






from dataclasses import dataclass
from typing import Callable, Optional

import datasets



@dataclass
class 							_A (    datasets.BuilderConfig							):
    """simple docstring"""

    lowerCamelCase  :       Optional[Callable]   =   None
    lowerCamelCase  :       Optional[dict]   =   None
    lowerCamelCase  :       Optional[datasets.Features]   =   None



    def _a       (							self			:    Dict			)			->       List[Any]:
           assert self.generator is not None, "generator must be specified"

           if self.gen_kwargs is None:
                  __UpperCAmelCase    							={}




class 							_A (    datasets.GeneratorBasedBuilder							):
    """simple docstring"""

    lowerCamelCase  :       List[Any]   =   GeneratorConfig



    def _a       (							self			:    Optional[int]			)			->       Optional[Any]:
           return datasets.DatasetInfo(features=self.config.features			)



    def _a       (							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			)			->       List[str]:
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs=self.config.gen_kwargs			)]



    def _a       (							self			:    List[str]   ,    **__SCREAMING_SNAKE_CASE			:    List[str]			)			->       Union[str, Any]:
           for idx, ex in enumerate(self.config.generator(**__SCREAMING_SNAKE_CASE			)			):
                  yield idx, ex












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)




