organized_edge_detection.hpp
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#ifndef PCL_FEATURES_IMPL_ORGANIZED_EDGE_DETECTION_H_
#define PCL_FEATURES_IMPL_ORGANIZED_EDGE_DETECTION_H_
#include <pcl/2d/edge.h>
#include <pcl/features/organized_edge_detection.h>
#include <pcl/console/print.h>
#include <pcl/console/time.h>
/**
* Directions: 1 2 3
* 0 x 4
* 7 6 5
* e.g. direction y means we came from pixel with label y to the center pixel x
*/
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointLT> void
pcl::OrganizedEdgeBase<PointT, PointLT>::compute (pcl::PointCloud<PointLT>& labels, std::vector<pcl::PointIndices>& label_indices) const
{
pcl::Label invalid_pt;
invalid_pt.label = unsigned (0);
labels.points.resize (input_->points.size (), invalid_pt);
labels.width = input_->width;
labels.height = input_->height;
extractEdges (labels);
assignLabelIndices (labels, label_indices);
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointLT> void
pcl::OrganizedEdgeBase<PointT, PointLT>::assignLabelIndices (pcl::PointCloud<PointLT>& labels, std::vector<pcl::PointIndices>& label_indices) const
{
const unsigned invalid_label = unsigned (0);
label_indices.resize (num_of_edgetype_);
for (unsigned idx = 0; idx < input_->points.size (); idx++)
{
if (labels[idx].label != invalid_label)
{
for (int edge_type = 0; edge_type < num_of_edgetype_; edge_type++)
{
if ((labels[idx].label >> edge_type) & 1)
label_indices[edge_type].indices.push_back (idx);
}
}
}
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointLT> void
pcl::OrganizedEdgeBase<PointT, PointLT>::extractEdges (pcl::PointCloud<PointLT>& labels) const
{
if ((detecting_edge_types_ & EDGELABEL_NAN_BOUNDARY) || (detecting_edge_types_ & EDGELABEL_OCCLUDING) || (detecting_edge_types_ & EDGELABEL_OCCLUDED))
{
// Fill lookup table for next points to visit
const int num_of_ngbr = 8;
Neighbor directions [num_of_ngbr] = {Neighbor(-1, 0, -1),
Neighbor(-1, -1, -labels.width - 1),
Neighbor( 0, -1, -labels.width ),
Neighbor( 1, -1, -labels.width + 1),
Neighbor( 1, 0, 1),
Neighbor( 1, 1, labels.width + 1),
Neighbor( 0, 1, labels.width ),
Neighbor(-1, 1, labels.width - 1)};
for (int row = 1; row < int(input_->height) - 1; row++)
{
for (int col = 1; col < int(input_->width) - 1; col++)
{
int curr_idx = row*int(input_->width) + col;
if (!pcl_isfinite (input_->points[curr_idx].z))
continue;
float curr_depth = fabsf (input_->points[curr_idx].z);
// Calculate depth distances between current point and neighboring points
std::vector<float> nghr_dist;
nghr_dist.resize (8);
bool found_invalid_neighbor = false;
for (int d_idx = 0; d_idx < num_of_ngbr; d_idx++)
{
int nghr_idx = curr_idx + directions[d_idx].d_index;
assert (nghr_idx >= 0 && nghr_idx < input_->points.size ());
if (!pcl_isfinite (input_->points[nghr_idx].z))
{
found_invalid_neighbor = true;
break;
}
nghr_dist[d_idx] = curr_depth - fabsf (input_->points[nghr_idx].z);
}
if (!found_invalid_neighbor)
{
// Every neighboring points are valid
std::vector<float>::iterator min_itr = std::min_element (nghr_dist.begin (), nghr_dist.end ());
std::vector<float>::iterator max_itr = std::max_element (nghr_dist.begin (), nghr_dist.end ());
float nghr_dist_min = *min_itr;
float nghr_dist_max = *max_itr;
float dist_dominant = fabsf (nghr_dist_min) > fabsf (nghr_dist_max) ? nghr_dist_min : nghr_dist_max;
if (fabsf (dist_dominant) > th_depth_discon_*fabsf (curr_depth))
{
// Found a depth discontinuity
if (dist_dominant > 0.f)
{
