mirror of
https://github.com/opencv/opencv.git
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1157 lines
33 KiB
C++
1157 lines
33 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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using namespace cvtest;
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// Integral
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PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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useRoi = GET_PARAM(2);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(Integral, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_8UC1);
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cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi);
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cv::gpu::integral(loadMat(src, useRoi), dst);
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cv::Mat dst_gold;
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cv::integral(src, dst_gold, CV_32S);
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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WHOLE_SUBMAT));
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// HistEven
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struct HistEven : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(HistEven, Accuracy)
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{
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cv::Mat img = readImage("stereobm/aloe-L.png");
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ASSERT_FALSE(img.empty());
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cv::Mat hsv;
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cv::cvtColor(img, hsv, CV_BGR2HSV);
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int hbins = 30;
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float hranges[] = {0.0f, 180.0f};
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std::vector<cv::gpu::GpuMat> srcs;
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cv::gpu::split(loadMat(hsv), srcs);
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cv::gpu::GpuMat hist;
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cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]);
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cv::MatND histnd;
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int histSize[] = {hbins};
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const float* ranges[] = {hranges};
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int channels[] = {0};
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cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges);
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cv::Mat hist_gold = histnd;
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hist_gold = hist_gold.t();
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hist_gold.convertTo(hist_gold, CV_32S);
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EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES);
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// CalcHist
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namespace
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{
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void calcHistGold(const cv::Mat& src, cv::Mat& hist)
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{
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hist.create(1, 256, CV_32SC1);
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hist.setTo(cv::Scalar::all(0));
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int* hist_row = hist.ptr<int>();
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for (int y = 0; y < src.rows; ++y)
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{
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const uchar* src_row = src.ptr(y);
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for (int x = 0; x < src.cols; ++x)
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++hist_row[src_row[x]];
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}
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}
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}
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PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(CalcHist, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_8UC1);
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cv::gpu::GpuMat hist;
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cv::gpu::calcHist(loadMat(src), hist);
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cv::Mat hist_gold;
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calcHistGold(src, hist_gold);
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EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES));
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// EqualizeHist
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PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(EqualizeHist, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_8UC1);
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cv::gpu::GpuMat dst;
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cv::gpu::equalizeHist(loadMat(src), dst);
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cv::Mat dst_gold;
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cv::equalizeHist(src, dst_gold);
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EXPECT_MAT_NEAR(dst_gold, dst, 3.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES));
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////////////////////////////////////////////////////////////////////////
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// ColumnSum
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PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(ColumnSum, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_32FC1);
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cv::gpu::GpuMat d_dst;
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cv::gpu::columnSum(loadMat(src), d_dst);
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cv::Mat dst(d_dst);
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for (int j = 0; j < src.cols; ++j)
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{
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float gold = src.at<float>(0, j);
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float res = dst.at<float>(0, j);
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ASSERT_NEAR(res, gold, 1e-5);
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}
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for (int i = 1; i < src.rows; ++i)
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{
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for (int j = 0; j < src.cols; ++j)
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{
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float gold = src.at<float>(i, j) += src.at<float>(i - 1, j);
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float res = dst.at<float>(i, j);
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ASSERT_NEAR(res, gold, 1e-5);
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES));
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////////////////////////////////////////////////////////
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// Canny
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namespace
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{
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IMPLEMENT_PARAM_CLASS(AppertureSize, int);
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IMPLEMENT_PARAM_CLASS(L2gradient, bool);
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}
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PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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int apperture_size;
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bool useL2gradient;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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apperture_size = GET_PARAM(1);
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useL2gradient = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(Canny, Accuracy)
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{
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cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(img.empty());
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double low_thresh = 50.0;
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double high_thresh = 100.0;
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if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
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{
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try
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{
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cv::gpu::GpuMat edges;
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cv::gpu::Canny(loadMat(img), edges, low_thresh, high_thresh, apperture_size, useL2gradient);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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cv::gpu::GpuMat edges;
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cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient);
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cv::Mat edges_gold;
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cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient);
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EXPECT_MAT_SIMILAR(edges_gold, edges, 2e-2);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine(
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ALL_DEVICES,
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testing::Values(AppertureSize(3), AppertureSize(5)),
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testing::Values(L2gradient(false), L2gradient(true)),
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WHOLE_SUBMAT));
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////////////////////////////////////////////////////////////////////////////////
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// MeanShift
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struct MeanShift : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat img;
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int spatialRad;
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int colorRad;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::gpu::setDevice(devInfo.deviceID());
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img = readImageType("meanshift/cones.png", CV_8UC4);
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ASSERT_FALSE(img.empty());
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spatialRad = 30;
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colorRad = 30;
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}
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};
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GPU_TEST_P(MeanShift, Filtering)
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{
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cv::Mat img_template;
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if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
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img_template = readImage("meanshift/con_result.png");
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else
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img_template = readImage("meanshift/con_result_CC1X.png");
