2012-07-17 01:08:14 +08:00
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/*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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#ifndef __OPENCV_TEST_UTILITY_HPP__
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#define __OPENCV_TEST_UTILITY_HPP__
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2013-09-11 17:35:39 +08:00
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2013-10-16 22:23:05 +08:00
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extern int LOOP_TIMES;
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2013-09-11 17:35:39 +08:00
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2012-07-17 01:08:14 +08:00
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#define MWIDTH 256
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#define MHEIGHT 256
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2013-09-11 17:35:39 +08:00
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#define MIN_VALUE 171
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2013-09-25 02:54:13 +08:00
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#define MAX_VALUE 357
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2013-09-11 17:35:39 +08:00
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2013-10-03 00:18:48 +08:00
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namespace cvtest {
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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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//void showDiff(cv::InputArray gold, cv::InputArray actual, double eps);
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cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi);
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cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi);
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2012-07-17 01:08:14 +08:00
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2013-06-19 13:03:35 +08:00
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// This function test if gpu_rst matches cpu_rst.
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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 cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
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// The smaller, the better matched
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double checkRectSimilarity(cv::Size sz, std::vector<cv::Rect>& ob1, std::vector<cv::Rect>& ob2);
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2012-07-17 01:08:14 +08:00
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//! read image from testdata folder.
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cv::Mat readImage(const std::string &fileName, int flags = cv::IMREAD_COLOR);
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cv::Mat readImageType(const std::string &fname, int type);
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double checkNorm(const cv::Mat &m);
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double checkNorm(const cv::Mat &m1, const cv::Mat &m2);
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double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2);
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#define EXPECT_MAT_NORM(mat, eps) \
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{ \
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EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \
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}
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2013-05-08 17:07:44 +08:00
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#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
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2013-01-25 18:31:34 +08:00
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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(cv::Mat(mat1), cv::Mat(mat2)), eps); \
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2013-05-08 17:07:44 +08:00
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}
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2013-09-11 17:35:39 +08:00
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2012-07-17 01:08:14 +08:00
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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(cv::Mat(mat1), cv::Mat(mat2)), eps); \
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}
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using perf::MatDepth;
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using perf::MatType;
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//! return vector with types from specified range.
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std::vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end);
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//! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4).
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2012-10-11 16:22:47 +08:00
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const std::vector<MatType> &all_types();
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2012-07-17 01:08:14 +08:00
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class Inverse
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{
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public:
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inline Inverse(bool val = false) : val_(val) {}
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inline operator bool() const
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{
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return val_;
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}
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private:
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bool val_;
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};
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void PrintTo(const Inverse &useRoi, std::ostream *os);
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2013-10-03 00:18:48 +08:00
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#define OCL_RNG_SEED 123456
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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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template <typename T>
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struct TSTestWithParam : public ::testing::TestWithParam<T>
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{
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cv::RNG rng;
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2013-04-15 23:39:49 +08:00
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2013-10-03 00:18:48 +08:00
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TSTestWithParam()
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{
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rng = cv::RNG(OCL_RNG_SEED);
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}
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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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(randomDoubleLog(minValX, maxValX), 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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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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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void generateOclMat(cv::ocl::oclMat& whole, cv::ocl::oclMat& subMat, const Mat& wholeMat, const Size& roiSize, const Border& border)
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{
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whole = wholeMat;
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subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
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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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#define ALL_TYPES testing::ValuesIn(all_types())
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#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
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2012-07-17 01:08:14 +08:00
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2013-10-03 00:18:48 +08:00
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#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300))
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2012-07-17 01:08:14 +08:00
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#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4))
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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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2013-10-03 00:18:48 +08:00
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} // namespace cvtest
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enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
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CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
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CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE)
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CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX)
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CV_ENUM(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN)
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CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
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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(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC)
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CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
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CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
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CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
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CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
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CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
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2013-10-09 22:05:09 +08:00
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# define OCL_TEST_P(test_case_name, test_name) \
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class GTEST_TEST_CLASS_NAME_(test_case_name, test_name) : \
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public test_case_name { \
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public: \
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GTEST_TEST_CLASS_NAME_(test_case_name, test_name)() { } \
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virtual void TestBody(); \
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void OCLTestBody(); \
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private: \
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static int AddToRegistry() \
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{ \
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::testing::UnitTest::GetInstance()->parameterized_test_registry(). \
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GetTestCasePatternHolder<test_case_name>(\
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#test_case_name, __FILE__, __LINE__)->AddTestPattern(\
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#test_case_name, \
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#test_name, \
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new ::testing::internal::TestMetaFactory< \
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GTEST_TEST_CLASS_NAME_(test_case_name, test_name)>()); \
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return 0; \
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} \
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\
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static int gtest_registering_dummy_; \
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GTEST_DISALLOW_COPY_AND_ASSIGN_(\
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GTEST_TEST_CLASS_NAME_(test_case_name, test_name)); \
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}; \
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\
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int GTEST_TEST_CLASS_NAME_(test_case_name, \
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test_name)::gtest_registering_dummy_ = \
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GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::AddToRegistry(); \
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\
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void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() \
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{ \
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try \
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{ \
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OCLTestBody(); \
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} \
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catch (const cv::Exception & ex) \
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{ \
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2013-10-11 04:00:01 +08:00
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if (ex.code == CV_OpenCLDoubleNotSupported)\
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2013-10-09 22:05:09 +08:00
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std::cout << "Test skipped (selected device does not support double)" << std::endl; \
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2013-10-11 04:00:01 +08:00
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else if (ex.code == CV_OpenCLNoAMDBlasFft) \
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std::cout << "Test skipped (AMD Blas / Fft libraries are not available)" << std::endl; \
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else \
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throw; \
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2013-10-09 22:05:09 +08:00
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} \
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} \
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\
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void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::OCLTestBody()
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2012-07-17 01:08:14 +08:00
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#endif // __OPENCV_TEST_UTILITY_HPP__
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