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Merge pull request #25469 from mshabunin:cpp-emd
imgproc: refactor EMD to reduce C-API usage #25469 - added more tests for EMD - refactored to remove CvArr - used BufferArea for memory allocations - renamed functions and variables and formatted the code - kept legacy functions intact in separate header
This commit is contained in:
Maksim Shabunin
GitHub
@@ -23,6 +23,14 @@ CV_EXPORTS void findContours_legacy(InputArray image,
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int method,
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Point offset = Point());
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CV_EXPORTS float EMD_legacy( InputArray _signature1, InputArray _signature2,
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int distType, InputArray _cost,
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float* lowerBound, OutputArray _flow );
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CV_EXPORTS float wrapperEMD_legacy(InputArray _signature1, InputArray _signature2,
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int distType, InputArray _cost,
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Ptr<float> lowerBound, OutputArray _flow);
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#endif
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} // namespace cv
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@@ -57,6 +57,7 @@
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==========================================================================
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*/
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#include "precomp.hpp"
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#include "opencv2/imgproc/detail/legacy.hpp"
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#define MAX_ITERATIONS 500
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#define CV_EMD_INF ((float)1e20)
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@@ -1147,7 +1148,7 @@ icvDistC( const float *x, const float *y, void *user_param )
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}
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float cv::EMD( InputArray _signature1, InputArray _signature2,
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float cv::EMD_legacy( InputArray _signature1, InputArray _signature2,
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int distType, InputArray _cost,
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float* lowerBound, OutputArray _flow )
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{
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@@ -1171,7 +1172,7 @@ float cv::EMD( InputArray _signature1, InputArray _signature2,
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_flow.needed() ? &_cflow : 0, lowerBound, 0 );
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}
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float cv::wrapperEMD(InputArray _signature1, InputArray _signature2,
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float cv::wrapperEMD_legacy(InputArray _signature1, InputArray _signature2,
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int distType, InputArray _cost,
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Ptr<float> lowerBound, OutputArray _flow)
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{
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File diff suppressed because it is too large
Load Diff
@@ -50,6 +50,8 @@
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#include "opencv2/core/private.hpp"
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#include "opencv2/core/ocl.hpp"
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#include "opencv2/core/hal/hal.hpp"
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#include "opencv2/core/check.hpp"
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#include "opencv2/core/utils/buffer_area.private.hpp"
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#include "opencv2/imgproc/hal/hal.hpp"
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#include "hal_replacement.hpp"
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@@ -1,93 +1,250 @@
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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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// * 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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// * 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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// * 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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// 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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// (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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// 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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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "opencv2/imgproc.hpp"
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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namespace opencv_test { namespace {
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class CV_EMDTest : public cvtest::BaseTest
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{
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public:
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CV_EMDTest();
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protected:
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void run(int);
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};
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//==============================================================================
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// Utility
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CV_EMDTest::CV_EMDTest()
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template <typename T>
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inline T sqr(T val)
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{
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return val * val;
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}
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void CV_EMDTest::run( int )
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inline static float calcEMD(Mat w1, Mat w2, Mat& flow, int dist, int dims)
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{
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int code = cvtest::TS::OK;
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const double success_error_level = 1e-6;
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#define M 10000
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double emd0 = 2460./210;
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static float cost[] =
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float mass1 = 0.f, mass2 = 0.f, work = 0.f;
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for (int i = 0; i < flow.rows; ++i)
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{
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16, 16, 13, 22, 17,
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14, 14, 13, 19, 15,
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19, 19, 20, 23, M,
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M , 0, M, 0, 0
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};
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static float w1[] = { 50, 60, 50, 50 },
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w2[] = { 30, 20, 70, 30, 60 };
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Mat _w1(4, 1, CV_32F, w1);
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Mat _w2(5, 1, CV_32F, w2);
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Mat _cost(_w1.rows, _w2.rows, CV_32F, cost);
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mass1 += w1.at<float>(i, 0);
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for (int j = 0; j < flow.cols; ++j)
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{
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if (i == 0)
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mass2 += w2.at<float>(j, 0);
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float dist_ = 0.f;
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switch (dist)
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{
