From 671233cd469fa010ee4fecc3a7d1a80d6732d16b Mon Sep 17 00:00:00 2001 From: Anatoly Baksheev Date: Tue, 22 May 2012 18:58:01 +0000 Subject: [PATCH] gpu: added Cascade and mulAndScaleSpectrums perf tests --- modules/gpu/perf/perf_imgproc.cpp | 69 + modules/gpu/perf/perf_objdetect.cpp | 28 + modules/gpu/perf_cpu/perf_arithm.cpp | 53 + modules/gpu/perf_cpu/perf_imgproc.cpp | 27 + modules/gpu/perf_cpu/perf_objdetect.cpp | 23 + modules/gpu/test/test_imgproc.cpp | 2258 +++++++++++++++---------------- 6 files changed, 1329 insertions(+), 1129 deletions(-) diff --git a/modules/gpu/perf/perf_imgproc.cpp b/modules/gpu/perf/perf_imgproc.cpp index 5472acf..5ef5fe8 100644 --- a/modules/gpu/perf/perf_imgproc.cpp +++ b/modules/gpu/perf/perf_imgproc.cpp @@ -1020,4 +1020,73 @@ INSTANTIATE_TEST_CASE_P(ImgProc, ImagePyramid_getLayer, testing::Combine( GPU_TYPICAL_MAT_SIZES, testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3, CV_32FC4))); + + +////////////////////////////////////////////////////////////////////// +// MulAndScaleSpectrums + +GPU_PERF_TEST(MulAndScaleSpectrums, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo = GET_PARAM(0); + cv::Size size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + + int type = CV_32FC2; + + cv::Mat src1_host(size, type); + cv::Mat src2_host(size, type); + declare.in(src1_host, src2_host, WARMUP_RNG); + + cv::gpu::GpuMat src1(src1_host); + cv::gpu::GpuMat src2(src2_host); + cv::gpu::GpuMat dst(size, type); + + TEST_CYCLE() + { + cv::gpu::mulSpectrums(src1, src2, dst, cv::DFT_ROWS, false); + } +} + +INSTANTIATE_TEST_CASE_P(ImgProc, MulAndScaleSpectrums, testing::Combine( + ALL_DEVICES, + GPU_TYPICAL_MAT_SIZES)); + + + +////////////////////////////////////////////////////////////////////// +// MulAndScaleSpectrumsScale + + +GPU_PERF_TEST(MulAndScaleSpectrumsScale, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo = GET_PARAM(0); + cv::Size size = GET_PARAM(1); + + float scale = 1.f / size.area(); + int type = CV_32FC2; + + cv::gpu::setDevice(devInfo.deviceID()); + + cv::Mat src1_host(size, type); + cv::Mat src2_host(size, type); + declare.in(src1_host, src2_host, WARMUP_RNG); + + cv::gpu::GpuMat src1(src1_host); + cv::gpu::GpuMat src2(src2_host); + cv::gpu::GpuMat dst(size, type); + + TEST_CYCLE() + { + cv::gpu::mulAndScaleSpectrums(src1, src2, dst, cv::DFT_ROWS, scale, false); + } +} + +INSTANTIATE_TEST_CASE_P(ImgProc, MulAndScaleSpectrumsScale, testing::Combine( + ALL_DEVICES, + GPU_TYPICAL_MAT_SIZES)); + + + + #endif diff --git a/modules/gpu/perf/perf_objdetect.cpp b/modules/gpu/perf/perf_objdetect.cpp index b6c02aa..9819123 100644 --- a/modules/gpu/perf/perf_objdetect.cpp +++ b/modules/gpu/perf/perf_objdetect.cpp @@ -24,4 +24,32 @@ GPU_PERF_TEST_1(HOG, cv::gpu::DeviceInfo) INSTANTIATE_TEST_CASE_P(ObjDetect, HOG, ALL_DEVICES); +CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) + +GPU_PERF_TEST_1(HaarClassifier, cv::gpu::DeviceInfo, DftFlags) +{ + cv::gpu::DeviceInfo devInfo = GetParam(); + cv::gpu::setDevice(devInfo.deviceID()); + + cv::Mat img_host = readImage("gpu/haarcascade/group_1_640x480_VGA.pgm", cv::IMREAD_GRAYSCALE); + + cv::gpu::CascadeClassifier_GPU cascade; + + if (!cascade.load("haarcascade_frontalface_alt.xml")) + CV_Error(0, "Can't load cascade"); + + cv::gpu::GpuMat img(img_host); + cv::gpu::GpuMat objects_buffer(1, 100, cv::DataType::type); + + TEST_CYCLE() + { + cascade.detectMultiScale(img, objects_buffer); + } +} + +INSTANTIATE_TEST_CASE_P(ObjDetect, HaarClassifier, ALL_DEVICES); + + + + #endif diff --git a/modules/gpu/perf_cpu/perf_arithm.cpp b/modules/gpu/perf_cpu/perf_arithm.cpp index b202ef2..6fd8919 100644 --- a/modules/gpu/perf_cpu/perf_arithm.cpp +++ b/modules/gpu/perf_cpu/perf_arithm.cpp @@ -636,6 +636,59 @@ INSTANTIATE_TEST_CASE_P(Arithm, BitwiseScalarOr, testing::Combine( testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32SC1, CV_32SC3, CV_32SC4))); +////////////////////////////////////////////////////////////////////// +// BitwiseXor + +GPU_PERF_TEST(BitwiseXor, cv::gpu::DeviceInfo, cv::Size, perf::MatType) +{ + cv::Size size = GET_PARAM(1); + int type = GET_PARAM(2); + + cv::Mat src1(size, type); + cv::Mat src2(size, type); + + declare.in(src1, src2, WARMUP_RNG); + + cv::Mat dst; + + TEST_CYCLE() + { + cv::bitwise_xor(src1, src2, dst); + } +} + +INSTANTIATE_TEST_CASE_P(Arithm, BitwiseXor, testing::Combine( + ALL_DEVICES, + GPU_TYPICAL_MAT_SIZES, + testing::Values(CV_8UC1, CV_16UC1, CV_32SC1))); + +////////////////////////////////////////////////////////////////////// +// BitwiseScalarXor + +GPU_PERF_TEST(BitwiseScalarXor, cv::gpu::DeviceInfo, cv::Size, perf::MatType) +{ + cv::Size size = GET_PARAM(1); + int type = GET_PARAM(2); + + cv::Mat src(size, type); + + declare.in(src, WARMUP_RNG); + + cv::Mat dst; + cv::Scalar sc = cv::Scalar(123, 123, 123, 123); + + TEST_CYCLE() + { + cv::bitwise_xor(src, sc, dst); + } +} + +INSTANTIATE_TEST_CASE_P(Arithm, BitwiseScalarXor, testing::Combine( + ALL_DEVICES, + GPU_TYPICAL_MAT_SIZES, + testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32SC1, CV_32SC3, CV_32SC4))); + + ////////////////////////////////////////////////////////////////////// // Min diff --git a/modules/gpu/perf_cpu/perf_imgproc.cpp b/modules/gpu/perf_cpu/perf_imgproc.cpp index 2c71699..4e48b11 100644 --- a/modules/gpu/perf_cpu/perf_imgproc.cpp +++ b/modules/gpu/perf_cpu/perf_imgproc.cpp @@ -533,4 +533,31 @@ INSTANTIATE_TEST_CASE_P(ImgProc, EqualizeHist, testing::Combine( ALL_DEVICES, GPU_TYPICAL_MAT_SIZES)); + +////////////////////////////////////////////////////////////////////// +// MulAndScaleSpectrums + + +GPU_PERF_TEST(MulAndScaleSpectrums, cv::gpu::DeviceInfo, cv::Size) +{ + cv::Size size = GET_PARAM(1); + + int type = CV_32FC2; + + cv::Mat src1(size, type); + cv::Mat src2(size, type); + cv::Mat dst(size, type); + declare.in(src1, src2, WARMUP_RNG); + + TEST_CYCLE() + { + cv::mulSpectrums(src1, src2, dst, cv::DFT_ROWS, false); + } +} + +INSTANTIATE_TEST_CASE_P(ImgProc, MulAndScaleSpectrums, testing::Combine( + ALL_DEVICES, + GPU_TYPICAL_MAT_SIZES)); + + #endif diff --git a/modules/gpu/perf_cpu/perf_objdetect.cpp b/modules/gpu/perf_cpu/perf_objdetect.cpp index 5b05966..e2e5d0e 100644 --- a/modules/gpu/perf_cpu/perf_objdetect.cpp +++ b/modules/gpu/perf_cpu/perf_objdetect.cpp @@ -19,4 +19,27 @@ GPU_PERF_TEST_1(HOG, cv::gpu::DeviceInfo) INSTANTIATE_TEST_CASE_P(ObjDetect, HOG, ALL_DEVICES); +GPU_PERF_TEST_1(HaarClassifier, cv::gpu::DeviceInfo) +{ + cv::Mat img = readImage("gpu/haarcascade/group_1_640x480_VGA.pgm", cv::IMREAD_GRAYSCALE); + + cv::CascadeClassifier cascade; + + if (!cascade.load("haarcascade_frontalface_alt.xml")) + CV_Error(0, "Can't load cascade"); + + + std::vector rects; + rects.reserve(1000); + + TEST_CYCLE() + { + cascade.detectMultiScale(img, rects); + } +} + +INSTANTIATE_TEST_CASE_P(ObjDetect, HaarClassifier, ALL_DEVICES); + + + #endif diff --git a/modules/gpu/test/test_imgproc.cpp b/modules/gpu/test/test_imgproc.cpp index 63b4527..52c1bd8 100644 --- a/modules/gpu/test/test_imgproc.cpp +++ b/modules/gpu/test/test_imgproc.cpp @@ -1,307 +1,307 @@ -/*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. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 "precomp.hpp" - -namespace { - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// Integral - -PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - bool useRoi; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - useRoi = GET_PARAM(2); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Integral, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); - cv::gpu::integral(loadMat(src, useRoi), dst); - - cv::Mat dst_gold; - cv::integral(src, dst_gold, CV_32S); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - WHOLE_SUBMAT)); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// HistEven - -struct HistEven : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(HistEven, Accuracy) -{ - cv::Mat img = readImage("stereobm/aloe-L.png"); - ASSERT_FALSE(img.empty()); - - cv::Mat hsv; - cv::cvtColor(img, hsv, CV_BGR2HSV); - - int hbins = 30; - float hranges[] = {0.0f, 180.0f}; - - std::vector srcs; - cv::gpu::split(loadMat(hsv), srcs); - - cv::gpu::GpuMat hist; - cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]); - - cv::MatND histnd; - int histSize[] = {hbins}; - const float* ranges[] = {hranges}; - int channels[] = {0}; - cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges); - - cv::Mat hist_gold = histnd; - hist_gold = hist_gold.t(); - hist_gold.convertTo(hist_gold, CV_32S); - - EXPECT_MAT_NEAR(hist_gold, hist, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// CalcHist - -void calcHistGold(const cv::Mat& src, cv::Mat& hist) -{ - hist.create(1, 256, CV_32SC1); - hist.setTo(cv::Scalar::all(0)); - - int* hist_row = hist.ptr(); - for (int y = 0; y < src.rows; ++y) - { - const uchar* src_row = src.ptr(y); - - for (int x = 0; x < src.cols; ++x) - ++hist_row[src_row[x]]; - } -} - -PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - - cv::Size size; - cv::Mat src; - cv::Mat hist_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CalcHist, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat hist; - cv::gpu::calcHist(loadMat(src), hist); - - cv::Mat hist_gold; - calcHistGold(src, hist_gold); - - EXPECT_MAT_NEAR(hist_gold, hist, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// EqualizeHist - -PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(EqualizeHist, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat dst; - cv::gpu::equalizeHist(loadMat(src), dst); - - cv::Mat dst_gold; - cv::equalizeHist(src, dst_gold); - - EXPECT_MAT_NEAR(dst_gold, dst, 3.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -//////////////////////////////////////////////////////////////////////// -// ColumnSum - -PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - - cv::Mat src; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(ColumnSum, Accuracy) -{ - cv::Mat src = randomMat(size, CV_32FC1); - - cv::gpu::GpuMat d_dst; - cv::gpu::columnSum(loadMat(src), d_dst); - - cv::Mat dst(d_dst); - - for (int j = 0; j < src.cols; ++j) - { - float gold = src.at(0, j); - float res = dst.at(0, j); - ASSERT_NEAR(res, gold, 1e-5); - } - - for (int i = 1; i < src.rows; ++i) - { - for (int j = 0; j < src.cols; ++j) - { - float gold = src.at(i, j) += src.at(i - 1, j); - float res = dst.at(i, j); - ASSERT_NEAR(res, gold, 1e-5); - } - } -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -//////////////////////////////////////////////////////// -// Canny - -IMPLEMENT_PARAM_CLASS(AppertureSize, int); -IMPLEMENT_PARAM_CLASS(L2gradient, bool); - -PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - int apperture_size; - bool useL2gradient; - bool useRoi; - - cv::Mat edges_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - apperture_size = GET_PARAM(1); - useL2gradient = GET_PARAM(2); - useRoi = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Canny, Accuracy) -{ - cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE); - ASSERT_FALSE(img.empty()); - - double low_thresh = 50.0; - double high_thresh = 100.0; +/*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. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 "precomp.hpp" + +namespace { + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// Integral + +PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + bool useRoi; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + useRoi = GET_PARAM(2); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Integral, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); + cv::gpu::integral(loadMat(src, useRoi), dst); + + cv::Mat dst_gold; + cv::integral(src, dst_gold, CV_32S); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + WHOLE_SUBMAT)); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// HistEven + +struct HistEven : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(HistEven, Accuracy) +{ + cv::Mat img = readImage("stereobm/aloe-L.png"); + ASSERT_FALSE(img.empty()); + + cv::Mat hsv; + cv::cvtColor(img, hsv, CV_BGR2HSV); + + int hbins = 30; + float hranges[] = {0.0f, 180.0f}; + + std::vector srcs; + cv::gpu::split(loadMat(hsv), srcs); + + cv::gpu::GpuMat hist; + cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]); + + cv::MatND histnd; + int histSize[] = {hbins}; + const float* ranges[] = {hranges}; + int channels[] = {0}; + cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges); + + cv::Mat hist_gold = histnd; + hist_gold = hist_gold.t(); + hist_gold.convertTo(hist_gold, CV_32S); + + EXPECT_MAT_NEAR(hist_gold, hist, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// CalcHist + +void calcHistGold(const cv::Mat& src, cv::Mat& hist) +{ + hist.create(1, 256, CV_32SC1); + hist.setTo(cv::Scalar::all(0)); + + int* hist_row = hist.ptr(); + for (int y = 0; y < src.rows; ++y) + { + const uchar* src_row = src.ptr(y); + + for (int x = 0; x < src.cols; ++x) + ++hist_row[src_row[x]]; + } +} + +PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + + cv::Size size; + cv::Mat src; + cv::Mat hist_gold; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CalcHist, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat hist; + cv::gpu::calcHist(loadMat(src), hist); + + cv::Mat hist_gold; + calcHistGold(src, hist_gold); + + EXPECT_MAT_NEAR(hist_gold, hist, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// EqualizeHist + +PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(EqualizeHist, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat dst; + cv::gpu::equalizeHist(loadMat(src), dst); + + cv::Mat dst_gold; + cv::equalizeHist(src, dst_gold); + + EXPECT_MAT_NEAR(dst_gold, dst, 3.