refactored opencv_stitching

modules/stitching/CMakeLists.txt

@@ -18,13 +18,7 @@ include_directories(
 
 set(stitching_libs opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_calib3d opencv_gpu)
 
-set(stitching_files blenders.hpp blenders.cpp
+FILE(GLOB stitching_files "*.hpp" "*.cpp")
-                    focal_estimators.hpp focal_estimators.cpp
-                    motion_estimators.hpp motion_estimators.cpp
-                    seam_finders.hpp seam_finders.cpp
-                    util.hpp util.cpp util_inl.hpp
-                    warpers.hpp warpers.cpp warpers_inl.hpp
-                    main.cpp)
 
 set(the_target opencv_stitching)    
 add_executable(${the_target} ${stitching_files})

modules/stitching/matchers.cpp

@@ -0,0 +1,358 @@
+#include <algorithm>
+#include <functional>
+#include <opencv2/calib3d/calib3d.hpp>
+#include <opencv2/gpu/gpu.hpp>
+#include "matchers.hpp"
+#include "util.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace cv::gpu;
+
+//////////////////////////////////////////////////////////////////////////////
+
+namespace
+{
+    class CpuSurfFeaturesFinder : public FeaturesFinder
+    {
+    public:
+        inline CpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers, 
+                                     int num_octaves_descr, int num_layers_descr) 
+        {
+            detector_ = new SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
+            extractor_ = new SurfDescriptorExtractor(num_octaves_descr, num_layers_descr);
+        }
+
+    protected:
+        void find(const vector<Mat> &images, vector<ImageFeatures> &features);
+
+    private:
+        Ptr<FeatureDetector> detector_;
+        Ptr<DescriptorExtractor> extractor_;
+    };
+
+    void CpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
+    {
+        // Make images gray
+        vector<Mat> gray_images(images.size());
+        for (size_t i = 0; i < images.size(); ++i)
+        {
+            CV_Assert(images[i].depth() == CV_8U);
+            cvtColor(images[i], gray_images[i], CV_BGR2GRAY);
+        }
+
+        features.resize(images.size());
+
+        // Find keypoints in all images
+        for (size_t i = 0; i < images.size(); ++i)
+        {
+            detector_->detect(gray_images[i], features[i].keypoints);
+            extractor_->compute(gray_images[i], features[i].keypoints, features[i].descriptors);
+        }
+    }
+    
+    class GpuSurfFeaturesFinder : public FeaturesFinder
+    {
+    public:
+        inline GpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers, 
+                                     int num_octaves_descr, int num_layers_descr) 
+        {
+            surf_.hessianThreshold = hess_thresh;
+            surf_.extended = false;
+            num_octaves_ = num_octaves;
+            num_layers_ = num_layers;
+            num_octaves_descr_ = num_octaves_descr;
+            num_layers_descr_ = num_layers_descr;
+        }
+
+    protected:
+        void find(const vector<Mat> &images, vector<ImageFeatures> &features);
+
+    private:
+        SURF_GPU surf_;
+        int num_octaves_, num_layers_;
+        int num_octaves_descr_, num_layers_descr_;
+    };
+
+    void GpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
+    {
+        // Make images gray
+        vector<GpuMat> gray_images(images.size());
+        for (size_t i = 0; i < images.size(); ++i)
+        {
+            CV_Assert(images[i].depth() == CV_8U);
+            cvtColor(GpuMat(images[i]), gray_images[i], CV_BGR2GRAY);
+        }
+
+        features.resize(images.size());
+
+        // Find keypoints in all images
+        GpuMat d_keypoints;
+        GpuMat d_descriptors;
+        for (size_t i = 0; i < images.size(); ++i)
+        {
+            surf_.nOctaves = num_octaves_;
+            surf_.nOctaveLayers = num_layers_;
+            surf_(gray_images[i], GpuMat(), d_keypoints);
+
+            surf_.nOctaves = num_octaves_descr_;
+            surf_.nOctaveLayers = num_layers_descr_;
+            surf_(gray_images[i], GpuMat(), d_keypoints, d_descriptors, true);
+
+            surf_.downloadKeypoints(d_keypoints, features[i].keypoints);
+            d_descriptors.download(features[i].descriptors);
+        }
+    }
+}
+