if (detecting_edge_types_ & EDGELABEL_OCCLUDED)
labels[curr_idx].label |= EDGELABEL_OCCLUDED;
}
else
{
if (detecting_edge_types_ & EDGELABEL_OCCLUDING)
labels[curr_idx].label |= EDGELABEL_OCCLUDING;
}
}
}
else
{
// Some neighboring points are not valid (nan points)
// Search for corresponding point across invalid points
// Search direction is determined by nan point locations with respect to current point
int dx = 0;
int dy = 0;
int num_of_invalid_pt = 0;
for (int d_idx = 0; d_idx < num_of_ngbr; d_idx++)
{
int nghr_idx = curr_idx + directions[d_idx].d_index;
assert (nghr_idx >= 0 && nghr_idx < input_->points.size ());
if (!pcl_isfinite (input_->points[nghr_idx].z))
{
dx += directions[d_idx].d_x;
dy += directions[d_idx].d_y;
num_of_invalid_pt++;
}
}
// Search directions
assert (num_of_invalid_pt > 0);
float f_dx = static_cast<float> (dx) / static_cast<float> (num_of_invalid_pt);
float f_dy = static_cast<float> (dy) / static_cast<float> (num_of_invalid_pt);
// Search for corresponding point across invalid points
float corr_depth = std::numeric_limits<float>::quiet_NaN ();
for (int s_idx = 1; s_idx < max_search_neighbors_; s_idx++)
{
int s_row = row + static_cast<int> (std::floor (f_dy*static_cast<float> (s_idx)));
int s_col = col + static_cast<int> (std::floor (f_dx*static_cast<float> (s_idx)));
if (s_row < 0 || s_row >= int(input_->height) || s_col < 0 || s_col >= int(input_->width))
break;
if (pcl_isfinite (input_->points[s_row*int(input_->width)+s_col].z))
{
corr_depth = fabsf (input_->points[s_row*int(input_->width)+s_col].z);
break;
}
}
if (!pcl_isnan (corr_depth))
{
// Found a corresponding point
float dist = curr_depth - corr_depth;
if (fabsf (dist) > th_depth_discon_*fabsf (curr_depth))
{
// Found a depth discontinuity
if (dist > 0.f)
{
if (detecting_edge_types_ & EDGELABEL_OCCLUDED)
labels[curr_idx].label |= EDGELABEL_OCCLUDED;
}
else
{
if (detecting_edge_types_ & EDGELABEL_OCCLUDING)
labels[curr_idx].label |= EDGELABEL_OCCLUDING;
}
}
}
else
{
// Not found a corresponding point, just nan boundary edge
if (detecting_edge_types_ & EDGELABEL_NAN_BOUNDARY)
labels[curr_idx].label |= EDGELABEL_NAN_BOUNDARY;
}
}
}
}
}
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointLT> void
pcl::OrganizedEdgeFromRGB<PointT, PointLT>::compute (pcl::PointCloud<PointLT>& labels, std::vector<pcl::PointIndices>& label_indices) const
{
pcl::Label invalid_pt;
invalid_pt.label = unsigned (0);
labels.points.resize (input_->points.size (), invalid_pt);
labels.width = input_->width;
labels.height = input_->height;
OrganizedEdgeBase<PointT, PointLT>::extractEdges (labels);
extractEdges (labels);
this->assignLabelIndices (labels, label_indices);
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointLT> void
pcl::OrganizedEdgeFromRGB<PointT, PointLT>::extractEdges (pcl::PointCloud<PointLT>& labels) const
{
if ((detecting_edge_types_ & EDGELABEL_RGB_CANNY))
{
pcl::PointCloud<PointXYZI>::Ptr gray (new pcl::PointCloud<PointXYZI>);
gray->width = input_->width;
gray->height = input_->height;
gray->resize (input_->height*input_->width);
for (size_t i = 0; i < input_->size (); ++i)
(*gray)[i].intensity = float (((*input_)[i].r + (*input_)[i].g + (*input_)[i].b) / 3);
pcl::PointCloud<pcl::PointXYZIEdge> img_edge_rgb;
pcl::Edge<PointXYZI, pcl::PointXYZIEdge> edge;
edge.setInputCloud (gray);
edge.setHysteresisThresholdLow (th_rgb_canny_low_);
edge.setHysteresisThresholdHigh (th_rgb_canny_high_);
edge.detectEdgeCanny (img_edge_rgb);
