Merge pull request #17119 from alalek:move_sift

doc/opencv.bib

@@ -620,7 +620,7 @@
   volume = {1},
   publisher = {IEEE}
 }
-@article{Lowe:2004:DIF:993451.996342,
+@article{Lowe04,
   author = {Lowe, David G.},
   title = {Distinctive Image Features from Scale-Invariant Keypoints},
   journal = {Int. J. Comput. Vision},

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -44,7 +44,7 @@ img1 = cv.imread('box.png',0)          # queryImage
 img2 = cv.imread('box_in_scene.png',0) # trainImage
 
 # Initiate SIFT detector
-sift = cv.xfeatures2d.SIFT_create()
+sift = cv.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)

doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown

@@ -110,7 +110,7 @@ img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE)          # queryImage
 img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate SIFT detector
-sift = cv.xfeatures2d.SIFT_create()
+sift = cv.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)
@@ -174,7 +174,7 @@ img1 = cv.imread('box.png',cv.IMREAD_GRAYSCALE)          # queryImage
 img2 = cv.imread('box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate SIFT detector
-sift = cv.xfeatures2d.SIFT_create()
+sift = cv.SIFT_create()
 
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1,None)

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -119,7 +119,7 @@ import cv2 as cv
 img = cv.imread('home.jpg')
 gray= cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 
-sift = cv.xfeatures2d.SIFT_create()
+sift = cv.SIFT_create()
 kp = sift.detect(gray,None)
 
 img=cv.drawKeypoints(gray,kp,img)
@@ -151,7 +151,7 @@ Now to calculate the descriptor, OpenCV provides two methods.
 
 We will see the second method:
 @code{.py}
-sift = cv.xfeatures2d.SIFT_create()
+sift = cv.SIFT_create()
 kp, des = sift.detectAndCompute(gray,None)
 @endcode
 Here kp will be a list of keypoints and des is a numpy array of shape

doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.markdown

@@ -27,7 +27,7 @@ Binary descriptors (ORB, BRISK, ...) are matched using the <a href="https://en.w
 This distance is equivalent to count the number of different elements for binary strings (population count after applying a XOR operation):
 \f[ d_{hamming} \left ( a,b \right ) = \sum_{i=0}^{n-1} \left ( a_i \oplus b_i \right ) \f]
 
-To filter the matches, Lowe proposed in @cite Lowe:2004:DIF:993451.996342 to use a distance ratio test to try to eliminate false matches.
+To filter the matches, Lowe proposed in @cite Lowe04 to use a distance ratio test to try to eliminate false matches.
 The distance ratio between the two nearest matches of a considered keypoint is computed and it is a good match when this value is below
 a threshold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors
 is close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct

modules/features2d/include/opencv2/features2d.hpp

@@ -244,6 +244,39 @@ typedef Feature2D DescriptorExtractor;
 //! @addtogroup features2d_main
 //! @{
 
+
+/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform
+(SIFT) algorithm by D. Lowe @cite Lowe04 .
+*/
+class CV_EXPORTS_W SIFT : public Feature2D
+{
+public:
+    /**
+    @param nfeatures The number of best features to retain. The features are ranked by their scores
+    (measured in SIFT algorithm as the local contrast)
+
+    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
+    number of octaves is computed automatically from the image resolution.
+
+    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
+    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
+
+    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
+    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
+    filtered out (more features are retained).
+
+    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
+    is captured with a weak camera with soft lenses, you might want to reduce the number.
+    */
+    CV_WRAP static Ptr<SIFT> create(int nfeatures = 0, int nOctaveLayers = 3,
+        double contrastThreshold = 0.04, double edgeThreshold = 10,
+        double sigma = 1.6);
+};
+
+typedef SIFT SiftFeatureDetector;
+typedef SIFT SiftDescriptorExtractor;
+
+
 /** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
  */
 class CV_EXPORTS_W BRISK : public Feature2D

modules/features2d/misc/java/test/SIFTDescriptorExtractorTest.java

@@ -6,6 +6,7 @@ import org.opencv.core.MatOfKeyPoint;
 import org.opencv.core.Point;
 import org.opencv.core.Scalar;
 import org.opencv.core.KeyPoint;
+import org.opencv.features2d.SIFT;
 import org.opencv.test.OpenCVTestCase;
 import org.opencv.test.OpenCVTestRunner;
 import org.opencv.imgproc.Imgproc;
@@ -29,7 +30,7 @@ public class SIFTDescriptorExtractorTest extends OpenCVTestCase {
     @Override
     protected void setUp() throws Exception {
         super.setUp();
-        extractor = createClassInstance(XFEATURES2D+"SIFT", DEFAULT_FACTORY, null, null);
+        extractor = SIFT.create();
         keypoint = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
         matSize = 100;
         truth = new Mat(1, 128, CvType.CV_32FC1) {

