Merge pull request #16717 from OrestChura:oc/goodFeatures

- cv::gapi::goodFeaturesToTrack() kernel is implemented
- tests (for exact check with cv::goodFeaturesToTrack() and for internal cases) are implemented
- a custom comparison function for vectors and a custom test fixture implemented
  - some posiible issues as wrong/inexact sorting of two compared vectors are
 not taken into account
- initializations of an input Mat using a picture from opencv_extra implemented (function from gapi_streaming_test used)

modules/gapi/include/opencv2/gapi/imgproc.hpp

@@ -2,7 +2,7 @@
 // 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.
 //
-// Copyright (C) 2018 Intel Corporation
+// Copyright (C) 2018-2020 Intel Corporation
 
 
 #ifndef OPENCV_GAPI_IMGPROC_HPP
@@ -102,6 +102,14 @@ namespace imgproc {
         }
     };
 
+    G_TYPED_KERNEL(GGoodFeatures,
+                   <cv::GArray<cv::Point2f>(GMat,int,double,double,Mat,int,bool,double)>,
+                   "org.opencv.imgproc.goodFeaturesToTrack") {
+        static GArrayDesc outMeta(GMatDesc, int, double, double, const Mat&, int, bool, double) {
+            return empty_array_desc();
+        }
+    };
+
     G_TYPED_KERNEL(GRGB2YUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2yuv") {
         static GMatDesc outMeta(GMatDesc in) {
             return in; // type still remains CV_8UC3;
@@ -251,7 +259,7 @@ namespace imgproc {
         }
     };
 
-}
+} //namespace imgproc
 
 
 //! @addtogroup gapi_filters
@@ -657,6 +665,59 @@ L2gradient=false ).
 GAPI_EXPORTS GMat Canny(const GMat& image, double threshold1, double threshold2,
                         int apertureSize = 3, bool L2gradient = false);
 
+/** @brief Determines strong corners on an image.
+
+The function finds the most prominent corners in the image or in the specified image region, as
+described in @cite Shi94
+
+-   Function calculates the corner quality measure at every source image pixel using the
+    #cornerMinEigenVal or #cornerHarris .
+-   Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
+    retained).
+-   The corners with the minimal eigenvalue less than
+    \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
+-   The remaining corners are sorted by the quality measure in the descending order.
+-   Function throws away each corner for which there is a stronger corner at a distance less than
+    maxDistance.
+
+The function can be used to initialize a point-based tracker of an object.
+
+@note If the function is called with different values A and B of the parameter qualityLevel , and
+A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
+with qualityLevel=B .
+
+@note Function textual ID is "org.opencv.imgproc.goodFeaturesToTrack"
+
+@param image Input 8-bit or floating-point 32-bit, single-channel image.
+@param maxCorners Maximum number of corners to return. If there are more corners than are found,
+the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
+and all detected corners are returned.
+@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
+parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
+(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
+quality measure less than the product are rejected. For example, if the best corner has the
+quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
+less than 15 are rejected.
+@param minDistance Minimum possible Euclidean distance between the returned corners.
+@param mask Optional region of interest. If the image is not empty (it needs to have the type
+CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
+@param blockSize Size of an average block for computing a derivative covariation matrix over each
+pixel neighborhood. See cornerEigenValsAndVecs .
+@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
+or #cornerMinEigenVal.
+@param k Free parameter of the Harris detector.
+
+@return vector of detected corners.
+ */
+GAPI_EXPORTS GArray<Point2f> goodFeaturesToTrack(const GMat  &image,
+                                                       int    maxCorners,
+                                                       double qualityLevel,
+                                                       double minDistance,
+                                                 const Mat   &mask = Mat(),
+                                                       int    blockSize = 3,
+                                                       bool   useHarrisDetector = false,
+                                                       double k = 0.04);
+
 /** @brief Equalizes the histogram of a grayscale image.
 
 The function equalizes the histogram of the input image using the following algorithm: