Merge pull request #4025 from vpisarev:features2d_fixes

modules/core/include/opencv2/core/persistence.hpp

@@ -660,6 +660,7 @@ CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value
 CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value );
 CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value );
 CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<KeyPoint>& value);
+CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<DMatch>& value);
 
 CV_EXPORTS void writeScalar( FileStorage& fs, int value );
 CV_EXPORTS void writeScalar( FileStorage& fs, float value );
@@ -678,6 +679,7 @@ CV_EXPORTS void read(const FileNode& node, String& value, const String& default_
 CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() );
 CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() );
 CV_EXPORTS void read(const FileNode& node, std::vector<KeyPoint>& keypoints);
+CV_EXPORTS void read(const FileNode& node, std::vector<DMatch>& matches);
 
 template<typename _Tp> static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value)
 {

modules/core/src/persistence.cpp

@@ -5594,6 +5594,35 @@ void read(const FileNode& node, std::vector<KeyPoint>& keypoints)
     }
 }
 
+
+void write(FileStorage& fs, const String& objname, const std::vector<DMatch>& matches)
+{
+    cv::internal::WriteStructContext ws(fs, objname, CV_NODE_SEQ + CV_NODE_FLOW);
+
+    int i, n = (int)matches.size();
+    for( i = 0; i < n; i++ )
+    {
+        const DMatch& m = matches[i];
+        cv::write(fs, m.queryIdx);
+        cv::write(fs, m.trainIdx);
+        cv::write(fs, m.imgIdx);
+        cv::write(fs, m.distance);
+    }
+}
+
+void read(const FileNode& node, std::vector<DMatch>& matches)
+{
+    matches.resize(0);
+    FileNodeIterator it = node.begin(), it_end = node.end();
+    for( ; it != it_end; )
+    {
+        DMatch m;
+        it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance;
+        matches.push_back(m);
+    }
+}
+
+
 int FileNode::type() const { return !node ? NONE : (node->tag & TYPE_MASK); }
 bool FileNode::isNamed() const { return !node ? false : (node->tag & NAMED) != 0; }
 

modules/features2d/test/test_descriptors_regression.cpp

@@ -347,3 +347,97 @@ TEST( Features2d_DescriptorExtractor_AKAZE, regression )
                                               Hamming(), AKAZE::create());
     test.safe_run();
 }
+
+TEST( Features2d_DescriptorExtractor, batch )
+{
+    string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
+    vector<Mat> imgs, descriptors;
+    vector<vector<KeyPoint> > keypoints;
+    int i, n = 6;
+    Ptr<ORB> orb = ORB::create();
+
+    for( i = 0; i < n; i++ )
+    {
+        string imgname = format("%s/img%d.png", path.c_str(), i+1);
+        Mat img = imread(imgname, 0);
+        imgs.push_back(img);
+    }
+
+    orb->detect(imgs, keypoints);
+    orb->compute(imgs, keypoints, descriptors);
+
+    ASSERT_EQ((int)keypoints.size(), n);
+    ASSERT_EQ((int)descriptors.size(), n);
+
+    for( i = 0; i < n; i++ )
+    {
+        EXPECT_GT((int)keypoints[i].size(), 100);
+        EXPECT_GT(descriptors[i].rows, 100);
+    }
+}
+
+TEST( Features2d_Feature2d, no_crash )
+{
+    const String& pattern = string(cvtest::TS::ptr()->get_data_path() + "shared/*.png");
+    vector<String> fnames;
+    glob(pattern, fnames, false);
+    sort(fnames.begin(), fnames.end());
+
+    Ptr<AKAZE> akaze = AKAZE::create();
+    Ptr<ORB> orb = ORB::create();
+    Ptr<KAZE> kaze = KAZE::create();
+    Ptr<BRISK> brisk = BRISK::create();
+    size_t i, n = fnames.size();
+    vector<KeyPoint> keypoints;
+    Mat descriptors;
+    orb->setMaxFeatures(5000);
+
+    for( i = 0; i < n; i++ )
+    {
+        printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
+        if( strstr(fnames[i].c_str(), "MP.png") != 0 )
+            continue;
+        bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
+
+        Mat img = imread(fnames[i], -1);
+        printf("\tAKAZE ... "); fflush(stdout);
+        akaze->detectAndCompute(img, noArray(), keypoints, descriptors);
+        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
+        if( checkCount )
+        {
+            EXPECT_GT((int)keypoints.size(), 0);
+        }
+        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
+        printf("ok\n");
+
+        printf("\tKAZE ... "); fflush(stdout);
+        kaze->detectAndCompute(img, noArray(), keypoints, descriptors);
+        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
+        if( checkCount )
+        {
+            EXPECT_GT((int)keypoints.size(), 0);
+        }
+        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
+        printf("ok\n");
+
+        printf("\tORB ... "); fflush(stdout);
+        orb->detectAndCompute(img, noArray(), keypoints, descriptors);
+        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
+        if( checkCount )
+        {
+            EXPECT_GT((int)keypoints.size(), 0);
+        }
+        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
+        printf("ok\n");
+
+        printf("\tBRISK ... "); fflush(stdout);
+        brisk->detectAndCompute(img, noArray(), keypoints, descriptors);
+        printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
+        if( checkCount )
+        {
+            EXPECT_GT((int)keypoints.size(), 0);
+        }
+        ASSERT_EQ(descriptors.rows, (int)keypoints.size());
+        printf("ok\n");
+    }
+}