for (uint32_t row=0; row<labels.height; row++)
{
for (uint32_t col=0; col<labels.width; col++)
{
if (img_edge_rgb (col, row).magnitude == 255.f)
labels[row * labels.width + col].label |= EDGELABEL_RGB_CANNY;
}
}
}
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT, typename PointLT> void
pcl::OrganizedEdgeFromNormals<PointT, PointNT, PointLT>::compute (pcl::PointCloud<PointLT>& labels, std::vector<pcl::PointIndices>& label_indices) const
{
pcl::Label invalid_pt;
invalid_pt.label = unsigned (0);
labels.points.resize (input_->points.size (), invalid_pt);
labels.width = input_->width;
labels.height = input_->height;
OrganizedEdgeBase<PointT, PointLT>::extractEdges (labels);
extractEdges (labels);
this->assignLabelIndices (labels, label_indices);
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT, typename PointLT> void
pcl::OrganizedEdgeFromNormals<PointT, PointNT, PointLT>::extractEdges (pcl::PointCloud<PointLT>& labels) const
{
if ((detecting_edge_types_ & EDGELABEL_HIGH_CURVATURE))
{
pcl::PointCloud<PointXYZI> nx, ny;
nx.width = normals_->width;
nx.height = normals_->height;
nx.resize (normals_->height*normals_->width);
ny.width = normals_->width;
ny.height = normals_->height;
ny.resize (normals_->height*normals_->width);
for (uint32_t row=0; row<normals_->height; row++)
{
for (uint32_t col=0; col<normals_->width; col++)
{
nx (col, row).intensity = normals_->points[row*normals_->width + col].normal_x;
ny (col, row).intensity = normals_->points[row*normals_->width + col].normal_y;
}
}
pcl::PointCloud<pcl::PointXYZIEdge> img_edge;
pcl::Edge<PointXYZI, pcl::PointXYZIEdge> edge;
edge.setHysteresisThresholdLow (th_hc_canny_low_);
edge.setHysteresisThresholdHigh (th_hc_canny_high_);
edge.canny (nx, ny, img_edge);
for (uint32_t row=0; row<labels.height; row++)
{
for (uint32_t col=0; col<labels.width; col++)
{
if (img_edge (col, row).magnitude == 255.f)
labels[row * labels.width + col].label |= EDGELABEL_HIGH_CURVATURE;
}
}
}
}
//////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT, typename PointLT> void
pcl::OrganizedEdgeFromRGBNormals<PointT, PointNT, PointLT>::compute (pcl::PointCloud<PointLT>& labels, std::vector<pcl::PointIndices>& label_indices) const
{
pcl::Label invalid_pt;
invalid_pt.label = unsigned (0);
labels.points.resize (input_->points.size (), invalid_pt);
labels.width = input_->width;
labels.height = input_->height;
OrganizedEdgeBase<PointT, PointLT>::extractEdges (labels);
OrganizedEdgeFromNormals<PointT, PointNT, PointLT>::extractEdges (labels);
OrganizedEdgeFromRGB<PointT, PointLT>::extractEdges (labels);
this->assignLabelIndices (labels, label_indices);
}
#define PCL_INSTANTIATE_OrganizedEdgeBase(T,LT) template class PCL_EXPORTS pcl::OrganizedEdgeBase<T,LT>;
#define PCL_INSTANTIATE_OrganizedEdgeFromRGB(T,LT) template class PCL_EXPORTS pcl::OrganizedEdgeFromRGB<T,LT>;
#define PCL_INSTANTIATE_OrganizedEdgeFromNormals(T,NT,LT) template class PCL_EXPORTS pcl::OrganizedEdgeFromNormals<T,NT,LT>;
#define PCL_INSTANTIATE_OrganizedEdgeFromRGBNormals(T,NT,LT) template class PCL_EXPORTS pcl::OrganizedEdgeFromRGBNormals<T,NT,LT>;
#endif //#ifndef PCL_FEATURES_IMPL_ORGANIZED_EDGE_DETECTION_H_
pcl::OrganizedEdgeFromNormals
Definition: organized_edge_detection.h:269
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::OrganizedEdgeBase::extractEdges
void extractEdges(pcl::PointCloud< PointLT > &labels) const
Perform the 3D edge detection (edges from depth discontinuities) and assign point indices for each ed...