+SurfFeaturesFinder::SurfFeaturesFinder(bool gpu_hint, double hess_thresh, int num_octaves, int num_layers, 
+                                       int num_octaves_descr, int num_layers_descr)
+{
+    if (gpu_hint && getCudaEnabledDeviceCount() > 0)
+        impl_ = new GpuSurfFeaturesFinder(hess_thresh, num_octaves, num_layers, num_octaves_descr, num_layers_descr);
+    else
+        impl_ = new CpuSurfFeaturesFinder(hess_thresh, num_octaves, num_layers, num_octaves_descr, num_layers_descr);
+}
+
+
+void SurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
+{
+    (*impl_)(images, features);
+}
+
+
+//////////////////////////////////////////////////////////////////////////////
+
+MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0) {}
+
+
+MatchesInfo::MatchesInfo(const MatchesInfo &other)
+{
+    *this = other;
+}
+
+
+const MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
+{
+    src_img_idx = other.src_img_idx;
+    dst_img_idx = other.dst_img_idx;
+    matches = other.matches;
+    num_inliers = other.num_inliers;
+    H = other.H.clone();
+    return *this;
+}
+
+
+//////////////////////////////////////////////////////////////////////////////
+
+void FeaturesMatcher::operator ()(const vector<Mat> &images, const vector<ImageFeatures> &features,
+                                  vector<MatchesInfo> &pairwise_matches)
+{
+    pairwise_matches.resize(images.size() * images.size());
+    for (size_t i = 0; i < images.size(); ++i)
+    {
+        LOGLN("Processing image " << i << "... ");
+        for (size_t j = 0; j < images.size(); ++j)
+        {
+            if (i == j)
+                continue;
+            size_t pair_idx = i * images.size() + j;
+            (*this)(images[i], features[i], images[j], features[j], pairwise_matches[pair_idx]);
+            pairwise_matches[pair_idx].src_img_idx = i;
+            pairwise_matches[pair_idx].dst_img_idx = j;
+        }
+    }
+}
+
+
+//////////////////////////////////////////////////////////////////////////////
+
+namespace
+{
+    class CpuMatcher : public FeaturesMatcher
+    {
+    public:
+        inline CpuMatcher(float match_conf) : match_conf_(match_conf) {}
+
+        void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info);
+
+    private:
+        float match_conf_;
+    };
+
+    void CpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
+    {
+        matches_info.matches.clear();
+
+        BruteForceMatcher< L2<float> > matcher;
+        vector< vector<DMatch> > pair_matches;
+
+        // Find 1->2 matches
+        matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
+        for (size_t i = 0; i < pair_matches.size(); ++i)
+        {
+            if (pair_matches[i].size() < 2)
+                continue;
+            const DMatch& m0 = pair_matches[i][0];
+            const DMatch& m1 = pair_matches[i][1];
+            if (m0.distance < (1.f - match_conf_) * m1.distance)
+                matches_info.matches.push_back(m0);
+        }
+
+        // Find 2->1 matches
+        pair_matches.clear();
+        matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
+        for (size_t i = 0; i < pair_matches.size(); ++i)
+        {
+            if (pair_matches[i].size() < 2)
+                continue;
+            const DMatch& m0 = pair_matches[i][0];
+            const DMatch& m1 = pair_matches[i][1];
+            if (m0.distance < (1.f - match_conf_) * m1.distance)
+                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
+        }
+    }
+        
+    class GpuMatcher : public FeaturesMatcher
+    {
+    public:
+        inline GpuMatcher(float match_conf) : match_conf_(match_conf) {}
+
+        void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info);
+
+    private:
+        float match_conf_;
+
+        GpuMat descriptors1_;
+        GpuMat descriptors2_;
+
+        GpuMat trainIdx_, distance_, allDist_;
+    };
+
+    void GpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
+    {
+        matches_info.matches.clear();
+
+        BruteForceMatcher_GPU< L2<float> > matcher;
+        
+        descriptors1_.upload(features1.descriptors);
+        descriptors2_.upload(features2.descriptors);
+
+        vector< vector<DMatch> > pair_matches;
+
+        // Find 1->2 matches
