void createTrainingDescriptors(vector<string> trainImages) {
//Mat img;
//string filepath;
cout << "create training descriptors" << endl;
Ptr<FeatureDetector> detector = FeatureDetector::create(detector_type);
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create(extractor_type);
if (detector_type == "SURF") {
detector->set("hessianThreshold", minHessian);
extractor->set("hessianThreshold", minHessian);
}
//Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create(extractor_type);
Mat *training_descriptors = new Mat();
/* Extracting features from training set thar contains all classes */
for (int i = 0; i < trainImages.size(); i++) {
string filepath = dataset_dir + trainImages[i];
if (!fileExists(filepath)) {
cout << "Error opening file! " << endl;
cout << "Error in: " << filepath << endl;
continue;
}
vector<KeyPoint> keypoints;
Mat img = imread(filepath, CV_LOAD_IMAGE_COLOR);
detector->detect(img, keypoints);
Mat descriptors;
extractor->compute(img, keypoints, descriptors);
training_descriptors->push_back(descriptors);
if (i % 500 == 0)
cout << "Iteration: " << i << " of " << trainImages.size() << endl;
}
cout << "Finished training descriptors" << endl;
// Creates vocabulary
cout << "Going to do vocabulary" << endl;
/* Creating the vocabulary (bag of words) */
BOWKMeansTrainer *bowtrainer = new BOWKMeansTrainer(numClusters);
bowtrainer->add(*training_descriptors);
delete training_descriptors;
vocabulary = bowtrainer->cluster();
delete bowtrainer;
FileStorage fs("vocabulary.yml", FileStorage::WRITE);
fs << "vocabulary" << vocabulary;
fs.release();
cout << "Finished vocabulary creation (1)" << endl;
}
void BOW::train(const char *imagepath, const char *imagelists) {
BOWKMeansTrainer trainer = BOWKMeansTrainer(200);
Ptr<DescriptorExtractor> desext = SIFT::create();
vector<KeyPoint> kps;
Mat desp;
Mat img;
static char fullpath[100], filename[100];
printf("Computing descriptors..\n");
FILE* fin = fopen(imagelists, "r");
int imgnum;
fscanf(fin, "%d", &imgnum);
int skipimg = 10;
double kpratio = 0.3;
for (int i = 0; i < imgnum; ++i)
{
fscanf(fin, "%s", filename);
printf("%s, %d of %d..", filename, i+1, imgnum);
if (i % skipimg)
{
printf("skipped\n");
continue;
}
sprintf(fullpath, "%s%s", imagepath, filename);
img = imread(fullpath);
// if (img.rows > 100 && img.cols > 100)
// img = Mat(img, Range(img.rows/5, img.rows*4/5), Range(img.cols/5, img.cols*4/5));
if (img.cols > 300 || img.rows > 300)
{
double fr = min(300.0/img.rows, 300.0/img.cols);
resize(img, img, Size(), fr, fr, INTER_AREA);
}
//GaussianBlur(img, img, Size(5, 5), 1.5, 1.5, BORDER_REPLICATE);
ImgBGReplace(img, img);
m_fd->detect(img, kps);
// Mat show;
// drawKeypoints(img, kps, show);
// imshow("", show);
// waitKey();
desext->compute(img, kps, desp);
static int idx[5000];
for (int j = 0; j < desp.rows; ++j) idx[j] = j;
int selectedkp = (int)(desp.rows * kpratio);
for (int j = 0; j < selectedkp; ++j)
{
int r = rand() % (desp.rows - j);
swap(idx[j], idx[r]);
trainer.add(desp.row(idx[j]));
}
printf("Complete\n");
}
fclose(fin);
printf("Complete\n");
printf("%d descriptors extracted.\n", trainer.descriptorsCount());
printf("Clustering...");
desp = trainer.cluster();
m_ext->setVocabulary(desp);
printf("Complete\n");
}
void clusterFeatures()
{
Mat dictionary = bowTrainer.cluster();
bowExtractor.setVocabulary(dictionary);
}
