Task02 Никон Парвицкий ITMO

.gitignore

@@ -2,3 +2,6 @@
 .idea
 build
 cmake-build*
+4.11.0.zip
+opencv-4.11.0
+.vscode

src/phg/matching/descriptor_matcher.cpp

@@ -1,14 +1,29 @@
 #include "descriptor_matcher.h"
 
+#include <algorithm>
 #include <opencv2/flann/miniflann.hpp>
 #include "flann_factory.h"
 
 void phg::DescriptorMatcher::filterMatchesRatioTest(const std::vector<std::vector<cv::DMatch>> &matches,
                                                     std::vector<cv::DMatch> &filtered_matches)
 {
     filtered_matches.clear();
+    filtered_matches.reserve(matches.size());
 
-    throw std::runtime_error("not implemented yet");
+    const float max_ratio = 0.7f;
+
+    for (const std::vector<cv::DMatch> &knn_matches : matches) {
+        if (knn_matches.size() < 2) {
+            continue;
+        }
+
+        const cv::DMatch &best = knn_matches[0];
+        const cv::DMatch &second_best = knn_matches[1];
+
+        if (best.distance < max_ratio * second_best.distance) {
+            filtered_matches.push_back(best);
+        }
+    }
 }
 
 
@@ -35,42 +50,69 @@ void phg::DescriptorMatcher::filterMatchesClusters(const std::vector<cv::DMatch>
         points_query.at<cv::Point2f>(i) = keypoints_query[matches[i].queryIdx].pt;
         points_train.at<cv::Point2f>(i) = keypoints_train[matches[i].trainIdx].pt;
     }
-//
-//    // размерность всего 2, так что точное KD-дерево
-//    std::shared_ptr<cv::flann::IndexParams> index_params = flannKdTreeIndexParams(TODO);
-//    std::shared_ptr<cv::flann::SearchParams> search_params = flannKsTreeSearchParams(TODO);
-//
-//    std::shared_ptr<cv::flann::Index> index_query = flannKdTreeIndex(points_query, index_params);
-//    std::shared_ptr<cv::flann::Index> 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<double> max_dists2_query(n_matches);
-//        std::vector<double> max_dists2_train(n_matches);
-//        for (int i = 0; i < n_matches; ++i) {
-//            max_dists2_query[i] = distances2_query.at<float>(i, total_neighbours - 1);
-//            max_dists2_train[i] = distances2_train.at<float>(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<cv::flann::IndexParams> index_params = flannKdTreeIndexParams(4);
+    std::shared_ptr<cv::flann::SearchParams> search_params = flannKsTreeSearchParams(32);
+
+    std::shared_ptr<cv::flann::Index> index_query = flannKdTreeIndex(points_query, index_params);
+    std::shared_ptr<cv::flann::Index> index_train = flannKdTreeIndex(points_train, index_params);
+
+    // для каждой точки найти total_neighbours 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<double> max_dists2_query(n_matches);
+        std::vector<double> max_dists2_train(n_matches);
+        for (int i = 0; i < n_matches; ++i) {
+            max_dists2_query[i] = distances2_query.at<float>(i, total_neighbours - 1);
+            max_dists2_train[i] = distances2_train.at<float>(i, total_neighbours - 1);
+        }
+
+        const 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_neighbours соседей
+    // в радиусах поиска имеют как минимум consistent_matches общих элементов
+    filtered_matches.reserve(n_matches);
+    for (int i = 0; i < n_matches; ++i) {
+        std::vector<int> neighbours_query;
+        std::vector<int> neighbours_train;
+        neighbours_query.reserve(total_neighbours);
+        neighbours_train.reserve(total_neighbours);
+
+        for (size_t k = 0; k < total_neighbours; ++k) {
+            if (distances2_query.at<float>(i, k) <= radius2_query) {
+                neighbours_query.push_back(indices_query.at<int>(i, k));
+            }
+            if (distances2_train.at<float>(i, k) <= radius2_train) {
+                neighbours_train.push_back(indices_train.at<int>(i, k));
+            }
+        }
+
+        int n_consistent = 0;
+        for (int idx_query : neighbours_query) {
+            if (std::find(neighbours_train.begin(), neighbours_train.end(), idx_query) != neighbours_train.end()) {
+                ++n_consistent;
+            }
+        }
+
+        if (n_consistent >= static_cast<int>(consistent_matches)) {
+            filtered_matches.push_back(matches[i]);
+        }
+    }
 }

src/phg/matching/flann_matcher.cpp

@@ -1,21 +1,34 @@
-#include <iostream>
 #include "flann_matcher.h"
 #include "flann_factory.h"
 
