mirror of https://github.com/opencv/opencv.git
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 headerpull/25486/head
Maksim Shabunin
10 months ago
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6 changed files with 1258 additions and 77 deletions
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@ -1,93 +1,250 @@ |
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// For Open Source Computer Vision Library
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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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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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CV_EMDTest::CV_EMDTest() |
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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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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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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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void CV_EMDTest::run( int ) |
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//==============================================================================
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TEST(Imgproc_EMD, regression) |
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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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// 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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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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// basic call with cost and flow output
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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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float emd = EMD( _w1, _w2, -1, _cost ); |
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if( fabs( emd - emd0 ) > success_error_level*emd0 ) |
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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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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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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, 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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// 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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if( code < 0 ) |
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ts->set_failed_test_info( code ); |
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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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TEST(Imgproc_EMD, regression) { CV_EMDTest test; test.safe_run(); } |
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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
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/* End of file. */ |
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}} // namespace opencv_test
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