opencv/modules/gpu/test/test_warp_perspective.cpp
Vladislav Vinogradov ade7394e77 refactored and fixed bugs in gpu warp functions (remap, resize, warpAffine, warpPerspective)
wrote more complicated tests for them
implemented own version of warpAffine and warpPerspective for different border interpolation types
refactored some gpu tests
2012-03-14 15:54:17 +00:00

276 lines
10 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "precomp.hpp"
#ifdef HAVE_CUDA
namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(3, 3, CV_64FC1);
M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2;
M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0;
M.at<double>(2, 0) = 0.0 ; M.at<double>(2, 1) = 0.0 ; M.at<double>(2, 2) = 1.0;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpPerspectiveMaps
PARAM_TEST_CASE(BuildWarpPerspectiveMaps, cv::gpu::DeviceInfo, cv::Size, Inverse)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BuildWarpPerspectiveMaps, Accuracy)
{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::gpu::GpuMat xmap, ymap;
cv::gpu::buildWarpPerspectiveMaps(M, inverse, size, xmap, ymap);
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), cv::INTER_NEAREST, cv::BORDER_CONSTANT);
int flags = cv::INTER_NEAREST;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Mat dst_gold;
cv::warpPerspective(src, dst_gold, M, size, flags, cv::BORDER_CONSTANT);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpPerspectiveMaps, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpPerspectiveImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal)
{
const int cn = src.channels();
dst.create(dsize, src.type());
for (int y = 0; y < dsize.height; ++y)
{
for (int x = 0; x < dsize.width; ++x)
{
float coeff = static_cast<float>(M.at<double>(2, 0) * x + M.at<double>(2, 1) * y + M.at<double>(2, 2));
float xcoo = static_cast<float>((M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2)) / coeff);
float ycoo = static_cast<float>((M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2)) / coeff);
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpPerspectiveGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal)
{
typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal);
static const func_t nearest_funcs[] =
{
warpPerspectiveImpl<unsigned char, NearestInterpolator>,
warpPerspectiveImpl<signed char, NearestInterpolator>,
warpPerspectiveImpl<unsigned short, NearestInterpolator>,
warpPerspectiveImpl<short, NearestInterpolator>,
warpPerspectiveImpl<int, NearestInterpolator>,
warpPerspectiveImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpPerspectiveImpl<unsigned char, LinearInterpolator>,
warpPerspectiveImpl<signed char, LinearInterpolator>,
warpPerspectiveImpl<unsigned short, LinearInterpolator>,
warpPerspectiveImpl<short, LinearInterpolator>,
warpPerspectiveImpl<int, LinearInterpolator>,
warpPerspectiveImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpPerspectiveImpl<unsigned char, CubicInterpolator>,
warpPerspectiveImpl<signed char, CubicInterpolator>,
warpPerspectiveImpl<unsigned short, CubicInterpolator>,
warpPerspectiveImpl<short, CubicInterpolator>,
warpPerspectiveImpl<int, CubicInterpolator>,
warpPerspectiveImpl<float, CubicInterpolator>
};
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
if (inverse)
funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal);
else
{
cv::Mat iM;
cv::invert(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
PARAM_TEST_CASE(WarpPerspective, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, Border, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
cv::Mat M;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
inverse = GET_PARAM(3);
interpolation = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspective, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::warpPerspective(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspective, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
testing::Values(Border(cv::BORDER_REFLECT101), Border(cv::BORDER_REPLICATE), Border(cv::BORDER_REFLECT), Border(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
PARAM_TEST_CASE(WarpPerspectiveNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation)
{
cv::gpu::DeviceInfo devInfo;
int type;
bool inverse;
int interpolation;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
type = GET_PARAM(1);
inverse = GET_PARAM(2);
interpolation = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(WarpPerspectiveNPP, Accuracy)
{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
cv::gpu::GpuMat dst;
cv::gpu::warpPerspective(loadMat(src), dst, M, src.size(), flags);
cv::Mat dst_gold;
warpPerspectiveGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpPerspectiveNPP, testing::Combine(
ALL_DEVICES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
DIRECT_INVERSE,
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))));
#endif // HAVE_CUDA