/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" namespace opencv_test { namespace { static string getDataDir() { return TS::ptr()->get_data_path(); } static string getRubberWhaleFrame1() { return getDataDir() + "optflow/RubberWhale1.png"; } static string getRubberWhaleFrame2() { return getDataDir() + "optflow/RubberWhale2.png"; } static string getRubberWhaleGroundTruth() { return getDataDir() + "optflow/RubberWhale.flo"; } static bool isFlowCorrect(float u) { return !cvIsNaN(u) && (fabs(u) < 1e9); } static float calcRMSE(Mat flow1, Mat flow2) { float sum = 0; int counter = 0; const int rows = flow1.rows; const int cols = flow1.cols; for (int y = 0; y < rows; ++y) { for (int x = 0; x < cols; ++x) { Vec2f flow1_at_point = flow1.at(y, x); Vec2f flow2_at_point = flow2.at(y, x); float u1 = flow1_at_point[0]; float v1 = flow1_at_point[1]; float u2 = flow2_at_point[0]; float v2 = flow2_at_point[1]; if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) { sum += (u1 - u2) * (u1 - u2) + (v1 - v2) * (v1 - v2); counter++; } } } return (float)sqrt(sum / (1e-9 + counter)); } bool readRubberWhale(Mat &dst_frame_1, Mat &dst_frame_2, Mat &dst_GT) { const string frame1_path = getRubberWhaleFrame1(); const string frame2_path = getRubberWhaleFrame2(); const string gt_flow_path = getRubberWhaleGroundTruth(); dst_frame_1 = imread(frame1_path); dst_frame_2 = imread(frame2_path); dst_GT = readOpticalFlow(gt_flow_path); if (dst_frame_1.empty() || dst_frame_2.empty() || dst_GT.empty()) return false; else return true; } TEST(DenseOpticalFlow_DIS, ReferenceAccuracy) { Mat frame1, frame2, GT; ASSERT_TRUE(readRubberWhale(frame1, frame2, GT)); int presets[] = {DISOpticalFlow::PRESET_ULTRAFAST, DISOpticalFlow::PRESET_FAST, DISOpticalFlow::PRESET_MEDIUM}; float target_RMSE[] = {0.86f, 0.74f, 0.49f}; cvtColor(frame1, frame1, COLOR_BGR2GRAY); cvtColor(frame2, frame2, COLOR_BGR2GRAY); Ptr algo; // iterate over presets: for (int i = 0; i < 3; i++) { Mat flow; algo = DISOpticalFlow::create(presets[i]); algo->calc(frame1, frame2, flow); ASSERT_EQ(GT.rows, flow.rows); ASSERT_EQ(GT.cols, flow.cols); EXPECT_LE(calcRMSE(GT, flow), target_RMSE[i]); } } TEST(DenseOpticalFlow_DIS, InvalidImgSize_CoarsestLevelLessThanZero) { cv::Ptr of = cv::DISOpticalFlow::create(); const int mat_size = 10; cv::Mat x(mat_size, mat_size, CV_8UC1, 42); cv::Mat y(mat_size, mat_size, CV_8UC1, 42); cv::Mat flow; ASSERT_THROW(of->calc(x, y, flow), cv::Exception); } // make sure that autoSelectPatchSizeAndScales() works properly. TEST(DenseOpticalFlow_DIS, InvalidImgSize_CoarsestLevelLessThanFinestLevel) { cv::Ptr of = cv::DISOpticalFlow::create(); const int mat_size = 80; cv::Mat x(mat_size, mat_size, CV_8UC1, 42); cv::Mat y(mat_size, mat_size, CV_8UC1, 42); cv::Mat flow; of->calc(x, y, flow); ASSERT_EQ(flow.rows, mat_size); ASSERT_EQ(flow.cols, mat_size); } TEST(DenseOpticalFlow_VariationalRefinement, ReferenceAccuracy) { Mat frame1, frame2, GT; ASSERT_TRUE(readRubberWhale(frame1, frame2, GT)); float target_RMSE = 0.86f; cvtColor(frame1, frame1, COLOR_BGR2GRAY); cvtColor(frame2, frame2, COLOR_BGR2GRAY); Ptr var_ref; var_ref = VariationalRefinement::create(); var_ref->setAlpha(20.0f); var_ref->setDelta(5.0f); var_ref->setGamma(10.0f); var_ref->setSorIterations(25); var_ref->setFixedPointIterations(25); Mat flow(frame1.size(), CV_32FC2); flow.setTo(0.0f); var_ref->calc(frame1, frame2, flow); ASSERT_EQ(GT.rows, flow.rows); ASSERT_EQ(GT.cols, flow.cols); EXPECT_LE(calcRMSE(GT, flow), target_RMSE); } }} // namespace