void Fern::train(const cv::Mat &frame, const cv::Rect &patchRect, const bool isPositive)
{
int leaf = getLeafIndex(frame, patchRect);
if (isPositive == true)
{
leafs[leaf].increment();
}
else
{
leafs[leaf].decrement();
}
}
void Fern::train(IntegralImage *image, int patchX, int patchY, int patchW, int patchH, int patchClass) {
// Apply all tests to find the leaf index this patch falls into
int leaf = getLeafIndex(image, patchX, patchY, patchW, patchH);
// Increment the number of positive or negative patches that fell into
// this leaf
if (patchClass == 0) {
n[leaf]++;
}
else {
p[leaf]++;
}
// Compute the posterior likelihood of a positive class for this leaf
if (p[leaf] > 0) {
posteriors[leaf] = (float)p[leaf] / (float)(p[leaf] + n[leaf]);
}
}
// Classify a given patch
double Fern::classify( cv::Mat const & image,
cv::Point & patchPt,
cv::Point & patchDims)
{
return m_posteriors[getLeafIndex(image,patchPt,patchDims)];
}
double Fern::classify(const cv::Mat &frame, const cv::Rect &patchRect) const
{
return leafs.at(getLeafIndex(frame, patchRect)).load();
}
float Fern::classify(IntegralImage *image, int patchX, int patchY, int patchW, int patchH) {
// Return the precomputed posterior likelihood of a positive class for
// this leaf
return posteriors[getLeafIndex(image, patchX, patchY, patchW, patchH)];
}