int main(int argc, char *argv[])
{
int time = getTimeMill();
QCoreApplication a(argc, argv);
QString dirPath = (argc > 1) ? a.arguments()[1] :
a.applicationDirPath();
QString patternPath = (argc > 2) ? a.arguments()[2] :
QString("input.jpg");
QImage pattern(patternPath);
if (pattern.isNull()) {
pattern = QImage("input.jpg");
}
if (pattern.isNull())
cout << "No pattern image." << endl;
else {
gpattern = GImage(pattern);
GPyramid pyr(gpattern, 1.6f, 0.5f, 3);
poivec p = getDOGDetection(pyr);
p = calculateOrientations(pyr, p);
pdescs = getDescriptors(pyr, p);
processDir(dirPath);
}
time = getTimeMill() - time;
cout << "Completed in " << time << "ms." << endl;
return 0;
// return a.exec();
}
void processFile(QString &path) {
QImage img(path);
if (img.isNull())
return;
GImage gimg = GImage(img);
GPyramid pyr(gimg, 1.6f, 0.5f, 3);
auto p = getDOGDetection(pyr);
p = calculateOrientations(pyr, p);
auto descs = getDescriptors(pyr, p);
auto m = getMatchingPOIs(pdescs, descs,
numeric_limits<float>::max());
int k = getHough(m.first, m.second, gimg.width, gimg.height,
1e-3f, 1e3f, 100, 100, 22, 10).second;
if (k >= 4)
cout << path.toStdString() << endl;
}
vector<InterestPoint> InterestPointsDetector::detectHarris(float threshold) {
Kernel sobelX = Kernel::createSobelKernelX();
Convolution convX = Convolution(sobelX, type);
FImage derXStore = convX.apply(source);
Kernel sobelY = Kernel::createSobelKernelY();
Convolution convY = Convolution(sobelY, type);
FImage derYStore = convY.apply(source);
FImage minLambdasStore = calculateMinLambdasStore(derXStore, derYStore);
vector<InterestPoint> points = determinePointsByRadius(
threshold, radiusOfNeighborhood, minLambdasStore);
vector<InterestPoint> mostPowerPoint = adaptiveNonMaximumSuppression(points);
vector<InterestPoint> mostPPWithOrientation = calculateOrientations(mostPowerPoint);
return mostPPWithOrientation;
}
