opencv/modules/gapi/test/common/gapi_video_tests_inl.hpp
Maxim Pashchenkov 2f6d2b08aa
Merge pull request #20995 from mpashchenkov:mp/ocv-gapi-tdp-skip
G-API: Removing G-API test code that is a reflection of ts module

* gapi: don't hijack testing infrastructure

* Removed initDataPath functionality (ts module exists)

* Removed false for ocv_extra data from findDataFile

Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
2021-11-16 18:27:42 +00:00

322 lines
11 KiB
C++

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