void OrbFeatures(cv::Mat in_image)
{
cv::OrbFeatureDetector orb(500);
std::vector< cv::KeyPoint > keypoints;
orb.detect(in_image, keypoints);
cv::OrbDescriptorExtractor extractor;
cv::Mat descriptors;
cv::Mat training_descriptors(1, extractor.descriptorSize(), extractor.descriptorType());
extractor.compute(in_image, keypoints, descriptors);
training_descriptors.push_back(descriptors);
cv::BOWKMeansTrainer bow_trainer(2);
bow_trainer.add(descriptors);
cv::Mat vocabulary = bow_trainer.cluster();
}
int main(int argc, char** argv) {
string dir = "foodcamimages/TRAIN", filepath;
DIR *dp;
struct dirent *dirp;
struct stat filestat;
dp = opendir( dir.c_str() );
// detecting keypoints
SurfFeatureDetector detector(400);
//FastFeatureDetector detector(1,true);
vector<KeyPoint> keypoints;
// computing descriptors
//Ptr<DescriptorExtractor > extractor(new SurfDescriptorExtractor());// extractor;
Ptr<DescriptorExtractor > extractor(
new OpponentColorDescriptorExtractor(
Ptr<DescriptorExtractor>(new SurfDescriptorExtractor())
)
);
Mat descriptors;
Mat training_descriptors(1,extractor->descriptorSize(),extractor->descriptorType());
Mat img;
cout << "------- build vocabulary ---------\n";
cout << "extract descriptors.."<<endl;
//int count = 0;
Rect clipping_rect = Rect(0,120,640,480-120);
Mat bg_ = imread("background.png")(clipping_rect), img_fg;
while (dirp = readdir( dp ))
{
// count++;
filepath = dir + "/" + dirp->d_name;
// If the file is a directory (or is in some way invalid) we'll skip it
if (stat( filepath.c_str(), &filestat )) continue;
if (S_ISDIR( filestat.st_mode )) continue;
img = imread(filepath);
if (!img.data) {
continue;
}
img = img(clipping_rect);
img_fg = img - bg_;
detector.detect(img_fg, keypoints);
// {
// Mat out; //img_fg.copyTo(out);
// drawKeypoints(img, keypoints, out, Scalar(255));
// imshow("fg",img_fg);
// imshow("keypoints", out);
// waitKey(0);
// }
extractor->compute(img, keypoints, descriptors);
training_descriptors.push_back(descriptors);
cout << ".";
}
cout << endl;
closedir( dp );
cout << "Total descriptors: " << training_descriptors.rows << endl;
FileStorage fs("training_descriptors.yml", FileStorage::WRITE);
fs << "training_descriptors" << training_descriptors;
fs.release();
BOWKMeansTrainer bowtrainer(1000); //num clusters
bowtrainer.add(training_descriptors);
cout << "cluster BOW features" << endl;
Mat vocabulary = bowtrainer.cluster();
FileStorage fs1("vocabulary_color_1000.yml", FileStorage::WRITE);
fs1 << "vocabulary" << vocabulary;
fs1.release();
}
