adaptive_threshold.py

# Python program to illustrate  
# adaptive thresholding type on an image 
       
# organizing imports  
import os

import cv2  
import numpy as np  

FILE_DIR = os.path.dirname(__file__)

# path to input image is specified and   
# image is loaded with imread command  
image1 = cv2.imread(os.path.join(FILE_DIR, 'input_book_image.jpg'))
   
# cv2.cvtColor is applied over the 
# image input with applied parameters 
# to convert the image in grayscale  
img = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) 
   
# applying different thresholding  
# techniques on the input image 
thresh1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, 
                                          cv2.THRESH_BINARY, 199, 5) 
  
thresh2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                          cv2.THRESH_BINARY, 199, 5) 
  
# the window showing output images 
# with the corresponding thresholding  
# techniques applied to the input image 
cv2.imshow('Adaptive Mean', thresh1) 
cv2.imshow('Adaptive Gaussian', thresh2) 
  
     
# De-allocate any associated memory usage   
if cv2.waitKey(0) & 0xff == 27:  
    cv2.destroyAllWindows()