opencv/modules/cudawarping/test/test_warp_affine.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
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// 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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// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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// derived from this software without specific prior written permission.
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//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace cvtest;
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namespace
{
cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
{
cv::Mat M(2, 3, CV_64FC1);
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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;
return M;
}
}
///////////////////////////////////////////////////////////////////
// Test buildWarpAffineMaps
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PARAM_TEST_CASE(BuildWarpAffineMaps, cv::cuda::DeviceInfo, cv::Size, Inverse)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
bool inverse;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
inverse = GET_PARAM(2);
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cv::cuda::setDevice(devInfo.deviceID());
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}
};
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CUDA_TEST_P(BuildWarpAffineMaps, Accuracy)
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{
cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
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cv::cuda::GpuMat xmap, ymap;
cv::cuda::buildWarpAffineMaps(M, inverse, size, xmap, ymap);
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int interpolation = cv::INTER_NEAREST;
int borderMode = cv::BORDER_CONSTANT;
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
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cv::Mat dst;
cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), interpolation, borderMode);
cv::Mat dst_gold;
cv::warpAffine(src, dst_gold, M, size, flags, borderMode);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
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INSTANTIATE_TEST_CASE_P(CUDA_Warping, BuildWarpAffineMaps, testing::Combine(
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ALL_DEVICES,
DIFFERENT_SIZES,
DIRECT_INVERSE));
///////////////////////////////////////////////////////////////////
// Gold implementation
namespace
{
template <typename T, template <typename> class Interpolator> void warpAffineImpl(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 xcoo = static_cast<float>(M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2));
float ycoo = static_cast<float>(M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2));
for (int c = 0; c < cn; ++c)
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal);
}
}
}
void warpAffineGold(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[] =
{
warpAffineImpl<unsigned char, NearestInterpolator>,
warpAffineImpl<signed char, NearestInterpolator>,
warpAffineImpl<unsigned short, NearestInterpolator>,
warpAffineImpl<short, NearestInterpolator>,
warpAffineImpl<int, NearestInterpolator>,
warpAffineImpl<float, NearestInterpolator>
};
static const func_t linear_funcs[] =
{
warpAffineImpl<unsigned char, LinearInterpolator>,
warpAffineImpl<signed char, LinearInterpolator>,
warpAffineImpl<unsigned short, LinearInterpolator>,
warpAffineImpl<short, LinearInterpolator>,
warpAffineImpl<int, LinearInterpolator>,
warpAffineImpl<float, LinearInterpolator>
};
static const func_t cubic_funcs[] =
{
warpAffineImpl<unsigned char, CubicInterpolator>,
warpAffineImpl<signed char, CubicInterpolator>,
warpAffineImpl<unsigned short, CubicInterpolator>,
warpAffineImpl<short, CubicInterpolator>,
warpAffineImpl<int, CubicInterpolator>,
warpAffineImpl<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::invertAffineTransform(M, iM);
funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal);
}
}
}
///////////////////////////////////////////////////////////////////
// Test
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PARAM_TEST_CASE(WarpAffine, cv::cuda::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, UseRoi)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
int type;
bool inverse;
int interpolation;
int borderType;
bool useRoi;
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);
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cv::cuda::setDevice(devInfo.deviceID());
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}
};
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CUDA_TEST_P(WarpAffine, Accuracy)
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{
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);
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cv::cuda::GpuMat dst = createMat(size, type, useRoi);
cv::cuda::warpAffine(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
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cv::Mat dst_gold;
warpAffineGold(src, M, inverse, size, dst_gold, interpolation, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0);
}
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INSTANTIATE_TEST_CASE_P(CUDA_Warping, WarpAffine, testing::Combine(
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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(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
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PARAM_TEST_CASE(WarpAffineNPP, cv::cuda::DeviceInfo, MatType, Inverse, Interpolation)
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{
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cv::cuda::DeviceInfo devInfo;
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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);
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cv::cuda::setDevice(devInfo.deviceID());
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}
};
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CUDA_TEST_P(WarpAffineNPP, Accuracy)
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{
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
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ASSERT_FALSE(src.empty());
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cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
int flags = interpolation;
if (inverse)
flags |= cv::WARP_INVERSE_MAP;
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cv::cuda::GpuMat dst;
cv::cuda::warpAffine(loadMat(src), dst, M, src.size(), flags);
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cv::Mat dst_gold;
warpAffineGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0));
EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2);
}
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INSTANTIATE_TEST_CASE_P(CUDA_Warping, WarpAffineNPP, testing::Combine(
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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