AngleAxis<float>(M_PI/2.0,Eigen::Vector3f(0.0,1.0,0.0)));
trackball_shader.setUniform("in_Color", colorVector);
glDrawArrays(GL_LINE_LOOP, 0, 200);
//Y:
colorVector << 0.0, 1.0, 0.0, 1.0;
trackball_shader.setUniform("modelMatrix", Eigen::Affine3f::Identity()*Eigen::AngleAxis<float>(M_PI/2.0,Eigen::Vector3f(1.0,0.0,0.0)));
trackball_shader.setUniform("in_Color", colorVector);
glDrawArrays(GL_LINE_LOOP, 0, 200);
//Z:
colorVector << 0.0, 0.0, 1.0, 1.0;
trackball_shader.setUniform("modelMatrix", Eigen::Affine3f::Identity());
trackball_shader.setUniform("in_Color", colorVector);
glDrawArrays(GL_LINE_LOOP, 0, 200);
unbindBuffers();
trackball_shader.unbind();
}
}
Eigen::Affine3f interpolateAffine(const Eigen::Affine3f &pose0,
const Eigen::Affine3f &pose1, float blend)
{
/* interpolate translation */
Eigen::Vector3f t0 = pose0.translation();
Eigen::Vector3f t1 = pose1.translation();
Eigen::Vector3f tIP = (t1 - t0)*blend;
/* interpolate rotation */
Eigen::Quaternionf r0(pose0.rotation());
Eigen::Quaternionf r1(pose1.rotation());
Eigen::Quaternionf rIP(r1.slerp(blend, r0));
/* compose resulting pose */
Eigen::Affine3f ipAff = pose0;
ipAff.rotate(rIP);
ipAff.translate(tIP);
return ipAff;
}
Eigen::Affine3f transRotVecToAffine3f(
const cv::Mat &translationVec,
const cv::Mat &rotationVec)
{
/* Copies the axis angle rotation
* and the translation to an
* Affine3f transformation matrix
*/
/* axis angle roation */
#if 1
Eigen::Vector3f axis(
rotationVec.at<float>(0,0),
rotationVec.at<float>(1,0),
rotationVec.at<float>(2,0));
float angle = axis.norm(); // length of the vector
axis.normalize();
Eigen::AngleAxisf rot(angle, axis);
#endif
#if 0
/* do euler angle rotation */
Eigen::Affine3f rot;
rot = Eigen::AngleAxisf(rotationVec.at<float>(0,0), Eigen::Vector3f::UnitX())
* Eigen::AngleAxisf(rotationVec.at<float>(1,0), Eigen::Vector3f::UnitY())
* Eigen::AngleAxisf(rotationVec.at<float>(2,0), Eigen::Vector3f::UnitZ());
#endif
/* compose new pose */
Eigen::Affine3f pose;
pose = Eigen::Affine3f (Eigen::Translation3f (
translationVec.at<float>(0, 0),
translationVec.at<float>(1, 0),
translationVec.at<float>(2, 0)));
pose.rotate(rot);
return pose;
}
bool normalizeOrientationAndTranslation(std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > &points, std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > &normals, Eigen::Affine3f &invTransform)
{
if (points.empty())
return false;
// Perform PCA on input to determine a canoncial coordinate frame for the given point cloud.
Eigen::Matrix3Xf::MapType pointsInMatrix(points.at(0).data(), 3, static_cast<int>(points.size()));
const Eigen::Vector3f centroid = pointsInMatrix.rowwise().mean();
pointsInMatrix = pointsInMatrix.colwise() - centroid;
const Eigen::Matrix3f cov = pointsInMatrix * pointsInMatrix.transpose();
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eig(cov);
const Eigen::Matrix3f rot = eig.eigenvectors().transpose();
for (size_t i = 0; i < points.size(); ++i) {
points[i] = rot * points[i];
normals[i] = rot * normals[i];
}
invTransform = Eigen::Affine3f::Identity();
invTransform = invTransform.rotate(rot).translate(-centroid); // applied in right to left order.
invTransform = invTransform.inverse();
return true;
}
void ObjectDetector::performSegmentation(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ>), cloud_plane_rotated (new pcl::PointCloud<pcl::PointXYZ>);
ROS_INFO("PointCloud before planar segmentation: %d data points.", cloud->width * cloud->height);
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);
// Fit plane
seg.setModelType (pcl::SACMODEL_PLANE);
// Use RANSAC
seg.setMethodType (pcl::SAC_RANSAC);
// Set maximum number of iterations
seg.setMaxIterations (max_it_calibration);
// Set distance to the model threshold
seg.setDistanceThreshold (floor_threshold);
// Segment the largest planar component from the cloud
seg.setInputCloud (cloud);
seg.segment (*inliers, *coefficients);
// Extract the inliers of the plane
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud (cloud);
extract.setIndices (inliers);
extract.setNegative (false);
extract.filter (*cloud_plane);
ROS_INFO("PointCloud representing the planar component: %d data points.", cloud_plane->width * cloud_plane->height);
// Create normal vector of the plane
Eigen::Matrix<float, 1, 3> normal_plane, normal_floor, r_axis;
normal_plane[0] = coefficients->values[0];
normal_plane[1] = coefficients->values[1];
normal_plane[2] = coefficients->values[2];
ROS_INFO("Plane normal: %f %f %f", normal_plane[0], normal_plane[1], normal_plane[2]);
// Create normal vector of the floor
normal_floor[0] = 0.0f;
normal_floor[1] = 1.0f;
normal_floor[2] = 0.0f;
ROS_INFO("Floor normal: %f %f %f", normal_floor[0], normal_floor[1], normal_floor[2]);
// Determine rotation axis
r_axis = normal_plane.cross(normal_floor);
ROS_INFO("Rotation axis: %f %f %f", r_axis[0], r_axis[1], r_axis[2]);
// Determine rotation angle theta
theta = acos(normal_plane.dot(normal_floor));
ROS_INFO("Rotation angle theta: %f", theta);
// Create rotation matrix
Eigen::Affine3f transform = Eigen::Affine3f::Identity();
transform.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitX()));
// Perform rotation on extracted plane
pcl::transformPointCloud(*cloud_plane, *cloud_plane_rotated,transform);
// Compute y translation by taking the average y values of the plane points
int num_of_points = cloud_plane_rotated->width * cloud_plane_rotated->height;
for (size_t i = 0; i < num_of_points; ++i)
{
y_translation += cloud_plane_rotated->points[i].y;
}
y_translation = y_translation / num_of_points;
}
int main(int argc, char** argv)
{
if( argc < 2 )
{
printPrompt( argv[0] );
return -1;
}
initModule_nonfree();
// Get Input Data
ifstream file(argv[1]);
if ( !file.is_open() )
return false;
string str;
// Image Name
getline( file, str ); getline( file, str );
string image_name = str;
// Cloud Name
getline( file, str ); getline( file, str );
string cloud_name = str;
// width of images to be created.
getline( file, str ); getline( file, str );
int w = atoi(str.c_str());
// height of images to be created
getline( file, str ); getline( file, str );
int h = atoi(str.c_str());
// resolution of voxel grids
getline( file, str ); getline( file, str );
float r = atof(str.c_str());
// f (distance from pinhole)
getline( file, str ); getline( file, str );
float f = atof(str.c_str());
// thetax (initial rotation about X Axis of map)
getline( file, str ); getline( file, str );
float thetaX = atof(str.c_str());
// thetay (initial rotation about Y Axis of map)
getline( file, str ); getline( file, str );
float thetaY = atof(str.c_str());
// number of points to go to
getline( file, str ); getline( file, str );
float nop = atoi(str.c_str());
// Number of divisions
getline( file, str ); getline( file, str );
float divs = atoi(str.c_str());
// Number of images to return
getline( file, str ); getline( file, str );
int numtoreturn = atoi(str.c_str());
// Should we load or create photos?
getline( file, str ); getline( file, str );
string lorc =str.c_str();
// Directory to look for photos
getline( file, str ); getline( file, str );
string dir =str.c_str();
// Directory to look for kp and descriptors
getline( file, str ); getline( file, str );
string kdir =str.c_str();
// save photos?
