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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 // Gold implementation
51
52 namespace
53 {
remapImpl(const cv::Mat & src,const cv::Mat & xmap,const cv::Mat & ymap,cv::Mat & dst,int borderType,cv::Scalar borderVal)54 template <typename T, template <typename> class Interpolator> void remapImpl(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal)
55 {
56 const int cn = src.channels();
57
58 cv::Size dsize = xmap.size();
59
60 dst.create(dsize, src.type());
61
62 for (int y = 0; y < dsize.height; ++y)
63 {
64 for (int x = 0; x < dsize.width; ++x)
65 {
66 for (int c = 0; c < cn; ++c)
67 dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ymap.at<float>(y, x), xmap.at<float>(y, x), c, borderType, borderVal);
68 }
69 }
70 }
71
remapGold(const cv::Mat & src,const cv::Mat & xmap,const cv::Mat & ymap,cv::Mat & dst,int interpolation,int borderType,cv::Scalar borderVal)72 void remapGold(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
73 {
74 typedef void (*func_t)(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal);
75
76 static const func_t nearest_funcs[] =
77 {
78 remapImpl<unsigned char, NearestInterpolator>,
79 remapImpl<signed char, NearestInterpolator>,
80 remapImpl<unsigned short, NearestInterpolator>,
81 remapImpl<short, NearestInterpolator>,
82 remapImpl<int, NearestInterpolator>,
83 remapImpl<float, NearestInterpolator>
84 };
85
86 static const func_t linear_funcs[] =
87 {
88 remapImpl<unsigned char, LinearInterpolator>,
89 remapImpl<signed char, LinearInterpolator>,
90 remapImpl<unsigned short, LinearInterpolator>,
91 remapImpl<short, LinearInterpolator>,
92 remapImpl<int, LinearInterpolator>,
93 remapImpl<float, LinearInterpolator>
94 };
95
96 static const func_t cubic_funcs[] =
97 {
98 remapImpl<unsigned char, CubicInterpolator>,
99 remapImpl<signed char, CubicInterpolator>,
100 remapImpl<unsigned short, CubicInterpolator>,
101 remapImpl<short, CubicInterpolator>,
102 remapImpl<int, CubicInterpolator>,
103 remapImpl<float, CubicInterpolator>
104 };
105
106 static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
107
108 funcs[interpolation][src.depth()](src, xmap, ymap, dst, borderType, borderVal);
109 }
110 }
111
112 ///////////////////////////////////////////////////////////////////
113 // Test
114
PARAM_TEST_CASE(Remap,cv::cuda::DeviceInfo,cv::Size,MatType,Interpolation,BorderType,UseRoi)115 PARAM_TEST_CASE(Remap, cv::cuda::DeviceInfo, cv::Size, MatType, Interpolation, BorderType, UseRoi)
116 {
117 cv::cuda::DeviceInfo devInfo;
118 cv::Size size;
119 int type;
120 int interpolation;
121 int borderType;
122 bool useRoi;
123
124 cv::Mat xmap;
125 cv::Mat ymap;
126
127 virtual void SetUp()
128 {
129 devInfo = GET_PARAM(0);
130 size = GET_PARAM(1);
131 type = GET_PARAM(2);
132 interpolation = GET_PARAM(3);
133 borderType = GET_PARAM(4);
134 useRoi = GET_PARAM(5);
135
136 cv::cuda::setDevice(devInfo.deviceID());
137
138 // rotation matrix
139
140 const double aplha = CV_PI / 4;
141 static double M[2][3] = { {std::cos(aplha), -std::sin(aplha), size.width / 2.0},
142 {std::sin(aplha), std::cos(aplha), 0.0}};
143
144 xmap.create(size, CV_32FC1);
145 ymap.create(size, CV_32FC1);
146
147 for (int y = 0; y < size.height; ++y)
148 {
149 for (int x = 0; x < size.width; ++x)
150 {
151 xmap.at<float>(y, x) = static_cast<float>(M[0][0] * x + M[0][1] * y + M[0][2]);
152 ymap.at<float>(y, x) = static_cast<float>(M[1][0] * x + M[1][1] * y + M[1][2]);
153 }
154 }
155 }
156 };
157
CUDA_TEST_P(Remap,Accuracy)158 CUDA_TEST_P(Remap, Accuracy)
159 {
160 cv::Mat src = randomMat(size, type);
161 cv::Scalar val = randomScalar(0.0, 255.0);
162
163 cv::cuda::GpuMat dst = createMat(xmap.size(), type, useRoi);
164 cv::cuda::remap(loadMat(src, useRoi), dst, loadMat(xmap, useRoi), loadMat(ymap, useRoi), interpolation, borderType, val);
165
166 cv::Mat dst_gold;
167 remapGold(src, xmap, ymap, dst_gold, interpolation, borderType, val);
168
169 EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
170 }
171
172 INSTANTIATE_TEST_CASE_P(CUDA_Warping, Remap, testing::Combine(
173 ALL_DEVICES,
174 DIFFERENT_SIZES,
175 testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
176 testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
177 testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
178 WHOLE_SUBMAT));
179
180 #endif // HAVE_CUDA
181