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +//////////////////////////////////////////////////////////////////////// +// ColumnSum + +PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + + cv::Mat src; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(ColumnSum, Accuracy) +{ + cv::Mat src = randomMat(size, CV_32FC1); + + cv::gpu::GpuMat d_dst; + cv::gpu::columnSum(loadMat(src), d_dst); + + cv::Mat dst(d_dst); + + for (int j = 0; j < src.cols; ++j) + { + float gold = src.at(0, j); + float res = dst.at(0, j); + ASSERT_NEAR(res, gold, 1e-5); + } + + for (int i = 1; i < src.rows; ++i) + { + for (int j = 0; j < src.cols; ++j) + { + float gold = src.at(i, j) += src.at(i - 1, j); + float res = dst.at(i, j); + ASSERT_NEAR(res, gold, 1e-5); + } + } +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +//////////////////////////////////////////////////////// +// Canny + +IMPLEMENT_PARAM_CLASS(AppertureSize, int); +IMPLEMENT_PARAM_CLASS(L2gradient, bool); + +PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + int apperture_size; + bool useL2gradient; + bool useRoi; + + cv::Mat edges_gold; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + apperture_size = GET_PARAM(1); + useL2gradient = GET_PARAM(2); + useRoi = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Canny, Accuracy) +{ + cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE); + ASSERT_FALSE(img.empty()); + + double low_thresh = 50.0; + double high_thresh = 100.0; if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS)) { try - { - cv::gpu::GpuMat edges; + { + cv::gpu::GpuMat edges; cv::gpu::Canny(loadMat(img), edges, low_thresh, high_thresh, apperture_size, useL2gradient); } catch (const cv::Exception& e) @@ -310,832 +310,832 @@ TEST_P(Canny, Accuracy) } } else - { - cv::gpu::GpuMat edges; - cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient); - - cv::Mat edges_gold; - cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient); - - EXPECT_MAT_SIMILAR(edges_gold, edges, 1e-2); - } -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine( - ALL_DEVICES, - testing::Values(AppertureSize(3), AppertureSize(5)), - testing::Values(L2gradient(false), L2gradient(true)), - WHOLE_SUBMAT)); - -//////////////////////////////////////////////////////////////////////////////// -// MeanShift - -struct MeanShift : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - cv::Mat img; - - int spatialRad; - int colorRad; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - - img = readImageType("meanshift/cones.png", CV_8UC4); - ASSERT_FALSE(img.empty()); - - spatialRad = 30; - colorRad = 30; - } -}; - -TEST_P(MeanShift, Filtering) -{ - cv::Mat img_template; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - img_template = readImage("meanshift/con_result.png"); - else - img_template = readImage("meanshift/con_result_CC1X.png"); - ASSERT_FALSE(img_template.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); - - ASSERT_EQ(CV_8UC4, d_dst.type()); - - cv::Mat dst(d_dst); - - cv::Mat result; - cv::cvtColor(dst, result, CV_BGRA2BGR); - - EXPECT_MAT_NEAR(img_template, result, 0.0); -} - -TEST_P(MeanShift, Proc) -{ - cv::FileStorage fs; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); - else - fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); - ASSERT_TRUE(fs.isOpened()); - - cv::Mat spmap_template; - fs["spmap"] >> spmap_template; - ASSERT_FALSE(spmap_template.empty()); - - cv::gpu::GpuMat rmap_filtered; - cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); - - cv::gpu::GpuMat rmap; - cv::gpu::GpuMat spmap; - cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); - - ASSERT_EQ(CV_8UC4, rmap.type()); - - EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); - EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES); - -//////////////////////////////////////////////////////////////////////////////// -// MeanShiftSegmentation - -IMPLEMENT_PARAM_CLASS(MinSize, int); - -PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize) -{ - cv::gpu::DeviceInfo devInfo; - int minsize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - minsize = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MeanShiftSegmentation, Regression) -{ - cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); - ASSERT_FALSE(img.empty()); - - std::ostringstream path; - path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - path << ".png"; - else - path << "_CC1X.png"; - cv::Mat dst_gold = readImage(path.str()); - ASSERT_FALSE(dst_gold.empty()); - - cv::Mat dst; - cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); - - cv::Mat dst_rgb; - cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR); - - EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine( - ALL_DEVICES, - testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); - -//////////////////////////////////////////////////////////////////////////// -// Blend - -template -void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold) -{ - result_gold.create(img1.size(), img1.type()); - - int cn = img1.channels(); - - for (int y = 0; y < img1.rows; ++y) - { - const float* weights1_row = weights1.ptr(y); - const float* weights2_row = weights2.ptr(y); - const T* img1_row = img1.ptr(y); - const T* img2_row = img2.ptr(y); - T* result_gold_row = result_gold.ptr(y); - - for (int x = 0; x < img1.cols * cn; ++x) - { - float w1 = weights1_row[x / cn]; - float w2 = weights2_row[x / cn]; - result_gold_row[x] = static_cast((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); - } - } -} - -PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int type; - bool