I'm using findHomography
on a list of points and sending the result to warpPerspective
.
The problem is that sometimes the result is complete garbage and the resulting image is represented by weird gray rectangles.
How can I detect when findHomography
sends me bad results?
There are several sanity tests you can perform on the output. On top of my head:
A common mistake that leads to garbage results is incorrect ordering of the lists of input and output points, that leads the fitting routine to work using wrong correspondences. Check that your indices are correct.
Understanding the degenerate homography cases is the key. You cannot get a good homography if your points are collinear or close to collinear, for example. Also, huge gray squares may indicate extreme scaling. Both cases may arise from the fact that there are very few inliers in your final homography calculation or the mapping is wrong.
To ensure that this never happens:
1. Make sure that points are well spread in both images.
2. Make sure that there are at least 10-30 correspondences (4 is enough if noise is small).
3. Make sure that points are correctly matched and the transformation is a homography.
To find bad homographies apply found H to your original points and see the separation from your expected points that is |x2-H*x1| < Tdist
, where Tdist
is your threshold for distance error. If there are only few points that satisfy this threshold your homography may be bad and you probably violated one of the above mentioned requirements.
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