import numpy as np
import cv2
def adjust_gamma(image, gamma=1.0):
	# build a lookup table mapping the pixel values [0, 255] to
	# their adjusted gamma values
	invGamma = 1.0 / gamma
	table = np.array([((i / 255.0) ** invGamma) * 255
		for i in np.arange(0, 256)]).astype("uint8")
 
	# apply gamma correction using the lookup table
	return cv2.LUT(image, table)

796dcb83966f57f0d9b7ca3457afbd1dbfc34b06
<mask token>

<mask token>


def adjust_gamma(image, gamma=1.0):
    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma * 255) for i in np.arange(0,
        256)]).astype('uint8')
    return cv2.LUT(image, table)

import numpy as np
import cv2


def adjust_gamma(image, gamma=1.0):
    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma * 255) for i in np.arange(0,
        256)]).astype('uint8')
    return cv2.LUT(image, table)

import numpy as np
import cv2
def adjust_gamma(image, gamma=1.0):
	# build a lookup table mapping the pixel values [0, 255] to
	# their adjusted gamma values
	invGamma = 1.0 / gamma
	table = np.array([((i / 255.0) ** invGamma) * 255
		for i in np.arange(0, 256)]).astype("uint8")
 
	# apply gamma correction using the lookup table
	return cv2.LUT(image, table)