modules/features2d/perf/perf_sift.cpp

@@ -0,0 +1,85 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#include "perf_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+typedef perf::TestBaseWithParam<std::string> SIFT_detect;
+typedef perf::TestBaseWithParam<std::string> SIFT_extract;
+typedef perf::TestBaseWithParam<std::string> SIFT_full;
+
+#define SIFT_IMAGES \
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png"
+
+PERF_TEST_P_(SIFT_detect, SIFT)
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame).time(90);
+    Ptr<SIFT> detector = SIFT::create();
+    vector<KeyPoint> points;
+
+    PERF_SAMPLE_BEGIN();
+        detector->detect(frame, points, mask);
+    PERF_SAMPLE_END();
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(SIFT_extract, SIFT)
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame).time(90);
+
+    Ptr<SIFT> detector = SIFT::create();
+    vector<KeyPoint> points;
+    Mat descriptors;
+    detector->detect(frame, points, mask);
+
+    PERF_SAMPLE_BEGIN();
+        detector->compute(frame, points, descriptors);
+    PERF_SAMPLE_END();
+
+    SANITY_CHECK_NOTHING();
+}
+
+PERF_TEST_P_(SIFT_full, SIFT)
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame).time(90);
+    Ptr<SIFT> detector = SIFT::create();
+    vector<KeyPoint> points;
+    Mat descriptors;
+
+    PERF_SAMPLE_BEGIN();
+        detector->detectAndCompute(frame, mask, points, descriptors, false);
+    PERF_SAMPLE_END();
+
+    SANITY_CHECK_NOTHING();
+}
+
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_detect,
+    testing::Values(SIFT_IMAGES)
+);
+INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_extract,
+    testing::Values(SIFT_IMAGES)
+);
+INSTANTIATE_TEST_CASE_P(/*nothing*/, SIFT_full,
+    testing::Values(SIFT_IMAGES)
+);
+
+}} // namespace

modules/features2d/test/test_descriptors_invariance.cpp

@@ -167,6 +167,9 @@ TEST_P(DescriptorScaleInvariance, scale)
  * Descriptors's rotation invariance check
  */
 
+INSTANTIATE_TEST_CASE_P(SIFT, DescriptorRotationInvariance,
+                        Value(IMAGE_TSUKUBA, SIFT::create(), SIFT::create(), 0.98f));
+
 INSTANTIATE_TEST_CASE_P(BRISK, DescriptorRotationInvariance,
                         Value(IMAGE_TSUKUBA, BRISK::create(), BRISK::create(), 0.99f));
 
@@ -183,6 +186,10 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DescriptorRotationInvariance,
  * Descriptor's scale invariance check
  */
 
+// TODO: Expected: (descInliersRatio) >= (minInliersRatio), actual: 0.330378 vs 0.78
+INSTANTIATE_TEST_CASE_P(DISABLED_SIFT, DescriptorScaleInvariance,
+                        Value(IMAGE_BIKES, SIFT::create(), SIFT::create(), 0.78f));
+
 INSTANTIATE_TEST_CASE_P(AKAZE, DescriptorScaleInvariance,
                         Value(IMAGE_BIKES, AKAZE::create(), AKAZE::create(), 0.6f));
 

modules/features2d/test/test_descriptors_regression.cpp

@@ -342,6 +342,13 @@ private:
 *                                Tests registrations                                     *
 \****************************************************************************************/
 
+TEST( Features2d_DescriptorExtractor_SIFT, regression )
+{
+    CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
+                                                SIFT::create() );
+    test.safe_run();
+}
+
 TEST( Features2d_DescriptorExtractor_BRISK, regression )
 {
     CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
@@ -388,7 +395,7 @@ TEST( Features2d_DescriptorExtractor_AKAZE_DESCRIPTOR_KAZE, regression )
     test.safe_run();
 }
 
-TEST( Features2d_DescriptorExtractor, batch )
+TEST( Features2d_DescriptorExtractor, batch_ORB )
 {
     string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
     vector<Mat> imgs, descriptors;
@@ -416,6 +423,35 @@ TEST( Features2d_DescriptorExtractor, batch )
     }
 }
 
+TEST( Features2d_DescriptorExtractor, batch_SIFT )
+{
+    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<SIFT> sift = SIFT::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);
+    }
+
+    sift->detect(imgs, keypoints);
+    sift->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);
+    }
+}
+
+
 class DescriptorImage : public TestWithParam<std::string>
 {
 protected:

modules/features2d/test/test_detectors_invariance.cpp

@@ -220,6 +220,9 @@ TEST_P(DetectorScaleInvariance, scale)
  * Detector's rotation invariance check
  */
 
