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322 lines
11 KiB
C++
322 lines
11 KiB
C++
// 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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//
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// Copyright (C) 2020 Intel Corporation
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#ifndef OPENCV_GAPI_VIDEO_TESTS_INL_HPP
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#define OPENCV_GAPI_VIDEO_TESTS_INL_HPP
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#include "gapi_video_tests.hpp"
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#include <opencv2/gapi/streaming/cap.hpp>
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namespace opencv_test
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{
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TEST_P(BuildOptFlowPyramidTest, AccuracyTest)
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{
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std::vector<Mat> outPyrOCV, outPyrGAPI;
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int outMaxLevelOCV = 0, outMaxLevelGAPI = 0;
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BuildOpticalFlowPyramidTestParams params { fileName, winSize, maxLevel,
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withDerivatives, pyrBorder, derivBorder,
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tryReuseInputImage, getCompileArgs() };
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BuildOpticalFlowPyramidTestOutput outOCV { outPyrOCV, outMaxLevelOCV };
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BuildOpticalFlowPyramidTestOutput outGAPI { outPyrGAPI, outMaxLevelGAPI };
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runOCVnGAPIBuildOptFlowPyramid(*this, params, outOCV, outGAPI);
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compareOutputPyramids(outGAPI, outOCV);
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}
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TEST_P(OptFlowLKTest, AccuracyTest)
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{
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std::vector<cv::Point2f> outPtsOCV, outPtsGAPI, inPts;
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std::vector<uchar> outStatusOCV, outStatusGAPI;
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std::vector<float> outErrOCV, outErrGAPI;
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OptFlowLKTestParams params { fileNamePattern, channels, pointsNum,
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winSize, criteria, getCompileArgs() };
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OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV };
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OptFlowLKTestOutput outGAPI { outPtsGAPI, outStatusGAPI, outErrGAPI };
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runOCVnGAPIOptFlowLK(*this, inPts, params, outOCV, outGAPI);
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compareOutputsOptFlow(outGAPI, outOCV);
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}
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TEST_P(OptFlowLKTestForPyr, AccuracyTest)
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{
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std::vector<cv::Mat> inPyr1, inPyr2;
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std::vector<cv::Point2f> outPtsOCV, outPtsGAPI, inPts;
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std::vector<uchar> outStatusOCV, outStatusGAPI;
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std::vector<float> outErrOCV, outErrGAPI;
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OptFlowLKTestParams params { fileNamePattern, channels, pointsNum,
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winSize, criteria, getCompileArgs() };
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OptFlowLKTestInput<std::vector<cv::Mat>> in { inPyr1, inPyr2, inPts };
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OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV };
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OptFlowLKTestOutput outGAPI { outPtsGAPI, outStatusGAPI, outErrGAPI };
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runOCVnGAPIOptFlowLKForPyr(*this, in, params, withDeriv, outOCV, outGAPI);
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compareOutputsOptFlow(outGAPI, outOCV);
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}
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TEST_P(BuildPyr_CalcOptFlow_PipelineTest, AccuracyTest)
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{
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std::vector<Point2f> outPtsOCV, outPtsGAPI, inPts;
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std::vector<uchar> outStatusOCV, outStatusGAPI;
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std::vector<float> outErrOCV, outErrGAPI;
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BuildOpticalFlowPyramidTestParams params { fileNamePattern, winSize, maxLevel,
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withDerivatives, BORDER_DEFAULT, BORDER_DEFAULT,
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true, getCompileArgs() };
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auto customKernel = gapi::kernels<GCPUMinScalar>();
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auto kernels = gapi::combine(customKernel,
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params.compileArgs[0].get<GKernelPackage>());
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params.compileArgs = compile_args(kernels);
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OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV };
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OptFlowLKTestOutput outGAPI { outPtsGAPI, outStatusGAPI, outErrGAPI };
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runOCVnGAPIOptFlowPipeline(*this, params, outOCV, outGAPI, inPts);
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compareOutputsOptFlow(outGAPI, outOCV);
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}
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#ifdef HAVE_OPENCV_VIDEO
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TEST_P(BackgroundSubtractorTest, AccuracyTest)
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{
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cv::gapi::video::BackgroundSubtractorType opType;
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double thr = -1;
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std::tie(opType, thr) = typeAndThreshold;
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cv::gapi::video::BackgroundSubtractorParams bsp(opType, histLength, thr,
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detectShadows, learningRate);
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// G-API graph declaration
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cv::GMat in;
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cv::GMat out = cv::gapi::BackgroundSubtractor(in, bsp);
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// Preserving 'in' in output to have possibility to compare with OpenCV reference
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cv::GComputation c(cv::GIn(in), cv::GOut(cv::gapi::copy(in), out));
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// G-API compilation of graph for streaming mode
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auto gapiBackSub = c.compileStreaming(getCompileArgs());
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// Testing G-API Background Substractor in streaming mode
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const auto path = findDataFile(filePath);
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try
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{
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gapiBackSub.setSource(gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(path));
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}
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catch (...)
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{ throw SkipTestException("Video file can't be opened."); }
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cv::Ptr<cv::BackgroundSubtractor> pOCVBackSub;
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if (opType == cv::gapi::video::TYPE_BS_MOG2)
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pOCVBackSub = cv::createBackgroundSubtractorMOG2(histLength, thr,
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detectShadows);
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else if (opType == cv::gapi::video::TYPE_BS_KNN)
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pOCVBackSub = cv::createBackgroundSubtractorKNN(histLength, thr,
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detectShadows);
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// Allowing 1% difference of all pixels between G-API and reference OpenCV results
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testBackgroundSubtractorStreaming(gapiBackSub, pOCVBackSub, 1, 1, learningRate, testNumFrames);
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}
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TEST_P(KalmanFilterTest, AccuracyTest)
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{
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cv::gapi::KalmanParams kp;
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initKalmanParams(type, dDim, mDim, cDim, kp);
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// OpenCV reference KalmanFilter initialization
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cv::KalmanFilter ocvKalman(dDim, mDim, cDim, type);
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initKalmanFilter(kp, true, ocvKalman);
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// measurement vector
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cv::Mat measure_vec(mDim, 1, type);
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// control vector
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cv::Mat ctrl_vec = Mat::zeros(cDim > 0 ? cDim : 2, 1, type);
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// G-API Kalman's output state
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cv::Mat gapiKState(dDim, 1, type);
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// OCV Kalman's output state
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cv::Mat ocvKState(dDim, 1, type);
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// G-API graph initialization
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cv::GMat m, ctrl;
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cv::GOpaque<bool> have_m;
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cv::GMat out = cv::gapi::KalmanFilter(m, have_m, ctrl, kp);
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cv::GComputation comp(cv::GIn(m, have_m, ctrl), cv::GOut(out));
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cv::RNG& rng = cv::theRNG();
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bool haveMeasure;
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for (int i = 0; i < numIter; i++)
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{
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haveMeasure = (rng(2u) == 1); // returns 0 or 1 - whether we have measurement at this iteration or not
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if (haveMeasure)
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cv::randu(measure_vec, Scalar::all(-1), Scalar::all(1));
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if (cDim > 0)
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cv::randu(ctrl_vec, Scalar::all(-1), Scalar::all(1));
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// G-API KalmanFilter call
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comp.apply(cv::gin(measure_vec, haveMeasure, ctrl_vec), cv::gout(gapiKState));
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// OpenCV KalmanFilter call
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ocvKState = cDim > 0 ? ocvKalman.predict(ctrl_vec) : ocvKalman.predict();
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if (haveMeasure)
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ocvKState = ocvKalman.correct(measure_vec);
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}
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// Comparison //////////////////////////////////////////////////////////////
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{
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiKState, ocvKState));
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}
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}
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TEST_P(KalmanFilterNoControlTest, AccuracyTest)
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{
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cv::gapi::KalmanParams kp;
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initKalmanParams(type, dDim, mDim, 0, kp);
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// OpenCV reference KalmanFilter initialization
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cv::KalmanFilter ocvKalman(dDim, mDim, 0, type);
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initKalmanFilter(kp, false, ocvKalman);
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// measurement vector
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cv::Mat measure_vec(mDim, 1, type);
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// G-API Kalman's output state
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cv::Mat gapiKState(dDim, 1, type);
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// OCV Kalman's output state
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cv::Mat ocvKState(dDim, 1, type);
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// G-API graph initialization
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cv::GMat m;
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cv::GOpaque<bool> have_m;
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cv::GMat out = cv::gapi::KalmanFilter(m, have_m, kp);
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cv::GComputation comp(cv::GIn(m, have_m), cv::GOut(out));
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cv::RNG& rng = cv::theRNG();
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bool haveMeasure;
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for (int i = 0; i < numIter; i++)
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{
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haveMeasure = (rng(2u) == 1); // returns 0 or 1 - whether we have measurement at this iteration or not
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if (haveMeasure)
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cv::randu(measure_vec, Scalar::all(-1), Scalar::all(1));
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// G-API