 
 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)
 {
-    flann_index = flannKdTreeIndex(train_desc, index_params);
+    train_desc_ = train_desc.clone();
+    flann_index = flannKdTreeIndex(train_desc_, index_params);
 }
 
 void phg::FlannMatcher::knnMatch(const cv::Mat &query_desc, std::vector<std::vector<cv::DMatch>> &matches, int k) const
 {
-    throw std::runtime_error("not implemented yet");
+    const int n_query_desc = query_desc.rows;
+    cv::Mat indices(n_query_desc, k, CV_32SC1);
+    cv::Mat distances2(n_query_desc, k, CV_32FC1);
+
+    flann_index->knnSearch(query_desc, indices, distances2, k, *search_params);
+
+    matches.assign(n_query_desc, {});
+    for (int qi = 0; qi < n_query_desc; ++qi) {
+        matches[qi].reserve(k);
+        for (int ki = 0; ki < k; ++ki) {
+            const int train_idx = indices.at<int>(qi, ki);
+            const float distance = std::sqrt(distances2.at<float>(qi, ki));
+            matches[qi].emplace_back(qi, train_idx, distance);
+        }
+    }
 }

src/phg/matching/flann_matcher.h

@@ -18,6 +18,7 @@ namespace phg {
         std::shared_ptr<cv::flann::IndexParams> index_params;
         std::shared_ptr<cv::flann::SearchParams> search_params;
         std::shared_ptr<cv::flann::Index> flann_index;
+        cv::Mat train_desc_;
     };
 
 }

src/phg/sfm/homography.cpp

@@ -1,5 +1,6 @@
 #include "homography.h"
 
+#include <cmath>
 #include <opencv2/calib3d/calib3d.hpp>
 #include <iostream>
 
@@ -84,8 +85,8 @@ 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({x0, y0, w0, 0.0, 0.0, 0.0, -x1 * x0 / w1, -x1 * y0 / w1, x1 * w0 / w1});
+            A.push_back({0.0, 0.0, 0.0, x0, y0, w0, -y1 * x0 / w1, -y1 * y0 / w1, y1 * w0 / w1});
         }
 
         int res = gauss(A, H);
@@ -162,63 +163,68 @@ namespace {
             throw std::runtime_error("findHomography: points_lhs.size() != points_rhs.size()");
         }
 
+        const int n_matches = points_lhs.size();
+
         // TODO Дополнительный балл, если вместо обычной версии будет использована модификация a-contrario RANSAC
         // * [1] Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers. (Lionel Moisan, Pierre Moulon, Pascal Monasse)
         // * [2] Adaptive Structure from Motion with a contrario model estimation. (Pierre Moulon, Pascal Monasse, Renaud Marlet)
         // * (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<int> 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;
+        // https://en.wikipedia.org/wiki/Random_sample_consensus#Parameters
+        const int n_trials = 2000;
+
+        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<int> sample;
+        for (int i_trial = 0; i_trial < n_trials; ++i_trial) {
+            randomSample(sample, n_matches, n_samples, &seed);
+
+            cv::Mat H;
+            try {
+                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]]);
+            } catch (const std::exception &) {
+                continue;
+            }
+
+            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 +244,14 @@ cv::Mat phg::findHomographyCV(const std::vector<cv::Point2f> &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");
+    const double x = pt.x;
+    const double y = pt.y;
+
+    const double xh = T.at<double>(0, 0) * x + T.at<double>(0, 1) * y + T.at<double>(0, 2);
+    const double yh = T.at<double>(1, 0) * x + T.at<double>(1, 1) * y + T.at<double>(1, 2);
+    const double wh = T.at<double>(2, 0) * x + T.at<double>(2, 1) * y + T.at<double>(2, 2);
+
+    return cv::Point2d(xh / wh, yh / wh);
 }
 
 cv::Point2d phg::transformPointCV(const cv::Point2d &pt, const cv::Mat &T) {