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ASSERT_FALSE(img_template.empty());
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cv::gpu::GpuMat d_dst;
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cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad);
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ASSERT_EQ(CV_8UC4, d_dst.type());
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cv::Mat dst(d_dst);
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cv::Mat result;
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cv::cvtColor(dst, result, CV_BGRA2BGR);
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EXPECT_MAT_NEAR(img_template, result, 0.0);
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}
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GPU_TEST_P(MeanShift, Proc)
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{
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cv::FileStorage fs;
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if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
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fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
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else
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fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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cv::Mat spmap_template;
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fs["spmap"] >> spmap_template;
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ASSERT_FALSE(spmap_template.empty());
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cv::gpu::GpuMat rmap_filtered;
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cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad);
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cv::gpu::GpuMat rmap;
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cv::gpu::GpuMat spmap;
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cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad);
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ASSERT_EQ(CV_8UC4, rmap.type());
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EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
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EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES);
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////////////////////////////////////////////////////////////////////////////////
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// MeanShiftSegmentation
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namespace
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{
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IMPLEMENT_PARAM_CLASS(MinSize, int);
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}
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PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize)
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{
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cv::gpu::DeviceInfo devInfo;
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int minsize;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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minsize = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(MeanShiftSegmentation, Regression)
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{
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cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4);
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ASSERT_FALSE(img.empty());
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std::ostringstream path;
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path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
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if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
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path << ".png";
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else
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path << "_CC1X.png";
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cv::Mat dst_gold = readImage(path.str());
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ASSERT_FALSE(dst_gold.empty());
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cv::Mat dst;
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cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize);
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cv::Mat dst_rgb;
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cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR);
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EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine(
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ALL_DEVICES,
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testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364))));
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////////////////////////////////////////////////////////////////////////////
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// Blend
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namespace
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{
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template <typename T>
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void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold)
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{
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result_gold.create(img1.size(), img1.type());
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int cn = img1.channels();
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for (int y = 0; y < img1.rows; ++y)
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{
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const float* weights1_row = weights1.ptr<float>(y);
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const float* weights2_row = weights2.ptr<float>(y);
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const T* img1_row = img1.ptr<T>(y);
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const T* img2_row = img2.ptr<T>(y);
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T* result_gold_row = result_gold.ptr<T>(y);
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for (int x = 0; x < img1.cols * cn; ++x)
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{
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float w1 = weights1_row[x / cn];
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float w2 = weights2_row[x / cn];
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result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
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}
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}
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}
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}
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PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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int type;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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type = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(Blend, Accuracy)
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{
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int depth = CV_MAT_DEPTH(type);
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cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
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cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
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cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
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cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
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cv::gpu::GpuMat result;
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cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result);
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cv::Mat result_gold;
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if (depth == CV_8U)
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blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
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else
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blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
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EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5);
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}
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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
|
|
|
|
namespace
|
|
{
|
|
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;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GET_PARAM(0);
|
|
size = GET_PARAM(1);
|
|
ksize = GET_PARAM(2);
|
|
ccorr = GET_PARAM(3);
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
GPU_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))
|
|
|
|
namespace
|
|
{
|
|
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());
|
|
}
|
|
};
|
|
|
|
GPU_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;
|
|
|
|
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());
|
|
}
|
|
};
|
|
|
|
GPU_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());
|
|
}
|
|
};
|
|
|
|
GPU_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<std::string, std::string>)
|
|
{
|
|
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());
|
|
}
|
|
};
|
|
|
|
GPU_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>
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GetParam();
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED)
|
|
{
|
|
cv::Mat scene = readImage("matchtemplate/scene.png");
|
|
ASSERT_FALSE(scene.empty());
|
|
|
|
cv::Mat templ = readImage("matchtemplate/template.png");
|
|
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);
|
|
}
|
|
|
|
GPU_TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF)
|
|
{
|
|
cv::Mat scene = readImage("matchtemplate/scene.png");
|
|
ASSERT_FALSE(scene.empty());
|
|
|
|
cv::Mat templ = readImage("matchtemplate/template.png");
|
|
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);
|
|
}
|
|
};
|
|
|
|
GPU_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);
|
|
}
|
|
|
|
GPU_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>
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GetParam();
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
}
|
|
};
|
|
|
|
namespace
|
|
{
|
|
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);
|
|
}
|
|
}
|
|
|
|
GPU_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);
|
|
}
|
|
}
|
|
|
|
namespace
|
|
{
|
|
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
|
|
{
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|
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
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|
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);
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|
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);
|
|
}
|
|
}
|
|
|
|
GPU_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
|
|
|
|
namespace
|
|
{
|
|
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());
|
|
}
|
|
};
|
|
|
|
GPU_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());
|
|
}
|
|
};
|
|
|
|
GPU_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))));
|
|
|
|
#endif // HAVE_CUDA
|