template <typename PointInT, typename PointOutT, typename NormalT> void
pcl::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)
{
#ifdef _OPENMP
#pragma omp parallel for 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 ¢er = 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 = sqrt (sn1);
sn2 = 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
{
#ifdef _OPENMP
#pragma omp parallel for 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 ¢er = 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
std::vector<int> indices = *indices_;
std::sort (indices.begin (), indices.end (),
boost::bind (&TrajkovicKeypoint3D::greaterCornernessAtIndices, this, _1, _2));
output.clear ();
output.reserve (input_->size ());
std::vector<bool> occupency_map (indices.size (), false);
const int width (input_->width);
const int height (input_->height);
const int occupency_map_size (indices.size ());
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads (threads_)
#endif
for (int i = 0; i < indices.size (); ++i)
{
int idx = indices[i];
if ((response_->points[idx] < second_threshold_) || occupency_map[idx])
continue;
PointOutT p;
p.getVector3fMap () = input_->points[idx].getVector3fMap ();
p.intensity = response_->points [idx];
#ifdef _OPENMP
#pragma omp critical
#endif
{
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 = static_cast<uint32_t> (output.size());
// we don not change the denseness
output.is_dense = true;
}
template <typename PointInT, typename PointOutT> void
pcl::IntegralImageNormalEstimation<PointInT, PointOutT>::computePointNormalMirror (
const int pos_x, const int pos_y, const unsigned point_index, PointOutT &normal)
{
float bad_point = std::numeric_limits<float>::quiet_NaN ();
const int width = input_->width;
const int height = input_->height;
// ==============================================================
if (normal_estimation_method_ == COVARIANCE_MATRIX)
{
if (!init_covariance_matrix_)
initCovarianceMatrixMethod ();
const int start_x = pos_x - rect_width_2_;
const int start_y = pos_y - rect_height_2_;
const int end_x = start_x + rect_width_;
const int end_y = start_y + rect_height_;
unsigned count = 0;
sumArea<unsigned>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFiniteElementsCountSE, &integral_image_XYZ_, _1, _2, _3, _4), count);
// no valid points within the rectangular reagion?
if (count == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = bad_point;
return;
}
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
Eigen::Vector3f center;
typename IntegralImage2D<float, 3>::SecondOrderType so_elements;
typename IntegralImage2D<float, 3>::ElementType tmp_center;
typename IntegralImage2D<float, 3>::SecondOrderType tmp_so_elements;
center[0] = 0;
center[1] = 0;
center[2] = 0;
tmp_center[0] = 0;
tmp_center[1] = 0;
tmp_center[2] = 0;
so_elements[0] = 0;
so_elements[1] = 0;
so_elements[2] = 0;
so_elements[3] = 0;
so_elements[4] = 0;
so_elements[5] = 0;
sumArea<typename IntegralImage2D<float, 3>::ElementType>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFirstOrderSumSE, &integral_image_XYZ_, _1, _2, _3, _4), tmp_center);
sumArea<typename IntegralImage2D<float, 3>::SecondOrderType>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getSecondOrderSumSE, &integral_image_XYZ_, _1, _2, _3, _4), so_elements);
center[0] = float (tmp_center[0]);
center[1] = float (tmp_center[1]);
center[2] = float (tmp_center[2]);
covariance_matrix.coeffRef (0) = static_cast<float> (so_elements [0]);