+        matcher.knnMatch(descriptors1_, descriptors2_, trainIdx_, distance_, allDist_, 2);
+        matcher.knnMatchDownload(trainIdx_, distance_, pair_matches);
+        for (size_t i = 0; i < pair_matches.size(); ++i)
+        {
+            if (pair_matches[i].size() < 2)
+                continue;
+            const DMatch& m0 = pair_matches[i][0];
+            const DMatch& m1 = pair_matches[i][1];
+
+            CV_Assert(m0.queryIdx < static_cast<int>(features1.keypoints.size()));
+            CV_Assert(m0.trainIdx < static_cast<int>(features2.keypoints.size()));
+
+            if (m0.distance < (1.f - match_conf_) * m1.distance)
+                matches_info.matches.push_back(m0);
+        }
+
+        // Find 2->1 matches
+        pair_matches.clear();
+        matcher.knnMatch(descriptors2_, descriptors1_, trainIdx_, distance_, allDist_, 2);
+        matcher.knnMatchDownload(trainIdx_, distance_, pair_matches);
+        for (size_t i = 0; i < pair_matches.size(); ++i)
+        {
+            if (pair_matches[i].size() < 2)
+                continue;
+            const DMatch& m0 = pair_matches[i][0];
+            const DMatch& m1 = pair_matches[i][1];
+
+            CV_Assert(m0.trainIdx < static_cast<int>(features1.keypoints.size()));
+            CV_Assert(m0.queryIdx < static_cast<int>(features2.keypoints.size()));
+
+            if (m0.distance < (1.f - match_conf_) * m1.distance)
+                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
+        }
+    }
+}
+
+BestOf2NearestMatcher::BestOf2NearestMatcher(bool gpu_hint, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
+{
+    if (gpu_hint && getCudaEnabledDeviceCount() > 0)
+        impl_ = new GpuMatcher(match_conf);
+    else
+        impl_ = new CpuMatcher(match_conf);
+
+    num_matches_thresh1_ = num_matches_thresh1;
+    num_matches_thresh2_ = num_matches_thresh2;
+}
+
+
+void BestOf2NearestMatcher::match(const Mat &img1, const ImageFeatures &features1, const Mat &img2, const ImageFeatures &features2,
+                                  MatchesInfo &matches_info)
+{
+    (*impl_)(img1, features1, img2, features2, matches_info);
+
+    // Check if it makes sense to find homography
+    if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
+        return;
+
+    // Construct point-point correspondences for homography estimation
+    Mat src_points(1, matches_info.matches.size(), CV_32FC2);
+    Mat dst_points(1, matches_info.matches.size(), CV_32FC2);
+    for (size_t i = 0; i < matches_info.matches.size(); ++i)
+    {
+        const DMatch& m = matches_info.matches[i];
+
+        Point2f p = features1.keypoints[m.queryIdx].pt;
+        p.x -= img1.cols * 0.5f;
+        p.y -= img1.rows * 0.5f;
+        src_points.at<Point2f>(0, i) = p;
+
+        p = features2.keypoints[m.trainIdx].pt;
+        p.x -= img2.cols * 0.5f;
+        p.y -= img2.rows * 0.5f;
+        dst_points.at<Point2f>(0, i) = p;
+    }
+
+    // Find pair-wise motion
+    matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
+
+    // Find number of inliers
+    matches_info.num_inliers = 0;
+    for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
+        if (matches_info.inliers_mask[i])
+            matches_info.num_inliers++;
+
+    // Check if we should try to refine motion
+    if (matches_info.num_inliers < num_matches_thresh2_)
+        return;
+
+    // Construct point-point correspondences for inliers only
+    src_points.create(1, matches_info.num_inliers, CV_32FC2);
+    dst_points.create(1, matches_info.num_inliers, CV_32FC2);
+    int inlier_idx = 0;
+    for (size_t i = 0; i < matches_info.matches.size(); ++i)
+    {
+        if (!matches_info.inliers_mask[i])
+            continue;
+
+        const DMatch& m = matches_info.matches[i];
+
+        Point2f p = features1.keypoints[m.queryIdx].pt;
+        p.x -= img1.cols * 0.5f;
+        p.y -= img2.rows * 0.5f;
+        src_points.at<Point2f>(0, inlier_idx) = p;
+
+        p = features2.keypoints[m.trainIdx].pt;
+        p.x -= img2.cols * 0.5f;
+        p.y -= img2.rows * 0.5f;
+        dst_points.at<Point2f>(0, inlier_idx) = p;
+
+        inlier_idx++;
+    }
+
+    // Rerun motion estimation on inliers only
+    matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
+}

