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11 // For Open Source Computer Vision Library
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41 //M*/
42
43 #include "test_precomp.hpp"
44
45 #ifdef HAVE_CUDA
46
47 using namespace cvtest;
48
49 ////////////////////////////////////////////////////////////////////////////////
50 // MeanShift
51
52 struct MeanShift : testing::TestWithParam<cv::cuda::DeviceInfo>
53 {
54 cv::cuda::DeviceInfo devInfo;
55
56 cv::Mat img;
57
58 int spatialRad;
59 int colorRad;
60
SetUpMeanShift61 virtual void SetUp()
62 {
63 devInfo = GetParam();
64
65 cv::cuda::setDevice(devInfo.deviceID());
66
67 img = readImageType("meanshift/cones.png", CV_8UC4);
68 ASSERT_FALSE(img.empty());
69
70 spatialRad = 30;
71 colorRad = 30;
72 }
73 };
74
CUDA_TEST_P(MeanShift,Filtering)75 CUDA_TEST_P(MeanShift, Filtering)
76 {
77 cv::Mat img_template;
78 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
79 img_template = readImage("meanshift/con_result.png");
80 else
81 img_template = readImage("meanshift/con_result_CC1X.png");
82 ASSERT_FALSE(img_template.empty());
83
84 cv::cuda::GpuMat d_dst;
85 cv::cuda::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad);
86
87 ASSERT_EQ(CV_8UC4, d_dst.type());
88
89 cv::Mat dst(d_dst);
90
91 cv::Mat result;
92 cv::cvtColor(dst, result, cv::COLOR_BGRA2BGR);
93
94 EXPECT_MAT_NEAR(img_template, result, 0.0);
95 }
96
CUDA_TEST_P(MeanShift,Proc)97 CUDA_TEST_P(MeanShift, Proc)
98 {
99 cv::FileStorage fs;
100 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
101 fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
102 else
103 fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
104 ASSERT_TRUE(fs.isOpened());
105
106 cv::Mat spmap_template;
107 fs["spmap"] >> spmap_template;
108 ASSERT_FALSE(spmap_template.empty());
109
110 cv::cuda::GpuMat rmap_filtered;
111 cv::cuda::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad);
112
113 cv::cuda::GpuMat rmap;
114 cv::cuda::GpuMat spmap;
115 cv::cuda::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad);
116
117 ASSERT_EQ(CV_8UC4, rmap.type());
118
119 EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
120 EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
121 }
122
123 INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShift, ALL_DEVICES);
124
125 ////////////////////////////////////////////////////////////////////////////////
126 // MeanShiftSegmentation
127
128 namespace
129 {
130 IMPLEMENT_PARAM_CLASS(MinSize, int);
131 }
132
PARAM_TEST_CASE(MeanShiftSegmentation,cv::cuda::DeviceInfo,MinSize)133 PARAM_TEST_CASE(MeanShiftSegmentation, cv::cuda::DeviceInfo, MinSize)
134 {
135 cv::cuda::DeviceInfo devInfo;
136 int minsize;
137
138 virtual void SetUp()
139 {
140 devInfo = GET_PARAM(0);
141 minsize = GET_PARAM(1);
142
143 cv::cuda::setDevice(devInfo.deviceID());
144 }
145 };
146
CUDA_TEST_P(MeanShiftSegmentation,Regression)147 CUDA_TEST_P(MeanShiftSegmentation, Regression)
148 {
149 cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4);
150 ASSERT_FALSE(img.empty());
151
152 std::ostringstream path;
153 path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
154 if (supportFeature(devInfo, cv::cuda::FEATURE_SET_COMPUTE_20))
155 path << ".png";
156 else
157 path << "_CC1X.png";
158 cv::Mat dst_gold = readImage(path.str());
159 ASSERT_FALSE(dst_gold.empty());
160
161 cv::Mat dst;
162 cv::cuda::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize);
163
164 cv::Mat dst_rgb;
165 cv::cvtColor(dst, dst_rgb, cv::COLOR_BGRA2BGR);
166
167 EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3);
168 }
169
170 INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, MeanShiftSegmentation, testing::Combine(
171 ALL_DEVICES,
172 testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364))));
173
174 #endif // HAVE_CUDA
175
