I can now take two pictures that is exactly the same, and tell that they are similar. The comparison is currently a mess I will have to make it somewhat smarter.
Currently I have a big pile of issues.
#1. Comparing shapes in pictures instead of comparing pictures.
#2. Pictures with varying number of shapes, how do you say they are similar?
#3. Different pictures have different ‘perfect parameters’.
#1 and #2 probably partially will be solved when I added fourier descriptors to the picture. #3 will be increased the more methods I add. Right now if I take out the contour of the picture with wrong parameters I will obtain tons of noise. I will probably have to improve the method for getting a countour, and/or I will have to dynamically find the best parameters. I have an idea for how to get the best parameters.
If I take out the contour with like 10 different parameters, then I just take the one with least amount of contours, more contours generally mean more noise.
The bigger problem is the Hu Invariants. Comparison done with them is a bit interesting. The seven hu invariants is seven float number somewhere from like 0.1 to like 1^-70. The numbers vary quite a bit with the shape, and the magnitudes are so vastly different. When I compare shapes I compare them using the numbers, the magnitudes are so extremely different though, so it’s a bit difficult.