bool extractFeatures(TTrainingFileList& fileList,
vector<TrainingExample*>& trainingSet,
const CommandOptions& opts) {
Timer t("extracting features from images",opts.getVerboseFlag());
HaarFeatures haar(opts.getVerboseFlag());
EdgeDetectionFeatures sobel;
Hough hough;
IplImage *image, *smallImage;
smallImage = cvCreateImage(cvSize(64, 64), IPL_DEPTH_8U, 1);
vector<string> labels;
getAllLabelStrings(labels);
for (int i = 0; i < (int)fileList.files.size(); i++) {
// show progress
if (i % 1000 == 0) {
showProgress(i, fileList.files.size());
}
if (find(labels.begin(),labels.end(),fileList.files[i].label)!=labels.end()) {
// load the image
image = cvLoadImage(fileList.files[i].filename.c_str(), 0);
if (image == NULL) {
cerr << "ERROR: could not load image "
<< fileList.files[i].filename.c_str() << endl;
continue;
}
// resize to 64 x 64
cvResize(image, smallImage);
assert(smallImage->nChannels==1); // assert its grayscale
// CS221 TO DO: extract features from image here
vector<double> values;
coreExtractFeatures(smallImage, values, opts);
trainingSet.push_back(new ImagesExample(values,
getLabel(fileList.files[i].label)));
// free memory
cvReleaseImage(&image);
}
}
// free memory
cvReleaseImage(&smallImage);
return fileList.files.size()==trainingSet.size();
}
void coreExtractFeatures(IplImage* image,
vector<double>& values,
const CommandOptions& opts) {
HaarFeatures haar(opts.getVerboseFlag());
haar.getFeatureValues(values,image);
if(opts.getBoolOption("circle_feature")) {
Hough hough;
hough.getCircles(values,image);
}
if(opts.getBoolOption("edge_feature")) {
Hough hough;
hough.getEdges(values,image);
}
if(opts.getBoolOption("corner_feature")) {
Hough hough;
hough.getCorners(values,image);
}
if(opts.getBoolOption("sobel_feature")) {
EdgeDetectionFeatures sobel;
sobel.getFeatureValues(values,image);
}
}