covariance_matrix.coeffRef (1) = covariance_matrix.coeffRef (3) = static_cast<float> (so_elements [1]);
covariance_matrix.coeffRef (2) = covariance_matrix.coeffRef (6) = static_cast<float> (so_elements [2]);
covariance_matrix.coeffRef (4) = static_cast<float> (so_elements [3]);
covariance_matrix.coeffRef (5) = covariance_matrix.coeffRef (7) = static_cast<float> (so_elements [4]);
covariance_matrix.coeffRef (8) = static_cast<float> (so_elements [5]);
covariance_matrix -= (center * center.transpose ()) / static_cast<float> (count);
float eigen_value;
Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, eigen_vector[0], eigen_vector[1], eigen_vector[2]);
normal.getNormalVector3fMap () = eigen_vector;
// Compute the curvature surface change
if (eigen_value > 0.0)
normal.curvature = fabsf (eigen_value / (covariance_matrix.coeff (0) + covariance_matrix.coeff (4) + covariance_matrix.coeff (8)));
else
normal.curvature = 0;
return;
}
// =======================================================
else if (normal_estimation_method_ == AVERAGE_3D_GRADIENT)
{
if (!init_average_3d_gradient_)
initAverage3DGradientMethod ();
const int start_x = pos_x - rect_width_2_;
const int start_y = pos_y - rect_height_2_;
const int end_x = start_x + rect_width_;
const int end_y = start_y + rect_height_;
unsigned count_x = 0;
unsigned count_y = 0;
sumArea<unsigned>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFiniteElementsCountSE, &integral_image_DX_, _1, _2, _3, _4), count_x);
sumArea<unsigned>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFiniteElementsCountSE, &integral_image_DY_, _1, _2, _3, _4), count_y);
if (count_x == 0 || count_y == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = bad_point;
return;
}
Eigen::Vector3d gradient_x (0, 0, 0);
Eigen::Vector3d gradient_y (0, 0, 0);
sumArea<typename IntegralImage2D<float, 3>::ElementType>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFirstOrderSumSE, &integral_image_DX_, _1, _2, _3, _4), gradient_x);
sumArea<typename IntegralImage2D<float, 3>::ElementType>(start_x, start_y, end_x, end_y, width, height, boost::bind(&IntegralImage2D<float, 3>::getFirstOrderSumSE, &integral_image_DY_, _1, _2, _3, _4), gradient_y);
Eigen::Vector3d normal_vector = gradient_y.cross (gradient_x);
double normal_length = normal_vector.squaredNorm ();
if (normal_length == 0.0f)
{
normal.getNormalVector3fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
normal_vector /= sqrt (normal_length);
float nx = static_cast<float> (normal_vector [0]);
float ny = static_cast<float> (normal_vector [1]);
float nz = static_cast<float> (normal_vector [2]);
flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, nx, ny, nz);
normal.normal_x = nx;
normal.normal_y = ny;
normal.normal_z = nz;
normal.curvature = bad_point;
return;
}
// ======================================================
else if (normal_estimation_method_ == AVERAGE_DEPTH_CHANGE)
{
if (!init_depth_change_)
initAverageDepthChangeMethod ();
int point_index_L_x = pos_x - rect_width_4_ - 1;
int point_index_L_y = pos_y;
int point_index_R_x = pos_x + rect_width_4_ + 1;
int point_index_R_y = pos_y;
int point_index_U_x = pos_x - 1;
int point_index_U_y = pos_y - rect_height_4_;
int point_index_D_x = pos_x + 1;
int point_index_D_y = pos_y + rect_height_4_;
if (point_index_L_x < 0)
point_index_L_x = -point_index_L_x;
if (point_index_U_x < 0)
point_index_U_x = -point_index_U_x;
if (point_index_U_y < 0)
point_index_U_y = -point_index_U_y;
if (point_index_R_x >= width)