useRoi; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - type = GET_PARAM(2); - useRoi = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Blend, Accuracy) -{ - int depth = CV_MAT_DEPTH(type); - - cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); - cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); - cv::Mat weights1 = randomMat(size, CV_32F, 0, 1); - cv::Mat weights2 = randomMat(size, CV_32F, 0, 1); - - cv::gpu::GpuMat result; - cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result); - - cv::Mat result_gold; - if (depth == CV_8U) - blendLinearGold(img1, img2, weights1, weights2, result_gold); - else - blendLinearGold(img1, img2, weights1, weights2, result_gold); - - EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), - WHOLE_SUBMAT)); - -//////////////////////////////////////////////////////// -// Convolve - -void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) -{ - // reallocate the output array if needed - C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); - cv::Size dftSize; - - // compute the size of DFT transform - dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); - dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); - - // allocate temporary buffers and initialize them with 0s - cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); - cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); - - // copy A and B to the top-left corners of tempA and tempB, respectively - cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); - A.copyTo(roiA); - cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); - B.copyTo(roiB); - - // now transform the padded A & B in-place; - // use "nonzeroRows" hint for faster processing - cv::dft(tempA, tempA, 0, A.rows); - cv::dft(tempB, tempB, 0, B.rows); - - // multiply the spectrums; - // the function handles packed spectrum representations well - cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); - - // transform the product back from the frequency domain. - // Even though all the result rows will be non-zero, - // you need only the first C.rows of them, and thus you - // pass nonzeroRows == C.rows - cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); - - // now copy the result back to C. - tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); -} - -IMPLEMENT_PARAM_CLASS(KSize, int); -IMPLEMENT_PARAM_CLASS(Ccorr, bool); - -PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int ksize; - bool ccorr; - - cv::Mat src; - cv::Mat kernel; - - cv::Mat dst_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - ksize = GET_PARAM(2); - ccorr = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Convolve, Accuracy) -{ - cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); - cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); - - cv::gpu::GpuMat dst; - cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr); - - cv::Mat dst_gold; - convolveDFT(src, kernel, dst_gold, ccorr); - - EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), - testing::Values(Ccorr(false), Ccorr(true)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate8U - -CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) -#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED)) - -IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); - -PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - cv::Size templ_size; - int cn; - int method; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - templ_size = GET_PARAM(2); - cn = GET_PARAM(3); - method = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate8U, Accuracy) -{ - cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); - cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); - - cv::gpu::GpuMat dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); - - cv::Mat dst_gold; - cv::matchTemplate(image, templ, dst_gold, method); - - EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), - testing::Values(Channels(1), Channels(3), Channels(4)), - ALL_TEMPLATE_METHODS)); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate32F - -PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - cv::Size templ_size; - int cn; - int method; - - int n, m, h, w; - cv::Mat image, templ; - - cv::Mat dst_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - templ_size = GET_PARAM(2); - cn = GET_PARAM(3); - method = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate32F, Regression) -{ - cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); - cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); - - cv::gpu::GpuMat dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); - - cv::Mat dst_gold; - cv::matchTemplate(image, templ, dst_gold, method); - - EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), - testing::Values(Channels(1), Channels(3), Channels(4)), - testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplateBlackSource - -PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - int method; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - method = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplateBlackSource, Accuracy) -{ - cv::Mat image = readImage("matchtemplate/black.png"); - ASSERT_FALSE(image.empty()); - - cv::Mat pattern = readImage("matchtemplate/cat.png"); - ASSERT_FALSE(pattern.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method); - - cv::Mat dst(d_dst); - - double maxValue; - cv::Point maxLoc; - cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); - - cv::Point maxLocGold = cv::Point(284, 12); - - ASSERT_EQ(maxLocGold, maxLoc); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( - ALL_DEVICES, - testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate_CCOEF_NORMED - -PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair) -{ - cv::gpu::DeviceInfo devInfo; - std::string imageName; - std::string patternName; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - imageName = GET_PARAM(1).first; - patternName = GET_PARAM(1).second; - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) -{ - cv::Mat image = readImage(imageName); - ASSERT_FALSE(image.empty()); - - cv::Mat pattern = readImage(patternName); - ASSERT_FALSE(pattern.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED); - - cv::Mat dst(d_dst); - - cv::Point minLoc, maxLoc; - double minVal, maxVal; - cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); - - cv::Mat dstGold; - cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED); - - double minValGold, maxValGold; - cv::Point minLocGold, maxLocGold; - cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); - - ASSERT_EQ(minLocGold, minLoc); - ASSERT_EQ(maxLocGold, maxLoc); - ASSERT_LE(maxVal, 1.0); - ASSERT_GE(minVal, -1.