void loadtemplate(std::string name) {
object = imread( name, CV_LOAD_IMAGE_GRAYSCALE );
if(object.empty()) {
loglne("Can not load template image, exit!");
exit(-1);
}
buildPyramid(object, objpyr, ldetector.nOctaves-1);
string model_filename = name + "_model.xml.gz";
loglni("[loadtemplate] try to load "<<model_filename<<"...");
FileStorage fs(model_filename, FileStorage::READ);
if( fs.isOpened() ) {
detector.read(fs.getFirstTopLevelNode());
loglni("[loadtemplate] successfully loaded "<<model_filename.c_str());
} else {
loglni("[loadtemplate] try to train the model...");
ldetector.setVerbose(true);
ldetector.getMostStable2D(object, objKeypoints, 100, gen);
detector.setVerbose(true);
detector.train(objpyr, objKeypoints, patchSize.width,
100, 11, 10000, ldetector, gen);
loglni("[loadtemplate] training DONE! saving...");
if( fs.open(model_filename, FileStorage::WRITE) )
detector.write(fs, "ferns_model");
}
fs.release();
cpts.push_back(Point2f(0,0));
cpts.push_back(Point2f(object.cols,0));
cpts.push_back(Point2f(object.cols,object.rows));
cpts.push_back(Point2f(0,object.rows));
}
int main(int argc, char** argv)
{
int i;
const char* object_filename = argc > 1 ? argv[1] : "box.png";
const char* scene_filename = argc > 2 ? argv[2] : "box_in_scene.png";
help();
Mat object = imread( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
Mat scene = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
if( !object.data || !scene.data )
{
fprintf( stderr, "Can not load %s and/or %s\n",
object_filename, scene_filename );
exit(-1);
}
double imgscale = 1;
Mat image;
resize(scene, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
cvNamedWindow("Object", 1);
cvNamedWindow("Image", 1);
cvNamedWindow("Object Correspondence", 1);
Size patchSize(32, 32);
LDetector ldetector(7, 20, 2, 2000, patchSize.width, 2);
ldetector.setVerbose(true);
PlanarObjectDetector detector;
vector<Mat> objpyr, imgpyr;
int blurKSize = 3;
double sigma = 0;
GaussianBlur(object, object, Size(blurKSize, blurKSize), sigma, sigma);
GaussianBlur(image, image, Size(blurKSize, blurKSize), sigma, sigma);
buildPyramid(object, objpyr, ldetector.nOctaves-1);
buildPyramid(image, imgpyr, ldetector.nOctaves-1);
vector<KeyPoint> objKeypoints, imgKeypoints;
PatchGenerator gen(0,256,5,true,0.8,1.2,-CV_PI/2,CV_PI/2,-CV_PI/2,CV_PI/2);
string model_filename = format("%s_model.xml.gz", object_filename);
printf("Trying to load %s ...\n", model_filename.c_str());
FileStorage fs(model_filename, FileStorage::READ);
if( fs.isOpened() )
{
detector.read(fs.getFirstTopLevelNode());
printf("Successfully loaded %s.\n", model_filename.c_str());
}
else
{
printf("The file not found and can not be read. Let's train the model.\n");
printf("Step 1. Finding the robust keypoints ...\n");
ldetector.setVerbose(true);
ldetector.getMostStable2D(object, objKeypoints, 100, gen);
printf("Done.\nStep 2. Training ferns-based planar object detector ...\n");
detector.setVerbose(true);
detector.train(objpyr, objKeypoints, patchSize.width, 100, 11, 10000, ldetector, gen);
printf("Done.\nStep 3. Saving the model to %s ...\n", model_filename.c_str());
if( fs.open(model_filename, FileStorage::WRITE) )
detector.write(fs, "ferns_model");
}
printf("Now find the keypoints in the image, try recognize them and compute the homography matrix\n");
fs.release();
vector<Point2f> dst_corners;
Mat correspond( object.rows + image.rows, std::max(object.cols, image.cols), CV_8UC3);
correspond = Scalar(0.);
Mat part(correspond, Rect(0, 0, object.cols, object.rows));
cvtColor(object, part, CV_GRAY2BGR);
part = Mat(correspond, Rect(0, object.rows, image.cols, image.rows));
cvtColor(image, part, CV_GRAY2BGR);
vector<int> pairs;
Mat H;
double t = (double)getTickCount();
objKeypoints = detector.getModelPoints();
ldetector(imgpyr, imgKeypoints, 300);
std::cout << "Object keypoints: " << objKeypoints.size() << "\n";
std::cout << "Image keypoints: " << imgKeypoints.size() << "\n";
bool found = detector(imgpyr, imgKeypoints, H, dst_corners, &pairs);
t = (double)getTickCount() - t;
printf("%gms\n", t*1000/getTickFrequency());
if( found )
{
for( i = 0; i < 4; i++ )
{
Point r1 = dst_corners[i%4];
Point r2 = dst_corners[(i+1)%4];
line( correspond, Point(r1.x, r1.y+object.rows),
Point(r2.x, r2.y+object.rows), Scalar(0,0,255) );
}
}
for( i = 0; i < (int)pairs.size(); i += 2 )
{
line( correspond, objKeypoints[pairs[i]].pt,
imgKeypoints[pairs[i+1]].pt + Point2f(0,(float)object.rows),
Scalar(0,255,0) );
}
imshow( "Object Correspondence", correspond );
Mat objectColor;
cvtColor(object, objectColor, CV_GRAY2BGR);
for( i = 0; i < (int)objKeypoints.size(); i++ )
{
circle( objectColor, objKeypoints[i].pt, 2, Scalar(0,0,255), -1 );
circle( objectColor, objKeypoints[i].pt, (1 << objKeypoints[i].octave)*15, Scalar(0,255,0), 1 );
}
Mat imageColor;
cvtColor(image, imageColor, CV_GRAY2BGR);
for( i = 0; i < (int)imgKeypoints.size(); i++ )
{
circle( imageColor, imgKeypoints[i].pt, 2, Scalar(0,0,255), -1 );
circle( imageColor, imgKeypoints[i].pt, (1 << imgKeypoints[i].octave)*15, Scalar(0,255,0), 1 );
}
imwrite("correspond.png", correspond );
imshow( "Object", objectColor );
imshow( "Image", imageColor );
waitKey(0);
return 0;
}