OpenCV 填充缺失的像素

如何解决OpenCV 填充缺失的像素

也许有人有想法，我们如何将白色数字上的黑色像素填充为白色，并使该图像更易于识别

我正在尝试使用内核大小 (1,1) 的 GaussianBlur，但它并没有有效帮助，有时图像上的数字会合并，这是最糟糕的结果

解决方法

您可以使用等效于 MATLAB imfill 的工具，但结果将是二进制图像。

我找到了 imfill here 的 Python 实现（它使用 Scikit-image）。

代码如下：

import cv2
import numpy as np
from skimage.morphology import reconstruction

def imfill(img):
    # https://stackoverflow.com/questions/36294025/python-equivalent-to-matlab-funciton-imfill-for-grayscale
    # 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:

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

    return fill_img

img = cv2.imread('5375.jpg',cv2.IMREAD_GRAYSCALE)  # Read image as grayscale

img_thresh = cv2.threshold(img,255,cv2.THRESH_OTSU)[1]  # Convert to B/W

fill_img = imfill(img_thresh)

cv2.imshow('img',img)
cv2.imshow('fill_img',fill_img)
cv2.waitKey()
cv2.destroyAllWindows()

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结果：

注意：
使用 cv2.findContours 和 drawContours 可能会得到相同的结果，但您应该在 findContours 上应用 img_thresh。

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如果你想要更接近原始图像的结果，你可以使用闭合形态学操作，并使用'fill_img'作为掩码：

closed_img = cv2.morphologyEx(img,cv2.MORPH_CLOSE,np.ones((35,35)))
closed_img[fill_img == 0] = 0  # Set to zero where fill_img is zero.

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结果：