How to do Histogram Equalization on specific area

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:

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 :