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trajkovic_3d.hpp
/*
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#ifndef PCL_TRAJKOVIC_KEYPOINT_3D_IMPL_H_
#define PCL_TRAJKOVIC_KEYPOINT_3D_IMPL_H_
#include <pcl/features/integral_image_normal.h>
namespace pcl
{
template <typename PointInT, typename PointOutT, typename NormalT> bool
TrajkovicKeypoint3D<PointInT, PointOutT, NormalT>::initCompute ()
{
if (!PCLBase<PointInT>::initCompute ())
return (false);
keypoints_indices_.reset (new pcl::PointIndices);
keypoints_indices_->indices.reserve (input_->size ());
if (indices_->size () != input_->size ())
{
PCL_ERROR ("[pcl::%s::initCompute] %s doesn't support setting indices!\n", name_.c_str ());
return (false);
}
if ((window_size_%2) == 0)
{
PCL_ERROR ("[pcl::%s::initCompute] Window size must be odd!\n", name_.c_str ());
return (false);
}
if (window_size_ < 3)
{
PCL_ERROR ("[pcl::%s::initCompute] Window size must be >= 3x3!\n", name_.c_str ());
return (false);
}
half_window_size_ = window_size_ / 2;
if (!normals_)
{
NormalsPtr normals (new Normals ());
pcl::IntegralImageNormalEstimation<PointInT, NormalT> normal_estimation;
normal_estimation.setNormalEstimationMethod (pcl::IntegralImageNormalEstimation<PointInT, NormalT>::SIMPLE_3D_GRADIENT);
normal_estimation.setInputCloud (input_);
normal_estimation.setNormalSmoothingSize (5.0);
normal_estimation.compute (*normals);
normals_ = normals;
}
if (normals_->size () != input_->size ())
{
PCL_ERROR ("[pcl::%s::initCompute] normals given, but the number of normals does not match the number of input points!\n", name_.c_str ());
return (false);
}
return (true);
}
template <typename PointInT, typename PointOutT, typename NormalT> void
TrajkovicKeypoint3D<PointInT, PointOutT, NormalT>::detectKeypoints (PointCloudOut &output)
{
response_.reset (new pcl::PointCloud<float> (input_->width, input_->height));
const Normals &normals = *normals_;
const PointCloudIn &input = *input_;
pcl::PointCloud<float>& response = *response_;
const int w = static_cast<int> (input_->width) - half_window_size_;
const int h = static_cast<int> (input_->height) - half_window_size_;
if (method_ == FOUR_CORNERS)
{
#if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
#pragma omp parallel for \
default(none) \
shared(input, normals, response) \
num_threads(threads_)
#else
#pragma omp parallel for \
default(none) \
shared(h, input, normals, response, w) \
num_threads(threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT &center = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
// Get rid of isolated points
if (!count) continue;
float sn1 = squaredNormalsDiff (up, center);
float sn2 = squaredNormalsDiff (down, center);
float r1 = sn1 + sn2;
float r2 = squaredNormalsDiff (right, center) + squaredNormalsDiff (left, center);
float d = std::min (r1, r2);
if (d < first_threshold_) continue;
sn1 = std::sqrt (sn1);
sn2 = std::sqrt (sn2);
float b1 = normalsDiff (right, up) * sn1;
b1+= normalsDiff (left, down) * sn2;
float b2 = normalsDiff (right, down) * sn2;
b2+= normalsDiff (left, up) * sn1;
float B = std::min (b1, b2);
float A = r2 - r1 - 2*B;
response (i,j) = ((B < 0) && ((B + A) > 0)) ? r1 - ((B*B)/A) : d;
}
}
}
else
{
#if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
#pragma omp parallel for \
default(none) \
shared(input, normals, response) \
num_threads(threads_)
#else
#pragma omp parallel for \
default(none) \
shared(h, input, normals, response, w) \
num_threads(threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT &center = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
const NormalT &upleft = getNormalOrNull (i-half_window_size_, j-half_window_size_, count);
const NormalT &upright = getNormalOrNull (i+half_window_size_, j-half_window_size_, count);
const NormalT &downleft = getNormalOrNull (i-half_window_size_, j+half_window_size_, count);
const NormalT &downright = getNormalOrNull (i+half_window_size_, j+half_window_size_, count);
// Get rid of isolated points
if (!count) continue;
std::vector<float> r (4,0);
r[0] = squaredNormalsDiff (up, center);
r[0]+= squaredNormalsDiff (down, center);
r[1] = squaredNormalsDiff (upright, center);
r[1]+= squaredNormalsDiff (downleft, center);
r[2] = squaredNormalsDiff (right, center);
r[2]+= squaredNormalsDiff (left, center);
r[3] = squaredNormalsDiff (downright, center);
r[3]+= squaredNormalsDiff (upleft, center);
float d = *(std::min_element (r.begin (), r.end ()));
if (d < first_threshold_) continue;
std::vector<float> B (4,0);
std::vector<float> A (4,0);
std::vector<float> sumAB (4,0);
B[0] = normalsDiff (upright, up) * normalsDiff (up, center);
