TEST_F(QueryTests, test_get_current_results) {
auto hQR = getSerializedHistoricalQueryResultsJSON();
auto query = getOsqueryScheduledQuery();
auto put_status = db->Put(kQueries, query.name, hQR.first);
EXPECT_TRUE(put_status.ok());
EXPECT_EQ(put_status.toString(), "OK");
auto cf = Query(query);
QueryData qd;
auto query_status = cf.getCurrentResults(qd, db);
EXPECT_TRUE(query_status.ok());
EXPECT_EQ(query_status.toString(), "OK");
EXPECT_EQ(qd, hQR.second.mostRecentResults.second);
}
TEST_F(QueryTests, test_add_and_get_current_results) {
auto query = getOsqueryScheduledQuery();
auto cf = Query(query);
auto s = cf.addNewResults(getTestDBExpectedResults(), std::time(0), db);
EXPECT_TRUE(s.ok());
EXPECT_EQ(s.toString(), "OK");
for (auto result : getTestDBResultStream()) {
DiffResults dr;
HistoricalQueryResults hQR;
auto hqr_status = cf.getHistoricalQueryResults(hQR, db);
EXPECT_TRUE(hqr_status.ok());
EXPECT_EQ(hqr_status.toString(), "OK");
auto s = cf.addNewResults(result.second, dr, true, std::time(0), db);
EXPECT_TRUE(s.ok());
DiffResults expected = diff(hQR.mostRecentResults.second, result.second);
EXPECT_EQ(dr, expected);
QueryData qd;
cf.getCurrentResults(qd, db);
EXPECT_EQ(qd, result.second);
}
}
//Function executed at each frame
bool segmentationEvaluationModule::run() {
if(firstTime)
{
firstTime = false;
QByteArray string8bit = this->RoiFileName.toLocal8Bit();
#ifdef __OPENCV3__
this->RoiPixelLevel = cv::imread(string8bit.data(), cv::IMREAD_GRAYSCALE );
#else
this->RoiPixelLevel = cv::imread(string8bit.data(), CV_LOAD_IMAGE_GRAYSCALE );
#endif
}
if(m_data->postedBestParameters) { //If the best parameters are posted, the job is done
if(m_setROI) {
QImage roi, scaledImg;
ImageTransforms::ImageROI(*m_data->fgImage, roi, m_dx, m_dy, m_w, m_h);
scaledImg = roi.scaledToWidth(m_wscale, Qt::SmoothTransformation);
QImage im = ImageTransforms::toGrayscale(scaledImg);
ImageTransforms::binarize(im);
getCurrentResults(&im);
} else
getCurrentResults(m_data->fgImage);
return true;
}
if(m_data->cycleActivated && m_data->firstOfCycle) { //Store previous data and init cycle and
NN++;
if(!first) { //Store previous data
writeToLog();
m_data->bestParametersSet = bestParameters;
}
previousParameters = m_data->tunedParametersSet;
if(m_data->tuningEnded) {
pauseApp();
m_data->postedBestParameters = true;
}
first = false;
mean_blob = N = mTP = mTN = mFP = mFN = sdTP = sdTN = sdFP = sdFN = 0;
accTP = accTN = accFP = accFN = 0;
}
if( m_data->fgImage == NULL || m_data->fgImage->isNull()
|| m_data->groundtruthImage == NULL || m_data->groundtruthImage->isNull())
return true;
if(m_setROI) {
QImage roi, scaledImg;
ImageTransforms::ImageROI(*m_data->fgImage, roi, m_dx, m_dy, m_w, m_h);
scaledImg = roi.scaledToWidth(m_wscale, Qt::SmoothTransformation);
QImage im = ImageTransforms::toGrayscale(scaledImg);
ImageTransforms::binarize(im);
getCurrentResults(&im);
} else
getCurrentResults(m_data->fgImage);
//Store stats
accTP += TP; accTN += TN; accFP += FP; accFN += FN;
N++; //number of frames in the cycle
mean_blob = MathFunctions::incrementalMean(mean_blob, m_data->blobs.size(), N);
mTP = MathFunctions::incrementalMean(mTP, TP, N);
sdTP = MathFunctions::incrementalSigma(mTP, sdTP, TP, N);
mTN = MathFunctions::incrementalMean(mTN, TN, N);
sdTN = MathFunctions::incrementalSigma(mTN, sdTN, TN, N);
mFP = MathFunctions::incrementalMean(mFP, FP, N);
sdFP = MathFunctions::incrementalSigma(mFP, sdFP, FP, N);
mFN = MathFunctions::incrementalMean(mFN, FN, N);
sdFN = MathFunctions::incrementalSigma(mFN, sdFN, FN, N);
return true;
}
osquery::Status Query::getCurrentResults(osquery::QueryData& qd) {
return getCurrentResults(qd, DBHandle::getInstance());
}
