Thinning contour lines in a binary image

Question

I have a binary image with contour lines and I need to clear every contour line of all unnecessary pixels, leaving a minimally connected line.

Can someone give me a source, sample code or additional information on this issue and where to look for help?

Answer 1

If you are looking for python implementations take a look at scikit-image .

One of their examples is, in fact, your precedent.

Alternatively, if you want to stick with the “straight” scipy, you can do this using the following erosion and extensions using scipy.ndimage . (As @AxezDNyde mentions.)

Edit: Fixed links.

Answer 2

There is actually an algorithm for the thinning algorithm Zhang-Suen. You can find its code here: http://rosettacode.org/wiki/Zhang-Suen_thinning_algorithm

I also wrote a vectorized version of this in Python, which is about 10 times faster than this code. Here is the code:

 def neighbours_vec(image): return image[2:,1:-1], image[2:,2:], image[1:-1,2:], image[:-2,2:], image[:-2,1:-1], image[:-2,:-2], image[1:-1,:-2], image[2:,:-2] def transitions_vec(P2, P3, P4, P5, P6, P7, P8, P9): return ((P3-P2) > 0).astype(int) + ((P4-P3) > 0).astype(int) + \ ((P5-P4) > 0).astype(int) + ((P6-P5) > 0).astype(int) + \ ((P7-P6) > 0).astype(int) + ((P8-P7) > 0).astype(int) + \ ((P9-P8) > 0).astype(int) + ((P2-P9) > 0).astype(int) def zhangSuen_vec(image, iterations): for iter in range (1, iterations): print iter # step 1 P2,P3,P4,P5,P6,P7,P8,P9 = neighbours_vec(image) condition0 = image[1:-1,1:-1] condition4 = P4*P6*P8 condition3 = P2*P4*P6 condition2 = transitions_vec(P2, P3, P4, P5, P6, P7, P8, P9) == 1 condition1 = (2 <= P2+P3+P4+P5+P6+P7+P8+P9) * (P2+P3+P4+P5+P6+P7+P8+P9 <= 6) cond = (condition0 == 1) * (condition4 == 0) * (condition3 == 0) * (condition2 == 1) * (condition1 == 1) changing1 = numpy.where(cond == 1) image[changing1[0]+1,changing1[1]+1] = 0 # step 2 P2,P3,P4,P5,P6,P7,P8,P9 = neighbours_vec(image) condition0 = image[1:-1,1:-1] condition4 = P2*P6*P8 condition3 = P2*P4*P8 condition2 = transitions_vec(P2, P3, P4, P5, P6, P7, P8, P9) == 1 condition1 = (2 <= P2+P3+P4+P5+P6+P7+P8+P9) * (P2+P3+P4+P5+P6+P7+P8+P9 <= 6) cond = (condition0 == 1) * (condition4 == 0) * (condition3 == 0) * (condition2 == 1) * (condition1 == 1) changing2 = numpy.where(cond == 1) image[changing2[0]+1,changing2[1]+1] = 0 return image 

Answer 3

The combination of erosion and dilatation (and vice versa) in a binary image can help get rid of salt pepper like noise, leaving small lines intact. Key words: "ranking filters" and "morphological filters".

Answer 4

PyPi has a package called thinning that you can simply install with pip. It implements Guo and Hall thinning algorithm for bumpy grayscale opencv arrays / images.