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comp.apply(cv::gin(measure_vec, haveMeasure), cv::gout(gapiKState));
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// OpenCV
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ocvKState = ocvKalman.predict();
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if (haveMeasure)
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ocvKState = ocvKalman.correct(measure_vec);
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}
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// Comparison //////////////////////////////////////////////////////////////
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{
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiKState, ocvKState));
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}
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}
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TEST_P(KalmanFilterCircleSampleTest, AccuracyTest)
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{
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// auxiliary variables
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cv::Mat processNoise(2, 1, type);
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// Input measurement
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cv::Mat measurement = Mat::zeros(1, 1, type);
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// Angle and it's delta(phi, delta_phi)
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cv::Mat state(2, 1, type);
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// G-API graph initialization
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cv::gapi::KalmanParams kp;
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kp.state = Mat::zeros(2, 1, type);
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cv::randn(kp.state, Scalar::all(0), Scalar::all(0.1));
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kp.errorCov = Mat::eye(2, 2, type);
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if (type == CV_32F)
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kp.transitionMatrix = (Mat_<float>(2, 2) << 1, 1, 0, 1);
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else
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kp.transitionMatrix = (Mat_<double>(2, 2) << 1, 1, 0, 1);
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kp.processNoiseCov = Mat::eye(2, 2, type) * (1e-5);
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kp.measurementMatrix = Mat::eye(1, 2, type);
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kp.measurementNoiseCov = Mat::eye(1, 1, type) * (1e-1);
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cv::GMat m;
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cv::GOpaque<bool> have_measure;
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cv::GMat out = cv::gapi::KalmanFilter(m, have_measure, kp);
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cv::GComputation comp(cv::GIn(m, have_measure), cv::GOut(out));
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// OCV Kalman initialization
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cv::KalmanFilter KF(2, 1, 0);
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initKalmanFilter(kp, false, KF);
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cv::randn(state, Scalar::all(0), Scalar::all(0.1));
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// GAPI Corrected state
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cv::Mat gapiState(2, 1, type);
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// OCV Corrected state
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cv::Mat ocvCorrState(2, 1, type);
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// OCV Predicted state
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cv::Mat ocvPreState(2, 1, type);
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bool haveMeasure;
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for (int i = 0; i < numIter; ++i)
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{
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// Get OCV Prediction
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ocvPreState = KF.predict();
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GAPI_DbgAssert(cv::norm(kp.measurementNoiseCov, KF.measurementNoiseCov, cv::NORM_INF) == 0);
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// generation measurement
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cv::randn(measurement, Scalar::all(0), Scalar::all((type == CV_32FC1) ?
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kp.measurementNoiseCov.at<float>(0) : kp.measurementNoiseCov.at<double>(0)));
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GAPI_DbgAssert(cv::norm(kp.measurementMatrix, KF.measurementMatrix, cv::NORM_INF) == 0);
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measurement += kp.measurementMatrix*state;
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if (cv::theRNG().uniform(0, 4) != 0)
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{
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haveMeasure = true;
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ocvCorrState = KF.correct(measurement);
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comp.apply(cv::gin(measurement, haveMeasure), cv::gout(gapiState));
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiState, ocvCorrState));
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}
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else
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{
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// Get GAPI Prediction
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haveMeasure = false;
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comp.apply(cv::gin(measurement, haveMeasure), cv::gout(gapiState));
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiState, ocvPreState));
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}
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GAPI_DbgAssert(cv::norm(kp.processNoiseCov, KF.processNoiseCov, cv::NORM_INF) == 0);
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cv::randn(processNoise, Scalar(0), Scalar::all(sqrt(type == CV_32FC1 ?
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kp.processNoiseCov.at<float>(0, 0):
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kp.processNoiseCov.at<double>(0, 0))));
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GAPI_DbgAssert(cv::norm(kp.transitionMatrix, KF.transitionMatrix, cv::NORM_INF) == 0);
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state = kp.transitionMatrix*state + processNoise;
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}
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}
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#endif
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} // opencv_test
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#endif // OPENCV_GAPI_VIDEO_TESTS_INL_HPP
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