getline( file, str ); getline( file, str );
string savePhotos =str.c_str();
file.close();
// Done Getting Input Data
map<vector<float>, Mat> imagemap;
map<vector<float>, Mat> surfmap;
map<vector<float>, Mat> siftmap;
map<vector<float>, Mat> orbmap;
map<vector<float>, Mat> fastmap;
imagemap.clear();
vector<KeyPoint> SurfKeypoints;
vector<KeyPoint> SiftKeypoints;
vector<KeyPoint> OrbKeypoints;
vector<KeyPoint> FastKeypoints;
Mat SurfDescriptors;
Mat SiftDescriptors;
Mat OrbDescriptors;
Mat FastDescriptors;
int minHessian = 300;
SurfFeatureDetector SurfDetector (minHessian);
SiftFeatureDetector SiftDetector (minHessian);
OrbFeatureDetector OrbDetector (minHessian);
FastFeatureDetector FastDetector (minHessian);
SurfDescriptorExtractor SurfExtractor;
SiftDescriptorExtractor SiftExtractor;
OrbDescriptorExtractor OrbExtractor;
if ( !fs::exists( dir ) || lorc == "c" )
{ // Load Point Cloud and render images
PointCloud<PT>::Ptr cloud (new pcl::PointCloud<PT>);
io::loadPCDFile<PT>(cloud_name, *cloud);
Eigen::Affine3f tf = Eigen::Affine3f::Identity();
tf.rotate (Eigen::AngleAxisf (thetaX, Eigen::Vector3f::UnitX()));
pcl::transformPointCloud (*cloud, *cloud, tf);
tf = Eigen::Affine3f::Identity();
tf.rotate (Eigen::AngleAxisf (thetaY, Eigen::Vector3f::UnitY()));
pcl::transformPointCloud (*cloud, *cloud, tf);
// Create images from point cloud
imagemap = render::createImages(cloud, nop, w, h, r, f);
if (savePhotos == "y")
{
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
// Create image name and storagename
string imfn = dir + "/";
string kpfn = kdir + "/";
for (int j = 0; j < i->first.size(); j++)
{
imfn += boost::to_string(i->first[j]) + " ";
kpfn += boost::to_string(i->first[j]) + " ";
}
imfn += ".jpg";
imwrite(imfn, i->second);
// Detect keypoints, add to keypoint map. Same with descriptors
SurfDetector.detect(i->second, SurfKeypoints);
SiftDetector.detect(i->second, SiftKeypoints);
OrbDetector.detect(i->second, OrbKeypoints);
FastDetector.detect(i->second, FastKeypoints);
SurfExtractor.compute(i->second, SurfKeypoints, SurfDescriptors);
SiftExtractor.compute(i->second, SiftKeypoints, SiftDescriptors);
OrbExtractor.compute(i->second, OrbKeypoints, OrbDescriptors);
SiftExtractor.compute(i->second, FastKeypoints, FastDescriptors);
// Store KP and Descriptors in yaml file.
kpfn += ".yml";
FileStorage store(kpfn, cv::FileStorage::WRITE);
write(store,"SurfKeypoints",SurfKeypoints);
write(store,"SiftKeypoints",SiftKeypoints);
write(store,"OrbKeypoints", OrbKeypoints);
write(store,"FastKeypoints",FastKeypoints);
write(store,"SurfDescriptors",SurfDescriptors);
write(store,"SiftDescriptors",SiftDescriptors);
write(store,"OrbDescriptors", OrbDescriptors);
write(store,"FastDescriptors",FastDescriptors);
store.release();
surfmap[i->first] = SurfDescriptors;
siftmap[i->first] = SiftDescriptors;
orbmap[i->first] = OrbDescriptors;
fastmap[i->first] = FastDescriptors;
}
}
}
else
{ // load images from the folder dir
// First look into the folder to get a list of filenames
vector<fs::path> ret;
const char * pstr = dir.c_str();
fs::path p(pstr);
get_all(pstr, ret);
for (int i = 0; i < ret.size(); i++)
{
// Load Image via filename
string fn = ret[i].string();
istringstream iss(fn);
vector<string> tokens;
copy(istream_iterator<string>(iss), istream_iterator<string>(), back_inserter<vector<string> >(tokens));
// Construct ID from filename
vector<float> ID;
for (int i = 0; i < 6; i++) // 6 because there are three location floats and three direction floats
ID.push_back(::atof(tokens[i].c_str()));
string imfn = dir + "/" + fn;
// Read image and add to imagemap.
Mat m = imread(imfn);
imagemap[ID] = m;
// Create Filename for loading Keypoints and descriptors
string kpfn = kdir + "/";
for (int j = 0; j < ID.size(); j++)
{
kpfn += boost::to_string(ID[j]) + " ";
}
kpfn = kpfn+ ".yml";
// Create filestorage item to read from and add to map.
FileStorage store(kpfn, cv::FileStorage::READ);
FileNode n1 = store["SurfKeypoints"];
read(n1,SurfKeypoints);
FileNode n2 = store["SiftKeypoints"];
read(n2,SiftKeypoints);
FileNode n3 = store["OrbKeypoints"];
read(n3,OrbKeypoints);
FileNode n4 = store["FastKeypoints"];
read(n4,FastKeypoints);
FileNode n5 = store["SurfDescriptors"];
read(n5,SurfDescriptors);
FileNode n6 = store["SiftDescriptors"];
read(n6,SiftDescriptors);
FileNode n7 = store["OrbDescriptors"];
read(n7,OrbDescriptors);
FileNode n8 = store["FastDescriptors"];
read(n8,FastDescriptors);
store.release();
surfmap[ID] = SurfDescriptors;
siftmap[ID] = SiftDescriptors;
orbmap[ID] = OrbDescriptors;
fastmap[ID] = FastDescriptors;
}
}
TickMeter tm;
tm.reset();
cout << "<\n Analyzing Images ..." << endl;
// We have a bunch of images, now we compute their grayscale and black and white.
map<vector<float>, Mat> gsmap;
map<vector<float>, Mat> bwmap;
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
vector<float> ID = i->first;
Mat Image = i-> second;
GaussianBlur( Image, Image, Size(5,5), 0, 0, BORDER_DEFAULT );
gsmap[ID] = averageImage::getPixSumFromImage(Image, divs);
bwmap[ID] = averageImage::aboveBelow(gsmap[ID]);
}
Mat image = imread(image_name);
Mat gsimage = averageImage::getPixSumFromImage(image, divs);
Mat bwimage = averageImage::aboveBelow(gsimage);
// cout << gsimage <<endl;
imwrite("GS.png", gsimage);
namedWindow("GSIMAGE (Line 319)");
imshow("GSIMAGE (Line 319)", gsimage);
waitKey(0);
vector<KeyPoint> imgSurfKeypoints;
vector<KeyPoint> imgSiftKeypoints;
vector<KeyPoint> imgOrbKeypoints;
vector<KeyPoint> imgFastKeypoints;
Mat imgSurfDescriptors;
Mat imgSiftDescriptors;
Mat imgOrbDescriptors;
Mat imgFastDescriptors;
SurfDetector.detect(image, imgSurfKeypoints);
SiftDetector.detect(image, imgSiftKeypoints);
OrbDetector.detect(image, imgOrbKeypoints);
FastDetector.detect(image, imgFastKeypoints);
SurfExtractor.compute(image, imgSurfKeypoints, imgSurfDescriptors);
SiftExtractor.compute(image, imgSiftKeypoints, imgSiftDescriptors);
OrbExtractor.compute(image, imgOrbKeypoints, imgOrbDescriptors);
SiftExtractor.compute(image, imgFastKeypoints, imgFastDescriptors);
tm.start();
cout << ">\n<\n Comparing Images ..." << endl;
// We have their features, now compare them!
map<vector<float>, float> gssim; // Gray Scale Similarity
map<vector<float>, float> bwsim; // Above Below Similarity
map<vector<float>, float> surfsim;
map<vector<float>, float> siftsim;
map<vector<float>, float> orbsim;
map<vector<float>, float> fastsim;
for (map<vector<float>, Mat>::iterator i = gsmap.begin(); i != gsmap.end(); ++i)
{
vector<float> ID = i->first;
gssim[ID] = similarities::getSimilarity(i->second, gsimage);
bwsim[ID] = similarities::getSimilarity(bwmap[ID], bwimage);
surfsim[ID] = similarities::compareDescriptors(surfmap[ID], imgSurfDescriptors);
siftsim[ID] = similarities::compareDescriptors(siftmap[ID], imgSiftDescriptors);
orbsim[ID] = 0;//similarities::compareDescriptors(orbmap[ID], imgOrbDescriptors);
fastsim[ID] = 0;//similarities::compareDescriptors(fastmap[ID], imgFastDescriptors);
}
map<vector<float>, int> top;
bool gotone = false;
typedef map<vector<float>, int>::iterator iter;
// Choose the best ones!
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
vector<float> ID = i->first;
int sim = /* gssim[ID] + 0.5*bwsim[ID] + */ 5*surfsim[ID] + 0.3*siftsim[ID] + orbsim[ID] + fastsim[ID];
// cout << surfsim[ID] << "\t";
// cout << siftsim[ID] << "\t";
// cout << orbsim[ID] << "\t";
// cout << fastsim[ID] << endl;
if (!gotone)
{
top[ID] = sim;
gotone = true;
}
iter it = top.begin();
iter end = top.end();
int max_value = it->second;
vector<float> max_ID = it->first;
for( ; it != end; ++it)
{
int current = it->second;
if(current > max_value)
{
max_value = it->second;
max_ID = it->first;
}
}
// cout << "Sim: " << sim << "\tmax_value: " << max_value << endl;
if (top.size() < numtoreturn)
top[ID] = sim;
else
{
if (sim < max_value)
{
top[ID] = sim;
top.erase(max_ID);
}
}
}
tm.stop();
double s = tm.getTimeSec();
cout << ">\n<\n Writing top " << numtoreturn << " images ..." << endl;
int count = 1;
namedWindow("Image");
namedWindow("Match");
namedWindow("ImageBW");
namedWindow("MatchBW");
namedWindow("ImageGS");
namedWindow("MatchGS");
imshow("Image", image);
imshow("ImageBW", bwimage);
imshow("ImageGS", gsimage);
vector<KeyPoint> currentPoints;
for (iter i = top.begin(); i != top.end(); ++i)
{
vector<float> ID = i->first;
cout << " Score: "<< i->second << "\tGrayScale: " << gssim[ID] << "\tBW: " << bwsim[ID] << " \tSURF: " << surfsim[ID] << "\tSIFT: " << siftsim[ID] << endl;
string fn = "Sim_" + boost::to_string(count) + "_" + boost::to_string(i->second) + ".png";
imwrite(fn, imagemap[ID]);
count++;
normalize(bwmap[ID], bwmap[ID], 0, 255, NORM_MINMAX, CV_64F);
normalize(gsmap[ID], gsmap[ID], 0, 255, NORM_MINMAX, CV_64F);
imshow("Match", imagemap[ID]);
imshow("MatchBW", bwmap[ID]);
imshow("MatchGS", gsmap[ID]);
waitKey(0);
}
cout << ">\nComparisons took " << s << " seconds for " << imagemap.size() << " images ("
<< (int) imagemap.size()/s << " images per second)." << endl;
return 0;
}
int
main (int argc, char** argv)
{
// Get input object and scene
if (argc < 2)
{
pcl::console::print_error ("Syntax is: %s cloud1.pcd (cloud2.pcd)\n", argv[0]);
return (1);
}
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZRGBA>);
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZRGBA>);
// Load object and scene
pcl::console::print_highlight ("Loading point clouds...\n");
if(argc<3)
{
if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[1], *cloud_in) < 0)
pcl::console::print_error ("Error loading first file!\n");
*cloud_out = *cloud_in;
//transform cloud
Eigen::Affine3f transformation;
transformation.setIdentity();
transformation.translate(Eigen::Vector3f(0.3,0.02,-0.1));
float roll, pitch, yaw;
roll = 0.02; pitch = 1.2; yaw = 0;
Eigen::AngleAxisf rollAngle(roll, Eigen::Vector3f::UnitX());
Eigen::AngleAxisf pitchAngle(pitch, Eigen::Vector3f::UnitY());
Eigen::AngleAxisf yawAngle(yaw, Eigen::Vector3f::UnitZ());
Eigen::Quaternion<float> q = rollAngle*pitchAngle*yawAngle;
transformation.rotate(q);
pcl::transformPointCloud<pcl::PointXYZRGBA>(*cloud_in, *cloud_out, transformation);
std::cout << "Transformed " << cloud_in->points.size () << " data points:"
<< std::endl;
}else{
if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[1], *cloud_in) < 0 ||
pcl::io::loadPCDFile<pcl::PointXYZRGBA> (argv[2], *cloud_out) < 0)
{
pcl::console::print_error ("Error loading files!\n");
return (1);
}
}
// Fill in the CloudIn data
// cloud_in->width = 100;
// cloud_in->height = 1;
// cloud_in->is_dense = false;
// cloud_in->points.resize (cloud_in->width * cloud_in->height);
// for (size_t i = 0; i < cloud_in->points.size (); ++i)
// {
// cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
// cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
// cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
// }
std::cout << "size:" << cloud_out->points.size() << std::endl;
{
pcl::ScopeTime("icp proces");
pcl::IterativeClosestPoint<pcl::PointXYZRGBA, pcl::PointXYZRGBA> icp;
icp.setInputSource(cloud_in);
icp.setInputTarget(cloud_out);
pcl::PointCloud<pcl::PointXYZRGBA> Final;
icp.setMaximumIterations(1000000);
icp.setRANSACOutlierRejectionThreshold(0.01);
icp.align(Final);
std::cout << "has converged:" << icp.hasConverged() << " score: " <<
icp.getFitnessScore() << std::endl;
std::cout << icp.getFinalTransformation() << std::endl;
//translation, rotation
Eigen::Matrix4f icp_transformation=icp.getFinalTransformation();
Eigen::Matrix3f icp_rotation = icp_transformation.block<3,3>(0,0);
Eigen::Vector3f euler = icp_rotation.eulerAngles(0,1,2);
std::cout << "rotation: " << euler.transpose() << std::endl;
std::cout << "translation:" << icp_transformation.block<3,1>(0,3).transpose() << std::endl;
}
return (0);
}
void cloud_cb (const sensor_msgs::PointCloud2Ptr& input)
{
int is_save = false;
int is_predict = true;
// Create a container for the data.
pcl::PointCloud<pcl::PointXYZI>::Ptr conv_input(new pcl::PointCloud<pcl::PointXYZI>());
input->fields[3].name = "intensity";
pcl::fromROSMsg(*input, *conv_input);
// Size of humansize cloud
int cloud_size = conv_input->points.size();
std::cout << "cloud_size: " << cloud_size << std::endl;
if( cloud_size <= 1 ){
return;
}
pcl::PCA<pcl::PointXYZI> pca;
pcl::PointCloud<pcl::PointXYZI>::Ptr transform_cloud_translate(new pcl::PointCloud<pcl::PointXYZI>());
pcl::PointCloud<pcl::PointXYZI>::Ptr transform_cloud_rotate(new pcl::PointCloud<pcl::PointXYZI>());
sensor_msgs::PointCloud2 output;
geometry_msgs::PointStamped::Ptr output_point(new geometry_msgs::PointStamped());
geometry_msgs::PointStamped output_point_;
Eigen::Vector3f eigen_values;
Eigen::Vector4f centroid;
Eigen::Matrix3f eigen_vectors;
Eigen::Affine3f translate = Eigen::Affine3f::Identity();
Eigen::Affine3f rotate = Eigen::Affine3f::Identity();
double theta;
std::vector<double> description;
description.push_back(cloud_size);
pcl::PointCloud<pcl::PointNormal>::Ptr normals(new pcl::PointCloud<pcl::PointNormal>());
pcl::NormalEstimation<pcl::PointXYZI, pcl::PointNormal> ne;
ne.setInputCloud(conv_input);
ne.setKSearch(24);
ne.compute(*normals);
pcl::FPFHEstimation<pcl::PointXYZI, pcl::PointNormal, pcl::FPFHSignature33> fpfh;
fpfh.setInputCloud(conv_input);
fpfh.setInputNormals(normals);
pcl::search::KdTree<pcl::PointXYZI>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZI>());
fpfh.setSearchMethod(tree);
pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs(new pcl::PointCloud<pcl::FPFHSignature33>());
fpfh.setRadiusSearch(0.05);
fpfh.compute(*fpfhs);
// for(int i=0;i<fpfhs.histogram.size();i++){
std::cout << fpfhs->points.size();
// }
std::cout << std::endl;
//PCA
pca.setInputCloud(conv_input);
centroid = pca.getMean();
std::cout << "Centroid of pointcloud: " << centroid[0] << ", " << centroid[1] << ", " << centroid[2] << std::endl;
eigen_values = pca.getEigenValues();
std::cout << "Eigen values of PCAed pointcloud: " << eigen_values[0] << ", " << eigen_values[1] << ", " << eigen_values[2] << std::endl;
eigen_vectors << pca.getEigenVectors();
std::cout << "Eigen Vectors of PCAed pointcloud:" << std::endl;
for(int i=0;i<eigen_vectors.rows()*eigen_vectors.cols();i++){
std::cout << eigen_vectors(i) << " ";
if(!((i+1)%3)){
std::cout << std::endl;
}
}
std::cout << std::endl;
//Rotation cloud from PCA data
theta = atan2(eigen_vectors(1,0), eigen_vectors(0,0));
std::cout << "Theta: " << theta << std::endl;
translate.translation() << -centroid[0], -centroid[1], -centroid[2];
pcl::transformPointCloud(*conv_input, *transform_cloud_translate, translate);
rotate.rotate(Eigen::AngleAxisf(-theta, Eigen::Vector3f::UnitZ()));
pcl::transformPointCloud(*transform_cloud_translate, *transform_cloud_rotate, rotate);
pcl::toROSMsg(*transform_cloud_rotate, output);
output.header = input -> header;
output.header.frame_id = "map";
pub.publish(output);
pcl::PointXYZI searchPoint;
searchPoint.x = 0.0;
searchPoint.y = 0.0;
searchPoint.z = 0.0;
float min_distance,min_distance_tmp;
min_distance = sqrt(
powf( conv_input->points[0].x - searchPoint.x, 2.0f ) +
powf( conv_input->points[0].y - searchPoint.y, 2.0f ) +
powf( conv_input->points[0].z - searchPoint.z, 2.0f )
);
//brute force nearest neighbor search
for(int i=1; i<conv_input->size(); i++){
min_distance_tmp = sqrt(
powf( conv_input->points[i].x - searchPoint.x, 2.0f ) +
powf( conv_input->points[i].y - searchPoint.y, 2.0f ) +
powf( conv_input->points[i].z - searchPoint.z, 2.0f )
);
if( min_distance < min_distance_tmp){
min_distance = min_distance_tmp;
}
}
std::cout << "min_distance: " << min_distance << std::endl;
std::cout << std::endl;
description.push_back(min_distance);
// Calculate center of mass of humansize cloud
Vector4f center_of_mass;
pcl::compute3DCentroid(*transform_cloud_rotate, center_of_mass);
MatrixXf convariance_matrix = MatrixXf::Zero(3,3);
MatrixXf convariance_matrix_tmp = MatrixXf::Zero(3,3);
MatrixXf moment_of_inertia_matrix_tmp = MatrixXf::Zero(3,3);
MatrixXf moment_of_inertia_matrix = MatrixXf::Zero(3,3);
Vector3f point_tmp;
Vector3f point_from_center_of_mass;
// intensity buffer
double intensity_sum=0, intensity_ave=0, intensity_pow_sum=0, intensity_std_dev=0, max_intensity = 5000;
std::vector<double> intensity_histgram(25);
for(int i=0; i<cloud_size; i++){
point_tmp << transform_cloud_rotate->points[i].x, transform_cloud_rotate->points[i].y, transform_cloud_rotate->points[i].z;
//Calculate three dimentional convariance matrix
point_from_center_of_mass <<
center_of_mass[1] - point_tmp[1],
center_of_mass[0] - point_tmp[0],
center_of_mass[2] - point_tmp[2];
convariance_matrix_tmp += point_tmp * point_tmp.transpose();
//Calculate three dimentional moment of inertia matrix
moment_of_inertia_matrix_tmp <<
powf(point_tmp[1],2.0f)+powf(point_tmp[2],2.0f), -point_tmp[0]*point_tmp[1], -point_tmp[0]*point_tmp[2],
-point_tmp[0]*point_tmp[1], powf(point_tmp[0],2.0f)+powf(point_tmp[2],2.0f), -point_tmp[1]*point_tmp[2],
-point_tmp[0]*point_tmp[2], -point_tmp[1]*point_tmp[2], powf(point_tmp[0],2.0f)+powf(point_tmp[1],2.0f);
moment_of_inertia_matrix = moment_of_inertia_matrix + moment_of_inertia_matrix_tmp;
// Calculate intensity distribution
intensity_sum += transform_cloud_rotate->points[i].intensity;
intensity_pow_sum += powf(transform_cloud_rotate->points[i].intensity,2);
intensity_histgram[transform_cloud_rotate->points[i].intensity / (max_intensity / intensity_histgram.size())] += 1;
if( g_max_inten < transform_cloud_rotate->points[i].intensity ){
g_max_inten = transform_cloud_rotate->points[i].intensity;
}
}
//Calculate Convariance matrix
convariance_matrix = (1.0f/cloud_size) * convariance_matrix_tmp.array();
// Calculate intensity distribution
intensity_ave = intensity_sum / cloud_size;
std::cout << "max_intensity: " << g_max_inten << std::endl;
std::cout << "intensity_ave: " << intensity_ave << std::endl;
intensity_std_dev = sqrt(fabs(intensity_pow_sum / cloud_size - powf(intensity_ave,2)));
std::cout << "intensity_std_dev: " << intensity_std_dev <<std::endl;
std::cout << "intensity_histgram: ";
description.push_back(intensity_ave);
description.push_back(intensity_std_dev);
for(int i=0; i<intensity_histgram.size(); i++){
intensity_histgram[i] = intensity_histgram[i] / cloud_size;
std::cout << intensity_histgram[i] << " ";
description.push_back(intensity_histgram[i]);
}
std::cout << std::endl;
std::cout << std::endl;
std::cout << "3D convariance matrix:" << std::endl;
for(int i=0;i<convariance_matrix.rows()*convariance_matrix.cols();i++){
if(i<5 || i==6){
description.push_back(convariance_matrix(i));
}
std::cout << convariance_matrix(i) << " ";
if(!((i+1)%3)){
std::cout << std::endl;
}
}
std::cout << std::endl;
std::cout << "moment of inertia:" << std::endl;
for(int i=0;i<moment_of_inertia_matrix.rows()*moment_of_inertia_matrix.cols();i++){
if(i<5 || i==6){
description.push_back(convariance_matrix(i));
}
std::cout << moment_of_inertia_matrix(i) << " ";
if(!((i+1)%3)){
std::cout << std::endl;
}
}
std::cout << std::endl;
//Calculate Slice distribution
pcl::PointXYZI min_pt,max_pt,sliced_min_pt,sliced_max_pt;
pcl::getMinMax3D(*transform_cloud_rotate, min_pt, max_pt);
int sectors = 10;
double sector_height = (max_pt.z - min_pt.z)/sectors;
pcl::PassThrough<pcl::PointXYZI> pass;
std::vector<std::vector<double> > slice_dist(sectors,std::vector<double> (2));
for(double sec_h = min_pt.z,i = 0; sec_h < max_pt.z - sector_height; sec_h += sector_height, i++){
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_sliced(new pcl::PointCloud<pcl::PointXYZI>());
pass.setInputCloud(transform_cloud_rotate);
pass.setFilterFieldName("z");
pass.setFilterLimits(sec_h, sec_h + sector_height);
pass.filter(*cloud_sliced);
pcl::getMinMax3D(*cloud_sliced,sliced_min_pt,sliced_max_pt);
slice_dist[i][0] = sliced_max_pt.x - sliced_min_pt.x;
slice_dist[i][1] = sliced_max_pt.y - sliced_min_pt.y;
}
std::cout << "slice distribution:" << std::endl;
for(int i=0;i<2;i++){
for(int j=0;j<10;j++){
description.push_back(slice_dist[j][i]);
std::cout << slice_dist[j][i] << " ";
}
std::cout << std::endl;
}
std::cout << std::endl;
if(is_save){
std::ofstream ofs;
ofs.open("description.txt", std::ios::ate | std::ios::app);
ofs << "1 ";
for(int i=0;i<description.size();i++){
ofs << i+1 << ":" << description[i] << " ";
}
ofs << std::endl;
ofs.close();
}
if(is_predict){
FILE *fp_scale, *fp_model;
int idx,max_index;
std::vector<double> scale_min;
std::vector<double> scale_max;
double scale_min_tmp;
double scale_max_tmp;
double lower,upper;
double predict;
struct svm_model* model = svm_load_model(
model_filename.c_str());
struct svm_node *nodes;
nodes = (struct svm_node *)malloc((description.size()+1)*sizeof(struct svm_node));
fp_scale = fopen(scale_filename.c_str(),"r");
if(fp_scale == NULL || model == NULL){
ROS_ERROR_STREAM("Can't find model or scale data");
return;
}
if(fgetc(fp_scale) != 'x'){
return;
}
fscanf(fp_scale,"%lf %lf",&lower,&upper);
// ROS_INFO_STREAM("lower: " << lower << "upper: "<< upper);
while(fscanf(fp_scale,"%d %lf %lf\n",&idx,&scale_min_tmp,&scale_max_tmp) == 3){
scale_min.push_back(scale_min_tmp);
scale_max.push_back(scale_max_tmp);
}
std::cout << "scaled description :";
int index = 0;
for(int i=0;i<description.size();i++){
if(i<24 || i>28){
if(description[i] == DBL_INF || description[i] == DBL_MINF){
description[i] = 0;
}
if(description[i] < scale_min[index]){
nodes[index].index = i+1;
nodes[index].value = lower;
}
else if(description[i] > scale_max[index]){
nodes[index].index = i+1;
nodes[index].value = upper;
}
else{
nodes[index].index = i+1;
nodes[index].value = lower + (upper-lower) *
(description[i]-scale_min[index])/
(scale_max[index]-scale_min[index]);
}
std::cout << nodes[index].index << ":" << nodes[index].value << ",";
index++;
}
}
nodes[index].index = -1;
std::cout << std::endl;
predict = svm_predict(model,nodes);
ROS_INFO_STREAM("Predict" << predict);
if(predict == 1){
Vector4f center_of_mass_base;
pcl::compute3DCentroid(*conv_input, center_of_mass_base);
output_point->point.x = center_of_mass_base[0];
output_point->point.y = center_of_mass_base[1];
if(std::abs(center_of_mass_base[1]) > 2.0){
if(center_of_mass_base[1] < 0){
output_point->point.y = center_of_mass_base[1] + 1.0;
}
else{
output_point->point.y = center_of_mass_base[1] - 1.0;
}
}
else{
output_point->point.x = center_of_mass_base[0] - 2.0;
}
output_point->point.z = 0.0;
ROS_INFO_STREAM("target_center :" << center_of_mass_base[1] << "," << center_of_mass_base[0] << "," << center_of_mass_base[2]);
output_point->header.frame_id = "base_link";
output_point->header.stamp = input->header.stamp;
try{
listener->transformPoint("/map", *output_point, output_point_);
}
catch(tf::TransformException &e){
ROS_WARN_STREAM("tf::TransformException: " << e.what());
}
pub_point.publish(output_point_);
}
}
}
int main(int argc, char** argv)
{
if (argc < 5)
{
PCL17_INFO ("Usage %s -input_file /in_file -output_file /out_file [options]\n", argv[0]);
PCL17_INFO (" * where options are:\n"
" -tilt <X> : tilt. Default : 30\n"
"");
return -1;
}
int tilt = 30;
std::string input_file;
std::string output_file;
pcl17::console::parse_argument(argc, argv, "-input_file", input_file);
pcl17::console::parse_argument(argc, argv, "-output_file", output_file);
