init - 初始化项目
This commit is contained in:
Lee Nony
398
modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp
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398
modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp
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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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//
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// Copyright (C) 2020 Intel Corporation
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#ifndef OPENCV_GAPI_VIDEO_PERF_TESTS_INL_HPP
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#define OPENCV_GAPI_VIDEO_PERF_TESTS_INL_HPP
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#include <iostream>
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#include "gapi_video_perf_tests.hpp"
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namespace opencv_test
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{
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using namespace perf;
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//------------------------------------------------------------------------------
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PERF_TEST_P_(BuildOptFlowPyramidPerfTest, TestPerformance)
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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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Scalar outMaxLevelSc;
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BuildOpticalFlowPyramidTestParams params;
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std::tie(params.fileName, params.winSize,
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params.maxLevel, params.withDerivatives,
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params.pyrBorder, params.derivBorder,
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params.tryReuseInputImage, params.compileArgs) = GetParam();
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BuildOpticalFlowPyramidTestOutput outOCV { outPyrOCV, outMaxLevelOCV };
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BuildOpticalFlowPyramidTestOutput outGAPI { outPyrGAPI, outMaxLevelGAPI };
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GComputation c = runOCVnGAPIBuildOptFlowPyramid(*this, params, outOCV, outGAPI);
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declare.in(in_mat1).out(outPyrGAPI);
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TEST_CYCLE()
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{
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c.apply(cv::gin(in_mat1), cv::gout(outPyrGAPI, outMaxLevelSc));
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}
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outMaxLevelGAPI = static_cast<int>(outMaxLevelSc[0]);
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// Comparison //////////////////////////////////////////////////////////////
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compareOutputPyramids(outGAPI, outOCV);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P_(OptFlowLKPerfTest, TestPerformance)
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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;
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std::tie(params.fileNamePattern, params.channels,
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params.pointsNum, params.winSize, params.criteria,
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params.compileArgs) = GetParam();
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OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV };
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OptFlowLKTestOutput outGAPI { outPtsGAPI, outStatusGAPI, outErrGAPI };
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cv::GComputation c = runOCVnGAPIOptFlowLK(*this, inPts, params, outOCV, outGAPI);
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declare.in(in_mat1, in_mat2, inPts).out(outPtsGAPI, outStatusGAPI, outErrGAPI);
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TEST_CYCLE()
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{
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c.apply(cv::gin(in_mat1, in_mat2, inPts, std::vector<cv::Point2f>{ }),
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cv::gout(outPtsGAPI, outStatusGAPI, outErrGAPI));
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}
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// Comparison //////////////////////////////////////////////////////////////
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compareOutputsOptFlow(outGAPI, outOCV);
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SANITY_CHECK_NOTHING();
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}
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//------------------------------------------------------------------------------
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PERF_TEST_P_(OptFlowLKForPyrPerfTest, TestPerformance)
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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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bool withDeriv = false;
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OptFlowLKTestParams params;
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std::tie(params.fileNamePattern, params.channels,
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params.pointsNum, params.winSize, params.criteria,
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withDeriv, params.compileArgs) = GetParam();
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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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cv::GComputation c = runOCVnGAPIOptFlowLKForPyr(*this, in, params, withDeriv, outOCV, outGAPI);
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declare.in(inPyr1, inPyr2, inPts).out(outPtsGAPI, outStatusGAPI, outErrGAPI);
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TEST_CYCLE()
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{
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c.apply(cv::gin(inPyr1, inPyr2, inPts, std::vector<cv::Point2f>{ }),
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cv::gout(outPtsGAPI, outStatusGAPI, outErrGAPI));
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}
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// Comparison //////////////////////////////////////////////////////////////
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compareOutputsOptFlow(outGAPI, outOCV);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P_(BuildPyr_CalcOptFlow_PipelinePerfTest, TestPerformance)
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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;
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params.pyrBorder = BORDER_DEFAULT;
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params.derivBorder = BORDER_DEFAULT;
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params.tryReuseInputImage = true;
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std::tie(params.fileName, params.winSize,
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params.maxLevel, params.withDerivatives,
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params.compileArgs) = GetParam();
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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<gapi::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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cv::GComputation c = runOCVnGAPIOptFlowPipeline(*this, params, outOCV, outGAPI, inPts);
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declare.in(in_mat1, in_mat2, inPts).out(outPtsGAPI, outStatusGAPI, outErrGAPI);
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TEST_CYCLE()
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{
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c.apply(cv::gin(in_mat1, in_mat2, inPts, std::vector<cv::Point2f>{ }),
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cv::gout(outPtsGAPI, outStatusGAPI, outErrGAPI));
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}
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// Comparison //////////////////////////////////////////////////////////////
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compareOutputsOptFlow(outGAPI, outOCV);
