320 lines
10 KiB
C++
320 lines
10 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., 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 the copyright holders 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 OpenCV Foundation 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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#ifndef __OPENCV_TS_OCL_TEST_HPP__
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#define __OPENCV_TS_OCL_TEST_HPP__
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#include "cvconfig.h" // to get definition of HAVE_OPENCL
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCL
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#include "opencv2/ts.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgproc/types_c.h"
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#include "opencv2/core/ocl.hpp"
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namespace cvtest {
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namespace ocl {
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using namespace cv;
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using namespace testing;
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namespace traits {
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template <typename T>
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struct GetMatForRead
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{
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};
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template <>
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struct GetMatForRead<Mat>
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{
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static const Mat get(const Mat& m) { return m; }
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};
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template <>
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struct GetMatForRead<UMat>
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{
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static const Mat get(const UMat& m) { return m.getMat(ACCESS_READ); }
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};
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} // namespace traits
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template <typename T>
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const Mat getMatForRead(const T& mat)
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{
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return traits::GetMatForRead<T>::get(mat);
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}
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extern int test_loop_times;
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#define MAX_VALUE 357
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#define EXPECT_MAT_NORM(mat, eps) \
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{ \
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EXPECT_LE(checkNorm(mat), eps) \
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}
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#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
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{ \
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ASSERT_EQ(mat1.type(), mat2.type()); \
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ASSERT_EQ(mat1.size(), mat2.size()); \
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EXPECT_LE(checkNorm(mat1, mat2), eps) \
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<< cv::format("Size: %d x %d", mat1.size().width, mat1.size().height) << std::endl; \
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}
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#define EXPECT_MAT_NEAR_RELATIVE(mat1, mat2, eps) \
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{ \
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ASSERT_EQ(mat1.type(), mat2.type()); \
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ASSERT_EQ(mat1.size(), mat2.size()); \
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EXPECT_LE(checkNormRelative(mat1, mat2), eps) \
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<< cv::format("Size: %d x %d", mat1.size().width, mat1.size().height) << std::endl; \
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}
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#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
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{ \
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ASSERT_EQ(mat1.type(), mat2.type()); \
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ASSERT_EQ(mat1.size(), mat2.size()); \
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EXPECT_LE(checkSimilarity(mat1, mat2), eps); \
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<< cv::format("Size: %d x %d", mat1.size().width, mat1.size().height) << std::endl; \
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}
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using perf::MatDepth;
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using perf::MatType;
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#define OCL_RNG_SEED 123456
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struct CV_EXPORTS TestUtils
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{
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cv::RNG rng;
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TestUtils()
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{
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rng = cv::RNG(OCL_RNG_SEED);
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}
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int randomInt(int minVal, int maxVal)
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{
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return rng.uniform(minVal, maxVal);
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}
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double randomDouble(double minVal, double maxVal)
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{
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return rng.uniform(minVal, maxVal);
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}
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double randomDoubleLog(double minVal, double maxVal)
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{
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double logMin = log((double)minVal + 1);
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double logMax = log((double)maxVal + 1);
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double pow = rng.uniform(logMin, logMax);
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double v = exp(pow) - 1;
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CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal)));
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return v;
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}
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Size randomSize(int minVal, int maxVal)
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{
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#if 1
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return cv::Size((int)randomDoubleLog(minVal, maxVal), (int)randomDoubleLog(minVal, maxVal));
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#else
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return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
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#endif
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}
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Size randomSize(int minValX, int maxValX, int minValY, int maxValY)
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{
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#if 1
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return cv::Size((int)randomDoubleLog(minValX, maxValX), (int)randomDoubleLog(minValY, maxValY));
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#else
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return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
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#endif
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}
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Scalar randomScalar(double minVal, double maxVal)
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{
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return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
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}
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Mat randomMat(Size size, int type, double minVal, double maxVal, bool useRoi = false)
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{
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RNG dataRng(rng.next());
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return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi);
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}
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struct Border
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{
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int top, bot, lef, rig;
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};
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Border randomBorder(int minValue = 0, int maxValue = MAX_VALUE)
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{
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Border border = {
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(int)randomDoubleLog(minValue, maxValue),
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(int)randomDoubleLog(minValue, maxValue),
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(int)randomDoubleLog(minValue, maxValue),
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(int)randomDoubleLog(minValue, maxValue)
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};
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return border;
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}
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void randomSubMat(Mat& whole, Mat& subMat, const Size& roiSize, const Border& border, int type, double minVal, double maxVal)
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{
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Size wholeSize = Size(roiSize.width + border.lef + border.rig, roiSize.height + border.top + border.bot);
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whole = randomMat(wholeSize, type, minVal, maxVal, false);
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subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
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}
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// If the two vectors are not equal, it will return the difference in vector size
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// Else it will return (total diff of each 1 and 2 rects covered pixels)/(total 1 rects covered pixels)
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// The smaller, the better matched
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static double checkRectSimilarity(cv::Size sz, std::vector<cv::Rect>& ob1, std::vector<cv::Rect>& ob2);
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//! read image from testdata folder.