point_index_R_x = width-(point_index_R_x-(width-1));
if (point_index_D_x >= width)
point_index_D_x = width-(point_index_D_x-(width-1));
if (point_index_D_y >= height)
point_index_D_y = height-(point_index_D_y-(height-1));
const int start_x_L = pos_x - rect_width_2_;
const int start_y_L = pos_y - rect_height_4_;
const int end_x_L = start_x_L + rect_width_2_;
const int end_y_L = start_y_L + rect_height_2_;
const int start_x_R = pos_x + 1;
const int start_y_R = pos_y - rect_height_4_;
const int end_x_R = start_x_R + rect_width_2_;
const int end_y_R = start_y_R + rect_height_2_;
const int start_x_U = pos_x - rect_width_4_;
const int start_y_U = pos_y - rect_height_2_;
const int end_x_U = start_x_U + rect_width_2_;
const int end_y_U = start_y_U + rect_height_2_;
const int start_x_D = pos_x - rect_width_4_;
const int start_y_D = pos_y + 1;
const int end_x_D = start_x_D + rect_width_2_;
const int end_y_D = start_y_D + rect_height_2_;
unsigned count_L_z = 0;
unsigned count_R_z = 0;
unsigned count_U_z = 0;
unsigned count_D_z = 0;
sumArea<unsigned>(start_x_L, start_y_L, end_x_L, end_y_L, width, height, boost::bind(&IntegralImage2D<float, 1>::getFiniteElementsCountSE, &integral_image_depth_, _1, _2, _3, _4), count_L_z);
sumArea<unsigned>(start_x_R, start_y_R, end_x_R, end_y_R, width, height, boost::bind(&IntegralImage2D<float, 1>::getFiniteElementsCountSE, &integral_image_depth_, _1, _2, _3, _4), count_R_z);
sumArea<unsigned>(start_x_U, start_y_U, end_x_U, end_y_U, width, height, boost::bind(&IntegralImage2D<float, 1>::getFiniteElementsCountSE, &integral_image_depth_, _1, _2, _3, _4), count_U_z);
sumArea<unsigned>(start_x_D, start_y_D, end_x_D, end_y_D, width, height, boost::bind(&IntegralImage2D<float, 1>::getFiniteElementsCountSE, &integral_image_depth_, _1, _2, _3, _4), count_D_z);
if (count_L_z == 0 || count_R_z == 0 || count_U_z == 0 || count_D_z == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = bad_point;
return;
}
float mean_L_z = 0;
float mean_R_z = 0;
float mean_U_z = 0;
float mean_D_z = 0;
sumArea<float>(start_x_L, start_y_L, end_x_L, end_y_L, width, height, boost::bind(&IntegralImage2D<float, 1>::getFirstOrderSumSE, &integral_image_depth_, _1, _2, _3, _4), mean_L_z);
sumArea<float>(start_x_R, start_y_R, end_x_R, end_y_R, width, height, boost::bind(&IntegralImage2D<float, 1>::getFirstOrderSumSE, &integral_image_depth_, _1, _2, _3, _4), mean_R_z);
sumArea<float>(start_x_U, start_y_U, end_x_U, end_y_U, width, height, boost::bind(&IntegralImage2D<float, 1>::getFirstOrderSumSE, &integral_image_depth_, _1, _2, _3, _4), mean_U_z);
sumArea<float>(start_x_D, start_y_D, end_x_D, end_y_D, width, height, boost::bind(&IntegralImage2D<float, 1>::getFirstOrderSumSE, &integral_image_depth_, _1, _2, _3, _4), mean_D_z);
mean_L_z /= float (count_L_z);
mean_R_z /= float (count_R_z);
mean_U_z /= float (count_U_z);
mean_D_z /= float (count_D_z);
PointInT pointL = input_->points[point_index_L_y*width + point_index_L_x];
PointInT pointR = input_->points[point_index_R_y*width + point_index_R_x];
PointInT pointU = input_->points[point_index_U_y*width + point_index_U_x];
PointInT pointD = input_->points[point_index_D_y*width + point_index_D_x];
const float mean_x_z = mean_R_z - mean_L_z;
const float mean_y_z = mean_D_z - mean_U_z;
const float mean_x_x = pointR.x - pointL.x;
const float mean_x_y = pointR.y - pointL.y;
const float mean_y_x = pointD.x - pointU.x;
const float mean_y_y = pointD.y - pointU.y;
float normal_x = mean_x_y * mean_y_z - mean_x_z * mean_y_y;