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SANITY_CHECK_NOTHING();
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}
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//------------------------------------------------------------------------------
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#ifdef HAVE_OPENCV_VIDEO
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PERF_TEST_P_(BackgroundSubtractorPerfTest, TestPerformance)
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{
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namespace gvideo = cv::gapi::video;
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initTestDataPath();
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gvideo::BackgroundSubtractorType opType;
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std::string filePath = "";
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bool detectShadows = false;
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double learningRate = -1.;
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std::size_t testNumFrames = 0;
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cv::GCompileArgs compileArgs;
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CompareMats cmpF;
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std::tie(opType, filePath, detectShadows, learningRate, testNumFrames,
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compileArgs, cmpF) = GetParam();
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const int histLength = 500;
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double thr = -1;
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switch (opType)
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{
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case gvideo::TYPE_BS_MOG2:
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{
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thr = 16.;
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break;
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}
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case gvideo::TYPE_BS_KNN:
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{
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thr = 400.;
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break;
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}
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default:
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FAIL() << "unsupported type of BackgroundSubtractor";
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}
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const gvideo::BackgroundSubtractorParams bsp(opType, histLength, thr, detectShadows,
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learningRate);
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// Retrieving frames
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std::vector<cv::Mat> frames;
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frames.reserve(testNumFrames);
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{
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cv::Mat frame;
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cv::VideoCapture cap;
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if (!cap.open(findDataFile(filePath)))
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throw SkipTestException("Video file can not be opened");
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for (std::size_t i = 0; i < testNumFrames && cap.read(frame); i++)
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{
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frames.push_back(frame);
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}
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}
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GAPI_Assert(testNumFrames == frames.size() && "Can't read required number of frames");
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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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cv::GComputation c(cv::GIn(in), cv::GOut(out));
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auto cc = c.compile(cv::descr_of(frames[0]), std::move(compileArgs));
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cv::Mat gapiForeground;
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TEST_CYCLE()
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{
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cc.prepareForNewStream();
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for (size_t i = 0; i < testNumFrames; i++)
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{
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cc(cv::gin(frames[i]), cv::gout(gapiForeground));
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}
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}
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// OpenCV Background Subtractor declaration
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cv::Ptr<cv::BackgroundSubtractor> pOCVBackSub;
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if (opType == gvideo::TYPE_BS_MOG2)
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pOCVBackSub = cv::createBackgroundSubtractorMOG2(histLength, thr, detectShadows);
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else if (opType == gvideo::TYPE_BS_KNN)
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pOCVBackSub = cv::createBackgroundSubtractorKNN(histLength, thr, detectShadows);
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cv::Mat ocvForeground;
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for (size_t i = 0; i < testNumFrames; i++)
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{
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pOCVBackSub->apply(frames[i], ocvForeground, learningRate);
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}
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// Validation
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EXPECT_TRUE(cmpF(gapiForeground, ocvForeground));
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SANITY_CHECK_NOTHING();
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}
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//------------------------------------------------------------------------------
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inline void generateInputKalman(const int mDim, const MatType2& type,
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const size_t testNumMeasurements, const bool receiveRandMeas,
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std::vector<bool>& haveMeasurements,
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std::vector<cv::Mat>& measurements)
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{
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cv::RNG& rng = cv::theRNG();
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measurements.clear();
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haveMeasurements = std::vector<bool>(testNumMeasurements, true);
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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if (receiveRandMeas)
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{
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haveMeasurements[i] = rng(2u) == 1; // returns 0 or 1 - whether we have measurement
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// at this iteration or not
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} // if not - testing the slowest case in which we have measurements at every iteration
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cv::Mat measurement = cv::Mat::zeros(mDim, 1, type);
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if (haveMeasurements[i])
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{
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cv::randu(measurement, cv::Scalar::all(-1), cv::Scalar::all(1));
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}
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measurements.push_back(measurement.clone());
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}
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}
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inline void generateInputKalman(const int mDim, const int cDim, const MatType2& type,
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const size_t testNumMeasurements, const bool receiveRandMeas,
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std::vector<bool>& haveMeasurements,
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std::vector<cv::Mat>& measurements,
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std::vector<cv::Mat>& ctrls)
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{
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generateInputKalman(mDim, type, testNumMeasurements, receiveRandMeas,
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haveMeasurements, measurements);
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ctrls.clear();
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cv::Mat ctrl(cDim, 1, type);
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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cv::randu(ctrl, cv::Scalar::all(-1), cv::Scalar::all(1));
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ctrls.push_back(ctrl.clone());
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}
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}
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PERF_TEST_P_(KalmanFilterControlPerfTest, TestPerformance)
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{
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MatType2 type = -1;
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int dDim = -1, mDim = -1;
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size_t testNumMeasurements = 0;
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bool receiveRandMeas = true;
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cv::GCompileArgs compileArgs;
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std::tie(type, dDim, mDim, testNumMeasurements, receiveRandMeas, compileArgs) = GetParam();
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const int cDim = 2;
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cv::gapi::KalmanParams kp;
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initKalmanParams(type, dDim, mDim, cDim, kp);
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// Generating input
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std::vector<bool> haveMeasurements;
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std::vector<cv::Mat> measurements, ctrls;
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generateInputKalman(mDim, cDim, type, testNumMeasurements, receiveRandMeas,
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haveMeasurements, measurements, ctrls);
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// G-API graph declaration
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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 c(cv::GIn(m, have_m, ctrl), cv::GOut(out));
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auto cc = c.compile(
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cv::descr_of(cv::gin(cv::Mat(mDim, 1, type), true, cv::Mat(cDim, 1, type))),
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std::move(compileArgs));
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cv::Mat gapiKState(dDim, 1, type);
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TEST_CYCLE()
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{
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cc.prepareForNewStream();
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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bool hvMeas = haveMeasurements[i];
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cc(cv::gin(measurements[i], hvMeas, ctrls[i]), cv::gout(gapiKState));
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}
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}
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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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cv::Mat ocvKState(dDim, 1, type);
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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ocvKState = ocvKalman.predict(ctrls[i]);
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if (haveMeasurements[i])
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ocvKState = ocvKalman.correct(measurements[i]);
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}
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// Validation
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiKState, ocvKState));
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P_(KalmanFilterNoControlPerfTest, TestPerformance)
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{
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MatType2 type = -1;
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int dDim = -1, mDim = -1;
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size_t testNumMeasurements = 0;
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bool receiveRandMeas = true;
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cv::GCompileArgs compileArgs;
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std::tie(type, dDim, mDim, testNumMeasurements, receiveRandMeas, compileArgs) = GetParam();
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const int cDim = 0;
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cv::gapi::KalmanParams kp;
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initKalmanParams(type, dDim, mDim, cDim, kp);
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// Generating input
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std::vector<bool> haveMeasurements;
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std::vector<cv::Mat> measurements;
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generateInputKalman(mDim, type, testNumMeasurements, receiveRandMeas,
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haveMeasurements, measurements);
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// G-API graph declaration
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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 c(cv::GIn(m, have_m), cv::GOut(out));
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auto cc = c.compile(cv::descr_of(cv::gin(cv::Mat(mDim, 1, type), true)),
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std::move(compileArgs));
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cv::Mat gapiKState(dDim, 1, type);
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TEST_CYCLE()
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{
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cc.prepareForNewStream();
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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bool hvMeas = haveMeasurements[i];
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cc(cv::gin(measurements[i], hvMeas), cv::gout(gapiKState));
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}
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}
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// OpenCV reference KalmanFilter declaration
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cv::KalmanFilter ocvKalman(dDim, mDim, cDim, type);
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initKalmanFilter(kp, false, ocvKalman);
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cv::Mat ocvKState(dDim, 1, type);
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for (size_t i = 0; i < testNumMeasurements; i++)
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{
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ocvKState = ocvKalman.predict();
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if (haveMeasurements[i])
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ocvKState = ocvKalman.correct(measurements[i]);
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}
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// Validation
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiKState, ocvKState));
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SANITY_CHECK_NOTHING();
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}
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#endif // HAVE_OPENCV_VIDEO
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} // opencv_test
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#endif // OPENCV_GAPI_VIDEO_PERF_TESTS_INL_HPP
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