






"""simple docstring"""


import numpy as np
from PIL import Image




def 				a (     UpperCamelCase_							:				np.array		)  ->		np.array:
 __A    ,					__A    ,					__A	:       int       =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2989 * r + 0.5870 * g + 0.1140 * b




def 				a (     UpperCamelCase_							:				np.array		)  ->		np.array:
 return (gray > 1_2_7) & (gray <= 2_5_5)




def 				a (     UpperCamelCase_							:				np.array     ,	UpperCamelCase_							:				np.array		)  ->		np.array:
 __A	:       Any       =	np.zeros_like(UpperCamelCase_		)
 __A	:       List[Any]       =	np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)		)

 # Copy image to padded image
 __A	:       List[Any]       =	image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]		):
  for y in range(image.shape[0]		):
   __A	:       Dict       =	(
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   __A	:       Optional[Any]       =	int(summation == 5		)
 return output


# kernel to be applied
_UpperCamelCase   :						List[Any]     						= np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
   # read original image
   _UpperCamelCase   :						int     						= np.array(Image.open(R"""..\image_data\lena.jpg"""))
   # Apply erosion operation to a binary image
   _UpperCamelCase   :						List[Any]     						= erosion(grayabinary(rgbagray(image)), structuring_element)
   # Save the output image
   _UpperCamelCase   :						Optional[int]     						= Image.fromarray(output).convert("""RGB""")
   pil_img.save("""result_erosion.png""")













"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_UpperCamelCase   :						str     						= logging.get_logger(__name__)






class __a   (   snake_case_				):



  def __init__(self  :		Union[str, Any]				,			*lowercase_  :		Any				,			**lowercase_  :		Optional[Any]   )		->							None:
   warnings.warn(
       'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
       ' Please use LayoutLMv3ImageProcessor instead.'				,			lowercase_				,			)
   super().__init__(*lowercase_				,			**lowercase_   )





