How to do Histogram Equalization on specific area

Question

I have a image and I want to do HE or CLAHE on specific area of the image. I already have a mask for the image. Is there any possible way to do so?

Answer 1

Here is the code to achieve that :

import cv2 as cv
import numpy as np

# Load your color  image
#src = cv.imread("___YourImagePath__.jpg", 
#cv.IMREAD_COLOR)

#Create random color image
src = np.random.randint(255, size=(800,800,3),dtype=np.uint8)
cv.imshow('Random Color Image',src)
cv.waitKey(0)


# conver to gray
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

# process gray image
equalized = cv.equalizeHist(gray)

# create a mask (binary image with same size as source image )
height,width,depth = src.shape
mask = np.zeros((height,width))
cv.circle(mask,( int(width/2),int(height/2)),int(width/3),1,thickness=-1)


# display mask
cv.imshow('Mask',mask)
cv.waitKey(0)

# Copy processed region using the mask
ProcessedRegion = np.where(mask!=0,equalized,gray)

#display result
cv.imshow('Processed region result', ProcessedRegion)
cv.waitKey(0)

Output :

Answer 2

To do so you need to perform the operation on the pixel intensities of the image which fall within the mask. For that these intensities must be stored separately.

Procedure:

• Get the pixel locations of those in white (255), within the mask.
• Pick intensity values (0 - 255) from the gray image present in these locations.
• Perform your operation (CLAHE or HE) on these intensities. The result is a different collection of intensities.
• Place these new intensity values in the collected locations.

Sample:

Input image:

Mask image:

Code:

import cv2
import numpy as np

# read sample image, convert to grayscale
img = cv2.imread('flower.jpg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# read mask image as binary image
mask = cv2.imread('flower_mask.jpg', 0)

# Step 1: store locations with value 255 (white)
loc = np.where(mask == 255)

# Step 2: Pick intensity values in these locations from the grayscale image:
values = gray[loc]

# Step 3: Histogram equalization on these values:
enhanced_values = cv2.equalizeHist(values)

# Step 4: Store these enhanced values in those locations:
gray2 = gray_img.copy()
for i, coord in enumerate(zip(loc[0], loc[1])):
    gray2[coord[0], coord[1]] = enhanced_values[i][0]

cv2.imshow('Enhanced image', gray2)

Enhance image:

Grayscale image: