implemented mean shift segmentation with elimination of small segments, added tests

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -473,6 +473,10 @@ namespace cv
         CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, 
             TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
 
+        //! Does mean shift segmentation with elimiation of small regions.
+        CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
+            TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
+
         //! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
         //! Supported types of input disparity: CV_8U, CV_16S.
         //! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).

tests/gpu/src/mssegmentation.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
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+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
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+
+#include <opencv2/opencv.hpp>
+#include <opencv2/gpu/gpu.hpp>
+#include <iostream>
+#include <string>
+#include <iosfwd>
+#include "gputest.hpp"
+using namespace cv;
+using namespace cv::gpu;
+using namespace std;
+
+struct CV_GpuMeanShiftSegmentationTest : public CvTest {
+    CV_GpuMeanShiftSegmentationTest() : CvTest( "GPU-MeanShiftSegmentation", "MeanShiftSegmentation" ) {}
+
+    void run(int) 
+    {
+        try 
+        {
+            Mat img_rgb = imread(string(ts->get_data_path()) + "meanshift/cones.png");
+            if (img_rgb.empty())
+            {
+                ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
+                return;
+            }
+
+            Mat img;
+            cvtColor(img_rgb, img, CV_BGR2BGRA);
+
+            for (int minsize = 0; minsize < 2000; minsize = (minsize + 1) * 2) 
+            {
+                stringstream path;
+                path << ts->get_data_path() << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize << ".png";
+
+                Mat dst;
+                meanShiftSegmentation((GpuMat)img, dst, 10, 10, minsize);
+                Mat dst_rgb;
+                cvtColor(dst, dst_rgb, CV_BGRA2BGR);
+
+                //imwrite(path.str(), dst_rgb);
+                Mat dst_ref = imread(path.str());
+                if (dst_ref.empty()) 
+                {
+                    ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
+                    return;
+                }
+                if (abs(cv::norm(dst_rgb - dst_ref, NORM_INF)) > 1e-3) 
+                {
+                    ts->printf(CvTS::LOG, "\ndiffers from image *minsize%d.png\n", minsize);
+                    ts->set_failed_test_info(CvTS::FAIL_BAD_ACCURACY);
+                    return;
+                }
+            }
+        }
+        catch (const cv::Exception& e) 
+        {
+            if (!check_and_treat_gpu_exception(e, ts))
+                throw;
+            return;
+        }
+
+        ts->set_failed_test_info(CvTS::OK);
+    }    
+} ms_segm_test;
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