pcl17::console::parse_argument(argc, argv, "-tilt", tilt);
pcl17::PointCloud<pcl17::PointXYZRGB> cloud, cloud_transformed, cloud_aligned, cloud_filtered;
pcl17::io::loadPCDFile(input_file, cloud);
Eigen::Affine3f view_transform;
view_transform.matrix() << 0, 0, 1, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 1;
Eigen::AngleAxis<float> rot(tilt * M_PI / 180, Eigen::Vector3f(0, 1, 0));
view_transform.prerotate(rot);
pcl17::transformPointCloud(cloud, cloud_transformed, view_transform);
pcl17::ModelCoefficients::Ptr coefficients(new pcl17::ModelCoefficients);
pcl17::PointIndices::Ptr inliers(new pcl17::PointIndices);
pcl17::SACSegmentation<pcl17::PointXYZRGB> seg;
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl17::SACMODEL_PLANE);
seg.setMethodType(pcl17::SAC_RANSAC);
seg.setDistanceThreshold(0.05);
seg.setProbability(0.99);
seg.setInputCloud(cloud_transformed.makeShared());
seg.segment(*inliers, *coefficients);
pcl17::ExtractIndices<pcl17::PointXYZRGB> extract;
extract.setInputCloud(cloud_transformed.makeShared());
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(cloud_transformed);
std::cout << "Z vector: " << coefficients->values[0] << " " << coefficients->values[1] << " "
<< coefficients->values[2] << " " << coefficients->values[3] << std::endl;
Eigen::Vector3f z_current(coefficients->values[0], coefficients->values[1], coefficients->values[2]);
Eigen::Vector3f y(0, 1, 0);
Eigen::Affine3f rotation;
rotation = pcl17::getTransFromUnitVectorsZY(z_current, y);
rotation.translate(Eigen::Vector3f(0, 0, coefficients->values[3]));
pcl17::transformPointCloud(cloud_transformed, cloud_aligned, rotation);
Eigen::Affine3f res = (rotation * view_transform);
cloud_aligned.sensor_origin_ = res * Eigen::Vector4f(0, 0, 0, 1);
cloud_aligned.sensor_orientation_ = res.rotate(Eigen::AngleAxisf(M_PI, Eigen::Vector3f(0, 0, 1))).rotation();
seg.setInputCloud(cloud_aligned.makeShared());
seg.segment(*inliers, *coefficients);
std::cout << "Z vector: " << coefficients->values[0] << " " << coefficients->values[1] << " "
<< coefficients->values[2] << " " << coefficients->values[3] << std::endl;
pcl17::io::savePCDFile(output_file, cloud_aligned);
return 0;
}
/**
* @brief Rotate the view matrix by a given quaternion.
* @param rotation Rotation to apply to view matrix.
*/
void rotate (const Eigen::Quaternion<float>& rotation)
{
view_matrix.rotate(rotation);
}
void rawCloudHandler(const sensor_msgs::PointCloud2ConstPtr& laserCloud)
{
//double timeScanCur = laserCloud->header.stamp.toSec();
ros::Time timestamp = laserCloud->header.stamp;
//bool returnBool;
pcl::fromROSMsg(*laserCloud, *laserCloudIn);
if (visualizationFlag)
{
viewer->removeAllPointClouds(0);
viewer->removeAllShapes(0);
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudLabeled(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZI>::Ptr cloudRaw(new pcl::PointCloud<pcl::PointXYZI>);
// std::vector<std::string> labelsC1;
// std::vector<std::string> labelsC2;
// pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<SAFEFeatures>);
// pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<SAFEFeatures>);
std::vector<std::string> labelsC1;
std::vector<std::string> labelsC2;
std::vector<std::string> labelsC3;
std::vector<std::string> labels;
pcl::PointCloud<AllFeatures>::Ptr featureCloudAcc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC3Acc(new pcl::PointCloud<AllFeatures>);
std::vector<Eigen::Matrix3f> confMats;
pcl::PointCloud<pcl::PointXYZRGB>::Ptr classificationCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
std::vector<DatasetContainer> dataset;
std::string folder = "/home/mikkel/catkin_ws/src/trainingSet/";
pcl::copyPointCloud(*laserCloudIn, *cloudRGB);
pcl::copyPointCloud(*laserCloudIn, *cloudRaw);
// Single colored
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color1(cloudRGB, 0, 0, 255);
viewer->addPointCloud<pcl::PointXYZRGB>(cloudRGB,color1,"cloudRGB",viewP(RawCloud));
}
//viewer->addText ("RawCloud", 10, 10, "vPP text", viewP(RawCloud));
// Ground modeling
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudStat(new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::PointCloud<pcl::PointXYZ>::Ptr planeDistCloud(new pcl::PointCloud<pcl::PointXYZ>);
// pcl::copyPointCloud(*cloudRGB,*cloudStat);
// pcl::copyPointCloud(*cloudRGB,*planeDistCloud);
// Remove everything outside the two lines
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZI>);
//pcl::copyPointCloud(*cloudRaw, *cloud_filtered);
// Rotation
float thetaZ = theta*PI/180; // The angle of rotation in radians
Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();
transform_2.rotate (Eigen::AngleAxisf (thetaZ, Eigen::Vector3f::UnitZ()));
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::transformPointCloud (*cloudRaw, *cloud_filtered, transform_2);
// Passthrough
pcl::PassThrough<pcl::PointXYZI> passX;
passX.setInputCloud (cloud_filtered);
passX.setFilterFieldName ("x");
passX.setFilterLimits (-0.5, 100.0);
//pass.setFilterLimitsNegative (true);
passX.filter (*cloud_filtered);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filteredRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
if (receivedHough)
{
pcl::PassThrough<pcl::PointXYZI> pass;
pass.setInputCloud (cloud_filtered);
pass.setFilterFieldName ("y");
pass.setFilterLimits (r1+WALL_CLEARANCE, r2-WALL_CLEARANCE);
//pass.setFilterLimitsNegative (true);
pass.filter (*cloud_filtered);
}
pcl::copyPointCloud(*cloud_filtered, *cloud_filteredRGB);
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color9(cloudRGB, 0, 0, 255);
viewer->addPointCloud<pcl::PointXYZRGB>(cloud_filteredRGB,color9,"LinesFiltered",viewP(LinesFiltered));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "LinesFiltered");
viewer->addText ("LinesFiltered", 10, 10, fontsize, 1, 1, 1, "LinesFiltered text", viewP(LinesFiltered));
}
// Grid Minimum
// pcl::PointCloud<pcl::PointXYZI>::Ptr gridCloud(new pcl::PointCloud<pcl::PointXYZI>);
// pcl::GridMinimum<pcl::PointXYZI> gridm(1.0); // Set grid resolution
// gridm.setInputCloud(cloud_filtered);
// gridm.filter(*gridCloud);
//*** Transform point cloud to adjust for a Ground Plane ***//
pcl::ModelCoefficients ground_coefficients;
pcl::PointIndices ground_indices;
pcl::SACSegmentation<pcl::PointXYZI> ground_finder;
ground_finder.setOptimizeCoefficients(true);
ground_finder.setModelType(pcl::SACMODEL_PLANE);
ground_finder.setMethodType(pcl::SAC_RANSAC);
ground_finder.setDistanceThreshold(0.15);
ground_finder.setInputCloud(cloud_filtered);
ground_finder.segment(ground_indices, ground_coefficients);
// Convert plane normal vector from type ModelCoefficients to Vector4f
Eigen::Vector3f np = Eigen::Vector3f(ground_coefficients.values[0],ground_coefficients.values[1],ground_coefficients.values[2]);
// Find rotation vector, u, and angle, theta
Eigen::Vector3f u = np.cross(Eigen::Vector3f(0,0,1));
u.normalize();
float theta = acos(np.dot(Eigen::Vector3f(0,0,1)));
// Construct transformation matrix (rotation matrix from axis and angle)
Eigen::Affine3f tf = Eigen::Affine3f::Identity();
float d = ground_coefficients.values[3];
tf.translation() << d*np[0], d*np[1], d*np[2];
tf.rotate (Eigen::AngleAxisf (theta, u));
// Execute the transformation
pcl::PointCloud<pcl::PointXYZI>::Ptr transformedCloud (new pcl::PointCloud<pcl::PointXYZI> ());
pcl::transformPointCloud(*cloud_filtered, *transformedCloud, tf);
// Compute statistical moments for least significant direction in neighborhood
#if (OLD_METHOD)
pcl::NeighborhoodFeatures<pcl::PointXYZI, AllFeatures> features;
pcl::PointCloud<AllFeatures>::Ptr featureCloud(new pcl::PointCloud<AllFeatures>);
features.setInputCloud(transformedCloud);
pcl::search::KdTree<pcl::PointXYZI>::Ptr search_tree5( new pcl::search::KdTree<pcl::PointXYZI>());
features.setSearchMethod(search_tree5);
//principalComponentsAnalysis.setRadiusSearch(0.6);
//features.setKSearch(30);
features.setRadiusSearch(0.01); // This value does not do anything! Look inside "NeighborgoodFeatures.h" to see adaptive radius calculation.