modules/features2d/test/test_matchers_algorithmic.cpp

@@ -543,3 +543,13 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
                                   DescriptorMatcher::create("FlannBased"), 0.04f );
     test.safe_run();
 }
+
+TEST( Features2d_DMatch, read_write )
+{
+    FileStorage fs(".xml", FileStorage::WRITE + FileStorage::MEMORY);
+    vector<DMatch> matches;
+    matches.push_back(DMatch(1,2,3,4.5f));
+    fs << "Match" << matches;
+    String str = fs.releaseAndGetString();
+    ASSERT_NE( strstr(str.c_str(), "4.5"), (char*)0 );
+}

modules/features2d/test/test_mser.cpp

@@ -41,171 +41,121 @@
 //M*/
 
 #include "test_precomp.hpp"
-#include "opencv2/imgproc/imgproc_c.h"
-
-#if 0
+#include "opencv2/highgui.hpp"
 
 #include <vector>
 #include <string>
 using namespace std;
 using namespace cv;
 
-class CV_MserTest : public cvtest::BaseTest
-{
-public:
-    CV_MserTest();
-protected:
-    void run(int);
-    int LoadBoxes(const char* path, vector<CvBox2D>& boxes);
-    int SaveBoxes(const char* path, const vector<CvBox2D>& boxes);
-    int CompareBoxes(const vector<CvBox2D>& boxes1,const vector<CvBox2D>& boxes2, float max_rel_diff = 0.01f);
-};
-
-CV_MserTest::CV_MserTest()
-{
-}
+#undef RENDER_MSERS
+#define RENDER_MSERS 0
 
-int CV_MserTest::LoadBoxes(const char* path, vector<CvBox2D>& boxes)
+#if defined RENDER_MSERS && RENDER_MSERS
+static void renderMSERs(const Mat& gray, Mat& img, const vector<vector<Point> >& msers)
 {
-    boxes.clear();
-    FILE* f = fopen(path,"r");
-
-    if (f==NULL)
+    cvtColor(gray, img, COLOR_GRAY2BGR);
+    RNG rng((uint64)1749583);
+    for( int i = 0; i < (int)msers.size(); i++ )
     {
-        return 0;
-    }
+        uchar b = rng.uniform(0, 256);
+        uchar g = rng.uniform(0, 256);
+        uchar r = rng.uniform(0, 256);
+        Vec3b color(b, g, r);
 
