from __future__ import print_function
from builtins import input
import cv2 as cv
import numpy as np
import argparse
# Read image given by user
parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
image = cv.imread(cv.samples.findFile(args.input))
if image is None:
  print('Could not open or find the image: ', args.input)
  exit(0)
new_image = np.zeros(image.shape, image.dtype)
alpha = 1.0 # Simple contrast control
beta = 0  # Simple brightness control
# Initialize values
print(' Basic Linear Transforms ')
print('-------------------------')
try:
  alpha = float(input('* Enter the alpha value [1.0-3.0]: '))
  beta = int(input('* Enter the beta value [0-100]: '))
except ValueError:
  print('Error, not a number')
# Do the operation new_image(i,j) = alpha*image(i,j) + beta
# Instead of these 'for' loops we could have used simply:
# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)
# but we wanted to show you how to access the pixels :)
for y in range(image.shape[0]):
  for x in range(image.shape[1]):
    for c in range(image.shape[2]):
      new_image[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)
cv.imshow('Original Image', image)
cv.imshow('New Image', new_image)
# Wait until user press some key
cv.waitKey()

lookUpTable = np.empty((1,256), np.uint8)
  for i in range(256):
    lookUpTable[0,i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)
  res = cv.LUT(img_original, lookUpTable)