DigitalHairRemoval.py

# -*- coding: utf-8 -*-
"""
Following are the DHR tasks followed in this example code:
    
    -- Applying Morphological Black-Hat transformation
    -- Creating the mask for InPainting task
    -- Applying inpainting algorithm on the image

"""

import cv2
src = cv2.imread("C:\\SkinHairRemovalPython\\inputImages\\sample1.jpg")

print( src.shape )
cv2.imshow("original Image" , src )


# Convert the original image to grayscale
grayScale = cv2.cvtColor( src, cv2.COLOR_RGB2GRAY )
cv2.imshow("GrayScale",grayScale)
cv2.imwrite('grayScale_sample1.jpg', grayScale, [int(cv2.IMWRITE_JPEG_QUALITY), 90])

# Kernel for the morphological filtering
kernel = cv2.getStructuringElement(1,(17,17))

# Perform the blackHat filtering on the grayscale image to find the 
# hair countours
blackhat = cv2.morphologyEx(grayScale, cv2.MORPH_BLACKHAT, kernel)
cv2.imshow("BlackHat",blackhat)
cv2.imwrite('blackhat_sample1.jpg', blackhat, [int(cv2.IMWRITE_JPEG_QUALITY), 90])

# intensify the hair countours in preparation for the inpainting 
# algorithm
ret,thresh2 = cv2.threshold(blackhat,10,255,cv2.THRESH_BINARY)
print( thresh2.shape )
cv2.imshow("Thresholded Mask",thresh2)
cv2.imwrite('thresholded_sample1.jpg', thresh2, [int(cv2.IMWRITE_JPEG_QUALITY), 90])

# inpaint the original image depending on the mask
dst = cv2.inpaint(src,thresh2,1,cv2.INPAINT_TELEA)
cv2.imshow("InPaint",dst)
cv2.imwrite('C:\\SkinHairRemovalPython\\InPainted_sample1.jpg', dst, [int(cv2.IMWRITE_JPEG_QUALITY), 90])