modules/stitching/matchers.hpp

@@ -0,0 +1,84 @@
+#ifndef __OPENCV_MATCHERS_HPP__
+#define __OPENCV_MATCHERS_HPP__
+
+#include <vector>
+#include <opencv2/core/core.hpp>
+#include <opencv2/features2d/features2d.hpp>
+
+struct ImageFeatures
+{
+    std::vector<cv::KeyPoint> keypoints;
+    cv::Mat descriptors;
+};
+
+
+class FeaturesFinder
+{
+public:
+    virtual ~FeaturesFinder() {}
+    void operator ()(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features) { find(images, features); }
+
+protected:
+    virtual void find(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features) = 0;
+};
+
+
+class SurfFeaturesFinder : public FeaturesFinder
+{
+public:
+    explicit SurfFeaturesFinder(bool gpu_hint = true, double hess_thresh = 500.0, 
+                                int num_octaves = 3, int num_layers = 4, 
+                                int num_octaves_descr = 4, int num_layers_descr = 2);
+
+protected:
+    void find(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features);
+
+    cv::Ptr<FeaturesFinder> impl_;
+};
+
+
+struct MatchesInfo
+{
+    MatchesInfo();
+    MatchesInfo(const MatchesInfo &other);
+    const MatchesInfo& operator =(const MatchesInfo &other);
+
+    int src_img_idx, dst_img_idx; // Optional images indices
+    std::vector<cv::DMatch> matches;
+    std::vector<uchar> inliers_mask;
+    int num_inliers; // Number of geometrically consistent matches
+    cv::Mat H; // Homography
+};
+
+
+class FeaturesMatcher
+{
+public:
+    virtual ~FeaturesMatcher() {}
+    void operator ()(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
+                     MatchesInfo& matches_info) { match(img1, features1, img2, features2, matches_info); }
+    void operator ()(const std::vector<cv::Mat> &images, const std::vector<ImageFeatures> &features,
+                     std::vector<MatchesInfo> &pairwise_matches);
+
+protected:
+    virtual void match(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
+                       MatchesInfo& matches_info) = 0;
+};
+
+
+class BestOf2NearestMatcher : public FeaturesMatcher
+{
+public:
+    explicit BestOf2NearestMatcher(bool gpu_hint = true, float match_conf = 0.55f, int num_matches_thresh1 = 5, int num_matches_thresh2 = 5);
+
+protected:
+    void match(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
+               MatchesInfo &matches_info);
+
+    int num_matches_thresh1_;
+    int num_matches_thresh2_;
+
+    cv::Ptr<FeaturesMatcher> impl_;
+};
+
+#endif // __OPENCV_MATCHERS_HPP__

modules/stitching/motion_estimators.cpp

@@ -1,353 +1,12 @@
 #include <algorithm>
-#include <functional>
+#include "opencv2/core/core_c.h"
 #include <opencv2/calib3d/calib3d.hpp>
-#include <opencv2/gpu/gpu.hpp>
 #include "focal_estimators.hpp"
 #include "motion_estimators.hpp"
 #include "util.hpp"
 
