diff --git a/src/phg/matching/descriptor_matcher.cpp b/src/phg/matching/descriptor_matcher.cpp index f4bcd87..2a2f15f 100644 --- a/src/phg/matching/descriptor_matcher.cpp +++ b/src/phg/matching/descriptor_matcher.cpp @@ -1,14 +1,23 @@ #include "descriptor_matcher.h" #include +#include +#include #include "flann_factory.h" void phg::DescriptorMatcher::filterMatchesRatioTest(const std::vector> &matches, std::vector &filtered_matches) { filtered_matches.clear(); + filtered_matches.reserve(matches.size()); - throw std::runtime_error("not implemented yet"); + const float ratio = 0.65f; + for (const std::vector &knn : matches) { + if (knn.size() < 2) + continue; + if (knn[0].distance < ratio*knn[1].distance) + filtered_matches.push_back(knn[0]); + } } @@ -35,42 +44,64 @@ void phg::DescriptorMatcher::filterMatchesClusters(const std::vector points_query.at(i) = keypoints_query[matches[i].queryIdx].pt; points_train.at(i) = keypoints_train[matches[i].trainIdx].pt; } -// -// // размерность всего 2, так что точное KD-дерево -// std::shared_ptr index_params = flannKdTreeIndexParams(TODO); -// std::shared_ptr search_params = flannKsTreeSearchParams(TODO); -// -// std::shared_ptr index_query = flannKdTreeIndex(points_query, index_params); -// std::shared_ptr index_train = flannKdTreeIndex(points_train, index_params); -// -// // для каждой точки найти total neighbors ближайших соседей -// cv::Mat indices_query(n_matches, total_neighbours, CV_32SC1); -// cv::Mat distances2_query(n_matches, total_neighbours, CV_32FC1); -// cv::Mat indices_train(n_matches, total_neighbours, CV_32SC1); -// cv::Mat distances2_train(n_matches, total_neighbours, CV_32FC1); -// -// index_query->knnSearch(points_query, indices_query, distances2_query, total_neighbours, *search_params); -// index_train->knnSearch(points_train, indices_train, distances2_train, total_neighbours, *search_params); -// -// // оценить радиус поиска для каждой картинки -// // NB: radius2_query, radius2_train: квадраты радиуса! -// float radius2_query, radius2_train; -// { -// std::vector max_dists2_query(n_matches); -// std::vector max_dists2_train(n_matches); -// for (int i = 0; i < n_matches; ++i) { -// max_dists2_query[i] = distances2_query.at(i, total_neighbours - 1); -// max_dists2_train[i] = distances2_train.at(i, total_neighbours - 1); -// } -// -// int median_pos = n_matches / 2; -// std::nth_element(max_dists2_query.begin(), max_dists2_query.begin() + median_pos, max_dists2_query.end()); -// std::nth_element(max_dists2_train.begin(), max_dists2_train.begin() + median_pos, max_dists2_train.end()); -// -// radius2_query = max_dists2_query[median_pos] * radius_limit_scale * radius_limit_scale; -// radius2_train = max_dists2_train[median_pos] * radius_limit_scale * radius_limit_scale; -// } -// -// метч остается, если левое и правое множества первых total_neighbors соседей в радиусах поиска(radius2_query, radius2_train) имеют как минимум consistent_matches общих элементов -// // TODO заполнить filtered_matches + + // размерность всего 2, так что точное KD-дерево + std::shared_ptr index_params = flannKdTreeIndexParams(1); + std::shared_ptr search_params = flannKsTreeSearchParams(std::max(n_matches, 64)); + + std::shared_ptr index_query = flannKdTreeIndex(points_query, index_params); + std::shared_ptr index_train = flannKdTreeIndex(points_train, index_params); + + // для каждой точки найти total neighbors ближайших соседей + cv::Mat indices_query(n_matches, (int)total_neighbours, CV_32SC1); + cv::Mat distances2_query(n_matches, (int)total_neighbours, CV_32FC1); + cv::Mat