+INSTANTIATE_TEST_CASE_P(SIFT, DetectorRotationInvariance,
+                        Value(IMAGE_TSUKUBA, SIFT::create(), 0.45f, 0.70f));
+
 INSTANTIATE_TEST_CASE_P(BRISK, DetectorRotationInvariance,
                         Value(IMAGE_TSUKUBA, BRISK::create(), 0.45f, 0.76f));
 
@@ -236,6 +239,10 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
  * Detector's scale invariance check
  */
 
+// TODO: Expected: (keyPointMatchesRatio) >= (minKeyPointMatchesRatio), actual: 0.596752 vs 0.69
+INSTANTIATE_TEST_CASE_P(DISABLED_SIFT, DetectorScaleInvariance,
+                        Value(IMAGE_BIKES, SIFT::create(), 0.69f, 0.98f));
+
 INSTANTIATE_TEST_CASE_P(BRISK, DetectorScaleInvariance,
                         Value(IMAGE_BIKES, BRISK::create(), 0.08f, 0.49f));
 

modules/features2d/test/test_detectors_regression.cpp

@@ -245,6 +245,12 @@ void CV_FeatureDetectorTest::run( int /*start_from*/ )
 *                                Tests registrations                                     *
 \****************************************************************************************/
 
+TEST( Features2d_Detector_SIFT, regression )
+{
+    CV_FeatureDetectorTest test( "detector-sift", SIFT::create() );
+    test.safe_run();
+}
+
 TEST( Features2d_Detector_BRISK, regression )
 {
     CV_FeatureDetectorTest test( "detector-brisk", BRISK::create() );

modules/features2d/test/test_keypoints.cpp

@@ -177,4 +177,11 @@ TEST(Features2d_Detector_Keypoints_AKAZE, validation)
     test_mldb.safe_run();
 }
 
+TEST(Features2d_Detector_Keypoints_SIFT, validation)
+{
+    CV_FeatureDetectorKeypointsTest test(SIFT::create());
+    test.safe_run();
+}
+
+
 }} // namespace

modules/stitching/src/matchers.cpp

@@ -51,7 +51,6 @@ using namespace cv::cuda;
 #ifdef HAVE_OPENCV_XFEATURES2D
 #include "opencv2/xfeatures2d.hpp"
 using xfeatures2d::SURF;
-using xfeatures2d::SIFT;
 #else
 #  if defined(_MSC_VER)
 #    pragma warning(disable:4702)  // unreachable code
@@ -487,14 +486,8 @@ void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
 
 SiftFeaturesFinder::SiftFeaturesFinder()
 {
-#ifdef HAVE_OPENCV_XFEATURES2D
     Ptr<SIFT> sift_ = SIFT::create();
-    if( !sift_ )
-        CV_Error( Error::StsNotImplemented, "OpenCV was built without SIFT support" );
     sift = sift_;
-#else
-    CV_Error( Error::StsNotImplemented, "OpenCV was built without SIFT support" );
-#endif
 }
 
 void SiftFeaturesFinder::find(InputArray image, ImageFeatures &features)

samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Utils.cpp

@@ -323,15 +323,8 @@ void createFeatures(const std::string &featureName, int numKeypoints, cv::Ptr<cv
     }
     else if (featureName == "SIFT")
     {
-#if defined (OPENCV_ENABLE_NONFREE) && defined (HAVE_OPENCV_XFEATURES2D)
+        detector = cv::SIFT::create();
-        detector = cv::xfeatures2d::SIFT::create();
+        descriptor = cv::SIFT::create();
-        descriptor = cv::xfeatures2d::SIFT::create();
-#else
-        std::cout << "xfeatures2d module is not available or nonfree is not enabled." << std::endl;
-        std::cout << "Default to ORB." << std::endl;
-        detector = cv::ORB::create(numKeypoints);
-        descriptor = cv::ORB::create(numKeypoints);
-#endif
     }
     else if (featureName == "SURF")
     {

samples/python/find_obj.py

@@ -29,7 +29,7 @@ FLANN_INDEX_LSH    = 6
 def init_feature(name):
     chunks = name.split('-')
     if chunks[0] == 'sift':
-        detector = cv.xfeatures2d.SIFT_create()
+        detector = cv.SIFT_create()
         norm = cv.NORM_L2
     elif chunks[0] == 'surf':
         detector = cv.xfeatures2d.SURF_create(800)