float normal_y = mean_x_z * mean_y_x - mean_x_x * mean_y_z;
float normal_z = mean_x_x * mean_y_y - mean_x_y * mean_y_x;
const float normal_length = (normal_x * normal_x + normal_y * normal_y + normal_z * normal_z);
if (normal_length == 0.0f)
{
normal.getNormalVector3fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, normal_x, normal_y, normal_z);
const float scale = 1.0f / sqrtf (normal_length);
normal.normal_x = normal_x * scale;
normal.normal_y = normal_y * scale;
normal.normal_z = normal_z * scale;
normal.curvature = bad_point;
return;
}
// ========================================================
else if (normal_estimation_method_ == SIMPLE_3D_GRADIENT)
{
PCL_THROW_EXCEPTION (PCLException, "BORDER_POLICY_MIRROR not supported for normal estimation method SIMPLE_3D_GRADIENT");
}
normal.getNormalVector3fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
template <typename PointInT, typename PointOutT> void
pcl::IntegralImageNormalEstimation<PointInT, PointOutT>::computePointNormal (
const int pos_x, const int pos_y, const unsigned point_index, PointOutT &normal)
{
float bad_point = std::numeric_limits<float>::quiet_NaN ();
if (normal_estimation_method_ == COVARIANCE_MATRIX)
{
if (!init_covariance_matrix_)
initCovarianceMatrixMethod ();
unsigned count = integral_image_XYZ_.getFiniteElementsCount (pos_x - (rect_width_2_), pos_y - (rect_height_2_), rect_width_, rect_height_);
// no valid points within the rectangular reagion?
if (count == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = std::numeric_limits<float>::quiet_NaN ();
return;
}
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
Eigen::Vector3f center;
typename IntegralImage2D<float, 3>::SecondOrderType so_elements;
center = integral_image_XYZ_.getFirstOrderSum(pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_).cast<float> ();
so_elements = integral_image_XYZ_.getSecondOrderSum(pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
covariance_matrix.coeffRef (0) = static_cast<float> (so_elements [0]);
covariance_matrix.coeffRef (1) = covariance_matrix.coeffRef (3) = static_cast<float> (so_elements [1]);
covariance_matrix.coeffRef (2) = covariance_matrix.coeffRef (6) = static_cast<float> (so_elements [2]);
covariance_matrix.coeffRef (4) = static_cast<float> (so_elements [3]);
covariance_matrix.coeffRef (5) = covariance_matrix.coeffRef (7) = static_cast<float> (so_elements [4]);
covariance_matrix.coeffRef (8) = static_cast<float> (so_elements [5]);
covariance_matrix -= (center * center.transpose ()) / static_cast<float> (count);
float eigen_value;
Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
//pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, eigen_vector);
pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, eigen_vector[0], eigen_vector[1], eigen_vector[2]);
normal.getNormalVector3fMap () = eigen_vector;
// Compute the curvature surface change
if (eigen_value > 0.0)
normal.curvature = fabsf (eigen_value / (covariance_matrix.coeff (0) + covariance_matrix.coeff (4) + covariance_matrix.coeff (8)));
else
normal.curvature = 0;
return;
}
else if (normal_estimation_method_ == AVERAGE_3D_GRADIENT)
{
if (!init_average_3d_gradient_)
initAverage3DGradientMethod ();
unsigned count_x = integral_image_DX_.getFiniteElementsCount (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
unsigned count_y = integral_image_DY_.getFiniteElementsCount (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
