template <template <typename> class Storage> void
cloud_cb (const boost::shared_ptr<openni_wrapper::Image>& image,
const boost::shared_ptr<openni_wrapper::DepthImage>& depth_image,
float constant)
{
static unsigned count = 0;
static double last = pcl::getTime ();
double now = pcl::getTime ();
if (++count == 30 || (now - last) > 5)
{
std::cerr << "Average framerate: " << double(count)/double(now - last) << " Hz" << std::endl;
count = 0;
last = now;
}
//static unsigned int nr_planes_last_time = 0;
std::cerr << "received callback" << std::endl;
typename PointCloudAOS<Storage>::Ptr data;
typename SampleConsensusModel1PointPlane<Storage>::Ptr sac_model;
{
pcl::ScopeTime timer ("All: ");
// Compute the PointCloud on the device
d2c.compute<Storage> (depth_image, image, constant, data, true, 2);
{
pcl::ScopeTime t ("creating sac_model");
sac_model.reset (new SampleConsensusModel1PointPlane<Storage> (data));
}
MultiRandomSampleConsensus<Storage> sac (sac_model);
sac.setMinimumCoverage (0.90); // at least 95% points should be explained by planes
sac.setMaximumBatches (5);
sac.setIerationsPerBatch (1000);
sac.setDistanceThreshold (0.05);
{
pcl::ScopeTime timer ("computeModel: ");
if (!sac.computeModel (0))
{
std::cerr << "Failed to compute model" << std::endl;
}
else
{
if (use_viewer)
{
std::cerr << "getting inliers.. ";
std::vector<typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr> planes;
// thrust::host_vector<int> regions_host = region_mask;
// std::copy (regions_host.begin (), regions_host.end(), std::ostream_iterator<int>(std::cerr, " "));
{
ScopeTime t ("retrieving inliers");
planes = sac.getAllInliers ();
}
typename Storage<int>::type region_mask;
markInliers<Storage> (data, region_mask, planes);
std::cerr << "image_size: " << data->width << " x " << data->height << " = " << region_mask.size () << std::endl;
typename StoragePointer<Storage,uchar>::type ptr = StorageAllocator<Storage,uchar>::alloc (data->width * data->height);
createIndicesImage<Storage> (ptr, region_mask);
typename ImageType<Storage>::type dst (data->height, data->width, CV_8UC1, thrust::raw_pointer_cast<char4>(ptr), data->width);
cv::imshow ("Result", cv::Mat(dst));
cv::waitKey (2);
std::vector<int> planes_inlier_counts = sac.getAllInlierCounts ();
std::vector<float4> coeffs = sac.getAllModelCoefficients ();
std::vector<float3> centroids = sac.getAllModelCentroids ();
std::cerr << "Found " << planes_inlier_counts.size () << " planes" << std::endl;
int best_plane = 0;
int best_plane_inliers_count = -1;
for (unsigned int i = 0; i < planes.size (); i++)
{
if (planes_inlier_counts[i] > best_plane_inliers_count)
{
best_plane = i;
best_plane_inliers_count = planes_inlier_counts[i];
}
typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr inliers_stencil;
inliers_stencil = planes[i];//sac.getInliersStencil ();
OpenNIRGB color;
double trand = 255 / (RAND_MAX + 1.0);
//int idx = (int)(rand () * trand);
color.r = (int)(rand () * trand);
color.g = (int)(rand () * trand);
color.b = (int)(rand () * trand);
{
ScopeTime t ("coloring planes");
colorIndices<Storage> (data, inliers_stencil, color);
}
}
}
//{
// ScopeTime t ("copying & transforming logo");
//for (unsigned int i = 0; i < planes.size (); i++)
//{
// // if (i == best_plane) // assume first plane is biggest plane
// {
// if (logo_cloud_)
// {
// boost::shared_ptr<pcl::PointCloud<pcl::PointXYZRGB> > transformed_logo (new pcl::PointCloud<pcl::PointXYZRGB>);
// Eigen::Affine3f transformation;
// Eigen::Vector3f plane_normal (coeffs[i].x, coeffs[i].y, coeffs[i].z);
// plane_normal.normalize ();
// if (plane_normal.dot (Eigen::Vector3f::Zero()) - coeffs[i].w > 0)
// plane_normal = -plane_normal;
// Eigen::Vector3f logo_normal (0,0,-1);
// Eigen::Vector3f trans (Eigen::Vector3f(centroids[i].x, centroids[i].y, centroids[i].z) * 0.97);
// Eigen::AngleAxisf rot (acos (logo_normal.dot (plane_normal)), logo_normal.cross (plane_normal).normalized ());
// transformation = Eigen::Affine3f::Identity();// = ....;
// transformation.translate (trans);// = ....;
// transformation.rotate (rot);// = ....;
// transformation.scale (0.001 * sqrt (planes_inlier_counts[i]));// = ....;
// std::cerr << "Plane centroid " << centroids[i].x << " " << centroids[i].y << " " << centroids[i].z << std::endl;
// pcl::getTransformedPointCloud<pcl::PointCloud<pcl::PointXYZRGB> > (*logo_cloud_, transformation, *transformed_logo);
// std::stringstream ss;
// ss << "logo" << i;
// //viewer.showCloud (transformed_logo, ss.str ());
// }
// }
//}
//}
//boost::shared_ptr<pcl::PointCloud<pcl::PointXYZRGB> > dummy_cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
//for (unsigned int i = planes.size (); i < nr_planes_last_time; i++)
//{
// std::stringstream ss;
// ss << "logo" << i;
// viewer.showCloud (dummy_cloud, ss.str ());
//}
//nr_planes_last_time = planes.size ();
}
}
}
if (use_viewer)
{
typename Storage<float4>::type normals = sac_model->getNormals ();
boost::mutex::scoped_lock l(m_mutex);
normal_cloud.reset (new pcl::PointCloud<pcl::PointXYZRGBNormal>);
pcl_cuda::toPCL (*data, normals, *normal_cloud);
new_cloud = true;
}
//pcl::PointCloud<pcl::PointXYZRGB>::Ptr output (new pcl::PointCloud<pcl::PointXYZRGB>);
//pcl_cuda::toPCL (*data, *output);
//viewer.showCloud (output);
}
template <template <typename> class Storage> void
cloud_cb (const boost::shared_ptr<openni_wrapper::Image>& image,
const boost::shared_ptr<openni_wrapper::DepthImage>& depth_image,
float constant)
{
static unsigned count = 0;
static double last = getTime ();
double now = getTime ();
//if (++count == 30 || (now - last) > 5)
{
std::cout << std::endl;
count = 1;
std::cout << "Average framerate: " << double(count)/double(now - last) << " Hz --- ";
last = now;
}
static int smoothing_nr_iterations = 10;
static int smoothing_filter_size = 2;
static int enable_visualization = 1;
static int enable_mean_shift = 0;
static int enable_plane_fitting = 0;
static int meanshift_sp=8;
static int meanshift_sr=20;
static int meanshift_minsize=100;
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output (new pcl::PointCloud<pcl::PointXYZRGB>);
typename PointCloudAOS<Storage>::Ptr data;
//ScopeTimeCPU timer ("All: ");
ScopeTimeCPU time ("everything");
// Compute the PointCloud on the device
{
ScopeTimeCPU time ("disparity smoothing");
d2c.compute<Storage> (depth_image, image, constant, data, false, 1, smoothing_nr_iterations, smoothing_filter_size);
}
boost::shared_ptr<typename Storage<float4>::type> normals;
float focallength = 580/2.0;
{
ScopeTimeCPU time ("Normal Estimation");
if (normal_method == 1)
normals = computeFastPointNormals<Storage> (data);
else
normals = computePointNormals<Storage> (data->points.begin (), data->points.end (), focallength, data, radius_cm / 100.0f, nr_neighbors);
cudaThreadSynchronize ();
}
// retrieve normals as an image..
