






import math
import os

import cva
import numpy as np

__A  							=  255.0







def 			lowercase__  (				A_     ,			A_		)  ->							float:







       """simple docstring"""






       __UpperCAmelCase    							=np.mean((original - contrast) ** 2		)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A_		)		)







def 			lowercase__  (				)  ->							None:







       """simple docstring"""






       __UpperCAmelCase    							=os.path.dirname(os.path.realpath(A_		)		)
       # Loading images (original image and compressed image)
       __UpperCAmelCase    							=cva.imread(os.path.join(A_     ,			"""image_data/original_image.png"""		)		)
       __UpperCAmelCase    							=cva.imread(os.path.join(A_     ,			"""image_data/compressed_image.png"""		)     ,			1		)

       __UpperCAmelCase    							=cva.imread(os.path.join(A_     ,			"""image_data/PSNR-example-base.png"""		)		)
       __UpperCAmelCase    							=cva.imread(
           os.path.join(A_     ,			"""image_data/PSNR-example-comp-10.jpg"""		)     ,			1		)

       # Value expected: 29.73dB
       print("""-- First Test --"""		)
       print(F'''PSNR value is {peak_signal_to_noise_ratio(A_     ,			A_		)} dB'''		)

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""		)
       print(F'''PSNR value is {peak_signal_to_noise_ratio(A_     ,			A_		)} dB'''		)


if __name__ == "__main__":
   main()












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like







def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:







       """simple docstring"""






       __UpperCAmelCase    							=int(mask / 2		)
       # copy image size
       __UpperCAmelCase    							=zeros_like(A_		)
       for i in range(A_     ,			gray_img.shape[0] - bd		):
              for j in range(A_     ,			gray_img.shape[1] - bd		):
                     # get mask according with mask
                     __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                     # calculate mask median
                     __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                     __UpperCAmelCase    							=median
       return median_img


if __name__ == "__main__":
   # read original image
   __A  							=  imread("../image_data/lena.jpg")
   # turn image in gray scale value
   __A  							=  cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   __A  							=  median_filter(gray, 3)
   __A  							=  median_filter(gray, 5)

   # show result images
   imshow("median filter with 3x3 mask", medianaxa)
   imshow("median filter with 5x5 mask", medianaxa)
   waitKey(0)




