Task02 Екатерина Алтунина ИТМО

.gitignore

@@ -2,3 +2,7 @@
 .idea
 build
 cmake-build*
+opencv-4.11.0
+4.11.0.zip
+data
+.cache

src/phg/matching/descriptor_matcher.cpp

@@ -1,27 +1,29 @@
 #include "descriptor_matcher.h"
 
-#include <opencv2/flann/miniflann.hpp>
 #include "flann_factory.h"
+#include <opencv2/flann/miniflann.hpp>
+#include <unordered_set>
 
-void phg::DescriptorMatcher::filterMatchesRatioTest(const std::vector<std::vector<cv::DMatch>> &matches,
-                                                    std::vector<cv::DMatch> &filtered_matches)
+void phg::DescriptorMatcher::filterMatchesRatioTest(const std::vector<std::vector<cv::DMatch>>& matches, std::vector<cv::DMatch>& filtered_matches)
 {
     filtered_matches.clear();
 
-    throw std::runtime_error("not implemented yet");
-}
+    const float threshold2 = 0.73f * 0.73f;
 
+    for(const auto& m : matches) {
+        if(m.size() >= 2 && m[0].distance < threshold2 * m[1].distance) {
+            filtered_matches.push_back(m[0]);
+        }
+    }
+}
 
-void phg::DescriptorMatcher::filterMatchesClusters(const std::vector<cv::DMatch> &matches,
-                                                   const std::vector<cv::KeyPoint> keypoints_query,
-                                                   const std::vector<cv::KeyPoint> keypoints_train,
-                                                   std::vector<cv::DMatch> &filtered_matches)
+void phg::DescriptorMatcher::filterMatchesClusters(const std::vector<cv::DMatch>& matches, const std::vector<cv::KeyPoint> keypoints_query, const std::vector<cv::KeyPoint> keypoints_train, std::vector<cv::DMatch>& filtered_matches)
 {
     filtered_matches.clear();
 
-    const size_t  total_neighbours  = 5;  // total number of neighbours to test (including candidate)
-    const size_t  consistent_matches  = 3;  // minimum number of consistent matches (including candidate)
-    const float  radius_limit_scale  = 2.f;  // limit search radius by scaled median
+    const size_t total_neighbours = 5; // total number of neighbours to test (including candidate)
+    const size_t consistent_matches = 3; // minimum number of consistent matches (including candidate)
+    const float radius_limit_scale = 2.f; // limit search radius by scaled median
 
     const int n_matches = matches.size();
 
@@ -35,42 +37,65 @@ 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(1);
+    std::shared_ptr<cv::flann::SearchParams> search_params = flannKsTreeSearchParams(64);
+
+    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
+    // DONE заполнить filtered_matches
+    std::unordered_set<int> query_set;
+    for (size_t mIdx = 0; mIdx < n_matches; ++mIdx) {
+
+        for (size_t nIdx = 0; nIdx < total_neighbours; ++nIdx) {
+            if(distances2_query.at<float>(mIdx, nIdx) <= radius2_query) {
+                query_set.insert(indices_query.at<int>(mIdx, nIdx));
+            }
+        }
+
+        size_t count = 0;
+        for (size_t nIdx = 0; nIdx < total_neighbours; ++nIdx) {
+            if(distances2_train.at<float>(mIdx, nIdx) <= radius2_train && query_set.count(indices_train.at<int>(mIdx, nIdx)) > 0) {
+                ++count;
+            }
+        }
+
+        if(count >= consistent_matches) {
+            filtered_matches.emplace_back(matches[mIdx]);
+        }
+
+        query_set.clear();
+    }
 }

src/phg/matching/flann_matcher.cpp

@@ -5,9 +5,9 @@
 
 phg::FlannMatcher::FlannMatcher()
 {
-    // параметры для приближенного поиска
-//    index_params = flannKdTreeIndexParams(TODO);
-//    search_params = flannKsTreeSearchParams(TODO);
+    // DONE параметры для приближенного поиска
+   index_params = flannKdTreeIndexParams(4);
+   search_params = flannKsTreeSearchParams(40);
 }
 
 void phg::FlannMatcher::train(const cv::Mat &train_desc)
@@ -17,5 +17,16 @@ void phg::FlannMatcher::train(const cv::Mat &train_desc)
 
 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");
+    cv::Mat indices_query(query_desc.rows, k, CV_32SC1);
+    cv::Mat distances2_query(query_desc.rows, k, CV_32FC1);
+
+    flann_index->knnSearch(query_desc, indices_query, distances2_query, k, *search_params);
+
+    matches.resize(query_desc.rows);
+    for(size_t mIdx = 0; mIdx < query_desc.rows; ++mIdx) {
+        matches[mIdx].clear();
+        for(size_t i = 0; i < k; ++i) {
+            matches[mIdx].emplace_back(mIdx, indices_query.at<int>(mIdx, i), distances2_query.at<float>(mIdx, i));
+        }
+    }
 }

src/phg/sfm/homography.cpp

@@ -74,7 +74,6 @@ namespace {
 
         for (int i = 0; i < 4; ++i) {
             // fill 2 rows of matrix A
-
             double x0 = xs0[i];
             double y0 = ys0[i];
             double w0 = ws0[i];
@@ -84,8 +83,8 @@ 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, -x1 * y0, x1 * w0});
+            A.push_back({0.0, 0.0, 0.0, x0, y0, w0, -y1 * x0, -y1 * y0, y1 * w0});
         }
 
         int res = gauss(A, H);
@@ -168,57 +167,57 @@ 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<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;
+       const int n_matches = points_lhs.size();
+
+       // 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<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;
     }
 
 }
@@ -238,7 +237,11 @@ 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");
+    cv::Mat perspTrans = T * cv::Mat({pt.x, pt.y, 1.0});
+    return {
+        perspTrans.at<double>(0) / perspTrans.at<double>(2),
+        perspTrans.at<double>(1) / perspTrans.at<double>(2)
+    };
 }
 
 cv::Point2d phg::transformPointCV(const cv::Point2d &pt, const cv::Mat &T) {