code: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  __A  							=  logging.get_logger(__name__)
  
  
  if is_vision_available():
     import PIL
  
  
  
  class 							_A (    UpperCamelCase_							):
      """simple docstring"""
  
      lowerCamelCase  :       Dict   =   ["""pixel_values"""]
  
  
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple = True   ,    __SCREAMING_SNAKE_CASE			:    Any = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any] = PILImageResampling.BICUBIC   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = None   ,    __SCREAMING_SNAKE_CASE			:    Any = True   ,    __SCREAMING_SNAKE_CASE			:    List[str] = 1 / 255   ,    __SCREAMING_SNAKE_CASE			:    Any = True   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any] = None   ,    __SCREAMING_SNAKE_CASE			:    List[str] = True   ,    **__SCREAMING_SNAKE_CASE			:    List[str]   ,    )			->       None:
             super().__init__(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=size if size is not None else {"""shortest_edge""": 224}
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=do_resize
             __UpperCAmelCase    							=size
             __UpperCAmelCase    							=resample
             __UpperCAmelCase    							=do_center_crop
             __UpperCAmelCase    							=crop_size
             __UpperCAmelCase    							=do_rescale
             __UpperCAmelCase    							=rescale_factor
             __UpperCAmelCase    							=do_normalize
             __UpperCAmelCase    							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             __UpperCAmelCase    							=image_std if image_std is not None else OPENAI_CLIP_STD
             __UpperCAmelCase    							=do_convert_rgb
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple = PILImageResampling.BICUBIC   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    **__SCREAMING_SNAKE_CASE			:    List[str]   ,    )			->       np.ndarray:
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=get_resize_output_image_size(
                 __SCREAMING_SNAKE_CASE   ,    size=(size["""height"""], size["""width"""])   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
             return resize(__SCREAMING_SNAKE_CASE   ,    size=__SCREAMING_SNAKE_CASE   ,    resample=__SCREAMING_SNAKE_CASE   ,    data_format=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str] = None   ,    **__SCREAMING_SNAKE_CASE			:    int   ,    )			->       np.ndarray:
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
             return center_crop(__SCREAMING_SNAKE_CASE   ,    size=(size["""height"""], size["""width"""])   ,    data_format=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str] = None   ,    **__SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    )			->       List[str]:
             return rescale(__SCREAMING_SNAKE_CASE   ,    scale=__SCREAMING_SNAKE_CASE   ,    data_format=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    int = None   ,    **__SCREAMING_SNAKE_CASE			:    List[str]   ,    )			->       np.ndarray:
             return normalize(__SCREAMING_SNAKE_CASE   ,    mean=__SCREAMING_SNAKE_CASE   ,    std=__SCREAMING_SNAKE_CASE   ,    data_format=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any] = None   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = None   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any] = None   ,    __SCREAMING_SNAKE_CASE			:    str = None   ,    __SCREAMING_SNAKE_CASE			:    str = None   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    List[Any] = None   ,    __SCREAMING_SNAKE_CASE			:    int = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = ChannelDimension.FIRST   ,    **__SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    )			->       PIL.Image.Image:
             __UpperCAmelCase    							=do_resize if do_resize is not None else self.do_resize
             __UpperCAmelCase    							=size if size is not None else self.size
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=resample if resample is not None else self.resample
             __UpperCAmelCase    							=do_center_crop if do_center_crop is not None else self.do_center_crop
             __UpperCAmelCase    							=crop_size if crop_size is not None else self.crop_size
             __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=do_rescale if do_rescale is not None else self.do_rescale
             __UpperCAmelCase    							=rescale_factor if rescale_factor is not None else self.rescale_factor
             __UpperCAmelCase    							=do_normalize if do_normalize is not None else self.do_normalize
             __UpperCAmelCase    							=image_mean if image_mean is not None else self.image_mean
             __UpperCAmelCase    							=image_std if image_std is not None else self.image_std
             __UpperCAmelCase    							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
             __UpperCAmelCase    							=make_list_of_images(__SCREAMING_SNAKE_CASE			)
  
             if not valid_images(__SCREAMING_SNAKE_CASE			):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""			)
  
             if do_resize and size is None:
                    raise ValueError("""Size must be specified if do_resize is True."""			)
  
             if do_center_crop and crop_size is None:
                    raise ValueError("""Crop size must be specified if do_center_crop is True."""			)
  
             if do_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)
  
             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError("""Image mean and std must be specified if do_normalize is True."""			)
  
             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    __UpperCAmelCase    							=[convert_to_rgb(__SCREAMING_SNAKE_CASE			) for image in images]
  
             # All transformations expect numpy arrays.
             __UpperCAmelCase    							=[to_numpy_array(__SCREAMING_SNAKE_CASE			) for image in images]
  
             if do_resize:
                    __UpperCAmelCase    							=[self.resize(image=__SCREAMING_SNAKE_CASE   ,    size=__SCREAMING_SNAKE_CASE   ,    resample=__SCREAMING_SNAKE_CASE			) for image in images]
  
             if do_center_crop:
                    __UpperCAmelCase    							=[self.center_crop(image=__SCREAMING_SNAKE_CASE   ,    size=__SCREAMING_SNAKE_CASE			) for image in images]
  
             if do_rescale:
                    __UpperCAmelCase    							=[self.rescale(image=__SCREAMING_SNAKE_CASE   ,    scale=__SCREAMING_SNAKE_CASE			) for image in images]
  
             if do_normalize:
                    __UpperCAmelCase    							=[self.normalize(image=__SCREAMING_SNAKE_CASE   ,    mean=__SCREAMING_SNAKE_CASE   ,    std=__SCREAMING_SNAKE_CASE			) for image in images]
  
             __UpperCAmelCase    							=[to_channel_dimension_format(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			) for image in images]
  
             __UpperCAmelCase    							={"""pixel_values""": images}
             return BatchFeature(data=__SCREAMING_SNAKE_CASE   ,    tensor_type=__SCREAMING_SNAKE_CASE			)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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)
  
  
  
  
  