Merge pull request #19712 from OrestChura:oc/Kalm_ptest
[G-API]: Performance tests for KalmanFilter * Kalman perf.tests and some tests refactoring * Input generation moved to a separate function; Slowest case sneario testing added * Generating refactored * Generating refactoring * Addressing comments
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Orest Chura
GitHub
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77bfd9434b
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ad2f5ccc66
@ -29,6 +29,12 @@ class BuildPyr_CalcOptFlow_PipelinePerfTest : public TestPerfParams<tuple<std::s
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class BackgroundSubtractorPerfTest:
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public TestPerfParams<tuple<cv::gapi::video::BackgroundSubtractorType, std::string,
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bool, double, std::size_t, cv::GCompileArgs, CompareMats>> {};
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class KalmanFilterControlPerfTest :
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public TestPerfParams<tuple<MatType2, int, int, size_t, bool, cv::GCompileArgs>> {};
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class KalmanFilterNoControlPerfTest :
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public TestPerfParams<tuple<MatType2, int, int, size_t, bool, cv::GCompileArgs>> {};
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} // opencv_test
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#endif // OPENCV_GAPI_VIDEO_PERF_TESTS_HPP
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@ -241,6 +241,156 @@ PERF_TEST_P_(BackgroundSubtractorPerfTest, TestPerformance)
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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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@ -111,4 +111,22 @@ INSTANTIATE_TEST_CASE_MACRO_P(WITH_VIDEO(BackgroundSubtractorPerfTestCPU),
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Values(5),
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Values(cv::compile_args(VIDEO_CPU)),
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Values(AbsExact().to_compare_obj())));
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INSTANTIATE_TEST_CASE_MACRO_P(WITH_VIDEO(KalmanFilterControlPerfTestCPU),
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KalmanFilterControlPerfTest,
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Combine(Values(CV_32FC1, CV_64FC1),
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Values(2, 5),
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Values(2, 5),
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Values(5),
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testing::Bool(),
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Values(cv::compile_args(VIDEO_CPU))));
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INSTANTIATE_TEST_CASE_MACRO_P(WITH_VIDEO(KalmanFilterNoControlPerfTestCPU),
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KalmanFilterNoControlPerfTest,
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Combine(Values(CV_32FC1, CV_64FC1),
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Values(2, 5),
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Values(2, 5),
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Values(5),
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testing::Bool(),
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Values(cv::compile_args(VIDEO_CPU))));
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} // opencv_test
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@ -117,15 +117,15 @@ GAPI_OCV_KERNEL_ST(GCPUKalmanFilter, cv::gapi::video::GKalmanFilter, cv::KalmanF
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kfParams.controlMatrix.cols, kfParams.transitionMatrix.type());
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// initial state
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state->statePost = kfParams.state;
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state->errorCovPost = kfParams.errorCov;
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kfParams.state.copyTo(state->statePost);
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kfParams.errorCov.copyTo(state->errorCovPost);
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// dynamic system initialization
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state->controlMatrix = kfParams.controlMatrix;
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state->measurementMatrix = kfParams.measurementMatrix;
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state->transitionMatrix = kfParams.transitionMatrix;
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state->processNoiseCov = kfParams.processNoiseCov;
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state->measurementNoiseCov = kfParams.measurementNoiseCov;
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kfParams.controlMatrix.copyTo(state->controlMatrix);
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kfParams.measurementMatrix.copyTo(state->measurementMatrix);
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kfParams.transitionMatrix.copyTo(state->transitionMatrix);
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kfParams.processNoiseCov.copyTo(state->processNoiseCov);
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kfParams.measurementNoiseCov.copyTo(state->measurementNoiseCov);
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}
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static void run(const cv::Mat& measurements, bool haveMeasurement,
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@ -151,14 +151,14 @@ GAPI_OCV_KERNEL_ST(GCPUKalmanFilterNoControl, cv::gapi::video::GKalmanFilterNoCo
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state = std::make_shared<cv::KalmanFilter>(kfParams.transitionMatrix.rows, kfParams.measurementMatrix.rows,
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0, kfParams.transitionMatrix.type());
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// initial state
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state->statePost = kfParams.state;
