353 lines
12 KiB
C++
353 lines
12 KiB
C++
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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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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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static string getDataDir() { return TS::ptr()->get_data_path(); }
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static string getRubberWhaleFrame1() { return getDataDir() + "optflow/RubberWhale1.png"; }
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static string getRubberWhaleFrame2() { return getDataDir() + "optflow/RubberWhale2.png"; }
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static string getRubberWhaleGroundTruth() { return getDataDir() + "optflow/RubberWhale.flo"; }
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static bool isFlowCorrect(float u) { return !cvIsNaN(u) && (fabs(u) < 1e9); }
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static bool isFlowCorrect(double u) { return !cvIsNaN(u) && (fabs(u) < 1e9); }
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static float calcRMSE(Mat flow1, Mat flow2)
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{
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float sum = 0;
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int counter = 0;
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const int rows = flow1.rows;
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const int cols = flow1.cols;
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for (int y = 0; y < rows; ++y)
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{
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for (int x = 0; x < cols; ++x)
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{
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Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
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Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
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float u1 = flow1_at_point[0];
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float v1 = flow1_at_point[1];
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float u2 = flow2_at_point[0];
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float v2 = flow2_at_point[1];
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if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2))
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{
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sum += (u1 - u2) * (u1 - u2) + (v1 - v2) * (v1 - v2);
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counter++;
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}
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}
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}
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return (float)sqrt(sum / (1e-9 + counter));
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}
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static float calcRMSE(vector<Point2f> prevPts, vector<Point2f> currPts, Mat flow)
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{
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vector<float> ee;
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for (unsigned int n = 0; n < prevPts.size(); n++)
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{
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Point2f gtFlow = flow.at<Point2f>(prevPts[n]);
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if (isFlowCorrect(gtFlow.x) && isFlowCorrect(gtFlow.y))
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{
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Point2f diffFlow = (currPts[n] - prevPts[n]) - gtFlow;
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ee.push_back(sqrt(diffFlow.x * diffFlow.x + diffFlow.y * diffFlow.y));
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}
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}
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return static_cast<float>(mean(ee).val[0]);
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}
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static float calcAvgEPE(vector< pair<Point2i, Point2i> > corr, Mat flow)
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{
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double sum = 0;
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int counter = 0;
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for (size_t i = 0; i < corr.size(); ++i)
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{
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Vec2f flow1_at_point = Point2f(corr[i].second - corr[i].first);
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Vec2f flow2_at_point = flow.at<Vec2f>(corr[i].first.y, corr[i].first.x);
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double u1 = (double)flow1_at_point[0];
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double v1 = (double)flow1_at_point[1];
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double u2 = (double)flow2_at_point[0];
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double v2 = (double)flow2_at_point[1];
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if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2))
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{
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sum += sqrt((u1 - u2) * (u1 - u2) + (v1 - v2) * (v1 - v2));
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counter++;
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}
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}
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return (float)(sum / counter);
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}
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bool readRubberWhale(Mat &dst_frame_1, Mat &dst_frame_2, Mat &dst_GT)
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{
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string frame1_path = getRubberWhaleFrame1();
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string frame2_path = getRubberWhaleFrame2();
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string gt_flow_path = getRubberWhaleGroundTruth();
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// removing space may be an issue on windows machines
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frame1_path.erase(std::remove_if(frame1_path.begin(), frame1_path.end(), isspace), frame1_path.end());
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frame2_path.erase(std::remove_if(frame2_path.begin(), frame2_path.end(), isspace), frame2_path.end());
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gt_flow_path.erase(std::remove_if(gt_flow_path.begin(), gt_flow_path.end(), isspace), gt_flow_path.end());
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dst_frame_1 = imread(frame1_path);
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dst_frame_2 = imread(frame2_path);
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dst_GT = readOpticalFlow(gt_flow_path);
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if (dst_frame_1.empty() || dst_frame_2.empty() || dst_GT.empty())
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return false;
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else
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return true;
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}
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TEST(DenseOpticalFlow_SimpleFlow, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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float target_RMSE = 0.37f;
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Mat flow;
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Ptr<DenseOpticalFlow> algo;
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algo = createOptFlow_SimpleFlow();
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
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}
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TEST(DenseOpticalFlow_DeepFlow, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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float target_RMSE = 0.35f;
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cvtColor(frame1, frame1, COLOR_BGR2GRAY);
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cvtColor(frame2, frame2, COLOR_BGR2GRAY);
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Mat flow;
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Ptr<DenseOpticalFlow> algo;
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algo = createOptFlow_DeepFlow();
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
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}
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TEST(SparseOpticalFlow, ReferenceAccuracy)
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{
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// with the following test each invoker class should be tested once
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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vector<Point2f> prevPts, currPts;
