Question

我仍然在Python上编写指纹图像预处理器。我在MATLAB中看到有一个特殊功能可以删除H break和spurs：

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

我搜索了scikit，OpenCV和其他人，但找不到这两种bwmorph使用的等价物。任何人都能指出我正确的方向还是我必须实现自己的方向？

Answer 1

2017年10月编辑

skimage模块现在至少有两个选项： skeletonize和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github找到了joefutrelle的解决方案。

看起来（视觉上）给出与Matlab版本类似的结果。

希望有所帮助！

修改

正如评论中指出的那样，由于上述链接可能会发生变化，我会延长我的初始职位：

在Python中寻找来自Matlab的bwmorph的替代品我偶然发现了Github上 joefutrelle 的以下代码（在这篇文章的末尾，因为它很长）。

我已经想出了两种方法将它实现到我的脚本中（我是初学者，我确信有更好的方法！）：

1）将整个代码复制到您的脚本中，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

然后你将用二进制图像提供函数：

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 """`

Answer 2

你必须自己实现这些，因为据我所知，它们不存在于OpenCV或skimage中。但是，检查MATLAB的工作原理应该是直截了当的，并在Python / NumPy中编写自己的版本。

这是一本专门为MATLAB用户详细描述NumPy函数的指南，提供了MATLAB和NumPy中等效函数的提示： http://wiki.scipy.org/NumPy_for_Matlab_Users

Python相当于bwmorph

2 个答案: