void build_model() {
vector<Mat> trainDesc;
FeatureDetector *detector = new SurfFeatureDetector();
DescriptorExtractor *extractor = new SurfDescriptorExtractor();
// Generate descriptors from the image db
fs::path p = fs::system_complete(IMAGE_DATABASE);
fs::directory_iterator end_iter;
for(fs::directory_iterator dir_itr(p); dir_itr != end_iter; ++dir_itr) {
string img_name(dir_itr->path().string());
Mat img = imread(img_name, CV_LOAD_IMAGE_GRAYSCALE);
// feature extraction
vector<KeyPoint> keypoints;
detector->detect(img, keypoints);
// feature description
Mat descriptor;
extractor->compute(img, keypoints, descriptor);
trainDesc.push_back(descriptor);
imgNames.push_back(img_name);
}
// train the model
matcher->add(trainDesc);
matcher->train();
// Clean up
delete detector;
delete extractor;
}
void DetectDescribe::performDescription(const cv::Mat& image,
std::vector<cv::KeyPoint> &v, cv::Mat & descriptors,
const int _descriptorType) {
DescriptorExtractor *extractor;
LDB *ldb;
cv::SiftDescriptorExtractor extractorSift;
switch (_descriptorType) {
case 0:
ldb = new LDB();
break;
case 1:
// SIFT is created statically
break;
case 2:
extractor = new cv::SurfDescriptorExtractor();
break;
case 3:
extractor = new cv::ORB();
break;
case 4:
extractor = new cv::BriefDescriptorExtractor();
break;
case 5:
extractor = new cv::BRISK();
break;
case 6:
extractor = new cv::FREAK();
break;
default:
extractor = new cv::ORB();
}
if (_descriptorType == 0) {
cv::Mat dst;
cv::cvtColor(image, dst, CV_BGR2GRAY);
ldb->compute(dst, v, descriptors);
delete ldb;
} else if (_descriptorType == 1) {
extractorSift.compute(image, v, descriptors);
} else {
extractor->compute(image, v, descriptors);
delete extractor;
}
}
void match_img(string &query_img) {
// save the query image into local disk
// gettimeofday(&tp, NULL);
// long int timestamp = tp.tv_sec * 1000000 + tp.tv_usec;
// ostringstream sstream;
// sstream << timestamp;
// string image_path = "input-" + sstream.str() + ".jpg";
// ofstream imagefile(image_path.c_str(), ios::binary);
// imagefile.write(query_img.c_str(), query_img.size());
// imagefile.close();
cout << "image query is " << query_img << endl;
string image_path = query_img;
gettimeofday(&tv1, NULL);
// feature extraction
Mat imgInput = imread(image_path, CV_LOAD_IMAGE_GRAYSCALE);
vector<KeyPoint> features;
// gettimeofday(&tv1, NULL);
detector->detect(imgInput, features);
// gettimeofday(&tv2, NULL);
// feature description
Mat descriptors;
// gettimeofday(&tv1, NULL);
extractor->compute(imgInput, features, descriptors);
descriptors.convertTo(descriptors, CV_32F);
// gettimeofday(&tv2, NULL);
// image matching
// gettimeofday(&tv1, NULL);
string response = exec_match(descriptors, MATCHING_METHOD);
gettimeofday(&tv2, NULL);
long int runtimematching = (tv2.tv_sec - tv1.tv_sec) * 1000000 + (tv2.tv_usec - tv1.tv_usec);
cout << "The matching image is " << response << endl;
cout << "Image Matching Time: " << fixed << setprecision(2) << (double)runtimematching / 1000 << "(ms)" << endl;
}