typename ImageType<Storage>::type normal_image;
typename StoragePointer<Storage,char4>::type ptr;
{
ScopeTimeCPU time ("Matrix Creation");
ImageType<Storage>::createContinuous ((int)data->height, (int)data->width, CV_8UC4, normal_image);
ptr = typename StoragePointer<Storage,char4>::type ((char4*)normal_image.data);
createNormalsImage<Storage> (ptr, *normals);
}
//TODO: this breaks for pcl::cuda::Host
cv::Mat seg;
{
ScopeTimeCPU time ("Mean Shift");
if (enable_mean_shift == 1)
{
cv::gpu::meanShiftSegmentation (normal_image, seg, meanshift_sp, meanshift_sr, meanshift_minsize);
typename Storage<char4>::type new_colors ((char4*)seg.datastart, (char4*)seg.dataend);
colorCloud<Storage> (data, new_colors);
}
}
typename SampleConsensusModel1PointPlane<Storage>::Ptr sac_model;
if (enable_plane_fitting == 1)
{
// Create sac_model
{
ScopeTimeCPU t ("creating sac_model");
sac_model.reset (new SampleConsensusModel1PointPlane<Storage> (data));
}
sac_model->setNormals (normals);
MultiRandomSampleConsensus<Storage> sac (sac_model);
sac.setMinimumCoverage (0.90); // at least 95% points should be explained by planes
sac.setMaximumBatches (1);
sac.setIerationsPerBatch (1024);
sac.setDistanceThreshold (0.05);
{
ScopeTimeCPU timer ("computeModel: ");
if (!sac.computeModel (0))
{
std::cerr << "Failed to compute model" << std::endl;
}
else
{
if (enable_visualization)
{
// std::cerr << "getting inliers.. ";
std::vector<typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr> planes;
typename Storage<int>::type region_mask;
markInliers<Storage> (data, region_mask, planes);
thrust::host_vector<int> regions_host;
std::copy (regions_host.begin (), regions_host.end(), std::ostream_iterator<int>(std::cerr, " "));
{
ScopeTimeCPU t ("retrieving inliers");
planes = sac.getAllInliers ();
}
std::vector<int> planes_inlier_counts = sac.getAllInlierCounts ();
std::vector<float4> coeffs = sac.getAllModelCoefficients ();
std::vector<float3> centroids = sac.getAllModelCentroids ();
std::cerr << "Found " << planes_inlier_counts.size () << " planes" << std::endl;
int best_plane = 0;
int best_plane_inliers_count = -1;
for (unsigned int i = 0; i < planes.size (); i++)
{
if (planes_inlier_counts[i] > best_plane_inliers_count)
{
best_plane = i;
best_plane_inliers_count = planes_inlier_counts[i];
}
typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr inliers_stencil;
inliers_stencil = planes[i];//sac.getInliersStencil ();
OpenNIRGB color;
//double trand = 255 / (RAND_MAX + 1.0);
//color.r = (int)(rand () * trand);
//color.g = (int)(rand () * trand);
//color.b = (int)(rand () * trand);
color.r = (1.0f + coeffs[i].x) * 128;
color.g = (1.0f + coeffs[i].y) * 128;
color.b = (1.0f + coeffs[i].z) * 128;
{
ScopeTimeCPU t ("coloring planes");
colorIndices<Storage> (data, inliers_stencil, color);
}
}
}
}
}
}
{
ScopeTimeCPU time ("Vis");
cv::namedWindow("NormalImage", CV_WINDOW_NORMAL);
cv::namedWindow("Parameters", CV_WINDOW_NORMAL);
cvCreateTrackbar( "iterations", "Parameters", &smoothing_nr_iterations, 50, NULL);
cvCreateTrackbar( "filter_size", "Parameters", &smoothing_filter_size, 10, NULL);
cvCreateTrackbar( "enable_visualization", "Parameters", &enable_visualization, 1, NULL);
cvCreateTrackbar( "enable_color", "Parameters", &enable_color, 1, NULL);
cvCreateTrackbar( "normal_method", "Parameters", &normal_method, 1, NULL);
cvCreateTrackbar( "neighborhood_radius", "Parameters", &radius_cm, 50, NULL);
cvCreateTrackbar( "nr_neighbors", "Parameters", &nr_neighbors, 400, NULL);
cvCreateTrackbar( "normal_viz_step", "Parameters", &normal_viz_step, 1000, NULL);
cvCreateTrackbar( "enable_mean_shift", "Parameters", &enable_mean_shift, 1, NULL);
cvCreateTrackbar( "meanshift_sp", "Parameters", &meanshift_sp, 100, NULL);