 using namespace std;
 using namespace cv;
-using namespace cv::gpu;
-
-//////////////////////////////////////////////////////////////////////////////
-
-namespace
-{
-    class CpuSurfFeaturesFinder : public FeaturesFinder
-    {
-    public:
-        inline CpuSurfFeaturesFinder() 
-        {
-            detector_ = new SurfFeatureDetector(500);
-            extractor_ = new SurfDescriptorExtractor();
-        }
-
-    protected:
-        void find(const vector<Mat> &images, vector<ImageFeatures> &features);
-
-    private:
-        Ptr<FeatureDetector> detector_;
-        Ptr<DescriptorExtractor> extractor_;
-    };
-
-    void CpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
-    {
-        // Make images gray
-        vector<Mat> gray_images(images.size());
-        for (size_t i = 0; i < images.size(); ++i)
-        {
-            CV_Assert(images[i].depth() == CV_8U);
-            cvtColor(images[i], gray_images[i], CV_BGR2GRAY);
-        }
-
-        features.resize(images.size());
-
-        // Find keypoints in all images
-        for (size_t i = 0; i < images.size(); ++i)
-        {
-            detector_->detect(gray_images[i], features[i].keypoints);
-            extractor_->compute(gray_images[i], features[i].keypoints, features[i].descriptors);
-        }
-    }
-    
-    class GpuSurfFeaturesFinder : public FeaturesFinder
-    {
-    public:
-        inline GpuSurfFeaturesFinder() 
-        {
-            surf.hessianThreshold = 500.0;
-            surf.extended = false;
-        }
-
-    protected:
-        void find(const vector<Mat> &images, vector<ImageFeatures> &features);
-
-    private:
-        SURF_GPU surf;
-    };
-
-    void GpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
-    {
-        // Make images gray
-        vector<GpuMat> gray_images(images.size());
-        for (size_t i = 0; i < images.size(); ++i)
-        {
-            CV_Assert(images[i].depth() == CV_8U);
-            cvtColor(GpuMat(images[i]), gray_images[i], CV_BGR2GRAY);
-        }
-
-        features.resize(images.size());
-
-        // Find keypoints in all images
-        GpuMat d_keypoints;
-        GpuMat d_descriptors;
-        for (size_t i = 0; i < images.size(); ++i)
-        {
-            surf.nOctaves = 3;
-            surf.nOctaveLayers = 4;
-            surf(gray_images[i], GpuMat(), d_keypoints);
-
-            surf.nOctaves = 4;
-            surf.nOctaveLayers = 2;
-            surf(gray_images[i], GpuMat(), d_keypoints, d_descriptors, true);
-
-            surf.downloadKeypoints(d_keypoints, features[i].keypoints);
-            d_descriptors.download(features[i].descriptors);
-        }
-    }
-}
-
-SurfFeaturesFinder::SurfFeaturesFinder(bool gpu_hint)
-{
-    if (gpu_hint && getCudaEnabledDeviceCount() > 0)
-        impl_ = new GpuSurfFeaturesFinder;
-    else
-        impl_ = new CpuSurfFeaturesFinder;
-}
-
-
-void SurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
-{
-    (*impl_)(images, features);
-}
-
-
-//////////////////////////////////////////////////////////////////////////////
-
-MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0) {}
-
-
-MatchesInfo::MatchesInfo(const MatchesInfo &other)
-{
-    *this = other;
-}
-
-
-const MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
-{
-    src_img_idx = other.src_img_idx;
-    dst_img_idx = other.dst_img_idx;
-    matches = other.matches;
-    num_inliers = other.num_inliers;
-    H = other.H.clone();
-    return *this;
-}
-
-
-//////////////////////////////////////////////////////////////////////////////
-
-void FeaturesMatcher::operator ()(const vector<Mat> &images, const vector<ImageFeatures> &features,
-                                  vector<MatchesInfo> &pairwise_matches)
-{
-    pairwise_matches.resize(images.size() * images.size());
-    for (size_t i = 0; i < images.size(); ++i)
-    {
-        LOGLN("Processing image " << i << "... ");
-        for (size_t j = 0; j < images.size(); ++j)
-        {
-            if (i == j)
-                continue;
-            size_t pair_idx = i * images.size() + j;
-            (*this)(images[i], features[i], images[j], features[j], pairwise_matches[pair_idx]);
-            pairwise_matches[pair_idx].src_img_idx = i;
-            pairwise_matches[pair_idx].dst_img_idx = j;
-        }
-    }
-}
-
-
-//////////////////////////////////////////////////////////////////////////////
-
-namespace
-{
-    class CpuMatcher : public FeaturesMatcher
-    {
-    public:
-        inline CpuMatcher(float match_conf) : match_conf_(match_conf) {}
-
-        void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info);
-
-    private:
-        float match_conf_;
-    };
-
-    void CpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
-    {
-        matches_info.matches.clear();
-
-        BruteForceMatcher< L2<float> > matcher;
-        vector< vector<DMatch> > pair_matches;
-
-        // Find 1->2 matches
-        matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
-        for (size_t i = 0; i < pair_matches.size(); ++i)
-        {
-            if (pair_matches[i].size() < 2)
-                continue;
-            const DMatch& m0 = pair_matches[i][0];
-            const DMatch& m1 = pair_matches[i][1];
-            if (m0.distance < (1.f - match_conf_) * m1.distance)
-                matches_info.matches.push_back(m0);
-        }
-
-        // Find 2->1 matches
-        pair_matches.clear();
-        matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
-        for (size_t i = 0; i < pair_matches.size(); ++i)
-        {
-            if (pair_matches[i].size() < 2)
-                continue;
-            const DMatch& m0 = pair_matches[i][0];
-            const DMatch& m1 = pair_matches[i][1];
-            if (m0.distance < (1.f - match_conf_) * m1.distance)
-                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
-        }
-    }
-        
-    class GpuMatcher : public FeaturesMatcher
-    {
-    public:
-        inline GpuMatcher(float match_conf) : match_conf_(match_conf) {}
-
-        void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info);
-
-    private:
-        float match_conf_;
-
-        GpuMat descriptors1_;
-        GpuMat descriptors2_;
-
-        GpuMat trainIdx_, distance_, allDist_;
-    };
-
-    void GpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
-    {
-        matches_info.matches.clear();
-
-        BruteForceMatcher_GPU< L2<float> > matcher;
-        
-        descriptors1_.upload(features1.descriptors);
-        descriptors2_.upload(features2.descriptors);
-
-        vector< vector<DMatch> > pair_matches;
-
-        // Find 1->2 matches
-        matcher.knnMatch(descriptors1_, descriptors2_, trainIdx_, distance_, allDist_, 2);
-        matcher.knnMatchDownload(trainIdx_, distance_, pair_matches);
-        for (size_t i = 0; i < pair_matches.size(); ++i)
-        {
-            if (pair_matches[i].size() < 2)
-                continue;
-            const DMatch& m0 = pair_matches[i][0];
-            const DMatch& m1 = pair_matches[i][1];
-
-            CV_Assert(m0.queryIdx < static_cast<int>(features1.keypoints.size()));
-            CV_Assert(m0.trainIdx < static_cast<int>(features2.keypoints.size()));
-
-            if (m0.distance < (1.f - match_conf_) * m1.distance)
-                matches_info.matches.push_back(m0);
-        }
-
-        // Find 2->1 matches
-        pair_matches.clear();
-        matcher.knnMatch(descriptors2_, descriptors1_, trainIdx_, distance_, allDist_, 2);
-        matcher.knnMatchDownload(trainIdx_, distance_, pair_matches);
-        for (size_t i = 0; i < pair_matches.size(); ++i)
-        {
-            if (pair_matches[i].size() < 2)
-                continue;
-            const DMatch& m0 = pair_matches[i][0];
-            const DMatch& m1 = pair_matches[i][1];
-
-            CV_Assert(m0.trainIdx < static_cast<int>(features1.keypoints.size()));
-            CV_Assert(m0.queryIdx < static_cast<int>(features2.keypoints.size()));
-
-            if (m0.distance < (1.f - match_conf_) * m1.distance)
-                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
-        }
-    }
-}
-
-BestOf2NearestMatcher::BestOf2NearestMatcher(bool gpu_hint, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
-{
-    if (gpu_hint && getCudaEnabledDeviceCount() > 0)
-        impl_ = new GpuMatcher(match_conf);
-    else
-        impl_ = new CpuMatcher(match_conf);
-
-    num_matches_thresh1_ = num_matches_thresh1;
-    num_matches_thresh2_ = num_matches_thresh2;
-}
-
-
-void BestOf2NearestMatcher::match(const Mat &img1, const ImageFeatures &features1, const Mat &img2, const ImageFeatures &features2,
-                                  MatchesInfo &matches_info)
-{
-    (*impl_)(img1, features1, img2, features2, matches_info);
-
-    // Check if it makes sense to find homography
-    if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
-        return;
-
-    // Construct point-point correspondences for homography estimation
-    Mat src_points(1, matches_info.matches.size(), CV_32FC2);
-    Mat dst_points(1, matches_info.matches.size(), CV_32FC2);
-    for (size_t i = 0; i < matches_info.matches.size(); ++i)
-    {
-        const DMatch& m = matches_info.matches[i];
-
-        Point2f p = features1.keypoints[m.queryIdx].pt;
-        p.x -= img1.cols * 0.5f;
-        p.y -= img1.rows * 0.5f;
-        src_points.at<Point2f>(0, i) = p;
-
-        p = features2.keypoints[m.trainIdx].pt;
-        p.x -= img2.cols * 0.5f;
-        p.y -= img2.rows * 0.5f;
-        dst_points.at<Point2f>(0, i) = p;
-    }
-
-    // Find pair-wise motion
-    matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
-
-    // Find number of inliers
-    matches_info.num_inliers = 0;
-    for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
-        if (matches_info.inliers_mask[i])
-            matches_info.num_inliers++;
-
-    // Check if we should try to refine motion
-    if (matches_info.num_inliers < num_matches_thresh2_)
-        return;
-
-    // Construct point-point correspondences for inliers only
-    src_points.create(1, matches_info.num_inliers, CV_32FC2);
-    dst_points.create(1, matches_info.num_inliers, CV_32FC2);
-    int inlier_idx = 0;
-    for (size_t i = 0; i < matches_info.matches.size(); ++i)
-    {
-        if (!matches_info.inliers_mask[i])
-            continue;
-
-        const DMatch& m = matches_info.matches[i];
-
-        Point2f p = features1.keypoints[m.queryIdx].pt;
-        p.x -= img1.cols * 0.5f;
-        p.y -= img2.rows * 0.5f;
-        src_points.at<Point2f>(0, inlier_idx) = p;
-
-        p = features2.keypoints[m.trainIdx].pt;
-        p.x -= img2.cols * 0.5f;
-        p.y -= img2.rows * 0.5f;
-        dst_points.at<Point2f>(0, inlier_idx) = p;
-
-        inlier_idx++;
-    }
-
-    // Rerun motion estimation on inliers only
-    matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
-}
 