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( - ALL_DEVICES, - testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate_CanFindBigTemplate - -struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) -{ - cv::Mat scene = readImage("matchtemplate/scene.jpg"); - ASSERT_FALSE(scene.empty()); - - cv::Mat templ = readImage("matchtemplate/template.jpg"); - ASSERT_FALSE(templ.empty()); - - cv::gpu::GpuMat d_result; - cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED); - - cv::Mat result(d_result); - - double minVal; - cv::Point minLoc; - cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); - - ASSERT_GE(minVal, 0); - ASSERT_LT(minVal, 1e-3); - ASSERT_EQ(344, minLoc.x); - ASSERT_EQ(0, minLoc.y); -} - -TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) -{ - cv::Mat scene = readImage("matchtemplate/scene.jpg"); - ASSERT_FALSE(scene.empty()); - - cv::Mat templ = readImage("matchtemplate/template.jpg"); - ASSERT_FALSE(templ.empty()); - - cv::gpu::GpuMat d_result; - cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF); - - cv::Mat result(d_result); - - double minVal; - cv::Point minLoc; - cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); - - ASSERT_GE(minVal, 0); - ASSERT_EQ(344, minLoc.x); - ASSERT_EQ(0, minLoc.y); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); - -//////////////////////////////////////////////////////////////////////////// -// MulSpectrums - -CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) - -PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int flag; - - cv::Mat a, b; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - flag = GET_PARAM(2); - - cv::gpu::setDevice(devInfo.deviceID()); - - a = randomMat(size, CV_32FC2); - b = randomMat(size, CV_32FC2); - } -}; - -TEST_P(MulSpectrums, Simple) -{ - cv::gpu::GpuMat c; - cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); - - cv::Mat c_gold; - cv::mulSpectrums(a, b, c_gold, flag, false); - - EXPECT_MAT_NEAR(c_gold, c, 1e-2); -} - -TEST_P(MulSpectrums, Scaled) -{ - float scale = 1.f / size.area(); - - cv::gpu::GpuMat c; - cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); - - cv::Mat c_gold; - cv::mulSpectrums(a, b, c_gold, flag, false); - c_gold.convertTo(c_gold, c_gold.type(), scale); - - EXPECT_MAT_NEAR(c_gold, c, 1e-2); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); - -//////////////////////////////////////////////////////////////////////////// -// Dft - -struct Dft : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) -{ - SCOPED_TRACE(hint); - - cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); - - cv::Mat b_gold; - cv::dft(a, b_gold, flags); - - cv::gpu::GpuMat d_b; - cv::gpu::GpuMat d_b_data; - if (inplace) - { - d_b_data.create(1, a.size().area(), CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); - } - cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); - - EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); - ASSERT_EQ(CV_32F, d_b.depth()); - ASSERT_EQ(2, d_b.channels()); - EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); -} - -TEST_P(Dft, C2C) -{ - int cols = randomInt(2, 100); - int rows = randomInt(2, 100); - - for (int i = 0; i < 2; ++i) - { - bool inplace = i != 0; - - testC2C("no flags", cols, rows, 0, inplace); - testC2C("no flags 0 1", cols, rows + 1, 0, inplace); - testC2C("no flags 1 0", cols, rows + 1, 0, inplace); - testC2C("no flags 1 1", cols + 1, rows, 0, inplace); - testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); - testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); - testC2C("single col", 1, rows, 0, inplace); - testC2C("single row", cols, 1, 0, inplace); - testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); - testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); - testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); - testC2C("size 1 2", 1, 2, 0, inplace); - testC2C("size 2 1", 2, 1, 0, inplace); - } -} - -void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) -{ - SCOPED_TRACE(hint); - - cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); - - cv::gpu::GpuMat d_b, d_c; - cv::gpu::GpuMat d_b_data, d_c_data; - if (inplace) - { - if (a.cols == 1) - { - d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); - } - else - { - d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); - } - d_c_data.create(1, a.size().area(), CV_32F); - d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); - } - - cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); - cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); - - EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); - EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); - ASSERT_EQ(CV_32F, d_c.depth()); - ASSERT_EQ(1, d_c.channels()); - - cv::Mat c(d_c); - EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); -} - -TEST_P(Dft, R2CThenC2R) -{ - int cols = randomInt(2, 100); - int rows = randomInt(2, 100); - - testR2CThenC2R("sanity", cols, rows, false); - testR2CThenC2R("sanity 0 1", cols, rows + 1, false); - testR2CThenC2R("sanity 1 0", cols + 1, rows, false); - testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); - testR2CThenC2R("single col", 1, rows, false); - testR2CThenC2R("single col 1", 1, rows + 1, false); - testR2CThenC2R("single row", cols, 1, false); - testR2CThenC2R("single row 1", cols + 1, 1, false); - - testR2CThenC2R("sanity", cols, rows, true); - testR2CThenC2R("sanity 0 1", cols, rows + 1, true); - testR2CThenC2R("sanity 1 0", cols + 1, rows, true); - testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); - testR2CThenC2R("single row", cols, 1, true); - testR2CThenC2R("single row 1", cols + 1, 1, true); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Dft, ALL_DEVICES); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// CornerHarris - -IMPLEMENT_PARAM_CLASS(BlockSize, int); -IMPLEMENT_PARAM_CLASS(ApertureSize, int); - -PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) -{ - cv::gpu::DeviceInfo devInfo; - int type; - int borderType; - int blockSize; - int apertureSize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - type = GET_PARAM(1); - borderType = GET_PARAM(2); - blockSize = GET_PARAM(3); - apertureSize = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CornerHarris, Accuracy) -{ - cv::Mat src = readImageType("stereobm/aloe-L.png", type); - ASSERT_FALSE(src.empty()); - - double k = randomDouble(0.1, 0.9); - - cv::gpu::GpuMat dst; - cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType); - - cv::Mat dst_gold; - cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.02); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerHarris, testing::Combine( - ALL_DEVICES, - testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), - testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), - testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), - testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// cornerMinEigen - -PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) -{ - cv::gpu::DeviceInfo devInfo; - int type; - int borderType; - int blockSize; - int apertureSize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - type = GET_PARAM(1); - borderType = GET_PARAM(2); - blockSize = GET_PARAM(3); - apertureSize = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CornerMinEigen, Accuracy) -{ - cv::Mat src = readImageType("stereobm/aloe-L.png", type); - ASSERT_FALSE(src.empty()); - - cv::gpu::GpuMat dst; - cv::gpu::cornerMinEigenVal(loadMat(src), dst, blockSize, apertureSize, borderType); - - cv::Mat dst_gold; - cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.02); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine( - ALL_DEVICES, - testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), - testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), - testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), - testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); - -} // namespace + { + cv::gpu::GpuMat edges; + cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient); + + cv::Mat edges_gold; + cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient); + + EXPECT_MAT_SIMILAR(edges_gold, edges, 1e-2); + } +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine( + ALL_DEVICES, + testing::Values(AppertureSize(3), AppertureSize(5)), + testing::Values(L2gradient(false), L2gradient(true)), + WHOLE_SUBMAT)); + +//////////////////////////////////////////////////////////////////////////////// +// MeanShift + +struct MeanShift : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + cv::Mat img; + + int spatialRad; + int colorRad; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + + img = readImageType("meanshift/cones.png", CV_8UC4); + ASSERT_FALSE(img.empty()); + + spatialRad = 30; + colorRad = 30; + } +}; + +TEST_P(MeanShift, Filtering) +{ + cv::Mat img_template; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + img_template = readImage("meanshift/con_result.png"); + else + img_template = readImage("meanshift/con_result_CC1X.png"); + ASSERT_FALSE(img_template.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); + + ASSERT_EQ(CV_8UC4, d_dst.type()); + + cv::Mat dst(d_dst); + + cv::Mat result; + cv::cvtColor(dst, result, CV_BGRA2BGR); + + EXPECT_MAT_NEAR(img_template, result, 0.0); +} + +TEST_P(MeanShift, Proc) +{ + cv::FileStorage fs; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); + else + fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); + ASSERT_TRUE(fs.isOpened()); + + cv::Mat spmap_template; + fs["spmap"] >> spmap_template; + ASSERT_FALSE(spmap_template.empty()); + + cv::gpu::GpuMat rmap_filtered; + cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); + + cv::gpu::GpuMat rmap; + cv::gpu::GpuMat spmap; + cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); + + ASSERT_EQ(CV_8UC4, rmap.type()); + + EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); + EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES); + +//////////////////////////////////////////////////////////////////////////////// +// MeanShiftSegmentation + +IMPLEMENT_PARAM_CLASS(MinSize, int); + +PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize) +{ + cv::gpu::DeviceInfo devInfo; + int minsize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + minsize = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MeanShiftSegmentation, Regression) +{ + cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); + ASSERT_FALSE(img.empty()); + + std::ostringstream path; + path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + path << ".png"; + else + path << "_CC1X.png"; + cv::Mat dst_gold = readImage(path.str()); + ASSERT_FALSE(dst_gold.empty()); + + cv::Mat dst; + cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); + + cv::Mat dst_rgb; + cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR); + + EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine( + ALL_DEVICES, + testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); + +//////////////////////////////////////////////////////////////////////////// +// Blend + +template +void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold) +{ + result_gold.create(img1.size(), img1.type()); + + int cn = img1.channels(); + + for (int y = 0; y < img1.rows; ++y) + { + const float* weights1_row = weights1.ptr(y); + const float* weights2_row = weights2.ptr(y); + const T* img1_row = img1.ptr(y); + const T* img2_row = img2.ptr(y); + T* result_gold_row = result_gold.ptr(y); + + for (int x = 0; x < img1.cols * cn; ++x) + { + float w1 = weights1_row[x / cn]; + float w2 = weights2_row[x / cn]; + result_gold_row[x] = static_cast((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); + } + } +} + +PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int type; + bool useRoi; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + type = GET_PARAM(2); + useRoi = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Blend, Accuracy) +{ + int depth = CV_MAT_DEPTH(type); + + cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); + cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); + cv::Mat weights1 = randomMat(size, CV_32F, 0, 1); + cv::Mat weights2 = randomMat(size, CV_32F, 0, 1); + + cv::gpu::GpuMat result; + cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result); + + cv::Mat result_gold; + if (depth == CV_8U) + blendLinearGold(img1, img2, weights1, weights2, result_gold); + else + blendLinearGold(img1, img2, weights1, weights2, result_gold); + + EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), + WHOLE_SUBMAT)); + +//////////////////////////////////////////////////////// +// Convolve + +void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) +{ + // reallocate the output array if needed + C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); + cv::Size dftSize; + + // compute the size of DFT transform + dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0s + cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); + cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); + A.copyTo(roiA); + cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + cv::dft(tempA, tempA, 0, A.rows); + cv::dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); +} + +IMPLEMENT_PARAM_CLASS(KSize, int); +IMPLEMENT_PARAM_CLASS(Ccorr, bool); + +PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int ksize; + bool ccorr; + + cv::Mat src; + cv::Mat kernel; + + cv::Mat dst_gold; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + ksize = GET_PARAM(2); + ccorr = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Convolve, Accuracy) +{ + cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); + cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); + + cv::gpu::GpuMat dst; + cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr); + + cv::Mat dst_gold; + convolveDFT(src, kernel, dst_gold, ccorr); + + EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), + testing::Values(Ccorr(false), Ccorr(true)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate8U + +CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) +#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED)) + +IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); + +PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + cv::Size templ_size; + int cn; + int method; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + templ_size = GET_PARAM(2); + cn = GET_PARAM(3); + method = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate8U, Accuracy) +{ + cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); + cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); + + cv::gpu::GpuMat dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); + + cv::Mat dst_gold; + cv::matchTemplate(image, templ, dst_gold, method); + + EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), + testing::Values(Channels(1), Channels(3), Channels(4)), + ALL_TEMPLATE_METHODS)); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate32F + +PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + cv::Size templ_size; + int cn; + int method; + + int n, m, h, w; + cv::Mat image, templ; + + cv::Mat dst_gold; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + templ_size = GET_PARAM(2); + cn = GET_PARAM(3); + method = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate32F, Regression) +{ + cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); + cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); + + cv::gpu::GpuMat dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); + + cv::Mat dst_gold; + cv::matchTemplate(image, templ, dst_gold, method); + + EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), + testing::Values(Channels(1), Channels(3), Channels(4)), + testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplateBlackSource + +PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + int method; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + method = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplateBlackSource, Accuracy) +{ + cv::Mat image = readImage("matchtemplate/black.png"); + ASSERT_FALSE(image.empty()); + + cv::Mat pattern = readImage("matchtemplate/cat.png"); + ASSERT_FALSE(pattern.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method); + + cv::Mat dst(d_dst); + + double maxValue; + cv::Point maxLoc; + cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); + + cv::Point maxLocGold = cv::Point(284, 12); + + ASSERT_EQ(maxLocGold, maxLoc); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( + ALL_DEVICES, + testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate_CCOEF_NORMED + +PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair) +{ + cv::gpu::DeviceInfo devInfo; + std::string imageName; + std::string patternName; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + imageName = GET_PARAM(1).first; + patternName = GET_PARAM(1).second; + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) +{ + cv::Mat image = readImage(imageName); + ASSERT_FALSE(image.empty()); + + cv::Mat pattern = readImage(patternName); + ASSERT_FALSE(pattern.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED); + + cv::Mat dst(d_dst); + + cv::Point minLoc, maxLoc; + double minVal, maxVal; + cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); + + cv::Mat dstGold; + cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED); + + double minValGold, maxValGold; + cv::Point minLocGold, maxLocGold; + cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); + + ASSERT_EQ(minLocGold, minLoc); + ASSERT_EQ(maxLocGold, maxLoc); + ASSERT_LE(maxVal, 1.0); + ASSERT_GE(minVal, -1.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( + ALL_DEVICES, + testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate_CanFindBigTemplate + +struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) +{ + cv::Mat scene = readImage("matchtemplate/scene.jpg"); + ASSERT_FALSE(scene.empty()); + + cv::Mat templ = readImage("matchtemplate/template.jpg"); + ASSERT_FALSE(templ.empty()); + + cv::gpu::GpuMat d_result; + cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED); + + cv::Mat result(d_result); + + double minVal; + cv::Point minLoc; + cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); + + ASSERT_GE(minVal, 0); + ASSERT_LT(minVal, 1e-3); + ASSERT_EQ(344, minLoc.x); + ASSERT_EQ(0, minLoc.y); +} + +TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) +{ + cv::Mat scene = readImage("matchtemplate/scene.jpg"); + ASSERT_FALSE(scene.empty()); + + cv::Mat templ = readImage("matchtemplate/template.jpg"); + ASSERT_FALSE(templ.empty()); + + cv::gpu::GpuMat d_result; + cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF); + + cv::Mat result(d_result); + + double minVal; + cv::Point minLoc; + cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); + + ASSERT_GE(minVal, 0); + ASSERT_EQ(344, minLoc.x); + ASSERT_EQ(0, minLoc.y); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); + +//////////////////////////////////////////////////////////////////////////// +// MulSpectrums + +CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) + +PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int