cvCreateTrackbar( "meanshift_sr", "Parameters", &meanshift_sr, 100, NULL);
cvCreateTrackbar( "meanshift_minsize", "Parameters", &meanshift_minsize, 500, NULL);
cvCreateTrackbar( "enable_plane_fitting", "Parameters", &enable_plane_fitting, 1, NULL);
cvCreateTrackbar( "enable_normal_viz", "Parameters", &enable_normal_viz, 1, NULL);
if (enable_visualization == 1)
{
cv::Mat temp;
if (enable_mean_shift == 1)
cv::cvtColor(seg,temp,CV_BGR2RGB);
else
cv::cvtColor(cv::Mat(normal_image),temp,CV_BGR2RGB);
cv::imshow ("NormalImage", temp);
boost::mutex::scoped_lock l(m_mutex);
normal_cloud.reset (new pcl::PointCloud<pcl::PointXYZRGBNormal>);
toPCL (*data, *normals, *normal_cloud);
new_cloud = true;
}
cv::waitKey (2);
}
}
// callback function from the OpenNIGrabber
template <template <typename> class Storage> void
cloud_cb (const boost::shared_ptr<openni_wrapper::Image>& image,
const boost::shared_ptr<openni_wrapper::DepthImage>& depth_image,
float constant)
{
// TIMING
static unsigned count = 0;
static double last = getTime ();
double now = getTime ();
//if (++count == 30 || (now - last) > 5)
{
std::cout << std::endl;
count = 1;
std::cout << "Average framerate: " << double(count)/double(now - last) << " Hz --- ";
last = now;
}
// PARAMETERS
static int smoothing_nr_iterations = 10;
static int smoothing_filter_size = 2;
static int enable_visualization = 0;
static int enable_mean_shift = 1;
static int enable_plane_fitting = 0;
static int meanshift_sp=8;
static int meanshift_sr=20;
static int meanshift_minsize=100;
// CPU AND GPU POINTCLOUD OBJECTS
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output (new pcl::PointCloud<pcl::PointXYZRGB>);
typename PointCloudAOS<Storage>::Ptr data;
ScopeTimeCPU time ("everything");
{
ScopeTimeCPU time ("disparity smoothing");
// Compute the PointCloud on the device
d2c.compute<Storage> (depth_image, image, constant, data, false, 1, smoothing_nr_iterations, smoothing_filter_size);
}
// GPU NORMAL CLOUD
boost::shared_ptr<typename Storage<float4>::type> normals;
float focallength = 580/2.0;
{
ScopeTimeCPU time ("Normal Estimation");
if (normal_method == 1)
// NORMAL ESTIMATION USING DIRECT PIXEL NEIGHBORS
normals = computeFastPointNormals<Storage> (data);
else
// NORMAL ESTIMATION USING NEIGHBORHOODS OF RADIUS "radius"
normals = computePointNormals<Storage> (data->points.begin (), data->points.end (), focallength, data, radius_cm / 100.0f, nr_neighbors);
cudaThreadSynchronize ();
}
// RETRIEVE NORMALS AS AN IMAGE
typename ImageType<Storage>::type normal_image;
typename StoragePointer<Storage,char4>::type ptr;
{
ScopeTimeCPU time ("Matrix Creation");
ImageType<Storage>::createContinuous ((int)data->height, (int)data->width, CV_8UC4, normal_image);
ptr = typename StoragePointer<Storage,char4>::type ((char4*)normal_image.data);
createNormalsImage<Storage> (ptr, *normals);
}
//urdf_filter->filter (data);
//TODO: this breaks for pcl::cuda::Host, meaning we have to run this on the GPU
std::vector<int> reg_labels;
pcl::cuda::detail::DjSets comps(0);
cv::Mat seg;
{
ScopeTimeCPU time ("Mean Shift");
if (enable_mean_shift == 1)
{
// USE GPU MEAN SHIFT SEGMENTATION FROM OPENCV
pcl::cuda::meanShiftSegmentation (normal_image, seg, reg_labels, meanshift_sp, meanshift_sr, meanshift_minsize, comps);
typename Storage<char4>::type new_colors ((char4*)seg.datastart, (char4*)seg.dataend);
colorCloud<Storage> (data, new_colors);
}
}
typename SampleConsensusModel1PointPlane<Storage>::Ptr sac_model;
if (enable_plane_fitting == 1)
{
// Create sac_model
{
ScopeTimeCPU t ("creating sac_model");
sac_model.reset (new SampleConsensusModel1PointPlane<Storage> (data));
}
sac_model->setNormals (normals);
MultiRandomSampleConsensus<Storage> sac (sac_model);
sac.setMinimumCoverage (0.90); // at least 95% points should be explained by planes
sac.setMaximumBatches (1);
sac.setIerationsPerBatch (1024);
sac.setDistanceThreshold (0.05);
{
ScopeTimeCPU timer ("computeModel: ");
if (!sac.computeModel (0))
{
std::cerr << "Failed to compute model" << std::endl;
}
else
{
if (enable_visualization)
{
// std::cerr << "getting inliers.. ";
std::vector<typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr> planes;
typename Storage<int>::type region_mask;
markInliers<Storage> (data, region_mask, planes);
thrust::host_vector<int> regions_host;
std::copy (regions_host.begin (), regions_host.end(), std::ostream_iterator<int>(std::cerr, " "));
{
ScopeTimeCPU t ("retrieving inliers");
planes = sac.getAllInliers ();
}
std::vector<int> planes_inlier_counts = sac.getAllInlierCounts ();
std::vector<float4> coeffs = sac.getAllModelCoefficients ();
std::vector<float3> centroids = sac.getAllModelCentroids ();
std::cerr << "Found " << planes_inlier_counts.size () << " planes" << std::endl;
int best_plane = 0;
int best_plane_inliers_count = -1;
for (unsigned int i = 0; i < planes.size (); i++)
{
if (planes_inlier_counts[i] > best_plane_inliers_count)
{
best_plane = i;
best_plane_inliers_count = planes_inlier_counts[i];
}
typename SampleConsensusModel1PointPlane<Storage>::IndicesPtr inliers_stencil;
inliers_stencil = planes[i];//sac.getInliersStencil ();
OpenNIRGB color;
//double trand = 255 / (RAND_MAX + 1.0);
//color.r = (int)(rand () * trand);
//color.g = (int)(rand () * trand);
//color.b = (int)(rand () * trand);
color.r = (1.0f + coeffs[i].x) * 128;
color.g = (1.0f + coeffs[i].y) * 128;
color.b = (1.0f + coeffs[i].z) * 128;
{
ScopeTimeCPU t ("coloring planes");
colorIndices<Storage> (data, inliers_stencil, color);
}
}
}
}
}
}
if (gui)
{
ScopeTimeCPU time ("Vis");
cv::namedWindow("NormalImage", CV_WINDOW_NORMAL);
cv::namedWindow("Parameters", CV_WINDOW_NORMAL);
cvCreateTrackbar( "iterations", "Parameters", &smoothing_nr_iterations, 50, NULL);
cvCreateTrackbar( "filter_size", "Parameters", &smoothing_filter_size, 10, NULL);
cvCreateTrackbar( "enable_visualization", "Parameters", &enable_visualization, 1, NULL);
cvCreateTrackbar( "enable_color", "Parameters", &enable_color, 1, NULL);
cvCreateTrackbar( "normal_method", "Parameters", &normal_method, 1, NULL);
cvCreateTrackbar( "neighborhood_radius", "Parameters", &radius_cm, 50, NULL);
cvCreateTrackbar( "nr_neighbors", "Parameters", &nr_neighbors, 400, NULL);
cvCreateTrackbar( "normal_viz_step", "Parameters", &normal_viz_step, 1000, NULL);
cvCreateTrackbar( "enable_mean_shift", "Parameters", &enable_mean_shift, 1, NULL);
cvCreateTrackbar( "meanshift_sp", "Parameters", &meanshift_sp, 100, NULL);
cvCreateTrackbar( "meanshift_sr", "Parameters", &meanshift_sr, 100, NULL);
cvCreateTrackbar( "meanshift_minsize", "Parameters", &meanshift_minsize, 500, NULL);
cvCreateTrackbar( "enable_plane_fitting", "Parameters", &enable_plane_fitting, 1, NULL);
if (enable_visualization == 1)
{
if (enable_mean_shift == 1)
cv::imshow ("NormalImage", seg);
else
cv::imshow ("NormalImage", cv::Mat(normal_image));
boost::mutex::scoped_lock l(m_mutex);
normal_cloud.reset (new pcl::PointCloud<pcl::PointXYZRGBNormal>);
toPCL (*data, *normals, *normal_cloud);
new_cloud = true;
}
cv::waitKey (2);
}
else
{
{
ScopeTimeCPU c_p ("Copying");
boost::mutex::scoped_lock l(m_mutex);
region_cloud.reset (new pcl::PointCloud<PointXYZRGBNormalRegion>);
region_cloud->header.frame_id = "/openni_rgb_optical_frame";
toPCL (*data, *normals, *region_cloud);
if (enable_mean_shift)
{
for (int cp = 0; cp < region_cloud->points.size (); cp++)
{
region_cloud->points[cp].region = reg_labels[cp];
}
}
new_cloud = true;
}
ScopeTimeCPU c_p ("Publishing");
publish_cloud ();
}
}
