CLAHE & CLAHE with LAB format

# BGR to Gray Scale
img_array = cv2.imread(os.path.join(path, img))
image_bw = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
        
    # The declaration of CLAHE
    # clipLimit -> Threshold for contrast limiting
clahe = cv2.createCLAHE(clipLimit = 5.0, tileGridSize=(16, 16))
final_img = clahe.apply(image_bw) + 0
cv2.imshow("CLAHE image", final_img)
plt.imshow(final_img)
cv2.waitKey(0)
cv2.destroyAllWindows()




#BGR to LAB
#image = cv2.imread("C://AiHints//apple.jpg")
#imread is use to read an image from a location

#img_array = cv2.imread(os.path.join(path, img))

img_array = cv2.imread("E:\FYP\Extra\DATASETS//25_left.jpeg")
lab_image = cv2.cvtColor(img_array, cv2.COLOR_BGR2LAB)
cv2.imshow("Original Image", img_array)
cv2.imshow("LAB Image", lab_image)
cv2.waitKey(0)
cv2.destroyAllWindows()





#CLAHE on LAB
#def histogram_equalization(img):

#img_array = cv2.imread(os.path.join(path, img))

img_array = cv2.imread("E:\FYP\Extra\DATA//25_left.jpeg")
lab = cv2.cvtColor(img_array, cv2.COLOR_BGR2Lab)

    # -----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)

    # -----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(32, 32))
cl = clahe.apply(l)

    # -----Merge the CLAHE enhanced L-channel with the a and b channel-----------
limg = cv2.merge((cl, a, b))

    # -----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)

cv2.imshow("Original Image", img_array)
cv2.imshow("LAB Image", final)
cv2.waitKey(0)
cv2.destroyAllWindows()