if (count_x == 0 || count_y == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = std::numeric_limits<float>::quiet_NaN ();
return;
}
Eigen::Vector3d gradient_x = integral_image_DX_.getFirstOrderSum (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
Eigen::Vector3d gradient_y = integral_image_DY_.getFirstOrderSum (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
Eigen::Vector3d normal_vector = gradient_y.cross (gradient_x);
double normal_length = normal_vector.squaredNorm ();
if (normal_length == 0.0f)
{
normal.getNormalVector4fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
normal_vector /= sqrt (normal_length);
float nx = static_cast<float> (normal_vector [0]);
float ny = static_cast<float> (normal_vector [1]);
float nz = static_cast<float> (normal_vector [2]);
//pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, normal_vector);
pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, nx, ny, nz);
normal.normal_x = nx;
normal.normal_y = ny;
normal.normal_z = nz;
normal.curvature = bad_point;
return;
}
else if (normal_estimation_method_ == AVERAGE_DEPTH_CHANGE)
{
if (!init_depth_change_)
initAverageDepthChangeMethod ();
// unsigned count = integral_image_depth_.getFiniteElementsCount (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, rect_height_);
// if (count == 0)
// {
// normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = std::numeric_limits<float>::quiet_NaN ();
// return;
// }
// const float mean_L_z = integral_image_depth_.getFirstOrderSum (pos_x - rect_width_2_ - 1, pos_y - rect_height_2_ , rect_width_ - 1, rect_height_ - 1) / ((rect_width_-1)*(rect_height_-1));
// const float mean_R_z = integral_image_depth_.getFirstOrderSum (pos_x - rect_width_2_ + 1, pos_y - rect_height_2_ , rect_width_ - 1, rect_height_ - 1) / ((rect_width_-1)*(rect_height_-1));
// const float mean_U_z = integral_image_depth_.getFirstOrderSum (pos_x - rect_width_2_ , pos_y - rect_height_2_ - 1, rect_width_ - 1, rect_height_ - 1) / ((rect_width_-1)*(rect_height_-1));
// const float mean_D_z = integral_image_depth_.getFirstOrderSum (pos_x - rect_width_2_ , pos_y - rect_height_2_ + 1, rect_width_ - 1, rect_height_ - 1) / ((rect_width_-1)*(rect_height_-1));
// width and height are at least 3 x 3
unsigned count_L_z = integral_image_depth_.getFiniteElementsCount (pos_x - rect_width_2_, pos_y - rect_height_4_, rect_width_2_, rect_height_2_);
unsigned count_R_z = integral_image_depth_.getFiniteElementsCount (pos_x + 1 , pos_y - rect_height_4_, rect_width_2_, rect_height_2_);
unsigned count_U_z = integral_image_depth_.getFiniteElementsCount (pos_x - rect_width_4_, pos_y - rect_height_2_, rect_width_2_, rect_height_2_);
unsigned count_D_z = integral_image_depth_.getFiniteElementsCount (pos_x - rect_width_4_, pos_y + 1 , rect_width_2_, rect_height_2_);
if (count_L_z == 0 || count_R_z == 0 || count_U_z == 0 || count_D_z == 0)
{
normal.normal_x = normal.normal_y = normal.normal_z = normal.curvature = std::numeric_limits<float>::quiet_NaN ();
return;
}
float mean_L_z = static_cast<float> (integral_image_depth_.getFirstOrderSum (pos_x - rect_width_2_, pos_y - rect_height_4_, rect_width_2_, rect_height_2_) / count_L_z);
float mean_R_z = static_cast<float> (integral_image_depth_.getFirstOrderSum (pos_x + 1 , pos_y - rect_height_4_, rect_width_2_, rect_height_2_) / count_R_z);
float mean_U_z = static_cast<float> (integral_image_depth_.getFirstOrderSum (pos_x - rect_width_4_, pos_y - rect_height_2_, rect_width_2_, rect_height_2_) / count_U_z);
