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 IntensityT> void pcl::HarrisKeypoint2D<PointInT, PointOutT, IntensityT>::detectKeypoints (PointCloudOut &output) { derivatives_cols_.resize (input_->width, input_->height); derivatives_rows_.resize (input_->width, input_->height); //Compute cloud intensities first derivatives along columns and rows //!!! nsallem 20120220 : we don't test here for density so if one term in nan the result is nan int w = static_cast<int> (input_->width) - 1; int h = static_cast<int> (input_->height) - 1; // j = 0 --> j-1 out of range ; use 0 // i = 0 --> i-1 out of range ; use 0 derivatives_cols_(0,0) = (intensity_ ((*input_) (0,1)) - intensity_ ((*input_) (0,0))) * 0.5; derivatives_rows_(0,0) = (intensity_ ((*input_) (1,0)) - intensity_ ((*input_) (0,0))) * 0.5; // #ifdef _OPENMP // //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_) // #pragma omp parallel for num_threads (threads_) // #endif for(int i = 1; i < w; ++i) { derivatives_cols_(i,0) = (intensity_ ((*input_) (i,1)) - intensity_ ((*input_) (i,0))) * 0.5; } derivatives_rows_(w,0) = (intensity_ ((*input_) (w,0)) - intensity_ ((*input_) (w-1,0))) * 0.5; derivatives_cols_(w,0) = (intensity_ ((*input_) (w,1)) - intensity_ ((*input_) (w,0))) * 0.5; // #ifdef _OPENMP // //#pragma omp parallel for shared (derivatives_cols_, derivatives_rows_, input_) num_threads (threads_) // #pragma omp parallel for num_threads (threads_) // #endif for(int j = 1; j < h; ++j) { // i = 0 --> i-1 out of range ; use 0 derivatives_rows_(0,j) = (intensity_ ((*input_) (1,j)) - intensity_ ((*input_) (0,j))) * 0.5; for(int i = 1; i < w; ++i) { // derivative with respect to rows derivatives_rows_(i,j) = (intensity_ ((*input_) (i+1,j)) - intensity_ ((*input_) (i-1,j))) * 0.5; // derivative with respect to cols derivatives_cols_(i,j) = (intensity_ ((*input_) (i,j+1)) - intensity_ ((*input_) (i,j-1))) * 0.5; } // i = w --> w+1 out of range ; use w derivatives_rows_(w,j) = (intensity_ ((*input_) (w,j)) - intensity_ ((*input_) (w-1,j))) * 0.5; } // j = h --> j+1 out of range use h derivatives_cols_(0,h) = (intensity_ ((*input_) (0,h)) - intensity_ ((*input_) (0,h-1))) * 0.5; derivatives_rows_(0,h) = (intensity_ ((*input_) (1,h)) - intensity_ ((*input_) (0,h))) * 0.5; // #ifdef _OPENMP // //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_) // #pragma omp parallel for num_threads (threads_) // #endif for(int i = 1; i < w; ++i) { derivatives_cols_(i,h) = (intensity_ ((*input_) (i,h)) - intensity_ ((*input_) (i,h-1))) * 0.5; } derivatives_rows_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w-1,h))) * 0.5; derivatives_cols_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w,h-1))) * 0.5; float highest_response_; switch (method_) { case HARRIS: responseHarris(*response_, highest_response_); break; case NOBLE: responseNoble(*response_, highest_response_); break; case LOWE: responseLowe(*response_, highest_response_); break; case TOMASI: responseTomasi(*response_, highest_response_); break; } if (!nonmax_) output = *response_; else { threshold_*= highest_response_; std::sort (indices_->begin (), indices_->end (), boost::bind (&HarrisKeypoint2D::greaterIntensityAtIndices, this, _1, _2)); output.clear (); output.reserve (response_->size()); std::vector<bool> occupency_map (response_->size (), false); int width (response_->width); int height (response_->height); const int occupency_map_size (occupency_map.size ()); #ifdef _OPENMP #pragma omp parallel for shared (output, occupency_map) private (width, height) num_threads(threads_) #endif for (int idx = 0; idx < occupency_map_size; ++idx) { if (occupency_map[idx] || response_->points [indices_->at (idx)].intensity < threshold_ || !isFinite (response_->points[idx])) continue; #ifdef _OPENMP #pragma omp critical #endif output.push_back (response_->at (indices_->at (idx))); int u_end = std::min (width, indices_->at (idx) % width + min_distance_); int v_end = std::min (height, indices_->at (idx) / width + min_distance_); for(int u = std::max (0, indices_->at (idx) % width - min_distance_); u < u_end; ++u) for(int v = std::max (0, indices_->at (idx) / width - min_distance_); v < v_end; ++v) occupency_map[v*input_->width+u] = true; } // if (refine_) // refineCorners (output); output.height = 1; output.width = static_cast<uint32_t> (output.size()); } // we don not change the denseness output.is_dense = input_->is_dense; }