Digital Image Processing: basic gradation conversion

The purpose is to review

Import CV2
 Import numpy NP AS 

# grayscale inversion 
DEF grayReversal (Gray): 
    gray_reversal = 255 - Gray # grayscale inversion 
    return gray_reversal 

# color image reversal 
DEF imgReversal (IMG): 
    img_reversal = np.zeros (IMG. Shape, np.uint8) # initial template 
    for I in Range (img.shape [0]):
         for J in Range (img.shape [. 1 ]): 
            B, G, R & lt = IMG [I, J] # note is bgr, not RGB 
            img_reversal [I, J] = 255 - B, 255 - G, 255 - R & lt
    return img_reversal 

# logarithmic transformation 
DEF logTrans (Gray, C): 
    gray_log = np.uint8 ((np.log C * (1.0 + Gray)))
     return gray_log 

# power law (gamma) conversion 
DEF PowerTrans (Gray, C, Y): 
    gray_power = np.uint8 (C * (Gray ** Y))
     return gray_power 

path = " _kdy.jpg " 
IMG = cv2.imread (path) 
Gray = cv2.cvtColor (IMG, cv2.COLOR_BGR2GRAY) # is converted to grayscale 
gray_reversal = grayReversal (Gray) 
img_reversal = imgReversal (IMG) 
gray_log= logTrans(gray, c=30)
gray_power = powerTrans(gray, c=30, y =0.35)

cv2.imshow("img", img)
cv2.imshow("gray", gray)
cv2.imshow("gray_reversal", gray_reversal)
cv2.imshow("img_reversal", img_reversal)
cv2.imshow("gray_log", gray_log)
cv2.imshow("gray_power", gray_power)

cv2.waitKey(0)

(FIG picture respectively, grayscale, FIG gradation inversion, FIG logarithmic transformation, power law (gamma) conversion FIG)