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
我尝试使用内核大小 (1,1) 的高斯模糊，但它没有有效帮助，有时图像上的数字会合并，这是最糟糕的结果
最佳答案
您可以使用 MATLAB imfill 的等效项，但结果将是二值图像。
我找到了 imfill 的 Python 实现 here (它使用 Scikit-image)。
这是代码:
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, 0, 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()
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 img_thresh 上应用 findContours。
如果您想要更接近原始图像的结果，您可以使用闭合形态学操作，并使用“fill_img”作为掩模:
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.
结果:
关于OpenCV 填充缺失像素，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68050682/