






def 			lowercase__  (				A_:	int		)  ->							str:







       """simple docstring"""






       if not isinstance(A_     ,			A_		):
              __UpperCAmelCase    							=(
                  """operation can not be conducted on a object of type """
                  F'''{type(A_		).__name__}'''
              )
              raise TypeError(A_		)
       __UpperCAmelCase    							=""""""
       for _ in range(0     ,			32		):
              bit_string += str(number % 2		)
              __UpperCAmelCase    							=number >> 1
       return bit_string







def 			lowercase__  (				A_:	int		)  ->							str:







       """simple docstring"""






       if number < 0:
              raise ValueError("""the value of input must be positive"""		)
       elif isinstance(A_     ,			A_		):
              raise TypeError("""Input value must be a 'int' type"""		)
       elif isinstance(A_     ,			A_		):
              raise TypeError("""'<' not supported between instances of 'str' and 'int'"""		)
       __UpperCAmelCase    							=0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1     ,			33		):
              # left shift the bits by unity
              __UpperCAmelCase    							=result << 1
              # get the end bit
              __UpperCAmelCase    							=number % 2
              # right shift the bits by unity
              __UpperCAmelCase    							=number >> 1
              # add that bit to our ans
              __UpperCAmelCase    							=result | end_bit
       return get_reverse_bit_string(A_		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()












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)