features.compute(*featureCloud);
// ros::Time tFeatureCalculation2 = ros::Time::now();
// ros::Duration tFeatureCalculation = tFeatureCalculation2-tPreprocessing2;
// if (executionTimes==true)
// ROS_INFO("Feature calculation time = %i",tFeatureCalculation.nsec);
visualizeFeature(*cloudRGB, *featureCloud, 0, viewP(GPDistMean), "GPDistMean");
visualizeFeature(*cloudRGB, *featureCloud, 1, viewP(GPDistMin), "GPDistMin");
visualizeFeature(*cloudRGB, *featureCloud, 2, viewP(GPDistPoint), "GPDistPoint");
visualizeFeature(*cloudRGB, *featureCloud, 3, viewP(GPDistVar), "GPDistVar");
visualizeFeature(*cloudRGB, *featureCloud, 4, viewP(PCA1), "PCA1"); // 4 = PCA1
visualizeFeature(*cloudRGB, *featureCloud, 5, viewP(PCA2), "PCA2"); // 3 = GPVar
visualizeFeature(*cloudRGB, *featureCloud, 6, viewP(PCA3), "PCA3"); // 0 = GPMean
visualizeFeature(*cloudRGB, *featureCloud, 7, viewP(PCANX), "PCANX");
visualizeFeature(*cloudRGB, *featureCloud, 8, viewP(PCANY), "PCANY");
visualizeFeature(*cloudRGB, *featureCloud, 9, viewP(PCANZ), "PCANZ"); // 9 = PCANZ
visualizeFeature(*cloudRGB, *featureCloud, 10, viewP(PointDist), "PointDist");
visualizeFeature(*cloudRGB, *featureCloud, 11, viewP(RSS), "RSS");
visualizeFeature(*cloudRGB, *featureCloud, 12, viewP(Reflectance), "Reflectance");
ROS_INFO("Computed all neighborhood features");
std::vector<float>* centroid = new std::vector<float>();
std::vector<float>* stds = new std::vector<float>();
//*** Testing ***//
if (training == false)
{
Eigen::Matrix3f confMat = Eigen::Matrix3f::Zero();
if (testdata==true)
{
*featureCloud = *featureCloudAcc;
}
pcl::copyPointCloud(*cloudRGB,*classificationCloud);
// Load feature normalization parameters
std::ifstream input_file((folder + "centroid").c_str());
std::istream_iterator<float> start(input_file), end;
std::vector<float> numbers(start, end);
std::copy(numbers.begin(), numbers.end(), std::back_inserter(*centroid));
std::ifstream input_file2((folder + "stds").c_str());
std::istream_iterator<float> start2(input_file2), end2;
std::vector<float> numbers2(start2, end2);
std::copy(numbers2.begin(), numbers2.end(), std::back_inserter(*stds));
// Normalize features
// if (pcl::io::savePCDFile ("testFeaturesNonNormalized.pcd", *featureCloud) != 0)
// return (false);
ros::Time tNormalization1 = ros::Time::now();
normalizeFeatures(*featureCloud,*featureCloud, ¢roid, &stds);
ros::Time tNormalization2 = ros::Time::now();
ros::Duration tNormalization = tNormalization2-tNormalization1;
if (executionTimes==true)
ROS_INFO("Normalization time = %f",(float)(tNormalization.nsec)/1000000);
pcl::PointIndices::Ptr unclassifiedIndices (new pcl::PointIndices ());
// if (pcl::io::savePCDFile ("testFeatures.pcd", *featureCloud) != 0)
// return (false);
CvSVM SVM;
//int dim = sizeof(featureCloud->points[0])/sizeof(float);
int dim = useFeatures.size();//sizeof(useFeatures)/sizeof(int);
SVM.load((folder + "svm").c_str());
//SVM.load((folder + "svm2014-11-06-11-22-59").c_str());
//SVM.load((folder + "svm2014-11-07-14-00-31").c_str());
//SVM.load((folder + "svm2014-11-07-13-16-28").c_str());
ros::Time tClassification1 = ros::Time::now();
//int nMissingLabels = 0;
for (size_t i=0;i<classificationCloud->points.size();i++)
{
float dataSVM[dim];
for (int d=0;d<dim;d++)
{
//dataSVM[d] = featureCloud->points[i].data[d];
dataSVM[d] = featureCloud->points[i].data[useFeatures[d]];
}
Mat labelsMat(1, dim, CV_32FC1, dataSVM);
float response = SVM.predict(labelsMat);
if (response == 1.0)
classificationCloud->points[i].b = 255;
else if (response == 2.0)
classificationCloud->points[i].g = 255;
else if (response == 3.0)
classificationCloud->points[i].r = 255;
else
ROS_INFO("Another class label = %f",response);
if (testdata==true)
{
int groundTruth;
if (labels[i].compare("ground") == 0)
groundTruth = 1;
else if (labels[i].compare("vegetation") == 0)
groundTruth = 2;
else if (labels[i].compare("object") == 0)
groundTruth = 3;
confMat(groundTruth-1,(int)response-1)++;
}
}
ros::Time tClassification2 = ros::Time::now();
ros::Duration tClassification = tClassification2-tClassification1;
if (executionTimes==true)
ROS_INFO("Classification time = %f",(float)(tClassification.nsec)/100000);
//ROS_INFO("Number of missing class labels = %i", nMissingLabels);
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));
// Test set - calculate performance statistics
if (testdata == true)
{
std::cout << confMat << std::endl;
confMats.push_back(confMat);
if (true)//k==files.size()-1)
{
ofstream myfile;
string resultsFolder = "/home/mikkel/catkin_ws/src/Results/";
myfile.open ((resultsFolder + "run_number_here.txt").c_str());
// Distance threshold
myfile << "DistanceThreshold:" << distThr << ";\n";
// Neighborhood parameters
myfile << "Neighborhood:" << rmin << ";" << rmindist << ";" << rmax << ";" << rmaxdist << ";\n";
// Features
myfile << "Features:";
for (int d=0;d<dim;d++)
myfile << useFeatures[d] << ";";
myfile << "\n";
myfile << "ConfusionMatrix:";
for (size_t i=0;i<confMats.size();i++)
{
for (int r=0;r<confMats[i].rows();r++)
for (int c=0;c<confMats[i].cols();c++)
myfile << confMats[i](r,c) << ";";
myfile << "\n";
}
myfile << "Accuracy:" << confMat.diagonal().sum()/confMat.sum() << ";\n";
myfile.close();
}
}
featureCloudAcc->clear();
labels.clear();
#else
for (size_t i=0;i<transformedCloud->size();i++)
{
pcl::PointXYZI pTmp2 = transformedCloud->points[i];
pcl::PointXYZRGB pTmp;
pTmp.x = pTmp2.x;
pTmp.y = pTmp2.y;
pTmp.z = pTmp2.z;
pTmp.r = 255;
pTmp.g = 255;
if (pTmp.z > 0.1) // Object
{
classificationCloud->push_back(pTmp);
}
}
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));
}
#endif
// REGION GROWING
//ROS_INFO("region growing 5");
pcl::PointCloud <pcl::PointXYZRGB>::Ptr objectCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
//pcl::PointCloud <pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud <pcl::PointXYZRGB>);
for (size_t i=0;i<classificationCloud->size();i++)
{
pcl::PointXYZRGB pTmp = classificationCloud->points[i];
if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
{
objectCloud->push_back(pTmp);
}
}
pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg;
reg.setInputCloud (objectCloud);
//reg.setIndices (indices);
pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> > (new pcl::search::KdTree<pcl::PointXYZRGB>);
reg.setSearchMethod (tree);
//reg.setDistanceThreshold (0.0001f);
//reg.setResidualThreshold(0.01f);
//ROS_INFO("res = %f",reg.getSmoothnessThreshold());
//reg.setPointColorThreshold (6);
//reg.setRegionColorThreshold (5);
reg.setMinClusterSize (1);
reg.setResidualThreshold(min_cluster_distance);
reg.setCurvatureTestFlag(false);
// reg.setResidualTestFlag(true);
// reg.setNormalTestFlag(false);
// reg.setSmoothModeFlag(false);
std::vector <pcl::PointIndices> clusters;
reg.extract (clusters);
//ROS_INFO("clusters = %d", clusters.size());
//std::cout << "testefter" << std::endl;
pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
pcl::PointCloud <pcl::PointXYZRGB>::Ptr clustersFilteredCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
std::vector <pcl::PointIndices> clustersFiltered;
