gpuimgproc module fixes

modules/gpuimgproc/test/test_mean_shift.cpp

@@ -0,0 +1,174 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
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+//  copy or use the software.
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+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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+//
+//M*/
+
+#include "test_precomp.hpp"
+
+#ifdef HAVE_CUDA
+
+using namespace cvtest;
+
+////////////////////////////////////////////////////////////////////////////////
+// MeanShift
+
+struct MeanShift : testing::TestWithParam<cv::gpu::DeviceInfo>
+{
+    cv::gpu::DeviceInfo devInfo;
+
+    cv::Mat img;
+
+    int spatialRad;
+    int colorRad;
+
+    virtual void SetUp()
+    {
+        devInfo = GetParam();
+
+        cv::gpu::setDevice(devInfo.deviceID());
+
+        img = readImageType("meanshift/cones.png", CV_8UC4);
+        ASSERT_FALSE(img.empty());
+
+        spatialRad = 30;
+        colorRad = 30;
+    }
+};
+
+GPU_TEST_P(MeanShift, Filtering)
+{
+    cv::Mat img_template;
+    if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
+        img_template = readImage("meanshift/con_result.png");
+    else
+        img_template = readImage("meanshift/con_result_CC1X.png");
+    ASSERT_FALSE(img_template.empty());
+
+    cv::gpu::GpuMat d_dst;
+    cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad);
+
+    ASSERT_EQ(CV_8UC4, d_dst.type());
+
+    cv::Mat dst(d_dst);
+
+    cv::Mat result;
+    cv::cvtColor(dst, result, cv::COLOR_BGRA2BGR);
+
+    EXPECT_MAT_NEAR(img_template, result, 0.0);
+}
+
+GPU_TEST_P(MeanShift, Proc)
+{
+    cv::FileStorage fs;
+    if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
+        fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
+    else
+        fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
+    ASSERT_TRUE(fs.isOpened());
+
+    cv::Mat spmap_template;
+    fs["spmap"] >> spmap_template;
+    ASSERT_FALSE(spmap_template.empty());
+
+    cv::gpu::GpuMat rmap_filtered;
+    cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad);
+
+    cv::gpu::GpuMat rmap;
+    cv::gpu::GpuMat spmap;
+    cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad);
+
+    ASSERT_EQ(CV_8UC4, rmap.type());
+
+    EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
+    EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
+}
+
+INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES);
+
+////////////////////////////////////////////////////////////////////////////////
+// MeanShiftSegmentation
+
+namespace
+{
+    IMPLEMENT_PARAM_CLASS(MinSize, int);
+}
+
+PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize)
+{
+    cv::gpu::DeviceInfo devInfo;
+    int minsize;
+
+    virtual void SetUp()
+    {
+        devInfo = GET_PARAM(0);
+        minsize = GET_PARAM(1);
+
+        cv::gpu::setDevice(devInfo.deviceID());
+    }
+};
+
+GPU_TEST_P(MeanShiftSegmentation, Regression)
+{
+    cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4);
+    ASSERT_FALSE(img.empty());
+
+    std::ostringstream path;
+    path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
+    if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
+        path << ".png";
+    else
+        path << "_CC1X.png";
+    cv::Mat dst_gold = readImage(path.str());
+    ASSERT_FALSE(dst_gold.empty());
+
+    cv::Mat dst;
+    cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize);
+
+    cv::Mat dst_rgb;
+    cv::cvtColor(dst, dst_rgb, cv::COLOR_BGRA2BGR);
+
+    EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3);
+}
+
+INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine(
+    ALL_DEVICES,
+    testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364))));
+
+#endif // HAVE_CUDA