Histogram equalization

I am doing face recognition. Here we have alot of faces with luminous variability. So for classification purpose I have pre-process the face with histogram equalization method, that is very useful for correct prediction in case of luminous variation.

since we used a normalized histogram of {x}. In order to map the values back into their original range, the following simple transformation needs to be applied on the result:

y’ = y (max{x} — min{x}) + min{x}

img = cv2.imread(‘image001.jpg’,0)

equ = cv2.equalizeHist(img)

res = np.hstack((img,equ)) #stacking images side-by-side

cv2.imwrite(‘response.png’,res)

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