






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			)












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)