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state->errorCovPost = kfParams.errorCov;
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kfParams.state.copyTo(state->statePost);
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kfParams.errorCov.copyTo(state->errorCovPost);
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// dynamic system initialization
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state->measurementMatrix = kfParams.measurementMatrix;
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state->transitionMatrix = kfParams.transitionMatrix;
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state->processNoiseCov = kfParams.processNoiseCov;
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state->measurementNoiseCov = kfParams.measurementNoiseCov;
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kfParams.measurementMatrix.copyTo(state->measurementMatrix);
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kfParams.transitionMatrix.copyTo(state->transitionMatrix);
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kfParams.processNoiseCov.copyTo(state->processNoiseCov);
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kfParams.measurementNoiseCov.copyTo(state->measurementNoiseCov);
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}
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static void run(const cv::Mat& measurements, bool haveMeasurement,
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@ -397,6 +397,37 @@ inline void testBackgroundSubtractorStreaming(cv::GStreamingCompiled& gapiBackSu
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EXPECT_FALSE(gapiBackSub.running());
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}
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inline void initKalmanParams(const int type, const int dDim, const int mDim, const int cDim,
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cv::gapi::KalmanParams& kp)
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{
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kp.state = Mat::zeros(dDim, 1, type);
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cv::randu(kp.state, Scalar::all(0), Scalar::all(0.1));
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kp.errorCov = Mat::eye(dDim, dDim, type);
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kp.transitionMatrix = Mat::ones(dDim, dDim, type) * 2;
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kp.processNoiseCov = Mat::eye(dDim, dDim, type) * (1e-5);
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kp.measurementMatrix = Mat::eye(mDim, dDim, type) * 2;
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kp.measurementNoiseCov = Mat::eye(mDim, mDim, type) * (1e-5);
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if (cDim > 0)
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kp.controlMatrix = Mat::eye(dDim, cDim, type) * (1e-3);
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}
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inline void initKalmanFilter(const cv::gapi::KalmanParams& kp, const bool control,
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cv::KalmanFilter& ocvKalman)
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{
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kp.state.copyTo(ocvKalman.statePost);
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kp.errorCov.copyTo(ocvKalman.errorCovPost);
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kp.transitionMatrix.copyTo(ocvKalman.transitionMatrix);
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kp.measurementMatrix.copyTo(ocvKalman.measurementMatrix);
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kp.measurementNoiseCov.copyTo(ocvKalman.measurementNoiseCov);
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kp.processNoiseCov.copyTo(ocvKalman.processNoiseCov);
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if (control)
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kp.controlMatrix.copyTo(ocvKalman.controlMatrix);
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}
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#else // !HAVE_OPENCV_VIDEO
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inline cv::GComputation runOCVnGAPIBuildOptFlowPyramid(TestFunctional&,
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@ -132,49 +132,19 @@ TEST_P(BackgroundSubtractorTest, AccuracyTest)
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testBackgroundSubtractorStreaming(gapiBackSub, pOCVBackSub, 1, 1, learningRate, testNumFrames);
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}
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inline void initKalmanParams(cv::gapi::KalmanParams& kp, int type, int dDim, int mDim, int cDim)
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{
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kp.state = Mat::zeros(dDim, 1, type);
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cv::randu(kp.state, Scalar::all(0), Scalar::all(0.1));
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kp.errorCov = Mat::eye(dDim, dDim, type);
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kp.transitionMatrix = Mat::ones(dDim, dDim, type) * 2;
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kp.processNoiseCov = Mat::eye(dDim, dDim, type)*(1e-5);
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kp.measurementMatrix = Mat::eye(mDim, dDim, type) * 2;
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kp.measurementNoiseCov = Mat::eye(mDim, mDim, type)*(1e-5);
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if (cDim > 0)
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kp.controlMatrix = Mat::eye(dDim, cDim, type)* (1e-3);
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}
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inline void initKalmanFilter(const cv::gapi::KalmanParams& kp,
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cv::KalmanFilter& ocvKalman, bool control)
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{
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kp.state.copyTo(ocvKalman.statePost);
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kp.errorCov.copyTo(ocvKalman.errorCovPost);
