Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/README.md

@@ -68,7 +68,7 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **tracking**: Vision Based Object Tracking -- Use and/or evaluate one of 5 different visual object tracking techniques.
 
-- **xfeatures2d**: Features2D extra -- Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms. SURF, SIFT, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.
+- **xfeatures2d**: Features2D extra -- Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms. SURF, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.
 
 - **ximgproc**: Extended Image Processing -- Structured Forests / Domain Transform Filter / Guided Filter / Adaptive Manifold Filter / Joint Bilateral Filter / Superpixels / Ridge Detection Filter.
 

modules/aruco/include/opencv2/aruco.hpp

@@ -99,7 +99,7 @@ enum CornerRefineMethod{
  * - maxMarkerPerimeterRate:  determine maximum perimeter for marker contour to be detected. This
  *   is defined as a rate respect to the maximum dimension of the input image (default 4.0).
  * - polygonalApproxAccuracyRate: minimum accuracy during the polygonal approximation process to
- *   determine which contours are squares.
+ *   determine which contours are squares. (default 0.03)
  * - minCornerDistanceRate: minimum distance between corners for detected markers relative to its
  *   perimeter (default 0.05)
  * - minDistanceToBorder: minimum distance of any corner to the image border for detected markers
@@ -109,7 +109,7 @@ enum CornerRefineMethod{
  *   of the two markers (default 0.05).
  * - cornerRefinementMethod: corner refinement method. (CORNER_REFINE_NONE, no refinement.
  *   CORNER_REFINE_SUBPIX, do subpixel refinement. CORNER_REFINE_CONTOUR use contour-Points,
- *   CORNER_REFINE_APRILTAG  use the AprilTag2 approach)
+ *   CORNER_REFINE_APRILTAG  use the AprilTag2 approach). (default CORNER_REFINE_NONE)
  * - cornerRefinementWinSize: window size for the corner refinement process (in pixels) (default 5).
  * - cornerRefinementMaxIterations: maximum number of iterations for stop criteria of the corner
  *   refinement process (default 30).
@@ -117,7 +117,7 @@ enum CornerRefineMethod{
  *   process (default: 0.1)
  * - markerBorderBits: number of bits of the marker border, i.e. marker border width (default 1).
  * - perspectiveRemovePixelPerCell: number of bits (per dimension) for each cell of the marker
- *   when removing the perspective (default 8).
+ *   when removing the perspective (default 4).
  * - perspectiveRemoveIgnoredMarginPerCell: width of the margin of pixels on each cell not
  *   considered for the determination of the cell bit. Represents the rate respect to the total
  *   size of the cell, i.e. perspectiveRemovePixelPerCell (default 0.13)
@@ -129,21 +129,21 @@ enum CornerRefineMethod{
  *   than 128 or not) (default 5.0)
  * - errorCorrectionRate error correction rate respect to the maximun error correction capability
  *   for each dictionary. (default 0.6).
- * - aprilTagMinClusterPixels: reject quads containing too few pixels.
- * - aprilTagMaxNmaxima: how many corner candidates to consider when segmenting a group of pixels into a quad.
+ * - aprilTagMinClusterPixels: reject quads containing too few pixels. (default 5)
+ * - aprilTagMaxNmaxima: how many corner candidates to consider when segmenting a group of pixels into a quad. (default 10)
  * - aprilTagCriticalRad: Reject quads where pairs of edges have angles that are close to straight or close to
- *   180 degrees. Zero means that no quads are rejected. (In radians).
+ *   180 degrees. Zero means that no quads are rejected. (In radians) (default 10*PI/180)
  * - aprilTagMaxLineFitMse:  When fitting lines to the contours, what is the maximum mean squared error
  *   allowed?  This is useful in rejecting contours that are far from being quad shaped; rejecting
- *   these quads "early" saves expensive decoding processing.
+ *   these quads "early" saves expensive decoding processing. (default 10.0)
  * - aprilTagMinWhiteBlackDiff: When we build our model of black & white pixels, we add an extra check that
- *   the white model must be (overall) brighter than the black model.  How much brighter? (in pixel values, [0,255]).
- * - aprilTagDeglitch:  should the thresholded image be deglitched? Only useful for very noisy images
+ *   the white model must be (overall) brighter than the black model.  How much brighter? (in pixel values, [0,255]). (default 5)
+ * - aprilTagDeglitch:  should the thresholded image be deglitched? Only useful for very noisy images. (default 0)
  * - aprilTagQuadDecimate: Detection of quads can be done on a lower-resolution image, improving speed at a
  *   cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still
- *   done at full resolution.
+ *   done at full resolution. (default 0.0)
  * - aprilTagQuadSigma: What Gaussian blur should be applied to the segmented image (used for quad detection?)
- *   Parameter is the standard deviation in pixels.  Very noisy images benefit from non-zero values (e.g. 0.8).
+ *   Parameter is the standard deviation in pixels.  Very noisy images benefit from non-zero values (e.g. 0.8). (default 0.0)
  * - detectInvertedMarker: to check if there is a white marker. In order to generate a "white" marker just
  *   invert a normal marker by using a tilde, ~markerImage. (default false)
  */

