void FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
const Mat &mask)
{
const int num_images = static_cast<int>(features.size());
CV_Assert(mask.empty() || (mask.type() == CV_8U && mask.cols == num_images && mask.rows));
Mat_<uchar> mask_(mask);
if (mask_.empty())
mask_ = Mat::ones(num_images, num_images, CV_8U);
vector<pair<int,int> > near_pairs;
for (int i = 0; i < num_images - 1; ++i)
for (int j = i + 1; j < num_images; ++j)
if (features[i].keypoints.size() > 0 && features[j].keypoints.size() > 0 && mask_(i, j))
near_pairs.push_back(make_pair(i, j));
pairwise_matches.resize(num_images * num_images);
MatchPairsBody body(*this, features, pairwise_matches, near_pairs);
if (is_thread_safe_)
parallel_for(BlockedRange(0, static_cast<int>(near_pairs.size())), body);
else
body(BlockedRange(0, static_cast<int>(near_pairs.size())));
LOGLN_CHAT("");
}
void BackgroundSubtractorMOG2::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
{
Mat image = _image.getMat();
bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
if( needToInitialize )
initialize(image.size(), image.type());
_fgmask.create( image.size(), CV_8U );
Mat fgmask = _fgmask.getMat();
++nframes;
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
CV_Assert(learningRate >= 0);
parallel_for(BlockedRange(0, image.rows),
MOG2Invoker(image, fgmask,
(GMM*)bgmodel.data,
(float*)(bgmodel.data + sizeof(GMM)*nmixtures*image.rows*image.cols),
bgmodelUsedModes.data, nmixtures, (float)learningRate,
(float)varThreshold,
backgroundRatio, varThresholdGen,
fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
bShadowDetection, nShadowDetection));
}
