OpenCV Good Features To Track with Scores

This code returns a std::vector<float> of detection scores from cv::goodFeaturesToTrack.

An interactive example is provided. The color changes between green (normalized score of 1.00) and red (normalized score of 0.00). Use +/- keys to choose the n-th greatest feature score to normalize by. Below is shown with n=1.

Background

Under the hood, cv::goodFeaturesToTrack runs the following core code:

if( useHarrisDetector )
    cornerHarris( image, eig, blockSize, gradientSize, harrisK );
else
    cornerMinEigenVal( image, eig, blockSize, gradientSize );

double maxVal = 0;
minMaxLoc( eig, 0, &maxVal, 0, 0, _mask );
threshold( eig, eig, maxVal*qualityLevel, 0, THRESH_TOZERO );
dilate( eig, tmp, Mat());

// ...

std::sort( tmpCorners.begin(), tmpCorners.end(), greaterThanPtr() );

This code first calculates the minimum eigenvalue of the gradient each block (i.e., 3x3 neighborhood of image), as required for finding a Shi-Tomasi / Harris corner. If this minimum eigenvalue is above some threshold, then the pixel where the block was centered is considered as a feature. Note that the magnitude of an eigenvalue corresponds with how good of a feature that pixel is.

However, since a desired qualityLevel was requested, the next block of code thresholds every eigenvalue response below maxVal*qualityLevel to 0, where maxVal is the greatest minimum eigenvalue from the entire image.

Since there are only so many features that can be selected (maxCorners), the next code block then sorts the features by their associated eigenvalue response.