Thresholding, Binarization & Adaptive Thresholding

In thresholding, we convert a grey scale image to it's binary form



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import cv2
import numpy as np

# Load our image as greyscale 
image = cv2.imread('images/gradient.jpg',0)
cv2.imshow('Original', image)

# Values below 127 goes to 0 (black, everything above goes to 255 (white)
ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
cv2.imshow('1 Threshold Binary', thresh1)

# Values below 127 go to 255 and values above 127 go to 0 (reverse of above)
ret,thresh2 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('2 Threshold Binary Inverse', thresh2)

# Values above 127 are truncated (held) at 127 (the 255 argument is unused)
ret,thresh3 = cv2.threshold(image, 127, 255, cv2.THRESH_TRUNC)
cv2.imshow('3 THRESH TRUNC', thresh3)

# Values below 127 go to 0, above 127 are unchanged  
ret,thresh4 = cv2.threshold(image, 127, 255, cv2.THRESH_TOZERO)
cv2.imshow('4 THRESH TOZERO', thresh4)

# Resever of above, below 127 is unchanged, above 127 goes to 0
ret,thresh5 = cv2.threshold(image, 127, 255, cv2.THRESH_TOZERO_INV)
cv2.imshow('5 THRESH TOZERO INV', thresh5)
cv2.waitKey(0) 
    
cv2.destroyAllWindows()

Is there a better way off thresholding?

The biggest downfall of those simple threshold methods is that we need to provide the threshold value (i.e. the 127 value we used previously).

What if there was a smarter way of doing this?

There is with, Adaptive thresholding.



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import cv2
import numpy as np

# Load our new image
image = cv2.imread('images/Origin_of_Species.jpg', 0)

cv2.imshow('Original', image)
cv2.waitKey(0) 

# Values below 127 goes to 0 (black, everything above goes to 255 (white)
ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
cv2.imshow('Threshold Binary', thresh1)
cv2.waitKey(0) 

# It's good practice to blur images as it removes noise
image = cv2.GaussianBlur(image, (3, 3), 0)

# Using adaptiveThreshold
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, 
                               cv2.THRESH_BINARY, 3, 5) 
cv2.imshow("Adaptive Mean Thresholding", thresh) 
cv2.waitKey(0) 

_, th2 = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow("Otsu's Thresholding", thresh) 
cv2.waitKey(0) 

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(image, (5,5), 0)
_, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow("Guassian Otsu's Thresholding", thresh) 
cv2.waitKey(0) 

cv2.destroyAllWindows()



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