B[0]+= normalsDiff (downleft, down) * normalsDiff (down, center);
B[1] = normalsDiff (right, upright) * normalsDiff (upright, center);
B[1]+= normalsDiff (left, downleft) * normalsDiff (downleft, center);
B[2] = normalsDiff (downright, right) * normalsDiff (downright, center);
B[2]+= normalsDiff (upleft, left) * normalsDiff (upleft, center);
B[3] = normalsDiff (down, downright) * normalsDiff (downright, center);
B[3]+= normalsDiff (up, upleft) * normalsDiff (upleft, center);
A[0] = r[1] - r[0] - B[0] - B[0];
A[1] = r[2] - r[1] - B[1] - B[1];
A[2] = r[3] - r[2] - B[2] - B[2];
A[3] = r[0] - r[3] - B[3] - B[3];
sumAB[0] = A[0] + B[0];
sumAB[1] = A[1] + B[1];
sumAB[2] = A[2] + B[2];
sumAB[3] = A[3] + B[3];
if ((*std::max_element (B.begin (), B.end ()) < 0) &&
(*std::min_element (sumAB.begin (), sumAB.end ()) > 0))
{
std::vector<float> D (4,0);
D[0] = B[0] * B[0] / A[0];
D[1] = B[1] * B[1] / A[1];
D[2] = B[2] * B[2] / A[2];
D[3] = B[3] * B[3] / A[3];
response (i,j) = *(std::min (D.begin (), D.end ()));
}
else
response (i,j) = d;
}
}
}
// Non maximas suppression
pcl::Indices indices = *indices_;
std::sort (indices.begin (), indices.end (), [this] (int p1, int p2) { return greaterCornernessAtIndices (p1, p2); });
output.clear ();
output.reserve (input_->size ());
std::vector<bool> occupency_map (indices.size (), false);
const int width (input_->width);
const int height (input_->height);
#if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
#pragma omp parallel for \
default(none) \
shared(indices, occupency_map, output) \
num_threads(threads_)
#else
#pragma omp parallel for \
default(none) \
shared(height, indices, occupency_map, output, width) \
num_threads(threads_)
#endif
for (int i = 0; i < static_cast<int>(indices.size ()); ++i)
{
int idx = indices[static_cast<std::size_t>(i)];
if (((*response_)[idx] < second_threshold_) || occupency_map[idx])
continue;
PointOutT p;
p.getVector3fMap () = (*input_)[idx].getVector3fMap ();
p.intensity = response_->points [idx];
#pragma omp critical
{
output.push_back (p);
keypoints_indices_->indices.push_back (idx);
}
const int x = idx % width;
const int y = idx / width;
const int u_end = std::min (width, x + half_window_size_);
const int v_end = std::min (height, y + half_window_size_);
for(int v = std::max (0, y - half_window_size_); v < v_end; ++v)
for(int u = std::max (0, x - half_window_size_); u < u_end; ++u)
occupency_map[v*width + u] = true;
}
output.height = 1;
output.width = output.size();
// we don not change the denseness
output.is_dense = true;
}
} // namespace pcl
#define PCL_INSTANTIATE_TrajkovicKeypoint3D(T,U,N) template class PCL_EXPORTS pcl::TrajkovicKeypoint3D<T,U,N>;
#endif
pcl::Feature::compute
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition feature.hpp:194
pcl::IntegralImageNormalEstimation
Surface normal estimation on organized data using integral images.
Definition integral_image_normal.h:66
pcl::IntegralImageNormalEstimation::setNormalEstimationMethod
void setNormalEstimationMethod(NormalEstimationMethod normal_estimation_method)
Set the normal estimation method.
Definition integral_image_normal.h:197
pcl::IntegralImageNormalEstimation::setInputCloud
void setInputCloud(const typename PointCloudIn::ConstPtr &cloud) override
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
Definition integral_image_normal.h:215
pcl::IntegralImageNormalEstimation::setNormalSmoothingSize
void setNormalSmoothingSize(float normal_smoothing_size)
Set the normal smoothing size.
Definition integral_image_normal.h:173
pcl::PCLBase
PCL base class.
Definition pcl_base.h:70
pcl::PointCloud< NormalT >
pcl::TrajkovicKeypoint3D::PointCloudOut
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition trajkovic_3d.h:60
pcl::TrajkovicKeypoint3D::PointCloudIn
typename Keypoint< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition trajkovic_3d.h:59
pcl::TrajkovicKeypoint3D::initCompute
bool initCompute() override
Definition trajkovic_3d.hpp:48
pcl::TrajkovicKeypoint3D::detectKeypoints
void detectKeypoints(PointCloudOut &output) override
Definition trajkovic_3d.hpp:98
pcl::TrajkovicKeypoint3D::NormalsPtr
typename Normals::Ptr NormalsPtr
Definition trajkovic_3d.h:63
pcl::B
@ B
Definition norms.h:54
pcl
Definition convolution.h:46
pcl::isFinite
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition point_tests.h:55
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::Normal
A point structure representing normal coordinates and the surface curvature estimate.
Definition point_types.hpp:794
pcl::PointIndices
Definition PointIndices.h:12