I am trying the quite new descriptor FREAK from the latest version of OpenCV following the freak_demo.cpp example. Instead of using SURF I use FAST. My basic code is something like this:
std::vector<KeyPoint> keypointsA, keypointsB;
Mat descriptorsA, descriptorsB;
std::vector<DMatch> matches;
FREAK extractor;
BruteForceMatcher<Hamming> matcher;
FAST(imgA,keypointsA,100);
FAST(imgB,keypointsB,20);
extractor.compute( imgA, keypointsA, descriptorsA );
extractor.compute( imgB, keypointsB, descriptorsB );
matcher.match(descriptorsA, descriptorsB, matches);
When doing matching there are always some refinement steps for getting rid out of outliers.
What I usually do is discarding matches that have a distance over a threshold, for example:
for (int i = 0; i < matches.size(); i++ )
{
if(matches[i].distance > 200)
{
matches.erase(matches.begin()+i-1);
}
}
Then, I use RANSAC to see which matches fit the homography model. OpenCV has a function for this:
for( int i = 0; i < matches.size(); i++ )
{
trainMatches.push_back( cv::Point2f(keypointsB[ matches[i].trainIdx ].pt.x/500.0f, keypointsB[ matches[i].trainIdx ].pt.y/500.0f) );
queryMatches.push_back( cv::Point2f(keypointsA[ matches[i].queryIdx ].pt.x/500.0f, keypointsA[ matches[i].queryIdx ].pt.y/500.0f) );
}
Mat h = cv::findHomography(trainMatches,queryMatches,CV_RANSAC,0.005, status);
And I just draw the inliers:
for(size_t i = 0; i < queryMatches.size(); i++)
{
if(status.at<char>(i) != 0)
{
inliers.push_back(matches[i]);
}
}
Mat imgMatch;
drawMatches(imgA, keypointsA, imgB, keypointsB, inliers, imgMatch);
Just try different thresholds and distances until you get the desired resutls.
You can also train the descriptor by giving your own selected pairs. And tune the parameters in the constructor.
explicit FREAK( bool orientationNormalized = true
, bool scaleNormalized = true
, float patternScale = 22.0f
, int nbOctave = 4
, const vector<int>& selectedPairs = vector<int>()
);
BTW, a more efficient version of FREAK is on the way :-)
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