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case DIST_L1:
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{
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for (int k = 1; k <= dims; ++k)
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{
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dist_ += abs(w1.at<float>(i, k) - w2.at<float>(j, k));
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}
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break;
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}
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case DIST_L2:
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{
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for (int k = 1; k <= dims; ++k)
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{
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dist_ += sqr(w1.at<float>(i, k) - w2.at<float>(j, k));
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}
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dist_ = sqrt(dist_);
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break;
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}
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case DIST_C:
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{
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for (int k = 1; k <= dims; ++k)
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{
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const float val = abs(w1.at<float>(i, k) - w2.at<float>(j, k));
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if (val > dist_)
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dist_ = val;
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}
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break;
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}
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}
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const float weight = flow.at<float>(i, j);
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work += dist_ * weight;
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}
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}
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return work / max(mass1, mass2);
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}
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float emd = EMD( _w1, _w2, -1, _cost );
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if( fabs( emd - emd0 ) > success_error_level*emd0 )
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//==============================================================================
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TEST(Imgproc_EMD, regression)
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{
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// input data
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const float M = 10000;
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Matx<float, 4, 1> w1 {50, 60, 50, 50};
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Matx<float, 5, 1> w2 {30, 20, 70, 30, 60};
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Matx<float, 4, 5> cost {16, 16, 13, 22, 17, 14, 14, 13, 19, 15,
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19, 19, 20, 23, M, M, 0, M, 0, 0};
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// expected results
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const double emd0 = 2460. / 210;
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Matx<float, 4, 5> flow0 {0, 0, 50, 0, 0, 0, 0, 20, 0, 40, 30, 20, 0, 0, 0, 0, 0, 0, 30, 20};
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// basic call with cost
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{
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ts->printf( cvtest::TS::LOG,
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"The computed distance is %.2f, while it should be %.2f\n", emd, emd0 );
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code = cvtest::TS::FAIL_BAD_ACCURACY;
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float emd = 0.f;
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ASSERT_NO_THROW(emd = EMD(w1, w2, DIST_USER, cost));
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EXPECT_NEAR(emd, emd0, 1e-6 * emd0);
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}
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if( code < 0 )
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ts->set_failed_test_info( code );
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// basic call with cost and flow output
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{
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Mat flow;
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float emd = 0.f;
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ASSERT_NO_THROW(emd = EMD(w1, w2, DIST_USER, cost, nullptr, flow));
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EXPECT_NEAR(emd, emd0, 1e-6 * emd0);
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EXPECT_MAT_NEAR(Mat(flow0), flow, 1e-6);
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}
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// no cost and DIST_USER - error
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{
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Mat flow;
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EXPECT_THROW(EMD(w1, w2, DIST_USER, noArray(), nullptr, flow), cv::Exception);
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EXPECT_THROW(EMD(w1, w2, DIST_USER), cv::Exception);
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}
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}
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TEST(Imgproc_EMD, regression) { CV_EMDTest test; test.safe_run(); }
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TEST(Imgproc_EMD, distance_types)
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{
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// 1D (sum = 210)
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Matx<float, 4, 2> w1 {50, 1, 60, 2, 50, 3, 50, 4};
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Matx<float, 5, 2> w2 {30, 1, 20, 2, 70, 3, 30, 4, 60, 5};
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}} // namespace
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/* End of file. */
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// 2D (sum = 210)
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Matx<float, 4, 3> w3 {50, 0, 0, 60, 0, 1, 50, 1, 0, 50, 1, 1};
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Matx<float, 5, 3> w4 {20, 0, 1, 70, 1, 0, 30, 1, 1, 60, 2, 2, 30, 3, 3};
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// basic call with all distance types
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{
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const vector<DistanceTypes> good_types {DIST_L1, DIST_L2, DIST_C};
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for (const auto& dt : good_types)
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{
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SCOPED_TRACE(cv::format("dt=%d", dt));
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float emd = 0.f;
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Mat flow;
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// 1D
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{
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ASSERT_NO_THROW(emd = EMD(w1, w2, dt, noArray(), nullptr, flow));
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const float emd0 = calcEMD(Mat(w1), Mat(w2), flow, dt, 1);
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EXPECT_NEAR(emd0, emd, 1e-6);
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}
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// 2D
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{
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ASSERT_NO_THROW(emd = EMD(w3, w4, dt, noArray(), nullptr, flow));
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const float emd0 = calcEMD(Mat(w3), Mat(w4), flow, dt, 2);
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EXPECT_NEAR(emd0, emd, 1e-6);
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}
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}
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}
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}
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typedef testing::TestWithParam<int> Imgproc_EMD_dist;
