cascade.cpp
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#include <openbr/plugins/openbr_internal.h>
#include <openbr/core/cascade.h>
using namespace cv;
namespace br
{
class _CascadeClassifier : public Classifier
{
Q_OBJECT
Q_PROPERTY(br::Classifier *stage READ get_stage WRITE set_stage RESET reset_stage STORED false)
Q_PROPERTY(int numStages READ get_numStages WRITE set_numStages RESET reset_numStages STORED false)
Q_PROPERTY(int numNegs READ get_numNegs WRITE set_numNegs RESET reset_numNegs STORED false)
Q_PROPERTY(float maxFAR READ get_maxFAR WRITE set_maxFAR RESET reset_maxFAR STORED false)
BR_PROPERTY(br::Classifier *, stage, NULL)
BR_PROPERTY(int, numStages, 20)
BR_PROPERTY(int, numNegs, 1000)
BR_PROPERTY(float, maxFAR, pow(0.5, numStages))
QList<Classifier *> stages;
void train(const QList<Mat> &images, const QList<float> &labels)
{
QList<Mat> posImages, negImages;
for (int i = 0; i < images.size(); i++)
labels[i] == 1 ? posImages.append(images[i]) : negImages.append(images[i]);
QList<Mat> trainingImages;
QList<float> trainingLabels;
for (int i = 0; i < numStages; i++) {
float currFAR = updateTrainingSet(posImages, negImages, trainingImages, trainingLabels);
if (currFAR < maxFAR) {
qDebug() << "FAR is below required level! Terminating early";
return;
}
Classifier *next_stage = stage->clone();
next_stage->train(trainingImages, trainingLabels);
stages.append(next_stage);
}
}
float classify(const Mat &image) const
{
(void) image;
return 0.;
}
float updateTrainingSet(const QList<Mat> &posImages, const QList<Mat> &negImages, QList<Mat> &trainingImages, QList<float> &trainingLabels)
{
trainingImages.clear();
trainingLabels.clear();
foreach (const Mat &pos, posImages) {
if (classify(pos) > 0) {
trainingImages.append(pos);
trainingLabels.append(1.);
}
}
NegFinder finder(negImages, Size(24, 24));
int totalNegs = 0, passedNegs = 0;
while (true) {
totalNegs++;
Mat neg = finder.get();
if (classify(neg) > 0) {
trainingImages.append(neg);
trainingLabels.append(0.);
passedNegs++;
}
if (passedNegs >= numNegs)
return passedNegs / (float)totalNegs;
}
}
private:
struct NegFinder
{
NegFinder(const QList<Mat> &_negs, Size _winSize)
{
negs = _negs;
winSize = _winSize;
negIdx = round = 0;
img.create( 0, 0, CV_8UC1 );
point = offset = Point( 0, 0 );
scale = 1.0F;
scaleFactor = 1.4142135623730950488016887242097F;
stepFactor = 0.5F;
}
void _next()
{
src = negs[negIdx++];
round += negIdx / negs.size();
round %= (winSize.width * winSize.height);
negIdx %= negs.size();
point = offset = Point(std::min(round % winSize.width, src.cols - winSize.width),
std::min(round / winSize.width, src.rows - winSize.height));
scale = max(((float)winSize.width + point.x) / ((float)src.cols),
((float)winSize.height + point.y) / ((float)src.rows));
Size sz((int)(scale*src.cols + 0.5F), (int)(scale*src.rows + 0.5F));
resize(src, img, sz);
}
Mat get()
{
if (img.empty())
_next();
Mat neg(winSize, CV_8UC1);
Mat m(winSize.height, winSize.width, CV_8UC1, (void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step);
m.copyTo(neg);
if ((int)(point.x + (1.0F + stepFactor ) * winSize.width) < img.cols)
point.x += (int)(stepFactor * winSize.width);
else {
point.x = offset.x;
if ((int)( point.y + (1.0F + stepFactor ) * winSize.height ) < img.rows)
point.y += (int)(stepFactor * winSize.height);
else {
point.y = offset.y;
scale *= scaleFactor;
if( scale <= 1.0F )
resize(src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows)));
else
_next();
}
}
return neg;
}
QList<Mat> negs;
int negIdx, round;
Mat src, img;
float scale;
float scaleFactor;
float stepFactor;
Size winSize;
Point offset, point;
};
};
BR_REGISTER(Classifier, _CascadeClassifier)
} // namespace br
#include "classification/cascade.moc"