bwmorph 的 Python 等效项

我仍在用 Python 编写指纹图像预处理器。我在 MATLAB 中看到有一个特殊的函数可以消除 H 中断和杂散：

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

我搜索了 scikit、OpenCV 等，但找不到这两种用法的等效项bwmorph。有人可以指出我正确的方向还是我必须实施自己的方向？

2017年10月编辑

skimage 模块现在至少有 2 个选项：骨架化 and thin

比较示例

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()

原帖

我在 joefutrelle 上找到了这个解决方案github.

它（视觉上）似乎给出了与 Matlab 版本类似的结果。

希望有帮助！

Edit:

正如评论中指出的那样，我将扩展我的最初帖子，因为提到的链接可能会发生变化：

在 Matlab 中寻找 Python 中 bwmorph 的替代品时，我偶然发现了以下代码乔·富特雷尔在 Github 上（在这篇文章的末尾，因为它很长）。

我已经找到了两种方法来将其实现到我的脚本中（我是初学者，我确信有更好的方法！）：

1）将整个代码复制到脚本中，然后调用该函数（但这会使脚本更难阅读）

2）将代码复制到新的Python文件“foo”中并保存。现在将其复制到 Python\Lib（例如 C:\Program Files\Python35\Lib）文件夹中。在原始脚本中，您可以通过编写以下内容来调用该函数：

from foo import bwmorph_thin

然后，您将向该函数提供二进制图像：

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
"""`