modules/xfeatures2d/README.md

@@ -5,4 +5,4 @@ Extra 2D Features Framework
 2. Non-free 2D feature algorithms
 
 Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms:
- SURF, SIFT, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.
+ SURF, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.

modules/xfeatures2d/doc/xfeatures2d.bib

@@ -60,17 +60,6 @@
   year = {2016}
 }
 
-@article{Lowe04,
-  title = {Distinctive image features from scale-invariant keypoints},
-  author = {Lowe, David G},
-  journal = {International journal of computer vision},
-  volume = {60},
-  number = {2},
-  pages = {91--110},
-  year = {2004},
-  publisher = {Kluwer Academic Publishers}
-}
-
 @article{Lowry2018LOGOSLG,
   title = {LOGOS: Local Geometric Support for High-Outlier Spatial Verification},
   author = {Stephanie Lowry and Henrik Andreasson},

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -67,39 +67,6 @@ namespace cv
 namespace xfeatures2d
 {
 
-
-/** @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;
-
-
 //! @addtogroup xfeatures2d_experiment
 //! @{
 

modules/xfeatures2d/misc/python/pyopencv_sift.hpp

@@ -0,0 +1,2 @@
+// Compatibility
+#include "shadow_sift.hpp"

modules/xfeatures2d/misc/python/shadow_sift.hpp

@@ -0,0 +1,20 @@
+// Compatibility
+// SIFT is moved to the main repository
+
+namespace cv {
+namespace xfeatures2d {
+
+/** Use cv.SIFT_create() instead */
+CV_WRAP static inline
+Ptr<cv::SIFT> SIFT_create(int nfeatures = 0, int nOctaveLayers = 3,
+        double contrastThreshold = 0.04, double edgeThreshold = 10,
+        double sigma = 1.6)
+{
+    CV_LOG_ONCE_WARNING(NULL, "DEPRECATED: cv.xfeatures2d.SIFT_create() is deprecated due SIFT tranfer to the main repository. "
+                              "https://github.com/opencv/opencv/issues/16736"
+    );
+
+    return SIFT::create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma);
+}
+
+}}  // namespace

modules/xfeatures2d/misc/python/test/test_sift_compatibility.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import os
+import numpy as np
+import cv2 as cv
+
+from tests_common import NewOpenCVTests
+
+class sift_compatibility_test(NewOpenCVTests):
+
+    def test_create(self):
+
+        sift = cv.xfeatures2d.SIFT_create()
+        self.assertFalse(sift is None)
+
+        img1 = np.zeros((100, 100, 3), dtype=np.uint8)
+        kp1_, des1_ = sift.detectAndCompute(img1, None)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/xfeatures2d/perf/perf_sift.cpp

@@ -1,72 +0,0 @@
-// 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;
-
-#define SIFT_IMAGES \
-    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
-    "stitching/a3.png"
-
-PERF_TEST_P(sift, detect, testing::Values(SIFT_IMAGES))
-{
-    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, testing::Values(SIFT_IMAGES))
-{
-    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, testing::Values(SIFT_IMAGES))
-{
-    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();
-}
-
-}} // namespace

modules/xfeatures2d/src/precomp.hpp

@@ -61,6 +61,4 @@
 
 #include "opencv2/core/private.hpp"
 
-#define USE_AVX2  (cv::checkHardwareSupport(CV_CPU_AVX2))
-
 #endif

modules/xfeatures2d/test/test_features2d.cpp

@@ -53,12 +53,6 @@ const string IMAGE_FILENAME = "tsukuba.png";
 
 namespace opencv_test { namespace {
 
-TEST( Features2d_Detector_SIFT, regression )
-{
-    CV_FeatureDetectorTest test( "detector-sift", SIFT::create() );
-    test.safe_run();
-}
-
 #ifdef OPENCV_ENABLE_NONFREE
 TEST( Features2d_Detector_SURF, regression )
 {
@@ -94,12 +88,6 @@ TEST( Features2d_Detector_Harris_Laplace_Affine, regression )
 /*
  * Descriptors
  */
-TEST( Features2d_DescriptorExtractor_SIFT, regression )
-{
-    CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
-                                                SIFT::create() );
-    test.safe_run();
-}
 
 #ifdef OPENCV_ENABLE_NONFREE
 TEST( Features2d_DescriptorExtractor_SURF, regression )
@@ -454,34 +442,6 @@ TEST(DISABLED_Features2d_SURF_using_mask, regression)
     FeatureDetectorUsingMaskTest test(SURF::create());
     test.safe_run();
 }
-
-TEST( XFeatures2d_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<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);
-    }
-}
 #endif // NONFREE
 
 }} // namespace

modules/xfeatures2d/test/test_keypoints.cpp

@@ -121,12 +121,6 @@ TEST(Features2d_Detector_Keypoints_SURF, validation)
     CV_FeatureDetectorKeypointsTest test(xfeatures2d::SURF::create());
     test.safe_run();
 }
-
-TEST(Features2d_Detector_Keypoints_SIFT, validation)
-{
-    CV_FeatureDetectorKeypointsTest test(xfeatures2d::SIFT::create());
-    test.safe_run();
-}
 #endif // NONFREE
 
 

modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp

@@ -20,24 +20,16 @@ INSTANTIATE_TEST_CASE_P(SURF, DetectorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA, SURF::create(), 0.40f, 0.76f)
 ));
 
-INSTANTIATE_TEST_CASE_P(SIFT, DetectorRotationInvariance, Values(
-    make_tuple(IMAGE_TSUKUBA, SIFT::create(), 0.45f, 0.70f)
-));
-
 INSTANTIATE_TEST_CASE_P(SURF, DescriptorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA, SURF::create(), SURF::create(), 0.83f)
 ));
 
-INSTANTIATE_TEST_CASE_P(SIFT, DescriptorRotationInvariance, Values(
-    make_tuple(IMAGE_TSUKUBA, SIFT::create(), SIFT::create(), 0.98f)
-));
+#endif // NONFREE
 
 INSTANTIATE_TEST_CASE_P(LATCH, DescriptorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA, SIFT::create(), LATCH::create(), 0.98f)
 ));
 
-#endif // NONFREE
-
 INSTANTIATE_TEST_CASE_P(DAISY, DescriptorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA,
                BRISK::create(),
@@ -159,16 +151,9 @@ INSTANTIATE_TEST_CASE_P(SURF, DetectorScaleInvariance, Values(
     make_tuple(IMAGE_BIKES, SURF::create(), 0.64f, 0.84f)
 ));
 
-INSTANTIATE_TEST_CASE_P(SIFT, DetectorScaleInvariance, Values(
-    make_tuple(IMAGE_BIKES, SIFT::create(), 0.55f, 0.99f)
-));
-
 INSTANTIATE_TEST_CASE_P(SURF, DescriptorScaleInvariance, Values(
     make_tuple(IMAGE_BIKES, SURF::create(), SURF::create(), 0.7f)
 ));
-INSTANTIATE_TEST_CASE_P(SIFT, DescriptorScaleInvariance, Values(
-    make_tuple(IMAGE_BIKES, SIFT::create(), SIFT::create(), 0.3f)
-));
 #endif // NONFREE