Performance improvements for copy-move detection

[AlexRiina]

I was looking through the code to understand what the individual parameters meant and saw a few things that looked like they could be tweaked for faster performance. I'm sure there are ways of doing the clustering in an even better way, but here's a quick attempt. On my sample image, the entire if self.clusters is None loop started at 84s and after this change takes 13s.

The first change I made was calculating all of the distances between matching keypoints. The previous loop structure meant that something like len(matches)^2 distances were calculated out for the original matches.

I also pre-filtered down the list of matches to only the ones where the distance between the point was above min_dist instead of handling that in both the i and j loops.

The final change at master...AlexRiina:master#diff-e06009cb5a3aa444ce736838cd58dd9cR237 prevents cases where a0 and a1 are really close and a0 and b1 are really close but a1 and b1 are not really close. I think goal of that loop is to pair a0 with either a1 or b1 and b0 with the other and the highlighted case is when b0 is not paired.

[GuidoBartoli]

Very nice, I merged into master and cleaned up the code, many thanks for investigating it! 👌