//pcl::PointXYZRGB min_pt, max_pt;
Eigen::Vector4f min_pt, max_pt;
obstacle_detection::boundingbox bbox;
obstacle_detection::boundingboxes bboxes;
bboxes.header.stamp = timestamp;
bboxes.header.frame_id = "/velodyne";
if (visualizationFlag)
viewer->removeAllShapes(viewP(SegmentsFiltered));
for (size_t i=0;i<clusters.size();i++)
{
//ROS_INFO("cluster size = %d",clusters[i].indices.size());
if (clusters[i].indices.size() > 100)
{
clustersFiltered.push_back(clusters[i]);
for (size_t pp=0;pp<clusters[i].indices.size();pp++)
{
clustersFilteredCloud->push_back(colored_cloud->points[clusters[i].indices[pp]]);
}
// Calculate bounding box
pcl::getMinMax3D(*colored_cloud, clusters[i], min_pt, max_pt);
bbox.start.x = min_pt[0];
bbox.start.y = min_pt[1];
bbox.start.z = min_pt[2];
bbox.width.x = max_pt[0]-min_pt[0];
bbox.width.y = max_pt[1]-min_pt[1];
bbox.width.z = max_pt[2]-min_pt[2];
bbox.header.stamp = timestamp;
bbox.header.frame_id = "/velodyne";
bboxes.boundingboxes.push_back(bbox);
if (visualizationFlag)
viewer->addCube (min_pt[0], max_pt[0], min_pt[1], max_pt[1], min_pt[2], max_pt[2], 1.0f, 1.0f, 1.0f, (boost::format("%04d") % i).str().c_str(), viewP(SegmentsFiltered));
}
}
pubBBoxesPointer->publish(bboxes);
//
// //pcl::visualization::CloudViewer viewer ("Cluster viewer");
// //viewer.showCloud (colored_cloud);
//
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC2(colored_cloud);
viewer->addPointCloud<pcl::PointXYZRGB>(colored_cloud,colorC2,"Segments",viewP(Segments));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Segments");
viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "Segments text", viewP(Segments));
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC3(clustersFilteredCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(clustersFilteredCloud,colorC3,"SegmentsFiltered",viewP(SegmentsFiltered));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "SegmentsFiltered");
viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "SegmentsFiltered text", viewP(SegmentsFiltered));
}
//
//
// // BOUNDING BOXES
// viewer->addCube (min_pt.x, max_pt.x, min_pt.y, max_pt.y, min_pt.z, max_pt.z);
// std_msgs::Float64 testF;
// //testF.data = 10;
// testF.data = 10;
//
// Compute principal directions
// Eigen::Vector4f pcaCentroid;
// pcl::compute3DCentroid(*clustersFilteredCloud, pcaCentroid);
// Eigen::Matrix3f covariance;
// computeCovarianceMatrixNormalized(*clustersFilteredCloud, pcaCentroid, covariance);
// Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance, Eigen::ComputeEigenvectors);
// Eigen::Matrix3f eigenVectorsPCA = eigen_solver.eigenvectors();
// eigenVectorsPCA.col(2) = eigenVectorsPCA.col(0).cross(eigenVectorsPCA.col(1));
//
// // Transform the original cloud to the origin where the principal components correspond to the axes.
// Eigen::Matrix4f projectionTransform(Eigen::Matrix4f::Identity());
// projectionTransform.block<3,3>(0,0) = eigenVectorsPCA.transpose();
// projectionTransform.block<3,1>(0,3) = -1.f * (projectionTransform.block<3,3>(0,0) * pcaCentroid.head<3>());
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudPointsProjected (new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::transformPointCloud(*clustersFilteredCloud, *cloudPointsProjected, projectionTransform);
// // Get the minimum and maximum points of the transformed cloud.
// pcl::PointXYZRGB minPoint, maxPoint;
// pcl::getMinMax3D(*cloudPointsProjected, minPoint, maxPoint);
// const Eigen::Vector3f meanDiagonal = 0.5f*(maxPoint.getVector3fMap() + minPoint.getVector3fMap());
//
// // Final transform
// const Eigen::Quaternionf bboxQuaternion(eigenVectorsPCA); //Quaternions are a way to do rotations https://www.youtube.com/watch?v=mHVwd8gYLnI
// const Eigen::Vector3f bboxTransform = eigenVectorsPCA * meanDiagonal + pcaCentroid.head<3>();
// viewer->addCube(bboxTransform, bboxQuaternion, maxPoint.x - minPoint.x, maxPoint.y - minPoint.y, maxPoint.z - minPoint.z, "bbox");
//pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF1(featureCloudTest, 0, 255, 0);
// pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorTTF1(featureCloudTest);
// PCL_INFO("featureCloudTest size = %i",featureCloudTest->points.size());
// pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF2(featureCloudTrain, 255, 0, 0);
// viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTest,colorTTF1,"TestTrainFeatures1",viewP(TestTrainFeatures));
// viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTrain,colorTTF2,"TestTrainFeatures2",viewP(TestTrainFeatures));
// viewer->addText ("TestTrainFeatures", 10, 10, "TestTrainFeatures text", viewP(TestTrainFeatures));
// sensor_msgs::PointCloud2 processedCloud;
// pcl::toROSMsg(*classificationCloud, processedCloud);
// processedCloud.header.stamp = ros::Time::now();//processedCloud->header.stamp;
// processedCloud.header.frame_id = "/velodyne";
// pubProcessedPointCloudPointer->publish(processedCloud);
//
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr groundCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr objectCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
//
// for (size_t i=0;i<classificationCloud->size();i++)
// {
// pcl::PointXYZRGB pTmp = classificationCloud->points[i];
// pTmp.z = 0; // 3D -> 2D
// if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
// {
// objectCloud2D->push_back(pTmp);
// }
// else if (pTmp.b == 255) // Object
// {
// groundCloud2D->push_back(pTmp);
// }
// }
//
// //std::vector<int> indexVector;
// unsigned int leafNodeCounter = 0;
// unsigned int voxelDensity = 0;
// unsigned int totalPoints = 0;
// //int occupancyW = OCCUPANCY_WIDTH/OCCUPANCY_GRID_RESOLUTION;
// //int occupancyH = OCCUPANCY_DEPTH/OCCUPANCY_GRID_RESOLUTION;
// std::vector<unsigned int> ocGroundGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
// std::vector<unsigned int> ocObjectGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
// std::vector<signed char> ocGridFinal(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH,-1);
//
// // Ground grid
// pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeGround (OCCUPANCY_GRID_RESOLUTION);
// octreeGround.setInputCloud (groundCloud2D);
// pcl::PointXYZ bot(-OCCUPANCY_DEPTH_M/2,-OCCUPANCY_WIDTH_M/2,-0.5);
// pcl::PointXYZ top(OCCUPANCY_DEPTH_M/2,OCCUPANCY_WIDTH_M/2,0.5);
// octreeGround.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z); // I have already calculated the BBox of my point cloud
// octreeGround.addPointsFromInputCloud ();
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1;
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1_end = octreeGround.leaf_end();
// int minx = 1000;
// int miny = 1000;
// int maxx = -1000;
// int maxy = -1000;
// for (it1 = octreeGround.leaf_begin(); it1 != it1_end; ++it1)
// {
// pcl::octree::OctreePointCloudDensityContainer& container = it1.getLeafContainer();
// voxelDensity = container.getPointCounter();
// Eigen::Vector3f min_pt, max_pt;
// octreeGround.getVoxelBounds (it1, min_pt, max_pt);
// //ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
