






__A  							=  10_00







def 			lowercase__  (				A_:	float		)  ->							float:







       """simple docstring"""






       return x * x







def 			lowercase__  (				A_:	List[Any]     ,			A_:	float     ,			A_:	float     ,			A_:	int = 4		)  ->							float:







       """simple docstring"""






       assert callable(
           A_		), F'''the function(object) passed should be callable your input : {function}'''
       assert isinstance(A_     ,			(float, int)		), F'''a should be float or integer your input : {a}'''
       assert isinstance(function(A_		)     ,			(float, int)		), (
           "the function should return integer or float return type of your function, "
           F'''{type(A_		)}'''
       )
       assert isinstance(A_     ,			(float, int)		), F'''b should be float or integer your input : {b}'''
       assert (
           isinstance(A_     ,			A_		) and precision > 0
       ), F'''precision should be positive integer your input : {precision}'''

       # just applying the formula of simpson for approximate integration written in
       # mentioned article in first comment of this file and above this function

       __UpperCAmelCase    							=(b - a) / N_STEPS
       __UpperCAmelCase    							=function(A_		) + function(A_		)

       for i in range(1     ,			A_		):
              __UpperCAmelCase    							=a + h * i
              result += function(A_		) * (4 if i % 2 else 2)

       result *= h / 3
       return round(A_     ,			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)




