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, 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; }