DynamicThreshold.py

import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import skimage.filter as skif
from PIL import Image
import numpy as np
from matplotlib import pylab as plt

#open image and convert fromRGB to mono_color
img =Image.open('sudoku.jpg')
img=img.convert('L')
img=np.array(img)
# Defining minimum neighboring size of objects
block_size = 111

# Adaptive threshold function which returns image
# map of structures that are different relative to
# background
adaptive_cut = skif.threshold_adaptive(img, block_size, offset=0)

# Global threshold('normal Binarization)
global_thresh = skif.threshold_otsu(img)
global_cut = img > global_thresh

# Creating figure to highlight difference between
# adaptive and global threshold methods
fig = plt.figure(figsize=(8, 4))
fig.subplots_adjust(hspace=0.05, wspace=0.05)

ax1 = fig.add_subplot(131)
ax1.imshow(img,cmap='gray')
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)

ax1 = fig.add_subplot(132)
ax1.imshow(global_cut,cmap='gray')
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)

ax1 = fig.add_subplot(133)
ax1.imshow(adaptive_cut,cmap='gray')
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)


plt.show()