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https://github.com/opencv/opencv.git
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8cbdd0c833
C-API cleanup: apps, imgproc_c and some constants #25075 Merge with https://github.com/opencv/opencv_contrib/pull/3642 * Removed obsolete apps - traincascade and createsamples (please use older OpenCV versions if you need them). These apps relied heavily on C-API * removed all mentions of imgproc C-API headers (imgproc_c.h, types_c.h) - they were empty, included core C-API headers * replaced usage of several C constants with C++ ones (error codes, norm modes, RNG modes, PCA modes, ...) - most part of this PR (split into two parts - all modules and calib+3d - for easier backporting) * removed imgproc C-API headers (as separate commit, so that other changes could be backported to 4.x) Most of these changes can be backported to 4.x.
393 lines
14 KiB
C++
393 lines
14 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 "opencv2/ts.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/videoio.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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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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inline std::vector<UMat> ToUMat(const std::vector<Mat>& src)
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{
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std::vector<UMat> dst;
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dst.resize(src.size());
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for (size_t i = 0; i < src.size(); ++i)
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{
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src[i].copyTo(dst[i]);
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}
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return dst;
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}
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inline UMat ToUMat(const Mat& src)
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{
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UMat dst;
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src.copyTo(dst);
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return dst;
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}
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inline UMat ToUMat(InputArray src)
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{
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UMat dst;
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src.getMat().copyTo(dst);
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return dst;
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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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do \
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{ \
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EXPECT_LE(cvtest::ocl::TestUtils::checkNorm1(mat), eps) \
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} while ((void)0, 0)
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#undef EXPECT_MAT_NEAR
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#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
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do \
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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(cvtest::ocl::TestUtils::checkNorm2(mat1, mat2), eps) \
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<< "Size: " << mat1.size() << std::endl; \
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} while ((void)0, 0)
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#define EXPECT_MAT_NEAR_RELATIVE(mat1, mat2, eps) \
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do \
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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(cvtest::ocl::TestUtils::checkNormRelative((mat1), (mat2)), eps) \
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<< "Size: " << (mat1).size() << std::endl; \
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} while ((void)0, 0)
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#define EXPECT_MAT_N_DIFF(mat1, mat2, num) \
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do \
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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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Mat diff; \
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absdiff(mat1, mat2, diff); \
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EXPECT_LE(countNonZero(diff.reshape(1)), num) \
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<< "Size: " << mat1.size() << std::endl; \
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} while ((void)0, 0)
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#define OCL_EXPECT_MAT_N_DIFF(name, eps) \
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do \
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{ \
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ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \
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ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \
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Mat diff, binary, binary_8; \
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absdiff(name ## _roi, u ## name ## _roi, diff); \
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Mat mask(diff.size(), CV_8UC(dst.channels()), cv::Scalar::all(255)); \
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if (mask.cols > 2 && mask.rows > 2) \
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mask(cv::Rect(1, 1, mask.cols - 2, mask.rows - 2)).setTo(0); \
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cv::threshold(diff, binary, (double)eps, 255, cv::THRESH_BINARY); \
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EXPECT_LE(countNonZero(binary.reshape(1)), (int)(binary.cols*binary.rows*5/1000)) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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binary.convertTo(binary_8, mask.type()); \
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binary_8 = binary_8 & mask; \
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EXPECT_LE(countNonZero(binary_8.reshape(1)), (int)((binary_8.cols+binary_8.rows)/100)) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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#define OCL_EXPECT_MATS_NEAR(name, eps) \
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do \
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{ \
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ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \
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ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \
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EXPECT_LE(cvtest::ocl::TestUtils::checkNorm2(name ## _roi, u ## name ## _roi), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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Point _offset; \
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Size _wholeSize; \
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u ## name ## _roi.locateROI(_wholeSize, _offset); \
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Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \
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_mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \
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ASSERT_EQ(name.type(), u ## name.type()); \
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ASSERT_EQ(name.size(), u ## name.size()); \
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EXPECT_LE(cvtest::ocl::TestUtils::checkNorm2(name, u ## name, _mask), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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#define OCL_EXPECT_MATS_NEAR_RELATIVE(name, eps) \
