void CascadeClassifier::detectMultiScale( const Mat& image, std::vector<Rect>& objects,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize)
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize );
}
int JoinCascador::Detect(const Mat& img, vector<Rect>& rects, vector<double>& scores, \
vector<Mat_<double> >& shapes) const {
vector<Rect> rects_;
vector<double> scores_;
vector<Mat_<double> > shapes_;
detectMultiScale(*this, img, rects_, scores_, shapes_);
const Config& c = Config::GetInstance();
//const double overlap = 0.3;
const double overlap = c.fddb_overlap;
vector<int> picked;
if (c.fddb_nms) {
picked = nms(rects_, scores_, overlap);
}
else {
const int n = rects_.size();
picked.resize(n);
for (int i = 0; i < n; i++) picked[i] = i;
}
const int n = picked.size();
rects.resize(n);
scores.resize(n);
shapes.resize(n);
// relocate the shape points
for (int i = 0; i < n; i++) {
const int index = picked[i];
const Rect& rect = rects_[index];
Mat_<double>& shape = shapes_[index];
const int landmark_n = shape.cols / 2;
for (int j = 0; j < landmark_n; j++) {
shape(0, 2 * j) = rect.x + shape(0, 2 * j)*rect.width;
shape(0, 2 * j + 1) = rect.y + shape(0, 2 * j + 1)*rect.height;
}
rects[i] = rect;
shapes[i] = shape;
scores[i] = scores_[index];
}
return n;
}
