mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 04:00:30 +08:00
251 lines
8.5 KiB
C++
251 lines
8.5 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// 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
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// 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:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
#ifdef HAVE_CUDA
|
|
|
|
using namespace cvtest;
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
// Gold implementation
|
|
|
|
namespace
|
|
{
|
|
template <typename T, template <typename> class Interpolator>
|
|
void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
|
|
{
|
|
const int cn = src.channels();
|
|
|
|
cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy));
|
|
|
|
dst.create(dsize, src.type());
|
|
|
|
float ifx = static_cast<float>(1.0 / fx);
|
|
float ify = static_cast<float>(1.0 / fy);
|
|
|
|
for (int y = 0; y < dsize.height; ++y)
|
|
{
|
|
for (int x = 0; x < dsize.width; ++x)
|
|
{
|
|
for (int c = 0; c < cn; ++c)
|
|
dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE);
|
|
}
|
|
}
|
|
}
|
|
|
|
void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation)
|
|
{
|
|
typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy);
|
|
|
|
static const func_t nearest_funcs[] =
|
|
{
|
|
resizeImpl<unsigned char, NearestInterpolator>,
|
|
resizeImpl<signed char, NearestInterpolator>,
|
|
resizeImpl<unsigned short, NearestInterpolator>,
|
|
resizeImpl<short, NearestInterpolator>,
|
|
resizeImpl<int, NearestInterpolator>,
|
|
resizeImpl<float, NearestInterpolator>
|
|
};
|
|
|
|
|
|
static const func_t linear_funcs[] =
|
|
{
|
|
resizeImpl<unsigned char, LinearInterpolator>,
|
|
resizeImpl<signed char, LinearInterpolator>,
|
|
resizeImpl<unsigned short, LinearInterpolator>,
|
|
resizeImpl<short, LinearInterpolator>,
|
|
resizeImpl<int, LinearInterpolator>,
|
|
resizeImpl<float, LinearInterpolator>
|
|
};
|
|
|
|
static const func_t cubic_funcs[] =
|
|
{
|
|
resizeImpl<unsigned char, CubicInterpolator>,
|
|
resizeImpl<signed char, CubicInterpolator>,
|
|
resizeImpl<unsigned short, CubicInterpolator>,
|
|
resizeImpl<short, CubicInterpolator>,
|
|
resizeImpl<int, CubicInterpolator>,
|
|
resizeImpl<float, CubicInterpolator>
|
|
};
|
|
|
|
static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};
|
|
|
|
funcs[interpolation][src.depth()](src, dst, fx, fy);
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
// Test
|
|
|
|
PARAM_TEST_CASE(Resize, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
cv::Size size;
|
|
double coeff;
|
|
int interpolation;
|
|
int type;
|
|
bool useRoi;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GET_PARAM(0);
|
|
size = GET_PARAM(1);
|
|
type = GET_PARAM(2);
|
|
coeff = GET_PARAM(3);
|
|
interpolation = GET_PARAM(4);
|
|
useRoi = GET_PARAM(5);
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
GPU_TEST_P(Resize, Accuracy)
|
|
{
|
|
cv::Mat src = randomMat(size, type);
|
|
|
|
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
|
|
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
|
|
|
|
cv::Mat dst_gold;
|
|
resizeGold(src, dst_gold, coeff, coeff, interpolation);
|
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine(
|
|
ALL_DEVICES,
|
|
DIFFERENT_SIZES,
|
|
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
|
|
testing::Values(0.3, 0.5, 1.5, 2.0),
|
|
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
|
|
WHOLE_SUBMAT));
|
|
|
|
/////////////////
|
|
|
|
PARAM_TEST_CASE(ResizeSameAsHost, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
cv::Size size;
|
|
double coeff;
|
|
int interpolation;
|
|
int type;
|
|
bool useRoi;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GET_PARAM(0);
|
|
size = GET_PARAM(1);
|
|
type = GET_PARAM(2);
|
|
coeff = GET_PARAM(3);
|
|
interpolation = GET_PARAM(4);
|
|
useRoi = GET_PARAM(5);
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
// downscaling only: used for classifiers
|
|
GPU_TEST_P(ResizeSameAsHost, Accuracy)
|
|
{
|
|
cv::Mat src = randomMat(size, type);
|
|
|
|
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
|
|
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
|
|
|
|
cv::Mat dst_gold;
|
|
cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation);
|
|
|
|
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeSameAsHost, testing::Combine(
|
|
ALL_DEVICES,
|
|
DIFFERENT_SIZES,
|
|
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
|
|
testing::Values(0.3, 0.5),
|
|
testing::Values(Interpolation(cv::INTER_AREA), Interpolation(cv::INTER_NEAREST)), //, Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)
|
|
WHOLE_SUBMAT));
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
// Test NPP
|
|
|
|
PARAM_TEST_CASE(ResizeNPP, cv::gpu::DeviceInfo, MatType, double, Interpolation)
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
double coeff;
|
|
int interpolation;
|
|
int type;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GET_PARAM(0);
|
|
type = GET_PARAM(1);
|
|
coeff = GET_PARAM(2);
|
|
interpolation = GET_PARAM(3);
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
GPU_TEST_P(ResizeNPP, Accuracy)
|
|
{
|
|
cv::Mat src = readImageType("stereobp/aloe-L.png", type);
|
|
ASSERT_FALSE(src.empty());
|
|
|
|
cv::gpu::GpuMat dst;
|
|
cv::gpu::resize(loadMat(src), dst, cv::Size(), coeff, coeff, interpolation);
|
|
|
|
cv::Mat dst_gold;
|
|
resizeGold(src, dst_gold, coeff, coeff, interpolation);
|
|
|
|
EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeNPP, testing::Combine(
|
|
ALL_DEVICES,
|
|
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
|
|
testing::Values(0.3, 0.5, 1.5, 2.0),
|
|
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR))));
|
|
|
|
#endif // HAVE_CUDA
|