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do \
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{ \
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ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \
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ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \
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EXPECT_LE(TestUtils::checkNormRelative(name ## _roi, u ## name ## _roi), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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Point _offset; \
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Size _wholeSize; \
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name ## _roi.locateROI(_wholeSize, _offset); \
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Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \
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_mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \
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ASSERT_EQ(name.type(), u ## name.type()); \
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ASSERT_EQ(name.size(), u ## name.size()); \
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EXPECT_LE(TestUtils::checkNormRelative(name, u ## name, _mask), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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//for sparse matrix
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#define OCL_EXPECT_MATS_NEAR_RELATIVE_SPARSE(name, eps) \
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do \
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{ \
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ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \
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ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \
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EXPECT_LE(cvtest::ocl::TestUtils::checkNormRelativeSparse(name ## _roi, u ## name ## _roi), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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Point _offset; \
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Size _wholeSize; \
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name ## _roi.locateROI(_wholeSize, _offset); \
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Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \
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_mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \
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ASSERT_EQ(name.type(), u ## name.type()); \
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ASSERT_EQ(name.size(), u ## name.size()); \
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EXPECT_LE(cvtest::ocl::TestUtils::checkNormRelativeSparse(name, u ## name, _mask), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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#undef EXPECT_MAT_SIMILAR
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#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
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do \
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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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<< "Size: " << mat1.size() << std::endl; \
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} while ((void)0, 0)
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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 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(const 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 checkNorm1(InputArray m, InputArray mask = noArray());
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static double checkNorm2(InputArray m1, InputArray m2, InputArray mask = noArray());
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static double checkSimilarity(InputArray m1, InputArray m2);
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static void showDiff(InputArray _src, InputArray _gold, InputArray _actual, double eps, bool alwaysShow);
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static inline double checkNormRelative(InputArray m1, InputArray m2, InputArray mask = noArray())
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{
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return cvtest::norm(m1.getMat(), m2.getMat(), cv::NORM_INF, mask) /
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std::max((double)std::numeric_limits<float>::epsilon(),
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(double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF)));
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}
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static inline double checkNormRelativeSparse(InputArray m1, InputArray m2, InputArray mask = noArray())
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{
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double norm_inf = cvtest::norm(m1.getMat(), m2.getMat(), cv::NORM_INF, mask);
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double norm_rel = norm_inf /
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std::max((double)std::numeric_limits<float>::epsilon(),
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(double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF)));
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return std::min(norm_inf, norm_rel);
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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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do \
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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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} while ((void)0, 0)
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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 TSTestWithParam : public TestUtils, public ::testing::TestWithParam<T>
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{
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};
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#undef PARAM_TEST_CASE
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#define PARAM_TEST_CASE(name, ...) struct name : public ::cvtest::ocl::TSTestWithParam< testing::tuple< __VA_ARGS__ > >
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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_TEST_F(name, ...) typedef name OCL_##name; TEST_F(OCL_##name, __VA_ARGS__)
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#define OCL_TEST(name, ...) TEST(OCL_##name, __VA_ARGS__)
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#define OCL_OFF(...) cv::ocl::setUseOpenCL(false); __VA_ARGS__ ;
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#define OCL_ON(...) cv::ocl::setUseOpenCL(true); __VA_ARGS__ ;
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#define OCL_ALL_DEPTHS Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)
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//, CV_16F, CV_16BF, CV_64U, CV_64S, CV_32U)
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#define OCL_ALL_DEPTHS_16F Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F, CV_16F)
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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, INTER_LINEAR_EXACT)
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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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namespace opencv_test {
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namespace ocl {
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using namespace cvtest::ocl;
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}} // namespace
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#endif // OPENCV_TS_OCL_TEST_HPP
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