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for (int r = 0; r < frame1.rows; r+=10)
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{
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for (int c = 0; c < frame1.cols; c+=10)
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{
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prevPts.push_back(Point2f(static_cast<float>(c), static_cast<float>(r)));
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}
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}
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vector<uchar> status(prevPts.size());
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vector<float> err(prevPts.size());
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Ptr<SparseRLOFOpticalFlow> algo = SparseRLOFOpticalFlow::create();
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algo->setForwardBackward(0.0f);
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Ptr<RLOFOpticalFlowParameter> param = Ptr<RLOFOpticalFlowParameter>(new RLOFOpticalFlowParameter);
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param->supportRegionType = SR_CROSS;
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param->useIlluminationModel = true;
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param->solverType = ST_BILINEAR;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.3f);
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param->solverType = ST_STANDART;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.34f);
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param->useIlluminationModel = false;
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param->solverType = ST_BILINEAR;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.27f);
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param->solverType = ST_STANDART;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.27f);
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param->normSigma0 = numeric_limits<float>::max();
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param->normSigma1 = numeric_limits<float>::max();
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param->useIlluminationModel = true;
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param->solverType = ST_BILINEAR;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.28f);
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param->solverType = ST_STANDART;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.28f);
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param->useIlluminationModel = false;
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param->solverType = ST_BILINEAR;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.80f);
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param->solverType = ST_STANDART;
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algo->setRLOFOpticalFlowParameter(param);
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algo->calc(frame1, frame2, prevPts, currPts, status, err);
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EXPECT_LE(calcRMSE(prevPts, currPts, GT), 0.28f);
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}
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TEST(DenseOpticalFlow_RLOF, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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Mat flow;
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Ptr<DenseRLOFOpticalFlow> algo = DenseRLOFOpticalFlow::create();
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Ptr<RLOFOpticalFlowParameter> param = Ptr<RLOFOpticalFlowParameter>(new RLOFOpticalFlowParameter);
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param->supportRegionType = SR_CROSS;
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param->solverType = ST_BILINEAR;
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algo->setRLOFOpticalFlowParameter(param);
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algo->setForwardBackward(1.0f);
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algo->setGridStep(cv::Size(4, 4));
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algo->setInterpolation(INTERP_EPIC);
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), 0.46f);
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algo->setInterpolation(INTERP_GEO);
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), 0.55f);
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}
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TEST(DenseOpticalFlow_SparseToDenseFlow, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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float target_RMSE = 0.52f;
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Mat flow;
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Ptr<DenseOpticalFlow> algo;
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algo = createOptFlow_SparseToDense();
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
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}
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TEST(DenseOpticalFlow_PCAFlow, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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const float target_RMSE = 0.55f;
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Mat flow;
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Ptr<DenseOpticalFlow> algo = createOptFlow_PCAFlow();
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algo->calc(frame1, frame2, flow);
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ASSERT_EQ(GT.rows, flow.rows);
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ASSERT_EQ(GT.cols, flow.cols);
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EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
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}
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TEST(DenseOpticalFlow_GlobalPatchColliderDCT, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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const Size sz = frame1.size() / 2;
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frame1 = frame1(Rect(0, 0, sz.width, sz.height));
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frame2 = frame2(Rect(0, 0, sz.width, sz.height));
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GT = GT(Rect(0, 0, sz.width, sz.height));
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vector<Mat> img1, img2, gt;
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vector< pair<Point2i, Point2i> > corr;
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img1.push_back(frame1);
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img2.push_back(frame2);
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gt.push_back(GT);
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Ptr< GPCForest<5> > forest = GPCForest<5>::create();
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forest->train(img1, img2, gt, GPCTrainingParams(8, 3, GPC_DESCRIPTOR_DCT, false));
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forest->findCorrespondences(frame1, frame2, corr);
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ASSERT_LE(7500U, corr.size());
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ASSERT_LE(calcAvgEPE(corr, GT), 0.5f);
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}
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TEST(DenseOpticalFlow_GlobalPatchColliderWHT, ReferenceAccuracy)
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{
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Mat frame1, frame2, GT;
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ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
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const Size sz = frame1.size() / 2;
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frame1 = frame1(Rect(0, 0, sz.width, sz.height));
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frame2 = frame2(Rect(0, 0, sz.width, sz.height));
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GT = GT(Rect(0, 0, sz.width, sz.height));
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vector<Mat> img1, img2, gt;
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vector< pair<Point2i, Point2i> > corr;
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img1.push_back(frame1);
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img2.push_back(frame2);
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gt.push_back(GT);
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Ptr< GPCForest<5> > forest = GPCForest<5>::create();
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forest->train(img1, img2, gt, GPCTrainingParams(8, 3, GPC_DESCRIPTOR_WHT, false));
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forest->findCorrespondences(frame1, frame2, corr);
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ASSERT_LE(7000U, corr.size());
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ASSERT_LE(calcAvgEPE(corr, GT), 0.5f);
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}
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}} // namespace
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