indices_train(n_matches, (int)total_neighbours, CV_32SC1); + cv::Mat distances2_train(n_matches, (int)total_neighbours, CV_32FC1); + + index_query->knnSearch(points_query, indices_query, distances2_query, (int)total_neighbours, *search_params); + index_train->knnSearch(points_train, indices_train, distances2_train, (int)total_neighbours, *search_params); + + // оценить радиус поиска для каждой картинки + // NB: radius2_query, radius2_train: квадраты радиуса! + float radius2_query, radius2_train; + { + std::vector max_dists2_query(n_matches); + std::vector max_dists2_train(n_matches); + for (int i = 0; i < n_matches; ++i) { + max_dists2_query[i] = distances2_query.at(i, (int)total_neighbours - 1); + max_dists2_train[i] = distances2_train.at(i, (int)total_neighbours - 1); + } + + int median_pos = n_matches / 2; + std::nth_element(max_dists2_query.begin(), max_dists2_query.begin() + median_pos, max_dists2_query.end()); + std::nth_element(max_dists2_train.begin(), max_dists2_train.begin() + median_pos, max_dists2_train.end()); + + radius2_query = max_dists2_query[median_pos] * radius_limit_scale * radius_limit_scale; + radius2_train = max_dists2_train[median_pos] * radius_limit_scale * radius_limit_scale; + } + + // метч остается, если левое и правое множества первых total_neighbors соседей в радиусах поиска(radius2_query, radius2_train) имеют как минимум consistent_matches общих элементов + // // TODO заполнить filtered_matches + std::vector tmp(n_matches, -1); + filtered_matches.reserve(n_matches); + + for (int i = 0; i < n_matches; ++i) { + for (int j = 0; j < total_neighbours; ++j) { + int idx_q = indices_query.at(i, (int)j); + float dist2_q = distances2_query.at(i, (int)j); + if (idx_q >= 0 && idx_q < n_matches && dist2_q <= radius2_query) + tmp[idx_q] = i; + } + + int n_common = 0; + for (int j = 0; j < total_neighbours; ++j) { + int idx_t = indices_train.at(i, (int)j); + float dist2_t = distances2_train.at(i, (int)j); + if (idx_t >= 0 && idx_t < n_matches && dist2_t <= radius2_train && tmp[idx_t] == i) + ++n_common; + } + + if (n_common >= (int)consistent_matches) + filtered_matches.push_back(matches[i]); + } } diff --git a/src/phg/matching/flann_matcher.cpp b/src/phg/matching/flann_matcher.cpp index 9e9f518..2081905 100644 --- a/src/phg/matching/flann_matcher.cpp +++ b/src/phg/matching/flann_matcher.cpp @@ -1,4 +1,5 @@ #include +#include #include "flann_matcher.h" #include "flann_factory.h" @@ -6,8 +7,8 @@ phg::FlannMatcher::FlannMatcher() { // параметры для приближенного поиска -// index_params = flannKdTreeIndexParams(TODO); -// search_params = flannKsTreeSearchParams(TODO); + index_params = flannKdTreeIndexParams(4); + search_params = flannKsTreeSearchParams(32); } void phg::FlannMatcher::train(const cv::Mat &train_desc) @@ -17,5 +18,24 @@ void phg::FlannMatcher::train(const cv::Mat &train_desc) void phg::FlannMatcher::knnMatch(const cv::Mat &query_desc, std::vector> &matches, int k) const { - throw std::runtime_error("not implemented yet"); + if (!flann_index) + throw std::runtime_error("not trained"); + if (query_desc.empty()) { + matches.clear(); + return; + } + + cv::Mat indexes(query_desc.rows, k, CV_32SC1); + cv::Mat distances2(query_desc.rows, k, CV_32FC1); + flann_index->knnSearch(query_desc, indexes, distances2, k, *search_params); + + matches.assign(query_desc.rows, {}); + for (int qi = 0; qi < query_desc.rows; ++qi) { + matches[qi].reserve(k); + for (int ki = 0; ki < k; ++ki) { + int train_idx = indexes.at(qi, ki); + float dist = std::sqrt(distances2.at(qi, ki)); + matches[qi].emplace_back(qi, train_idx, 0, dist); + } + } } diff --git a/src/phg/sfm/homography.cpp b/src/phg/sfm/homography.cpp index 5cbc780..b0080a8 100644 --- a/src/phg/sfm/homography.cpp +++ b/src/phg/sfm/homography.cpp @@ -2,6 +2,8 @@ #include #include +#include +#include namespace { @@ -84,8 +86,8 @@ namespace { double w1 = ws1[i]; // 8 elements of matrix + free term as needed by gauss routine -// A.push_back({TODO}); -// A.push_back({TODO}); + A.push_back({w1*x0, w1*y0, w1*w0, 0, 0, 0, -x1*x0, -x1*y0, x1*w0}); + A.push_back({0, 0, 0, w1*x0, w1*y0, w1*w0, -y1*x0, -y1*y0, y1*w0}); } int res = gauss(A, H); @@ -168,57 +170,59 @@ namespace { // * (простое описание для понимания) // * [3] http://ikrisoft.blogspot.com/2015/01/ransac-with-contrario-approach.html -// const int n_matches = points_lhs.size(); -// -// // https://en.wikipedia.org/wiki/Random_sample_consensus#Parameters -// const int n_trials = TODO; -// -// const int n_samples = TODO; -// uint64_t seed = 1; -// const double reprojection_error_threshold_px = 2; -// -// int best_support = 0; -// cv::Mat best_H; -// -// std::vector sample; -// for (int i_trial = 0; i_trial < n_trials; ++i_trial) { -// randomSample(sample, n_matches, n_samples, &seed); -// -// cv::Mat H = estimateHomography4Points(points_lhs[sample[0]], points_lhs[sample[1]], points_lhs[sample[2]], points_lhs[sample[3]], -// points_rhs[sample[0]], points_rhs[sample[1]], points_rhs[sample[2]], points_rhs[sample[3]]); -// -// int support = 0; -// for (int i_point = 0; i_point < n_matches; ++i_point) { -// try { -// cv::Point2d proj = phg::transformPoint(points_lhs[i_point], H); -// if (cv::norm(proj - cv::Point2d(points_rhs[i_point])) < reprojection_error_threshold_px) { -// ++support; -// } -// } catch (const std::exception &e) -// { -// std::cerr << e.what() << std::endl; -// } -// } -// -// if (support > best_support) { -// best_support = support; -// best_H = H; -// -// std::cout << "estimateHomographyRANSAC : support: " << best_support << "/" << n_matches << std::endl; -// -// if (best_support == n_matches) { -// break; -// } -// } -// } -// -// std::cout << "estimateHomographyRANSAC : best support: " << best_support << "/" << n_matches << std::endl; -// -// if (best_support == 0) { -// throw std::runtime_error("estimateHomographyRANSAC : failed to estimate homography"); -// } -// -// return best_H; + const int n_matches = points_lhs.size(); + if (n_matches < 4) + throw std::runtime_error("need more"); + + // https://en.wikipedia.org/wiki/Random_sample_consensus#Parameters + const int n_trials = 1000; + + const int n_samples = 4; + uint64_t seed = 1; + const double reprojection_error_threshold_px = 2; + + int best_support = 0; + cv::Mat best_H; + + std::vector sample; + for (int i_trial = 0; i_trial < n_trials; ++i_trial) { + randomSample(sample, n_matches, n_samples, &seed); + + cv::Mat H = estimateHomography4Points(points_lhs[sample[0]], points_lhs[sample[1]], points_lhs[sample[2]], points_lhs[sample[3]], + points_rhs[sample[0]], points_rhs[sample[1]], points_rhs[sample[2]], points_rhs[sample[3]]); + + int support = 0; + for (int i_point = 0; i_point < n_matches; ++i_point) { + try { + cv::Point2d proj = phg::transformPoint(points_lhs[i_point], H); + if (cv::norm(proj - cv::Point2d(points_rhs[i_point])) < reprojection_error_threshold_px) { + ++support; + } + } catch (const std::exception &e) + { + std::cerr << e.what() << std::endl; + } + } + + if (support > best_support) { + best_support = support; + best_H = H; + + std::cout << "estimateHomographyRANSAC : support: " << best_support << "/" << n_matches << std::endl; + + if (best_support == n_matches) { + break; + } + } + } + + std::cout << "estimateHomographyRANSAC : best support: " << best_support << "/" << n_matches << std::endl; + + if (best_support == 0) { + throw std::runtime_error("estimateHomographyRANSAC : failed to estimate homography"); + } + + return best_H; } } @@ -238,7 +242,34 @@ cv::Mat phg::findHomographyCV(const std::vector &points_lhs, const // таким преобразованием внутри занимается функции cv::perspectiveTransform и cv::warpPerspective cv::Point2d phg::transformPoint(const cv::Point2d &pt, const cv::Mat &T) { - throw std::runtime_error("not implemented yet"); + if (T.rows != 3 || T.cols != 3) { + throw std::runtime_error("not 3x3 matrix"); + } + + auto x = pt.x; + auto y = pt.y; + + double tx, ty, tw; + if (T.type() == CV_64FC1) { + tx = T.at(0, 0)*x + T.at(0, 1)*y + T.at(0, 2); + ty = T.at(1, 0)*x + T.at(1, 1)*y + T.at(1, 2); + tw = T.at(2, 0)*x + T.at(2, 1)*y + T.at(2, 2); + } else if (T.type() == CV_32FC1) { + tx = T.at(0, 0)*x + T.at(0, 1)*y + T.at(0, 2); + ty = T.at(1, 0)*x + T.at(1, 1)*y + T.at(1, 2); + tw = T.at(2, 0)*x + T.at(2, 1)*y + T.at(2, 2); + } else { + cv::Mat Td; + T.convertTo(Td, CV_64FC1); + tx = Td.at(0, 0)*x + Td.at(0, 1)*y + Td.at(0, 2); + ty = Td.at(1, 0)*x + Td.at(1, 1)*y + Td.at(1, 2); + tw = Td.at(2, 0)*x + Td.at(2, 1)*y + Td.at(2, 2); + } + + if (std::abs(tw) < 1e-12) + throw std::runtime_error("zero div"); + + return {tx/tw, ty/tw}; } cv::Point2d phg::transformPointCV(const cv::Point2d &pt, const cv::Mat &T) { diff --git a/src/phg/sfm/panorama_stitcher.cpp b/src/phg/sfm/panorama_stitcher.cpp index 8d76939..a791f50 100644 --- a/src/phg/sfm/panorama_stitcher.cpp +++ b/src/phg/sfm/panorama_stitcher.cpp @@ -23,7 +23,45 @@ cv::Mat phg::stitchPanorama(const std::vector &imgs, { // здесь надо посчитать вектор Hs // при этом можно обойтись n_images - 1 вызовами функтора homography_builder - throw std::runtime_error("not implemented yet"); + std::vector state(n_images, 0); + for (int i = 0; i < n_images; ++i) { + if (state[i] == 2) + continue; + + std::vector stack; + int v = i; + while (v >= 0) { + if (v >= n_images) + throw std::runtime_error("invalid index"); + if (state[v] == 2) + break; + if (state[v] == 1) + throw std::runtime_error("cycle"); + + state[v] = 1; + stack.push_back(v); + v = parent[v]; + } + + while (!stack.empty()) { + int node = stack.back(); + stack.pop_back(); + + int p = parent[node]; + if (p < 0) { + Hs[node] = cv::Mat::eye(3, 3, CV_64FC1); + } else { + cv::Mat H_i_to_p = homography_builder(imgs[node], imgs[p]); + + if (H_i_to_p.empty()) + throw std::runtime_error("empty"); + + H_i_to_p.convertTo(H_i_to_p, CV_64FC1); + Hs[node] = Hs[p] * H_i_to_p; + } + state[node] = 2; + } + } } bbox2 bbox; diff --git a/src/phg/sift/sift.cpp b/src/phg/sift/sift.cpp index 7204771..354f750 100755 --- a/src/phg/sift/sift.cpp +++ b/src/phg/sift/sift.cpp @@ -112,12 +112,16 @@ std::vector phg::buildOctaves(const cv::Mat& img, const phg:: // // вычтем sigma0 чтобы размыть ровно до нужной суммарной сигмы // TODO sigma_layer = ... (вычитаем как в sigma base); // cv::GaussianBlur(oct.layers[0], oct.layers[i], cv::Size(), sigma_layer, sigma_layer); + double sigma_layer = sigma0 * std::pow(2.0, static_cast(i) / s); + sigma_layer = std::sqrt(sigma_layer * sigma_layer - sigma0 * sigma0); + cv::GaussianBlur(oct.layers[0], oct.layers[i], cv::Size(), sigma_layer, sigma_layer); } // подготавливаем базовый слой для следующей октавы if (o + 1 < n_octaves) { // используется в opencv, формула для пересчета ключевых точек: pt_upscaled = 2^o * pt_downscaled // TODO cv::resize(даунскейлим текущий слой в два раза, без интерполяции, просто сабсепмлинг); + cv::resize(oct.layers[s], base, cv::Size(oct.layers[s].cols / 2, oct.layers[s].rows / 2), 0, 0, cv::INTER_NEAREST); // можно использовать и downsample2x_avg(oct.layers[s]), это позволяет потом заапскейлить слои обратно до оригинального разрешения без сдвига // но потребуется везде изменить формулу для пересчета ключевых точек: pt_upscaled = (pt_downscaled + 0.5) * 2^o - 0.5 @@ -139,6 +143,9 @@ std::vector phg::buildDoG(const std::vector phg::findScaleSpaceExtrema(const std::vector phg::findScaleSpaceExtrema(const std::vector(yi, xi + 1) + cL.at(yi, xi - 1) - 2.f * resp_center; -// float dyy = TODO; -// float dss = TODO; -// -// float dxy = (cL.at(yi + 1, xi + 1) - cL.at(yi + 1, xi - 1) - cL.at(yi - 1, xi + 1) + cL.at(yi - 1, xi - 1)) * 0.25f; -// float dxs = TODO; -// float dys = TODO; + dxx = cL.at(yi, xi + 1) + cL.at(yi, xi - 1) - 2.f * resp_center; + dyy = cL.at(yi + 1, xi) + cL.at(yi - 1, xi) - 2.f * resp_center; + dss = nL.at(yi, xi) + pL.at(yi, xi) - 2.f * resp_center; + + dxy = (cL.at(yi + 1, xi + 1) - cL.at(yi + 1, xi - 1) - cL.at(yi - 1, xi + 1) + cL.at(yi - 1, xi - 1)) * 0.25f; + dxs = (nL.at(yi, xi + 1) - nL.at(yi, xi - 1) - pL.at(yi, xi + 1) + pL.at(yi, xi - 1)) * 0.25f; + dys = (nL.at(yi + 1, xi) - nL.at(yi - 1, xi) - pL.at(yi + 1, xi) + pL.at(yi - 1, xi)) * 0.25f; cv::Matx33f H(dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss); @@ -273,21 +289,21 @@ std::vector phg::findScaleSpaceExtrema(const std::vector= (r + 1) * (r + 1) * det) + break; } // скейлим координаты точек обратно до родных размеров картинки @@ -379,39 +395,39 @@ std::vector phg::computeOrientations(const std::vector(py, px + 1) - img.at(py, px - 1); -// float gy = img.at(py + 1, px) - img.at(py - 1, px); -// -// float mag = TODO; -// float angle = std::atan2(TODO); // [-pi, pi] -// -// float angle_deg = angle * 180.f / (float) CV_PI; -// if (angle_deg < 0.f) angle_deg += 360.f; -// -// // гауссово взвешивание голоса точки с затуханием к краям -// float weight = std::exp(-(TODO) / (2.f * sigma_win * sigma_win)); -// if (!params.enable_orientation_gaussian_weighting) { -// weight = 1.f; -// } -// -// // голосуем в гистограмме направлений. находим два ближайших бина и гладко распределяем голос между ними -// // в таком случае, голос попавший близко к границе между бинами, проголосует поровну за оба бина -// float bin = TODO; -// if (bin >= n_bins) bin -= n_bins; -// int bin0 = (int) bin; -// int bin1 = (bin0 + 1) % n_bins; -// -// float frac = bin - bin0; -// if (!params.enable_orientation_bin_interpolation) { -// frac = 0.f; -// } -// -// histogram[bin0] += TODO; -// histogram[bin1] += TODO; + int px = xi + dx; + int py = yi + dy; + + // градиент + float gx = img.at(py, px + 1) - img.at(py, px - 1); + float gy = img.at(py + 1, px) - img.at(py - 1, px); + + float mag = std::sqrt(gx * gx + gy * gy); + float angle = std::atan2(gy, gx); // [-pi, pi] + + float angle_deg = angle * 180.f / (float) CV_PI; + if (angle_deg < 0.f) angle_deg += 360.f; + + // гауссово взвешивание голоса точки с затуханием к краям + float weight = std::exp(-(dx * dx + dy * dy) / (2.f * sigma_win * sigma_win)); + if (!params.enable_orientation_gaussian_weighting) { + weight = 1.f; + } + + // голосуем в гистограмме направлений. находим два ближайших бина и гладко распределяем голос между ними + // в таком случае, голос