// totalPoints += voxelDensity;
// if (min_pt[0] < minx)
// minx = min_pt[0];
// if (min_pt[0] > maxx)
// maxx = min_pt[0];
// if (min_pt[1] < miny)
// miny = min_pt[1];
// if (min_pt[1] > maxy)
// maxy = min_pt[1];
//
// int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
//
// // if ((r == 51) || (c==51))
// // ROS_INFO("r = %i,c = %i",r,c);
//
// //if (((int)min_pt[0] == -1) || ((int)min_pt[1]==-1))
// //ROS_INFO("min_pt[0] = %f, min_pt[1]=%f",min_pt[0],min_pt[1]);
// if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
// ocGroundGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
// //ROS_INFO("x (%f) -> r (%i), y (%f) -> c (%i)",min_pt[0],r,min_pt[1],c);
// leafNodeCounter++;
// }
//
//
// // Object grid
// pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeObject (OCCUPANCY_GRID_RESOLUTION);
// octreeObject.setInputCloud (objectCloud2D);
// octreeObject.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z); // I have already calculated the BBox of my point cloud
// octreeObject.addPointsFromInputCloud ();
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2;
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2_end = octreeObject.leaf_end();
// minx = 1000;
// miny = 1000;
// maxx = -1000;
// maxy = -1000;
// leafNodeCounter = 0;
// voxelDensity = 0;
// totalPoints = 0;
// int t1 = 0;
// int t2 = 0;
// int t3 = 0;
// for (it2 = octreeObject.leaf_begin(); it2 != it2_end; ++it2)
// {
// pcl::octree::OctreePointCloudDensityContainer& container = it2.getLeafContainer();
// voxelDensity = container.getPointCounter();
// Eigen::Vector3f min_pt, max_pt;
// octreeObject.getVoxelBounds (it2, min_pt, max_pt);
// //ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
// totalPoints += voxelDensity;
// if (min_pt[0] < minx)
// minx = min_pt[0];
// if (min_pt[0] > maxx)
// maxx = min_pt[0];
// if (min_pt[1] < miny)
// miny = min_pt[1];
// if (min_pt[1] > maxy)
// maxy = min_pt[1];
//
// int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
// ocObjectGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("object points = %i -> %i: x (%f) -> r (%i), y (%f) -> c (%i)",voxelDensity,ocGridFinal[r+OCCUPANCY_DEPTH*c],min_pt[0],r,min_pt[1],c);
// //ocGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
//
// leafNodeCounter++;
// }
//
// for (int r=0;r<OCCUPANCY_DEPTH;r++)
// {
// for (int c=0;c<OCCUPANCY_WIDTH;c++)
// {
// if ((ocGroundGrid[r+OCCUPANCY_DEPTH*c] == 0) && (ocObjectGrid[r+OCCUPANCY_DEPTH*c] <= 1))
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = -1.0; // 0.5 default likelihood
// t1++;
// }
// else if ((ocObjectGrid[r+OCCUPANCY_DEPTH*c] == 0))// && (ocGroundGrid[r+OCCUPANCY_DEPTH*c] > 0))
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = OCCUPANCY_MIN_PROB;
// t2++;
// }
// else
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = 50+(OCCUPANCY_MAX_PROB-50)*ocObjectGrid[r+OCCUPANCY_DEPTH*c]/(ocObjectGrid[r+OCCUPANCY_DEPTH*c]+ocGroundGrid[r+OCCUPANCY_DEPTH*c]);
// t3++;
// }
// }
// }
//
// nav_msgs::OccupancyGrid occupancyGrid;
// occupancyGrid.info.resolution = OCCUPANCY_GRID_RESOLUTION;
// occupancyGrid.header.stamp = timestamp;//ros::Time::now();
// occupancyGrid.header.frame_id = "/velodyne";
// occupancyGrid.info.width = OCCUPANCY_WIDTH;
// occupancyGrid.info.height = OCCUPANCY_DEPTH;
// //geometry_msgs::Pose lidarPose;
// geometry_msgs::Point lidarPoint;
// lidarPoint.x = 0;
// lidarPoint.y = 0;
// lidarPoint.z = 0;
// geometry_msgs::Quaternion lidarQuaternion;
// lidarQuaternion.w = 1;
// lidarQuaternion.x = 0;
// lidarQuaternion.y = 0;
// lidarQuaternion.z = 0;
// occupancyGrid.info.origin.position = lidarPoint;
// occupancyGrid.info.origin.orientation = lidarQuaternion;
// occupancyGrid.data = ocGridFinal;
//
// //ROS_INFO("total points = %i, total leaves = %i",totalPoints,leafNodeCounter);
// //ROS_INFO("minx = %i, maxx = %i, miny = %i, maxy = %i",minx,maxx,miny,maxy);
// //ROS_INFO("t1 = %i, t2 = %i, t3 = %i",t1,t2,t3);
//
//
// pubOccupancyGridPointer->publish(occupancyGrid);
// int l = 0;
// while (*++itLeafs)
// {
// //Iteratively explore only the leaf nodes..
// std::vector<PointXYZ> points;
// itLeafs->getData(points);
// l++;
// }
//
// ROS_INFO("l = %i",l);
//pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (OCCUPANCY_GRID_RESOLUTION);
// sensor_msgs::PointCloud2::Ptr cloud_filtered (new sensor_msgs::PointCloud2 ());
// pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
// sor.setInputCloud (processedCloud);
// sor.setLeafSize (0.01f, 0.01f, 0.01f);
// sor.filter (*cloud_filtered);
if (n==0)
{
//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v1);
//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v2);
//viewer->loadCameraParameters("pcl_video.cam");
}
#if (OLD_METHOD)
}
#endif
if (visualizationFlag)
viewer->spinOnce();
// Save screenshot
// std::stringstream tmp;
// tmp << n;
// viewer->saveScreenshot("/home/mikkel/images/" + (boost::format("%04d") % n).str() + ".png");
// n++;
}
void cloud_cb(
const typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr& cloud) {
iter++;
if(iter != skip) return;
iter = 0;
pcl::PointCloud<pcl::PointXYZRGB> cloud_transformed,
cloud_aligned, cloud_filtered;
Eigen::Affine3f view_transform;
view_transform.matrix() << 0, 0, 1, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 1;
Eigen::AngleAxis<float> rot(tilt * M_PI / 180,
Eigen::Vector3f(0, 1, 0));
view_transform.prerotate(rot);
pcl::transformPointCloud(*cloud, cloud_transformed, view_transform);
pcl::ModelCoefficients::Ptr coefficients(
new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
pcl::SACSegmentation<pcl::PointXYZRGB> seg;
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_PLANE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setDistanceThreshold(0.05);
seg.setProbability(0.99);
seg.setInputCloud(cloud_transformed.makeShared());
seg.segment(*inliers, *coefficients);
pcl::ExtractIndices<pcl::PointXYZRGB> extract;
extract.setInputCloud(cloud_transformed.makeShared());
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(cloud_transformed);
std::cout << "Z vector: " << coefficients->values[0] << " "
<< coefficients->values[1] << " " << coefficients->values[2]
<< " " << coefficients->values[3] << std::endl;
Eigen::Vector3f z_current(coefficients->values[0],
coefficients->values[1], coefficients->values[2]);
Eigen::Vector3f y(0, 1, 0);
Eigen::Affine3f rotation;
rotation = pcl::getTransFromUnitVectorsZY(z_current, y);
rotation.translate(Eigen::Vector3f(0, 0, coefficients->values[3]));
pcl::transformPointCloud(cloud_transformed, cloud_aligned, rotation);
Eigen::Affine3f res = (rotation * view_transform);
cloud_aligned.sensor_origin_ = res * Eigen::Vector4f(0, 0, 0, 1);
cloud_aligned.sensor_orientation_ = res.rotate(
Eigen::AngleAxisf(M_PI, Eigen::Vector3f(0, 0, 1))).rotation();
seg.setInputCloud(cloud_aligned.makeShared());
seg.segment(*inliers, *coefficients);
std::cout << "Z vector: " << coefficients->values[0] << " "
<< coefficients->values[1] << " " << coefficients->values[2]
<< " " << coefficients->values[3] << std::endl;
pub.publish(cloud_aligned);
}