






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image



def 					_A			(__a							) ->       np.ndarray:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def 					_A			(__a							) ->       np.ndarray:
    """simple docstring"""






    return (gray > 1_27) & (gray <= 2_55)



def 					_A			(__a    ,				__a							) ->       np.ndarray:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		np.zeros_like(__a							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            SCREAMING_SNAKE_CASE_      :  int    						=		(
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            SCREAMING_SNAKE_CASE_      :  Any    						=		int(summation > 0							)
    return output


if __name__ == "__main__":
  # read original image
  UpperCAmelCase_   : Dict   			= Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
  UpperCAmelCase_   : int   			= np.array(Image.open(lena_path))
  # kernel to be applied
  UpperCAmelCase_   : Union[str, Any]   			= np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  UpperCAmelCase_   : Union[str, Any]   			= dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
  # Save the output image
  UpperCAmelCase_   : List[Any]   			= Image.fromarray(output).convert("""RGB""")
  pil_img.save("""result_dilation.png""")










"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


UpperCAmelCase_   : Any   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Union[str, Any]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          warnings.warn(
              '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use SegformerImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