-    while (!feof(f))
-    {
-        CvBox2D box;
-        int values_read = fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height);
-        CV_Assert(values_read == 5);
-        boxes.push_back(box);
+        const Point* pt = &msers[i][0];
+        size_t j, n = msers[i].size();
+        for( j = 0; j < n; j++ )
+            img.at<Vec3b>(pt[j]) = color;
     }
-    fclose(f);
-    return 1;
-}
-
-int CV_MserTest::SaveBoxes(const char* path, const vector<CvBox2D>& boxes)
-{
-    FILE* f = fopen(path,"w");
-    if (f==NULL)
-    {
-        return 0;
-    }
-    for (int i=0;i<(int)boxes.size();i++)
-    {
-        fprintf(f,"%f,%f,%f,%f,%f\n",boxes[i].angle,boxes[i].center.x,boxes[i].center.y,boxes[i].size.width,boxes[i].size.height);
-    }
-    fclose(f);
-    return 1;
 }
+#endif
 
-int CV_MserTest::CompareBoxes(const vector<CvBox2D>& boxes1,const vector<CvBox2D>& boxes2, float max_rel_diff)
+TEST(Features2d_MSER, cases)
 {
-    if (boxes1.size() != boxes2.size())
-        return 0;
-
-    for (int i=0; i<(int)boxes1.size();i++)
-    {
-        float rel_diff;
-        if (!((boxes1[i].angle == 0.0f) && (abs(boxes2[i].angle) < max_rel_diff)))
-        {
-            float angle_diff = (float)fmod(boxes1[i].angle - boxes2[i].angle, 180);
-            // for angular correctness, it makes no sense to use a "relative" error.
-            // a 1-degree error around 5 degrees is equally bas as around 250 degrees.
-            // in correct cases, angle_diff can now be a bit above 0 or a bit below 180
-            if (angle_diff > 90.0f)
-            {
-                    angle_diff -= 180.0f;
-            }
-            rel_diff = (float)fabs(angle_diff);
-            if (rel_diff > max_rel_diff)
-                return i;
-        }
+    uchar buf[] =
+    {
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255,
+         255, 255, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255,
+         255, 255, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255,
+         255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+         255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255
+    };
+    Mat big_image = imread(cvtest::TS::ptr()->get_data_path() + "mser/puzzle.png", 0);
+    Mat small_image(14, 26, CV_8U, buf);
+    static const int thresharr[] = { 0, 70, 120, 180, 255 };
+
+    const int kDelta = 5;
+    Ptr<MSER> mserExtractor = MSER::create( kDelta );
+    vector<vector<Point> > msers;
+    vector<Rect> boxes;
 
-        if (!((boxes1[i].center.x == 0.0f) && (abs(boxes2[i].center.x) < max_rel_diff)))
-        {
-            rel_diff = abs(boxes1[i].center.x-boxes2[i].center.x)/abs(boxes1[i].center.x);
-            if (rel_diff > max_rel_diff)
-                return i;
-        }
+    RNG rng((uint64)123456);
 
-        if (!((boxes1[i].center.y == 0.0f) && (abs(boxes2[i].center.y) < max_rel_diff)))
+    for( int i = 0; i < 100; i++ )
     {
-            rel_diff = abs(boxes1[i].center.y-boxes2[i].center.y)/abs(boxes1[i].center.y);
-            if (rel_diff > max_rel_diff)
-                return i;
-        }
-        if (!((boxes1[i].size.width == 0.0f) && (abs(boxes2[i].size.width) < max_rel_diff)))
-        {
-            rel_diff = abs(boxes1[i].size.width-boxes2[i].size.width)/abs(boxes1[i].size.width);
-            if (rel_diff > max_rel_diff)
-            return i;
-        }
+        bool use_big_image = rng.uniform(0, 7) != 0;
+        bool invert = rng.uniform(0, 2) != 0;
+        bool binarize = use_big_image ? rng.uniform(0, 5) != 0 : false;
+        bool blur = rng.uniform(0, 2) != 0;
+        int thresh = thresharr[rng.uniform(0, 5)];
 