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kp.transitionMatrix.copyTo(ocvKalman.transitionMatrix);
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kp.measurementMatrix.copyTo(ocvKalman.measurementMatrix);
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kp.measurementNoiseCov.copyTo(ocvKalman.measurementNoiseCov);
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kp.processNoiseCov.copyTo(ocvKalman.processNoiseCov);
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if (control)
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kp.controlMatrix.copyTo(ocvKalman.controlMatrix);
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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(kp, type, dDim, mDim, cDim);
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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, ocvKalman, true);
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initKalmanFilter(kp, true, ocvKalman);
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//measurement vector
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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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// 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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@ -193,7 +163,7 @@ TEST_P(KalmanFilterTest, AccuracyTest)
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for (int i = 0; i < numIter; i++)
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{
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haveMeasure = (rng(2u) == 1) ? true : false; // returns 0 or 1 - whether we have measurement at this iteration or not
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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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@ -210,22 +180,20 @@ TEST_P(KalmanFilterTest, AccuracyTest)
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// Comparison //////////////////////////////////////////////////////////////
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{
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double diff = 0;
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vector<int> idx;
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EXPECT_TRUE(cmpEps(gapiKState, ocvKState, &diff, 1.0, &idx, false) >= 0);
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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(kp, type, dDim, mDim, 0);
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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, ocvKalman, false);
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initKalmanFilter(kp, false, ocvKalman);
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//measurement vector
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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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@ -244,7 +212,7 @@ TEST_P(KalmanFilterNoControlTest, AccuracyTest)
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for (int i = 0; i < numIter; i++)
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{
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haveMeasure = (rng(2u) == 1) ? true : false; // returns 0 or 1 - whether we have measurement at this iteration or not
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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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@ -260,9 +228,7 @@ TEST_P(KalmanFilterNoControlTest, AccuracyTest)
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// Comparison //////////////////////////////////////////////////////////////
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{
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double diff = 0;
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vector<int> idx;
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EXPECT_TRUE(cmpEps(gapiKState, ocvKState, &diff, 1.0, &idx, false) >= 0);
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EXPECT_TRUE(AbsExact().to_compare_f()(gapiKState, ocvKState));
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}
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}
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@ -270,11 +236,8 @@ 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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// For comparison
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double diff = 0;
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vector<int> idx;
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// Input mesurement
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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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@ -303,7 +266,7 @@ TEST_P(KalmanFilterCircleSampleTest, AccuracyTest)
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// OCV Kalman initialization
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cv::KalmanFilter KF(2, 1, 0);
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initKalmanFilter(kp, KF, false);
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initKalmanFilter(kp, false, KF);
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cv::randn(state, Scalar::all(0), Scalar::all(0.1));
|
||||
|
||||
@ -334,14 +297,14 @@ TEST_P(KalmanFilterCircleSampleTest, AccuracyTest)
|
||||
haveMeasure = true;
|
||||
ocvCorrState = KF.correct(measurement);
|
||||
comp.apply(cv::gin(measurement, haveMeasure), cv::gout(gapiState));
|
||||
EXPECT_TRUE(cmpEps(gapiState, ocvCorrState, &diff, 1.0, &idx, false) >= 0);
|
||||
EXPECT_TRUE(AbsExact().to_compare_f()(gapiState, ocvCorrState));
|
||||
}
|
||||
else
|
||||
{
|
||||
// Get GAPI Prediction
|
||||
haveMeasure = false;
|
||||
comp.apply(cv::gin(measurement, haveMeasure), cv::gout(gapiState));
|
||||
EXPECT_TRUE(cmpEps(gapiState, ocvPreState, &diff, 1.0, &idx, false) >= 0);
|
||||
EXPECT_TRUE(AbsExact().to_compare_f()(gapiState, ocvPreState));
|
||||
}
|
||||
|
||||
GAPI_DbgAssert(cv::norm(kp.processNoiseCov, KF.processNoiseCov, cv::NORM_INF) == 0);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user