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opencvadaptive-threshold

Get darker lines of an image using opencv

How can we remove lighter lines of an image and get only darker lines?

There is a sudoku game in the following image (taken from here). Adaptive Gaussian Thresholding gives really good result but it includes gray (lighter) lines too.

Note: I tried canny but it removes numbers too.

ee

Solution

  • I get the best result if I do a background equalization first so I could use the global threshold and then use morphological operations like dilate and erode.

    The following example runs in my iPython notebook with python 3.4 and opencv 3.1-dev on Ubuntu 15.10:

    import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

image = cv2.imread('sudokubig.jpg', 0)

if image is None:
    raise ValueError('Image not found!')

# background equalization
max_value = np.max(image)
backgroundRemoved = image.astype(float)
blur = cv2.GaussianBlur(backgroundRemoved, (151,151), 50)
backgroundRemoved = backgroundRemoved/blur
backgroundRemoved = (backgroundRemoved*max_value/np.max(backgroundRemoved)).astype(np.uint8)


fig = plt.figure(figsize=(20, 20))
plt.subplot(311),plt.imshow(image, 'gray'),plt.title('Input'),plt.axis('off')
plt.subplot(312),plt.imshow(backgroundRemoved, 'gray'),plt.title('Background Removed'),plt.axis('off')

ret, thres = cv2.threshold(backgroundRemoved,130,255,cv2.THRESH_BINARY)

# remove horizontal lines
kernel = np.ones((4, 1),np.uint8)
dilation1 = cv2.dilate(thres, kernel, iterations = 1)

# remove vertical lines
kernel = np.ones((1, 4),np.uint8)
dilation2 = cv2.dilate(dilation1, kernel, iterations = 1)

kernel = np.ones((3, 3),np.uint8)
erosion = cv2.erode(dilation2, kernel, iterations = 1)

plt.subplot(313),plt.imshow(erosion, 'gray'),plt.title('Final'),plt.axis('off')
plt.show()

kernel = np.ones((1, 4),np.uint8)
dilation = cv2.dilate(dilation, kernel, iterations = 1)

kernel = np.ones((3, 3),np.uint8)
erosion = cv2.erode(dilation, kernel, iterations = 1)

fig = plt.figure() 
plt.imshow(erosion, cmap='gray'),plt.title('missmatch')
plt.show()

    jupyter output

    Maybe you will find a smarter way or better params. I would love to see your improvements here, but I hope this short snippet helps you a little bit.