Definition: organized_edge_detection.hpp:88
pcl::OrganizedEdgeBase::Neighbor
Definition: organized_edge_detection.h:156
pcl::OrganizedEdgeBase::Neighbor::d_x
int d_x
Definition: organized_edge_detection.h:164
pcl::OrganizedEdgeFromRGBNormals::compute
void compute(pcl::PointCloud< PointLT > &labels, std::vector< pcl::PointIndices > &label_indices) const
Perform the 3D edge detection (edges from depth discontinuities, RGB Canny edge, and high curvature r...
Definition: organized_edge_detection.hpp:334
pcl::Edge::setHysteresisThresholdHigh
void setHysteresisThresholdHigh(float threshold)
Definition: edge.h:153
pcl::Edge
Definition: edge.h:48
pcl::PointCloud::height
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
pcl::OrganizedEdgeBase::compute
void compute(pcl::PointCloud< PointLT > &labels, std::vector< pcl::PointIndices > &label_indices) const
Perform the 3D edge detection (edges from depth discontinuities)
Definition: organized_edge_detection.hpp:54
pcl::PointCloud::Ptr
boost::shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:428
pcl::Edge::setInputCloud
void setInputCloud(PointCloudInPtr input)
Set the input point cloud pointer.
Definition: edge.h:297
pcl::PointCloud::width
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
pcl::OrganizedEdgeFromNormals::extractEdges
void extractEdges(pcl::PointCloud< PointLT > &labels) const
Perform the 3D edge detection (edges from depth discontinuities and high curvature regions) ...
Definition: organized_edge_detection.hpp:292
pcl::OrganizedEdgeFromRGB
Definition: organized_edge_detection.h:183
pcl::Label::label
uint32_t label
Definition: point_types.hpp:505
pcl::Edge::setHysteresisThresholdLow
void setHysteresisThresholdLow(float threshold)
Definition: edge.h:147
pcl::OrganizedEdgeBase::Neighbor::d_y
int d_y
Definition: organized_edge_detection.h:165
pcl::Edge::canny
void canny(const pcl::PointCloud< PointInT > &input_x, const pcl::PointCloud< PointInT > &input_y, pcl::PointCloud< PointOutT > &output)
Perform Canny edge detection with two separated input images for horizontal and vertical derivatives...
Definition: edge.hpp:403
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points. ...
Definition: projection_matrix.h:53
pcl::OrganizedEdgeBase
OrganizedEdgeBase, OrganizedEdgeFromRGB, OrganizedEdgeFromNormals, and OrganizedEdgeFromRGBNormals fi...
Definition: organized_edge_detection.h:58
pcl::OrganizedEdgeFromNormals::compute
void compute(pcl::PointCloud< PointLT > &labels, std::vector< pcl::PointIndices > &label_indices) const
Perform the 3D edge detection (edges from depth discontinuities and high curvature regions) and assig...
Definition: organized_edge_detection.hpp:276
pcl::OrganizedEdgeFromRGB::extractEdges
void extractEdges(pcl::PointCloud< PointLT > &labels) const
Perform the 3D edge detection (edges from depth discontinuities and RGB Canny edge) ...
Definition: organized_edge_detection.hpp:244
pcl::Label
Definition: point_types.hpp:503
pcl::OrganizedEdgeFromRGB::compute
void compute(pcl::PointCloud< PointLT > &labels, std::vector< pcl::PointIndices > &label_indices) const
Perform the 3D edge detection (edges from depth discontinuities and RGB Canny edge) and assign point ...
Definition: organized_edge_detection.hpp:228
pcl::PointCloud::resize
void resize(size_t n)
Resize the cloud.
Definition: point_cloud.h:455
pcl::OrganizedEdgeBase::assignLabelIndices
void assignLabelIndices(pcl::PointCloud< PointLT > &labels, std::vector< pcl::PointIndices > &label_indices) const
Assign point indices for each edge label.
Definition: organized_edge_detection.hpp:69
pcl::Edge::detectEdgeCanny
void detectEdgeCanny(pcl::PointCloud< PointOutT > &output)
All edges of magnitude above t_high are always classified as edges.
Definition: edge.hpp:329
pcl::OrganizedEdgeBase::Neighbor::d_index
int d_index
Definition: organized_edge_detection.h:166