void prepare(cv::Rect dst_roi)
{
using namespace cv;
dst_roi_final_ = dst_roi;
// Crop unnecessary bands
double max_len = static_cast<double>(max(dst_roi.width, dst_roi.height));
num_bands_ = min(actual_num_bands_, static_cast<int>(ceil(log(max_len) / log(2.0))));
// Add border to the final image, to ensure sizes are divided by (1 << num_bands_)
dst_roi.width += ((1 << num_bands_) - dst_roi.width % (1 << num_bands_)) % (1 << num_bands_);
dst_roi.height += ((1 << num_bands_) - dst_roi.height % (1 << num_bands_)) % (1 << num_bands_);
Blender::prepare(dst_roi);
dst_pyr_laplace_.resize(num_bands_ + 1);
dst_pyr_laplace_[0] = dst_;
dst_band_weights_.resize(num_bands_ + 1);
dst_band_weights_[0].create(dst_roi.size(), weight_type_);
dst_band_weights_[0].setTo(0);
for (int i = 1; i <= num_bands_; ++i)
{
dst_pyr_laplace_[i].create((dst_pyr_laplace_[i - 1].rows + 1) / 2,
(dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);
dst_band_weights_[i].create((dst_band_weights_[i - 1].rows + 1) / 2,
(dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);
dst_pyr_laplace_[i].setTo(Scalar::all(0));
dst_band_weights_[i].setTo(0);
}
}
//RETURNS THE TOP-LEFT CORNER OF THE REFLECTION OF sourceTemplate ON image
cv::Rect findBestMatchLocation( const cv::Mat& image, const cv::Rect& source_rect,
double* nsigma, const cv::Mat& mask )
{
cv::Mat image_gray;
cvtColor( image, image_gray, CV_RGB2GRAY, 1 );
cv::Mat image_copy = image_gray.clone();
// Create template.
cv::Mat image_template_copy = image_gray.clone();
cv::Mat sourceTemplate = image_template_copy( source_rect );
flip( sourceTemplate, sourceTemplate, 0 );
// Creates results matrix where the top left corner of the
// template is slid across each pixel of the source
int result_cols = image.cols-sourceTemplate.cols+1;
int result_rows = image.rows-sourceTemplate.rows+1;
cv::Mat result;
result.create( result_cols,result_rows, CV_32FC1 );
// Mask image to match only in selected ROI.
if( !mask.empty() )
{
cv::Mat tmp;
image_copy.copyTo( tmp, mask );
image_copy = tmp;
}
//0:CV_TM_SQDIFF
//1:CV_TM_SQDIFF_NORMED
//2:CV_TM_CORR
//3:CV_TM_CCOR_NORMED
//4:CV_TM_CCOEFF
//5:CV_TM_CCOEFF_NORMED <----Most succesful at finding reflections
int match_method = CV_TM_CCOEFF_NORMED; // 4 seemed good for stddev thresholding.
//matchTemplate( masked_scene, sourceTemplate, result, match_method );
matchTemplate( image_gray, sourceTemplate, result, match_method );
double minVal, maxVal;
cv::Point minLoc, maxLoc, matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, cv::Mat() );
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{
matchLoc = minLoc;
}
else
{
matchLoc = maxLoc;
}
cv::Scalar mean, stddev;
meanStdDev( result, mean, stddev, cv::Mat() );
*nsigma = ( maxVal-mean[0] )/ stddev[0];
// matchLoc is the location of the top left corner of the reflection
// that matchTemplate found
return cv::Rect( matchLoc, source_rect.size() );
}
//--------------------------------------------------------------------------------------
// Class: ARC_Pair
// Method: ARC_Pair :: convert_to_point
// Description: Returns the coordinate of a feature in the center of a rect bounded in
// the center by Size s.
//--------------------------------------------------------------------------------------
cv::Point
ARC_Pair::convert_to_point ( const cv::Rect& r, const cv::Mat& img, const cv::Size& s )
{
std::vector<cv::Point> lv;
cv::Rect little;
cv::Mat gray;
cv::Mat mask;
little = r;
little += 0.5*cv::Point( r.size()-s );
little -= r.size() - s;
cvtColor( img, gray, CV_BGR2GRAY );
mask = cv::Mat::zeros( img.size(), CV_8UC1 );
rectangle( mask, little, 255, CV_FILLED );
goodFeaturesToTrack( gray, lv, 1, 0.01, 10, mask, 3, 0, 0.04);
return ( lv.size()>0 ) ? lv[0] : cv::Point( -1, -1 ) ;
} // ----- end of method ARC_Pair::convert_to_point -----
MatCluster::MatCluster(const std::vector<cv::Point> &points,
const cv::Rect &boundsInWhole,
std::vector<cv::Point> deepest,
int smoothing)
: Cluster(points.size(), boundsInWhole, deepest, smoothing),
mat(boundsInWhole.size())
{
mat.setTo(0);
for (vector<Point>::const_iterator it = points.begin(), itEnd = points.end();
it != itEnd; ++it)
mat(*it - offset) = 1;
}
const ImageLandmarkDataPtr ImageTransformer::TransformData(const ImageLandmarkDataPtr& landmarkData,
const cv::Rect& activeRect,
const cv::Size& destSize,
bool shouldSaveNewImage)
{
double scaleFactor = (double)destSize.width / (double)activeRect.size().width;
// double scaleY = destSize.height / activeRect.size().height;
const cv::Point2i center(activeRect.x + activeRect.width / 2, activeRect.y + activeRect.height / 2);
cv::Mat transformMat = cv::getRotationMatrix2D(center, 0.f, scaleFactor);
const ImageLandmarkDataPtr newLandmarkData = TransformDataWithMat(landmarkData, transformMat, shouldSaveNewImage);
return newLandmarkData;
}
const LandmarkCollectionDataPtr ImageTransformer::TransformCollectionUsingFaceCrop(const LandmarkCollectionDataPtr& collectionData)
{
LandmarkCollectionDataPtr newCollection(new LandmarkCollectionData(collectionData->CollectionSize()));
CascadeClassifier classifier;
collectionData->EnumerateConstColectionWithCallback([&] (const ImageLandmarkDataPtr& landmarkData, const int index, bool* stop) {
const cv::Rect activeRect = classifier.DetectFace(landmarkData->ImageSource());
if (activeRect.size().width != 0)
{
const ImageLandmarkDataPtr newData = TransformData(landmarkData, activeRect, {kImageSize, kImageSize}, true);
newCollection->AddImageLandmarkData(newData);
}
else
{
std::cout<<"Face not found: "<<landmarkData->ImagePath()<<std::endl;
}
std::cout<<index<<std::endl;
});
return newCollection;
}