flag; + + cv::Mat a, b; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + flag = GET_PARAM(2); + + cv::gpu::setDevice(devInfo.deviceID()); + + a = randomMat(size, CV_32FC2); + b = randomMat(size, CV_32FC2); + } +}; + +TEST_P(MulSpectrums, Simple) +{ + cv::gpu::GpuMat c; + cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); + + cv::Mat c_gold; + cv::mulSpectrums(a, b, c_gold, flag, false); + + EXPECT_MAT_NEAR(c_gold, c, 1e-2); +} + +TEST_P(MulSpectrums, Scaled) +{ + float scale = 1.f / size.area(); + + cv::gpu::GpuMat c; + cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); + + cv::Mat c_gold; + cv::mulSpectrums(a, b, c_gold, flag, false); + c_gold.convertTo(c_gold, c_gold.type(), scale); + + EXPECT_MAT_NEAR(c_gold, c, 1e-2); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); + +//////////////////////////////////////////////////////////////////////////// +// Dft + +struct Dft : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) +{ + SCOPED_TRACE(hint); + + cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); + + cv::Mat b_gold; + cv::dft(a, b_gold, flags); + + cv::gpu::GpuMat d_b; + cv::gpu::GpuMat d_b_data; + if (inplace) + { + d_b_data.create(1, a.size().area(), CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); + } + cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); + + EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); + ASSERT_EQ(CV_32F, d_b.depth()); + ASSERT_EQ(2, d_b.channels()); + EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); +} + +TEST_P(Dft, C2C) +{ + int cols = randomInt(2, 100); + int rows = randomInt(2, 100); + + for (int i = 0; i < 2; ++i) + { + bool inplace = i != 0; + + testC2C("no flags", cols, rows, 0, inplace); + testC2C("no flags 0 1", cols, rows + 1, 0, inplace); + testC2C("no flags 1 0", cols, rows + 1, 0, inplace); + testC2C("no flags 1 1", cols + 1, rows, 0, inplace); + testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); + testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); + testC2C("single col", 1, rows, 0, inplace); + testC2C("single row", cols, 1, 0, inplace); + testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); + testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); + testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); + testC2C("size 1 2", 1, 2, 0, inplace); + testC2C("size 2 1", 2, 1, 0, inplace); + } +} + +void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) +{ + SCOPED_TRACE(hint); + + cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); + + cv::gpu::GpuMat d_b, d_c; + cv::gpu::GpuMat d_b_data, d_c_data; + if (inplace) + { + if (a.cols == 1) + { + d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); + } + else + { + d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); + } + d_c_data.create(1, a.size().area(), CV_32F); + d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); + } + + cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); + cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); + + EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); + EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); + ASSERT_EQ(CV_32F, d_c.depth()); + ASSERT_EQ(1, d_c.channels()); + + cv::Mat c(d_c); + EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); +} + +TEST_P(Dft, R2CThenC2R) +{ + int cols = randomInt(2, 100); + int rows = randomInt(2, 100); + + testR2CThenC2R("sanity", cols, rows, false); + testR2CThenC2R("sanity 0 1", cols, rows + 1, false); + testR2CThenC2R("sanity 1 0", cols + 1, rows, false); + testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); + testR2CThenC2R("single col", 1, rows, false); + testR2CThenC2R("single col 1", 1, rows + 1, false); + testR2CThenC2R("single row", cols, 1, false); + testR2CThenC2R("single row 1", cols + 1, 1, false); + + testR2CThenC2R("sanity", cols, rows, true); + testR2CThenC2R("sanity 0 1", cols, rows + 1, true); + testR2CThenC2R("sanity 1 0", cols + 1, rows, true); + testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); + testR2CThenC2R("single row", cols, 1, true); + testR2CThenC2R("single row 1", cols + 1, 1, true); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Dft, ALL_DEVICES); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// CornerHarris + +IMPLEMENT_PARAM_CLASS(BlockSize, int); +IMPLEMENT_PARAM_CLASS(ApertureSize, int); + +PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) +{ + cv::gpu::DeviceInfo devInfo; + int type; + int borderType; + int blockSize; + int apertureSize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + type = GET_PARAM(1); + borderType = GET_PARAM(2); + blockSize = GET_PARAM(3); + apertureSize = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CornerHarris, Accuracy) +{ + cv::Mat src = readImageType("stereobm/aloe-L.png", type); + ASSERT_FALSE(src.empty()); + + double k = randomDouble(0.1, 0.9); + + cv::gpu::GpuMat dst; + cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType); + + cv::Mat dst_gold; + cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.02); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerHarris, testing::Combine( + ALL_DEVICES, + testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), + testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), + testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), + testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// cornerMinEigen + +PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) +{ + cv::gpu::DeviceInfo devInfo; + int type; + int borderType; + int blockSize; + int apertureSize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + type = GET_PARAM(1); + borderType = GET_PARAM(2); + blockSize = GET_PARAM(3); + apertureSize = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CornerMinEigen, Accuracy) +{ + cv::Mat src = readImageType("stereobm/aloe-L.png", type); + ASSERT_FALSE(src.empty()); + + cv::gpu::GpuMat dst; + cv::gpu::cornerMinEigenVal(loadMat(src), dst, blockSize, apertureSize, borderType); + + cv::Mat dst_gold; + cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.02); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine( + ALL_DEVICES, + testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), + testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), + testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), + testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); + +} // namespace -- 2.7.4