попавший близко к границе между бинами, проголосует поровну за оба бина + float bin = angle_deg * n_bins / 360.f; + if (bin >= n_bins) bin -= n_bins; + int bin0 = (int) bin; + int bin1 = (bin0 + 1) % n_bins; + + float frac = bin - bin0; + if (!params.enable_orientation_bin_interpolation) { + frac = 0.f; + } + + histogram[bin0] += (1.f - frac) * weight * mag; + histogram[bin1] += frac * weight * mag; } } @@ -450,20 +466,20 @@ std::vector phg::computeOrientations(const std::vector a = (left + right - 2 * center) / 2 // f(1) - f(-1) = 2b -> b = (right - left) / 2 -// float offset = TODO; -// if (!params.enable_orientation_subpixel_localization) { -// offset = 0.f; -// } -// -// float bin_real = i + offset; -// if (bin_real < 0.f) bin_real += n_bins; -// if (bin_real >= n_bins) bin_real -= n_bins; -// -// float angle = bin_real * 360.f / n_bins; -// -// cv::KeyPoint new_kp = kp; -// new_kp.angle = angle; -// oriented_kpts.push_back(new_kp); + float offset = ((left + right - 2.f * center) != 0.f) ? (0.5f * (left - right) / (left + right - 2.f * center)) : 0.f; + if (!params.enable_orientation_subpixel_localization) { + offset = 0.f; + } + + float bin_real = i + offset; + if (bin_real < 0.f) bin_real += n_bins; + if (bin_real >= n_bins) bin_real -= n_bins; + + float angle = bin_real * 360.f / n_bins; + + cv::KeyPoint new_kp = kp; + new_kp.angle = angle; + oriented_kpts.push_back(new_kp); } } } @@ -574,11 +590,11 @@ std::pair> phg::computeDescriptors(const std: bin_o -= n_orient_bins; // семплы вблизи края патча взвешиваем с меньшим весом -// float weight = std::exp(-(TODO) / (2.f * sigma_desc * sigma_desc)); -// if (!params.enable_descriptor_gaussian_weighting) { -// weight = 1.f; -// } -// float weighted_mag = mag * weight; + float weight = std::exp(-(rot_x * rot_x + rot_y * rot_y) / (2.f * sigma_desc * sigma_desc)); + if (!params.enable_descriptor_gaussian_weighting) { + weight = 1.f; + } + float weighted_mag = mag * weight; if (params.enable_descriptor_bin_interpolation) { // размажем вклад weighted_mag по пространственным бинам и по бинам гистограммок трилинейной интерполяцией @@ -609,8 +625,8 @@ std::pair> phg::computeDescriptors(const std: io += n_orient_bins; float wo = (dio == 0) ? (1.f - fo) : fo; -// int idx = TODO; -// desc[idx] += TODO; + int idx = (iy * n_spatial_bins + ix) * n_orient_bins + io; + desc[idx] += weighted_mag * wx * wy * wo; } } } @@ -621,8 +637,8 @@ std::pair> phg::computeDescriptors(const std: if (ix_nearest >= 0 && ix_nearest < n_spatial_bins && iy_nearest >= 0 && iy_nearest < n_spatial_bins) { // TODO uncomment -// int idx = (iy_nearest * n_spatial_bins + ix_nearest) * n_orient_bins + io_nearest; -// desc[idx] += weighted_mag; + int idx = (iy_nearest * n_spatial_bins + ix_nearest) * n_orient_bins + io_nearest; + desc[idx] += weighted_mag; } } } diff --git a/tests/test_matching.cpp b/tests/test_matching.cpp index 158fd2c..13657a0 100644 --- a/tests/test_matching.cpp +++ b/tests/test_matching.cpp @@ -19,8 +19,8 @@ // TODO enable both toggles for testing custom detector & matcher -#define ENABLE_MY_DESCRIPTOR 0 -#define ENABLE_MY_MATCHING 0 +#define ENABLE_MY_DESCRIPTOR 1 +#define ENABLE_MY_MATCHING 1 #define ENABLE_GPU_BRUTEFORCE_MATCHER 0 #if ENABLE_MY_MATCHING diff --git a/tests/test_sift.cpp b/tests/test_sift.cpp index cf3bd7d..7433f9b 100755 --- a/tests/test_sift.cpp +++ b/tests/test_sift.cpp @@ -28,7 +28,7 @@ // TODO ENABLE ME // TODO ENABLE ME // TODO ENABLE ME -#define ENABLE_MY_SIFT_TESTING 0 +#define ENABLE_MY_SIFT_TESTING 1 #define DENY_CREATE_REF_DATA 1