float mean_D_z = static_cast<float> (integral_image_depth_.getFirstOrderSum (pos_x - rect_width_4_, pos_y + 1 , rect_width_2_, rect_height_2_) / count_D_z);
PointInT pointL = input_->points[point_index - rect_width_4_ - 1];
PointInT pointR = input_->points[point_index + rect_width_4_ + 1];
PointInT pointU = input_->points[point_index - rect_height_4_ * input_->width - 1];
PointInT pointD = input_->points[point_index + rect_height_4_ * input_->width + 1];
const float mean_x_z = mean_R_z - mean_L_z;
const float mean_y_z = mean_D_z - mean_U_z;
const float mean_x_x = pointR.x - pointL.x;
const float mean_x_y = pointR.y - pointL.y;
const float mean_y_x = pointD.x - pointU.x;
const float mean_y_y = pointD.y - pointU.y;
float normal_x = mean_x_y * mean_y_z - mean_x_z * mean_y_y;
float normal_y = mean_x_z * mean_y_x - mean_x_x * mean_y_z;
float normal_z = mean_x_x * mean_y_y - mean_x_y * mean_y_x;
const float normal_length = (normal_x * normal_x + normal_y * normal_y + normal_z * normal_z);
if (normal_length == 0.0f)
{
normal.getNormalVector4fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, normal_x, normal_y, normal_z);
const float scale = 1.0f / sqrt (normal_length);
normal.normal_x = normal_x * scale;
normal.normal_y = normal_y * scale;
normal.normal_z = normal_z * scale;
normal.curvature = bad_point;
return;
}
else if (normal_estimation_method_ == SIMPLE_3D_GRADIENT)
{
if (!init_simple_3d_gradient_)
initSimple3DGradientMethod ();
// this method does not work if lots of NaNs are in the neighborhood of the point
Eigen::Vector3d gradient_x = integral_image_XYZ_.getFirstOrderSum (pos_x + rect_width_2_, pos_y - rect_height_2_, 1, rect_height_) -
integral_image_XYZ_.getFirstOrderSum (pos_x - rect_width_2_, pos_y - rect_height_2_, 1, rect_height_);
Eigen::Vector3d gradient_y = integral_image_XYZ_.getFirstOrderSum (pos_x - rect_width_2_, pos_y + rect_height_2_, rect_width_, 1) -
integral_image_XYZ_.getFirstOrderSum (pos_x - rect_width_2_, pos_y - rect_height_2_, rect_width_, 1);
Eigen::Vector3d normal_vector = gradient_y.cross (gradient_x);
double normal_length = normal_vector.squaredNorm ();
if (normal_length == 0.0f)
{
normal.getNormalVector4fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
normal_vector /= sqrt (normal_length);
float nx = static_cast<float> (normal_vector [0]);
float ny = static_cast<float> (normal_vector [1]);
float nz = static_cast<float> (normal_vector [2]);
//pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, normal_vector);
pcl::flipNormalTowardsViewpoint (input_->points[point_index], vpx_, vpy_, vpz_, nx, ny, nz);
normal.normal_x = nx;
normal.normal_y = ny;
normal.normal_z = nz;
normal.curvature = bad_point;
return;
}
normal.getNormalVector4fMap ().setConstant (bad_point);
normal.curvature = bad_point;
return;
}
template<typename PointInT, typename PointOutT, typename NormalT> void
pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::detectKeypoints (PointCloudOut &output)
{
// Make sure the output cloud is empty
output.points.clear ();
if (border_radius_ > 0.0)
edge_points_ = getBoundaryPoints (*(input_->makeShared ()), border_radius_, angle_threshold_);
bool* borders = new bool [input_->size()];
int index;
#ifdef _OPENMP
#pragma omp parallel for num_threads(threads_)
#endif
for (index = 0; index < int (input_->size ()); index++)
{
borders[index] = false;
PointInT current_point = input_->points[index];
if ((border_radius_ > 0.0) && (pcl::isFinite(current_point)))
{
std::vector<int> nn_indices;
std::vector<float> nn_distances;