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TEST_P(Imgproc_EMD_dist, random_flow_verify)
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{
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const int dist = GetParam();
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for (size_t iter = 0; iter < 100; ++iter)
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{
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SCOPED_TRACE(cv::format("iter=%zu", iter));
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RNG& rng = TS::ptr()->get_rng();
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const int dims = rng.uniform(1, 10);
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Mat w1(rng.uniform(1, 10), dims + 1, CV_32FC1);
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Mat w2(rng.uniform(1, 10), dims + 1, CV_32FC1);
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// weights > 0
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{
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Mat w1_weights = w1.col(0);
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Mat w2_weights = w2.col(0);
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cvtest::randUni(rng, w1_weights, 0, 100);
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cvtest::randUni(rng, w2_weights, 0, 100);
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}
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// coord
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{
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Mat w1_coord = w1.colRange(1, dims + 1);
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Mat w2_coord = w2.colRange(1, dims + 1);
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cvtest::randUni(rng, w1_coord, -10, +10);
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cvtest::randUni(rng, w2_coord, -10, +10);
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}
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float emd1 = 0.f, emd2 = 0.f;
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const float eps = 1e-5f;
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Mat flow;
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{
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ASSERT_NO_THROW(emd1 = EMD(w1, w2, dist, noArray(), nullptr, flow));
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const float emd0 = calcEMD(w1, w2, flow, dist, dims);
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EXPECT_NEAR(emd0, emd1, eps);
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}
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{
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ASSERT_NO_THROW(emd2 = EMD(w2, w1, dist, noArray(), nullptr, flow));
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const float emd0 = calcEMD(w2, w1, flow, dist, dims);
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EXPECT_NEAR(emd0, emd2, eps);
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}
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EXPECT_NEAR(emd1, emd2, eps);
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}
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}
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INSTANTIATE_TEST_CASE_P(, Imgproc_EMD_dist, testing::Values(DIST_L1, DIST_L2, DIST_C));
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TEST(Imgproc_EMD, invalid)
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{
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Matx<float, 4, 2> w1 {50, 1, 60, 2, 50, 3, 50, 4};
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Matx<float, 5, 2> w2 {30, 1, 20, 2, 70, 3, 30, 4, 60, 5};
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// empty signature
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{
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Mat empty;
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EXPECT_THROW(EMD(empty, w2, DIST_USER), cv::Exception);
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EXPECT_THROW(EMD(w1, empty, DIST_USER), cv::Exception);
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}
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// zero total weight, negative weight
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{
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Matx<float, 3, 1> wz {0, 0, 0};
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Matx<float, 3, 2> wz1 {0, 1, 0, 2, 0, 3};
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Matx<float, 3, 1> wn {0, 3, -2};
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Matx<float, 3, 2> wn1 {0, 1, 3, 2, -2, 3};
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EXPECT_THROW(EMD(wz, w2, DIST_USER), cv::Exception);
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EXPECT_THROW(EMD(wz1, w2, DIST_USER), cv::Exception);
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EXPECT_THROW(EMD(wn, w2, DIST_USER), cv::Exception);
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EXPECT_THROW(EMD(wn1, w2, DIST_USER), cv::Exception);
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}
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// user distance type, but no cost matrix provided or is wrong
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{
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Mat cost(3, 3, CV_32FC1, Scalar::all(0)), cost8u(4, 5, CV_8UC1, Scalar::all(0)), empty;
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EXPECT_THROW(EMD(w1, w2, DIST_USER, noArray()), cv::Exception);
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EXPECT_THROW(EMD(w1, w2, DIST_USER, empty), cv::Exception);
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EXPECT_THROW(EMD(w1, w2, DIST_USER, cost8u), cv::Exception);
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EXPECT_THROW(EMD(w1, w2, DIST_USER, cost), cv::Exception);
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}
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// lower_bound is set together with cost
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{
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Mat cost(4, 5, CV_32FC1, Scalar::all(0));
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float bound = 0.f;
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EXPECT_THROW(EMD(w1, w2, DIST_USER, cost, &bound), cv::Exception);
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}
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// zero dimensions with non-user distance type
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const vector<DistanceTypes> good_types {DIST_L1, DIST_L2, DIST_C};
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for (const auto& dt : good_types)
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{
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SCOPED_TRACE(cv::format("dt=%d", dt));
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Matx<float, 4, 1> w01 {20, 30, 40, 50};
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Matx<float, 5, 1> w02 {20, 30, 40, 50, 10};
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EXPECT_THROW(EMD(w01, w02, dt), cv::Exception);
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}
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// wrong distance type
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const vector<DistanceTypes> bad_types {DIST_L12, DIST_FAIR, DIST_WELSCH, DIST_HUBER};
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for (const auto& dt : bad_types)
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{
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SCOPED_TRACE(cv::format("dt=%d", dt));
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EXPECT_THROW(EMD(w1, w2, dt), cv::Exception);
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}
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}
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}} // namespace opencv_test
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@@ -5,8 +5,11 @@
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#define __OPENCV_TEST_PRECOMP_HPP__
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#include "opencv2/ts.hpp"
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#include "opencv2/ts/ts_gtest.h"
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#include "opencv2/ts/ocl_test.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/core.hpp"
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#include "opencv2/core/private.hpp"
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