Python等效于Matlab函数'imfill'用于灰度？

Question

是否有使用OpenCV或scikit-image的实现等效于Matlab的灰度图像imfill函数（即灰度孔填充）？

有关灰度（I2 = imfill（I））的imfill部分，请参阅以下示例链接matlab_imfill。或者看图像：matlab_tire_ex

这是示例中轮胎图像的链接

我一直在尝试使用scipy.ndimage.grey_closing函数复制Matlab输出，并改变大小参数，但是没有成功。

我正在使用Python 3.5。

Answer 1

这里有两个版本的泛洪填充算法：

http://arcgisandpython.blogspot.de/2012/01/python-flood-fill-algorithm.html

第一个，更简单的一个包含两个未定义的变量，但这是一个工作版本：

import numpy as np
import scipy as sp
import scipy.ndimage

def flood_fill(test_array,h_max=255):
    input_array = np.copy(test_array) 
    el = sp.ndimage.generate_binary_structure(2,2).astype(np.int)
    inside_mask = sp.ndimage.binary_erosion(~np.isnan(input_array), structure=el)
    output_array = np.copy(input_array)
    output_array[inside_mask]=h_max
    output_old_array = np.copy(input_array)
    output_old_array.fill(0)   
    el = sp.ndimage.generate_binary_structure(2,1).astype(np.int)
    while not np.array_equal(output_old_array, output_array):
        output_old_array = np.copy(output_array)
        output_array = np.maximum(input_array,sp.ndimage.grey_erosion(output_array, size=(3,3), footprint=el))
    return output_array

Answer 2

Matlab infill（）依次使用函数IM = imreconstruct（marker，mask）

Scikit-image具有类似的功能...... skimage.morphology.reconstruction(seed, mask, method='dilation', selem=None, offset=None)

该算法在Soille，P.，Morphological Image Analysis：Principles and Applications，Springer-Verlag，1999，pp.208-209中有详细描述。第6.3.7节“Fillhole”

import numpy as np
from skimage.morphology import reconstruction
import matplotlib.pyplot as plt
from skimage.io import imread, imsave


# Use the matlab reference Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp. 208-209.
#  6.3.7  Fillhole
# The holes of a binary image correspond to the set of its regional minima which
# are  not  connected  to  the image  border.  This  definition  holds  for  grey scale
# images.  Hence,  filling  the holes of a  grey scale image comes down  to remove
# all  minima  which  are  not  connected  to  the  image  border, or,  equivalently,
# impose  the  set  of minima  which  are  connected  to  the  image  border.  The
# marker image 1m  used  in  the morphological reconstruction by erosion is set
# to the maximum image value except along its border where the values of the
# original image are kept:

img = imread("tyre.jpg")

seed = np.ones_like(img)*255
img[ : ,0] = 0
img[ : ,-1] = 0
img[ 0 ,:] = 0
img[ -1 ,:] = 0
seed[ : ,0] = 0
seed[ : ,-1] = 0
seed[ 0 ,:] = 0
seed[ -1 ,:] = 0


fill = reconstruction(seed, img, method='erosion')

f, (ax0, ax1) = plt.subplots(1, 2,
    subplot_kw={'xticks': [], 'yticks': []},
    figsize=(12, 8))
ax0.imshow(img)
ax1.imshow(fill)
plt.show()

Link to tyre image and filled image