this->searchForNeighbors (static_cast<int> (index), border_radius_, nn_indices, nn_distances);
for (size_t j = 0 ; j < nn_indices.size (); j++)
{
if (edge_points_[nn_indices[j]])
{
borders[index] = true;
break;
}
}
}
}
#ifdef _OPENMP
Eigen::Vector3d *omp_mem = new Eigen::Vector3d[threads_];
for (size_t i = 0; i < threads_; i++)
omp_mem[i].setZero (3);
#else
Eigen::Vector3d *omp_mem = new Eigen::Vector3d[1];
omp_mem[0].setZero (3);
#endif
double *prg_local_mem = new double[input_->size () * 3];
double **prg_mem = new double * [input_->size ()];
for (size_t i = 0; i < input_->size (); i++)
prg_mem[i] = prg_local_mem + 3 * i;
#ifdef _OPENMP
#pragma omp parallel for num_threads(threads_)
#endif
for (index = 0; index < static_cast<int> (input_->size ()); index++)
{
#ifdef _OPENMP
int tid = omp_get_thread_num ();
#else
int tid = 0;
#endif
PointInT current_point = input_->points[index];
if ((!borders[index]) && pcl::isFinite(current_point))
{
//if the considered point is not a border point and the point is "finite", then compute the scatter matrix
Eigen::Matrix3d cov_m = Eigen::Matrix3d::Zero ();
getScatterMatrix (static_cast<int> (index), cov_m);
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver (cov_m);
const double& e1c = solver.eigenvalues ()[2];
const double& e2c = solver.eigenvalues ()[1];
const double& e3c = solver.eigenvalues ()[0];
if (!pcl_isfinite (e1c) || !pcl_isfinite (e2c) || !pcl_isfinite (e3c))
continue;
if (e3c < 0)
{
PCL_WARN ("[pcl::%s::detectKeypoints] : The third eigenvalue is negative! Skipping the point with index %i.\n",
name_.c_str (), index);
continue;
}
omp_mem[tid][0] = e2c / e1c;
omp_mem[tid][1] = e3c / e2c;;
omp_mem[tid][2] = e3c;
}
for (int d = 0; d < omp_mem[tid].size (); d++)
prg_mem[index][d] = omp_mem[tid][d];
}
for (index = 0; index < int (input_->size ()); index++)
{
if (!borders[index])
{
if ((prg_mem[index][0] < gamma_21_) && (prg_mem[index][1] < gamma_32_))
third_eigen_value_[index] = prg_mem[index][2];
}
}
bool* feat_max = new bool [input_->size()];
bool is_max;
#ifdef _OPENMP
#pragma omp parallel for private(is_max) num_threads(threads_)
#endif
for (index = 0; index < int (input_->size ()); index++)
{
feat_max [index] = false;
PointInT current_point = input_->points[index];
if ((third_eigen_value_[index] > 0.0) && (pcl::isFinite(current_point)))
{
std::vector<int> nn_indices;
std::vector<float> nn_distances;
int n_neighbors;
this->searchForNeighbors (static_cast<int> (index), non_max_radius_, nn_indices, nn_distances);
n_neighbors = static_cast<int> (nn_indices.size ());
if (n_neighbors >= min_neighbors_)
{
is_max = true;
for (int j = 0 ; j < n_neighbors; j++)
if (third_eigen_value_[index] < third_eigen_value_[nn_indices[j]])
is_max = false;
if (is_max)
feat_max[index] = true;
}
}
}
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads(threads_)
#endif
for (index = 0; index < int (input_->size ()); index++)
{
if (feat_max[index])
#ifdef _OPENMP
#pragma omp critical
#endif
{
PointOutT p;
p.getVector3fMap () = input_->points[index].getVector3fMap ();
output.points.push_back(p);
keypoints_indices_->indices.push_back (index);
}
}
output.header = input_->header;
output.width = static_cast<uint32_t> (output.points.size ());
output.height = 1;
// Clear the contents of variables and arrays before the beginning of the next computation.
if (border_radius_ > 0.0)
normals_.reset (new pcl::PointCloud<NormalT>);
delete[] borders;
delete[] prg_mem;
delete[] prg_local_mem;
delete[] feat_max;
}
