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further simplify the logics in filter tests

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This commit is contained in:
yao 2013-05-08 16:08:33 +08:00
parent 1a53e2cfb2
commit 35c6860f06
1 changed files with 28 additions and 95 deletions
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123
modules/ocl/test/test_filters.cpp
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@ -116,6 +116,19 @@ PARAM_TEST_CASE(FilterTestBase,
gmat1 = mat1_roi;
}
void Init(int mat_type)
{
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(size, mat_type, 5, 16);
dst = randomMat(size, mat_type, 5, 16);
}
void Near(double threshold)
{
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, threshold, "");
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
@ -131,12 +144,7 @@ struct Blur : FilterTestBase
type = GET_PARAM(0);
ksize = GET_PARAM(1);
bordertype = GET_PARAM(3);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
Init(type);
}
};
@ -145,20 +153,13 @@ TEST_P(Blur, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::blur(mat1_roi, dst_roi, ksize, Point(-1, -1), bordertype);
cv::ocl::blur(gmat1, gdst, ksize, Point(-1, -1), bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, "");
Near(1.0);
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
//Laplacian
struct Laplacian : FilterTestBase
@ -170,15 +171,8 @@ struct Laplacian : FilterTestBase
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
Init(type);
}
};
TEST_P(Laplacian, Accuracy)
@ -186,14 +180,9 @@ TEST_P(Laplacian, Accuracy)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Laplacian(mat1_roi, dst_roi, -1, ksize.width, 1);
cv::ocl::Laplacian(gmat1, gdst, -1, ksize.width, 1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, "");
Near(1e-5);
}
}
@ -213,45 +202,28 @@ struct ErodeDilate : FilterTestBase
{
type = GET_PARAM(0);
iterations = GET_PARAM(3);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
Init(type);
// rng.fill(kernel, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
kernel = randomMat(rng, Size(3, 3), CV_8UC1, 0, 3, false);
kernel = randomMat(Size(3, 3), CV_8UC1, 0, 3);
}
};
// erode
TEST_P(ErodeDilate, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::erode(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
cv::ocl::erode(gmat1, gdst, kernel, Point(-1, -1), iterations);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, "");
Near(1e-5);
}
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::dilate(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
cv::ocl::dilate(gmat1, gdst, kernel, Point(-1, -1), iterations);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, "");
Near(1e-5);
}
}
@ -272,15 +244,8 @@ struct Sobel : FilterTestBase
dx = s.width;
dy = s.height;
bordertype = GET_PARAM(3);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
Init(type);
}
};
TEST_P(Sobel, Mat)
@ -288,16 +253,10 @@ TEST_P(Sobel, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Sobel(mat1_roi, dst_roi, -1, dx, dy, ksize, /*scale*/0.00001,/*delta*/0, bordertype);
cv::ocl::Sobel(gmat1, gdst, -1, dx, dy, ksize,/*scale*/0.00001,/*delta*/0, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, "");
Near(1);
}
}
@ -315,17 +274,8 @@ struct Scharr : FilterTestBase
dx = s.width;
dy = s.height;
bordertype = GET_PARAM(3);
dx = 1;
dy = 0;
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
Init(type);
}
};
TEST_P(Scharr, Mat)
@ -333,14 +283,9 @@ TEST_P(Scharr, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Scharr(mat1_roi, dst_roi, -1, dx, dy, /*scale*/1,/*delta*/0, bordertype);
cv::ocl::Scharr(gmat1, gdst, -1, dx, dy,/*scale*/1,/*delta*/0, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, "");
Near(1);
}
}
@ -360,18 +305,11 @@ struct GaussianBlur : FilterTestBase
type = GET_PARAM(0);
ksize = GET_PARAM(1);
bordertype = GET_PARAM(3);
Init(type);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
sigma1 = rng.uniform(0.1, 1.0);
sigma2 = rng.uniform(0.1, 1.0);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
}
};
TEST_P(GaussianBlur, Mat)
@ -379,14 +317,9 @@ TEST_P(GaussianBlur, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::GaussianBlur(mat1_roi, dst_roi, ksize, sigma1, sigma2, bordertype);
cv::ocl::GaussianBlur(gmat1, gdst, ksize, sigma1, sigma2, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, "");
Near(1);
}
}
@ -423,7 +356,7 @@ INSTANTIATE_TEST_CASE_P(Filter, Sobel, Combine(
INSTANTIATE_TEST_CASE_P(Filter, Scharr, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
Values(Size(0, 0)), //not use
Values(Size(0, 0), Size(0, 1), Size(1, 0), Size(1, 1)),
Values(Size(0, 1), Size(1, 0)),
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
INSTANTIATE_TEST_CASE_P(Filter, GaussianBlur, Combine(