Python Equivalent for bwmorph

Question

I am still coding a fingerprint image preprocessor on Python. I see in MATLAB there is a special function to remove H breaks and spurs:

bwmorph(a , 'hbreak')
bwmorph(a , 'spur')

I have searched scikit, OpenCV and others but couldn't find an equivalent for these two use of bwmorph. Can anybody point me to right direction or do i have to implement my own?

Answer 1

Edit October 2017

the skimage module now has at least 2 options: skeletonize and thin

Example with comparison

from skimage.morphology import thin, skeletonize
import numpy as np
import matplotlib.pyplot as plt

square = np.zeros((7, 7), dtype=np.uint8)
square[1:-1, 2:-2] = 1
square[0, 1] =  1
thinned = thin(square)
skel = skeletonize(square)

f, ax = plt.subplots(2, 2)
ax[0,0].imshow(square)
ax[0,0].set_title('original')
ax[0,0].get_xaxis().set_visible(False)
ax[0,1].axis('off')
ax[1,0].imshow(thinned)
ax[1,0].set_title('morphology.thin')
ax[1,1].imshow(skel)
ax[1,1].set_title('morphology.skeletonize')
plt.show()

Original post

I have found this solution by joefutrelle on github.

It seems (visually) to give similar results as the Matlab version.

Hope that helps!

Edit:

As it was pointed out in the comments, I'll extend my initial post as the mentioned link might change:

Looking for a substitute in Python for bwmorph from Matlab I stumbled upon the following code from joefutrelle on Github (at the end of this post as it's very long).

I have figured out two ways to implement this into my script (I'm a beginner and I'm sure there are better ways!):

1) copy the whole code into your script and then call the function (but this makes the script harder to read)

2) copy the code it in a new python file 'foo' and save it. Now copy it in the Python\Lib (eg. C:\Program Files\Python35\Lib) folder. In your original script you can call the function by writing:

from foo import bwmorph_thin

Then you'll feed the function with your binary image:

skeleton = bwmorph_thin(foo_image, n_iter = math.inf)

import numpy as np
from scipy import ndimage as ndi

# lookup tables for bwmorph_thin

G123_LUT = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1,
       0, 0, 0], dtype=np.bool)

G123P_LUT = np.array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0], dtype=np.bool)

def bwmorph_thin(image, n_iter=None):
    """
    Perform morphological thinning of a binary image

    Parameters
    ----------
    image : binary (M, N) ndarray
        The image to be thinned.

    n_iter : int, number of iterations, optional
        Regardless of the value of this parameter, the thinned image
        is returned immediately if an iteration produces no change.
        If this parameter is specified it thus sets an upper bound on
        the number of iterations performed.

    Returns
    -------
    out : ndarray of bools
        Thinned image.

    See also
    --------
    skeletonize

    Notes
    -----
    This algorithm [1]_ works by making multiple passes over the image,
    removing pixels matching a set of criteria designed to thin
    connected regions while preserving eight-connected components and
    2 x 2 squares [2]_. In each of the two sub-iterations the algorithm
    correlates the intermediate skeleton image with a neighborhood mask,
    then looks up each neighborhood in a lookup table indicating whether
    the central pixel should be deleted in that sub-iteration.

    References
    ----------
    .. [1] Z. Guo and R. W. Hall, "Parallel thinning with
           two-subiteration algorithms," Comm. ACM, vol. 32, no. 3,
           pp. 359-373, 1989.
    .. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning
           Methodologies-A Comprehensive Survey," IEEE Transactions on
           Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
           September 1992, p. 879

    Examples
    --------
    >>> square = np.zeros((7, 7), dtype=np.uint8)
    >>> square[1:-1, 2:-2] = 1
    >>> square[0,1] =  1
    >>> square
    array([[0, 1, 0, 0, 0, 0, 0],
           [0, 0, 1, 1, 1, 0, 0],
           [0, 0, 1, 1, 1, 0, 0],
           [0, 0, 1, 1, 1, 0, 0],
           [0, 0, 1, 1, 1, 0, 0],
           [0, 0, 1, 1, 1, 0, 0],
           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
    >>> skel = bwmorph_thin(square)
    >>> skel.astype(np.uint8)
    array([[0, 1, 0, 0, 0, 0, 0],
           [0, 0, 1, 0, 0, 0, 0],
           [0, 0, 0, 1, 0, 0, 0],
           [0, 0, 0, 1, 0, 0, 0],
           [0, 0, 0, 1, 0, 0, 0],
           [0, 0, 0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
    """
    # check parameters
    if n_iter is None:
        n = -1
    elif n_iter <= 0:
        raise ValueError('n_iter must be > 0')
    else:
        n = n_iter

    # check that we have a 2d binary image, and convert it
    # to uint8
    skel = np.array(image).astype(np.uint8)

    if skel.ndim != 2:
        raise ValueError('2D array required')
    if not np.all(np.in1d(image.flat,(0,1))):
        raise ValueError('Image contains values other than 0 and 1')

    # neighborhood mask
    mask = np.array([[ 8,  4,  2],
                     [16,  0,  1],
                     [32, 64,128]],dtype=np.uint8)

    # iterate either 1) indefinitely or 2) up to iteration limit
    while n != 0:
        before = np.sum(skel) # count points before thinning

        # for each subiteration
        for lut in [G123_LUT, G123P_LUT]:
            # correlate image with neighborhood mask
            N = ndi.correlate(skel, mask, mode='constant')
            # take deletion decision from this subiteration's LUT
            D = np.take(lut, N)
            # perform deletion
            skel[D] = 0

        after = np.sum(skel) # coint points after thinning

        if before == after:
            # iteration had no effect: finish
            break

        # count down to iteration limit (or endlessly negative)
        n -= 1

    return skel.astype(np.bool)

"""
# here's how to make the LUTs

def nabe(n):
    return np.array([n>>i&1 for i in range(0,9)]).astype(np.bool)

def hood(n):
    return np.take(nabe(n), np.array([[3, 2, 1],
                                      [4, 8, 0],
                                      [5, 6, 7]]))
def G1(n):
    s = 0
    bits = nabe(n)
    for i in (0,2,4,6):
        if not(bits[i]) and (bits[i+1] or bits[(i+2) % 8]):
            s += 1
    return s==1

g1_lut = np.array([G1(n) for n in range(256)])

def G2(n):
    n1, n2 = 0, 0
    bits = nabe(n)
    for k in (1,3,5,7):
        if bits[k] or bits[k-1]:
            n1 += 1
        if bits[k] or bits[(k+1) % 8]:
            n2 += 1
    return min(n1,n2) in [2,3]

g2_lut = np.array([G2(n) for n in range(256)])

g12_lut = g1_lut & g2_lut

def G3(n):
    bits = nabe(n)
    return not((bits[1] or bits[2] or not(bits[7])) and bits[0])

def G3p(n):
    bits = nabe(n)
    return not((bits[5] or bits[6] or not(bits[3])) and bits[4])

g3_lut = np.array([G3(n) for n in range(256)])
g3p_lut = np.array([G3p(n) for n in range(256)])

g123_lut  = g12_lut & g3_lut
g123p_lut = g12_lut & g3p_lut
"""`