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static cv::Mat readImage(const String &fileName, int flags = cv::IMREAD_COLOR);
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static cv::Mat readImageType(const String &fname, int type);
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static double checkNorm(const cv::Mat &m);
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static double checkNorm(const cv::Mat &m1, const cv::Mat &m2);
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static double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2);
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static inline double checkNormRelative(const Mat &m1, const Mat &m2)
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{
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return cv::norm(m1, m2, cv::NORM_INF) /
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std::max((double)std::numeric_limits<float>::epsilon(),
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(double)std::max(cv::norm(m1, cv::NORM_INF), norm(m2, cv::NORM_INF)));
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}
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static void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow = false);
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template <typename T1>
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static double checkNorm(const T1& m)
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{
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return checkNorm(getMatForRead(m));
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}
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template <typename T1, typename T2>
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static double checkNorm(const T1& m1, const T2& m2)
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{
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return checkNorm(getMatForRead(m1), getMatForRead(m2));
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}
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template <typename T1, typename T2>
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static double checkSimilarity(const T1& m1, const T2& m2)
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{
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return checkSimilarity(getMatForRead(m1), getMatForRead(m2));
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}
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template <typename T1, typename T2>
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static inline double checkNormRelative(const T1& m1, const T2& m2)
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{
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const Mat _m1 = getMatForRead(m1);
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const Mat _m2 = getMatForRead(m2);
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return checkNormRelative(_m1, _m2);
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}
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template <typename T1, typename T2, typename T3>
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static void showDiff(const T1& src, const T2& gold, const T3& actual, double eps, bool alwaysShow = false)
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{
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const Mat _src = getMatForRead(src);
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const Mat _gold = getMatForRead(gold);
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const Mat _actual = getMatForRead(actual);
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showDiff(_src, _gold, _actual, eps, alwaysShow);
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}
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};
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#define TEST_DECLARE_INPUT_PARAMETER(name) Mat name, name ## _roi; UMat u ## name, u ## name ## _roi;
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#define TEST_DECLARE_OUTPUT_PARAMETER(name) TEST_DECLARE_INPUT_PARAMETER(name)
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#define UMAT_UPLOAD_INPUT_PARAMETER(name) \
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{ \
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name.copyTo(u ## name); \
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Size _wholeSize; Point ofs; name ## _roi.locateROI(_wholeSize, ofs); \
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u ## name ## _roi = u ## name(Rect(ofs.x, ofs.y, name ## _roi.size().width, name ## _roi.size().height)); \
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}
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#define UMAT_UPLOAD_OUTPUT_PARAMETER(name) UMAT_UPLOAD_INPUT_PARAMETER(name)
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template <typename T>
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struct CV_EXPORTS TSTestWithParam : public TestUtils, public ::testing::TestWithParam<T>
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{
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};
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#define PARAM_TEST_CASE(name, ...) struct name : public TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > >
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#define GET_PARAM(k) std::tr1::get< k >(GetParam())
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#ifndef IMPLEMENT_PARAM_CLASS
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#define IMPLEMENT_PARAM_CLASS(name, type) \
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class name \
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{ \
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public: \
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name ( type arg = type ()) : val_(arg) {} \
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operator type () const {return val_;} \
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private: \
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type val_; \
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}; \
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inline void PrintTo( name param, std::ostream* os) \
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{ \
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*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
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}
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IMPLEMENT_PARAM_CLASS(Channels, int)
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#endif // IMPLEMENT_PARAM_CLASS
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#define OCL_TEST_P TEST_P
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#define OCL_OFF(fn) cv::ocl::setUseOpenCL(false); fn
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#define OCL_ON(fn) cv::ocl::setUseOpenCL(true); fn
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#define OCL_ALL_DEPTHS Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F)
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#define OCL_ALL_CHANNELS Values(1, 2, 3, 4)
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CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA)
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CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV)
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CV_ENUM(BorderType, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101)
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#define OCL_INSTANTIATE_TEST_CASE_P(prefix, test_case_name, generator) \
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INSTANTIATE_TEST_CASE_P(OCL_ ## prefix, test_case_name, generator)
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}} // namespace cvtest::ocl
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#endif // HAVE_OPENCL
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#endif // __OPENCV_TS_OCL_TEST_HPP__
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