-        if (!((boxes1[i].size.height == 0.0f) && (abs(boxes2[i].size.height) < max_rel_diff)))
+        /*if( i == 0 )
         {
-            rel_diff = abs(boxes1[i].size.height-boxes2[i].size.height)/abs(boxes1[i].size.height);
-            if (rel_diff > max_rel_diff)
-                return i;
-        }
-    }
+            use_big_image = true;
+            invert = binarize = blur = false;
+        }*/
 
-    return -1;
-}
+        const Mat& src0 = use_big_image ? big_image : small_image;
+        Mat src = src0.clone();
 
-void CV_MserTest::run(int)
-{
-    string image_path = string(ts->get_data_path()) + "mser/puzzle.png";
+        int kMinArea = use_big_image ? 256 : 10;
+        int kMaxArea = (int)src.total()/4;
 
-    Mat img = imread( image_path );
-    if (img.empty())
-    {
-        ts->printf( cvtest::TS::LOG, "Unable to open image mser/puzzle.png\n");
-        ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-        return;
-    }
+        mserExtractor->setMinArea(kMinArea);
+        mserExtractor->setMaxArea(kMaxArea);
 
-    Mat yuv;
-    cvtColor(img, yuv, COLOR_BGR2YCrCb);
-    vector<vector<Point> > msers;
-    MSER()(yuv, msers);
+        if( invert )
+            bitwise_not(src, src);
+        if( binarize )
+            threshold(src, src, thresh, 255, THRESH_BINARY);
+        if( blur )
+            GaussianBlur(src, src, Size(5, 5), 1.5, 1.5);
 
-    vector<CvBox2D> boxes;
-    vector<CvBox2D> boxes_orig;
-    for ( size_t i = 0; i < msers.size(); i++ )
-    {
-        RotatedRect box = fitEllipse(msers[i]);
-        box.angle=(float)CV_PI/2-box.angle;
-        boxes.push_back(box);
-    }
+        int minRegs = use_big_image ? 7 : 2;
+        int maxRegs = use_big_image ? 1000 : 15;
+        if( binarize && (thresh == 0 || thresh == 255) )
+            minRegs = maxRegs = 0;
 
-    string boxes_path = string(ts->get_data_path()) + "mser/boxes.txt";
-    string calc_boxes_path = string(ts->get_data_path()) + "mser/boxes.calc.txt";
+        mserExtractor->detectRegions( src, msers, boxes );
+        int nmsers = (int)msers.size();
+        ASSERT_EQ(nmsers, (int)boxes.size());
 
-    if (!LoadBoxes(boxes_path.c_str(),boxes_orig))
+        if( maxRegs < nmsers || minRegs > nmsers )
         {
-        SaveBoxes(boxes_path.c_str(),boxes);
-        ts->printf( cvtest::TS::LOG, "Unable to open data file mser/boxes.txt\n");
-        ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
-        return;
+            printf("%d. minArea=%d, maxArea=%d, nmsers=%d, minRegs=%d, maxRegs=%d, "
+                   "image=%s, invert=%d, binarize=%d, thresh=%d, blur=%d\n",
+                   i, kMinArea, kMaxArea, nmsers, minRegs, maxRegs, use_big_image ? "big" : "small",
+                   (int)invert, (int)binarize, thresh, (int)blur);
+    #if defined RENDER_MSERS && RENDER_MSERS
+            Mat image;
+            imshow("source", src);
+            renderMSERs(src, image, msers);
+            imshow("result", image);
+            waitKey();
+    #endif
         }
 
-    const float dissimularity = 0.01f;
-    int n_box = CompareBoxes(boxes_orig,boxes,dissimularity);
-    if (n_box < 0)
-    {
-        ts->set_failed_test_info(cvtest::TS::OK);
-    }
-    else
-    {
-        SaveBoxes(calc_boxes_path.c_str(), boxes);
-        ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
-        ts->printf( cvtest::TS::LOG, "Incorrect correspondence in box %d\n",n_box);
+        ASSERT_LE(minRegs, nmsers);
+        ASSERT_GE(maxRegs, nmsers);
     }
 }
-
-TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
-
-#endif