 
 //////////////////////////////////////////////////////////////////////////////

modules/stitching/motion_estimators.hpp

@@ -3,84 +3,9 @@
 
 #include <vector>
 #include <opencv2/core/core.hpp>
-#include <opencv2/features2d/features2d.hpp>
+#include "matchers.hpp"
 #include "util.hpp"
 
-struct ImageFeatures
-{
-    std::vector<cv::KeyPoint> keypoints;
-    cv::Mat descriptors;
-};
-
-
-class FeaturesFinder
-{
-public:
-    virtual ~FeaturesFinder() {}
-    void operator ()(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features) { find(images, features); }
-
-protected:
-    virtual void find(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features) = 0;
-};
-
-
-class SurfFeaturesFinder : public FeaturesFinder
-{
-public:
-    explicit SurfFeaturesFinder(bool gpu_hint = true);
-
-protected:
-    void find(const std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features);
-
-    cv::Ptr<FeaturesFinder> impl_;
-};
-
-
-struct MatchesInfo
-{
-    MatchesInfo();
-    MatchesInfo(const MatchesInfo &other);
-    const MatchesInfo& operator =(const MatchesInfo &other);
-
-    int src_img_idx, dst_img_idx; // Optional images indices
-    std::vector<cv::DMatch> matches;
-    std::vector<uchar> inliers_mask;
-    int num_inliers; // Number of geometrically consistent matches
-    cv::Mat H; // Homography
-};
-
-
-class FeaturesMatcher
-{
-public:
-    virtual ~FeaturesMatcher() {}
-    void operator ()(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
-                     MatchesInfo& matches_info) { match(img1, features1, img2, features2, matches_info); }
-    void operator ()(const std::vector<cv::Mat> &images, const std::vector<ImageFeatures> &features,
-                     std::vector<MatchesInfo> &pairwise_matches);
-
-protected:
-    virtual void match(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
-                       MatchesInfo& matches_info) = 0;
-};
-
-
-class BestOf2NearestMatcher : public FeaturesMatcher
-{
-public:
-    explicit BestOf2NearestMatcher(bool gpu_hint = true, float match_conf = 0.55f, int num_matches_thresh1 = 5, int num_matches_thresh2 = 5);
-
-protected:
-    void match(const cv::Mat &img1, const ImageFeatures &features1, const cv::Mat &img2, const ImageFeatures &features2,
-               MatchesInfo &matches_info);
-
-    int num_matches_thresh1_;
-    int num_matches_thresh2_;
-
-    cv::Ptr<FeaturesMatcher> impl_;
-};
-
-
 struct CameraParams
 {
     CameraParams();

modules/stitching/seam_finders.hpp

@@ -52,7 +52,7 @@ class GraphCutSeamFinder : public PairwiseSeamFinder
 {
 public:
     // TODO add COST_COLOR_GRAD support
-    enum { COST_COLOR/*, COST_COLOR_GRAD*/ };
+    enum { COST_COLOR };
 
     GraphCutSeamFinder(int cost_type = COST_COLOR, float terminal_cost = 10000.f,
                        float bad_region_penalty = 1000.f);