void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserveData) {
if(_in_src.kind() != _InputArray::STD_VECTOR_MAT && _in_src.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< std::vector<...> >).";
CV_Error(Error::StsBadArg, error_message);
}
if(_in_src.total() == 0) {
String error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(Error::StsUnsupportedFormat, error_message);
} else if(_in_labels.getMat().type() != CV_32SC1) {
String error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _in_labels.type());
CV_Error(Error::StsUnsupportedFormat, error_message);
}
// get the vector of matrices
std::vector<Mat> src;
_in_src.getMatVector(src);
// get the label matrix
Mat labels = _in_labels.getMat();
// check if data is well- aligned
if(labels.total() != src.size()) {
String error_message = format("The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.", src.size(), _labels.total());
CV_Error(Error::StsBadArg, error_message);
}
// if this model should be trained without preserving old data, delete old model data
if(!preserveData) {
_labels.release();
_histograms.clear();
}
// append labels to _labels matrix
for(size_t labelIdx = 0; labelIdx < labels.total(); labelIdx++) {
_labels.push_back(labels.at<int>((int)labelIdx));
}
// store the spatial histograms of the original data
for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) {
// calculate lbp image
Mat lbp_image = elbp(src[sampleIdx], _radius, _neighbors);
// get spatial histogram from this lbp image
Mat p = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true);
// add to templates
_histograms.push_back(p);
}
}
void LBPH::predict(InputArray _src, Ptr<PredictCollector> collector) const {
if(_histograms.empty()) {
// throw error if no data (or simply return -1?)
String error_message = "This LBPH model is not computed yet. Did you call the train method?";
CV_Error(Error::StsBadArg, error_message);
}
Mat src = _src.getMat();
// get the spatial histogram from input image
Mat lbp_image = elbp(src, _radius, _neighbors);
Mat query = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true /* normed histograms */);
// find 1-nearest neighbor
collector->init((int)_histograms.size());
for (size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
double dist = compareHist(_histograms[sampleIdx], query, HISTCMP_CHISQR_ALT);
int label = _labels.at<int>((int)sampleIdx);
if (!collector->collect(label, dist))return;
}
}
Mat lbp::spatial_histogram(const Mat& src, int numPatterns, int gridx, int gridy, int overlap) {
Mat hist;
spatial_histogram(src, hist, numPatterns, gridx, gridy);
return hist;
}
Mat lbp::spatial_histogram(const Mat& src, int numPatterns, const Size& window, int overlap) {
Mat hist;
spatial_histogram(src, hist, numPatterns, window, overlap);
return hist;
}
void lbp::spatial_histogram(const Mat& src, Mat& dst, int numPatterns, int gridx, int gridy, int overlap) {
int width = static_cast<int>(floor((float)src.cols/gridx));
int height = static_cast<int>(floor((float)src.rows / gridy));
spatial_histogram(src, dst, numPatterns, Size_<int>(width, height), overlap);
}
cv::Mat spatial_histogram(const cv::Mat& src, int numPatterns, const cv::Size& window, int overlap) {
cv::Mat hist;
spatial_histogram(src, hist, numPatterns, window, overlap);
return hist;
}
void spatial_histogram(const cv::Mat& src, cv::Mat& dst, int numPatterns, int gridx, int gridy, int overlap) {
int width = static_cast<int>(floor((double)src.cols / gridx));
int height = static_cast<int>(floor((double)src.rows / gridy));
spatial_histogram(src, dst, numPatterns, cv::Size_<int>(width, height), overlap);
}
void lbp::spatial_histogram(const Mat& src, Mat& dst, int numPatterns, int gridx, int gridy, int overlap) {
//int width = src.cols/gridx;
//int height =src.rows / gridy;
spatial_histogram(src, dst, numPatterns, Size_<int>(gridx, gridy), overlap);
}