'''simple docstring'''




import numpy as np
from PIL import Image



def 						__snake_case (       lowerCAmelCase       :     np.array			):
 __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def 						__snake_case (       lowerCAmelCase       :     np.array			):
 return (gray > 127) & (gray <= 255)



def 						__snake_case (       lowerCAmelCase       :     np.array      ,  lowerCAmelCase       :     np.array			):
 __UpperCAmelCase         =					np.zeros_like(lowerCAmelCase			)
 __UpperCAmelCase         =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

 # Copy image to padded image
 __UpperCAmelCase         =					image

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


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

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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCamelCase				:		int       			=		{
    'configuration_align': [
        'ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AlignConfig',
        'AlignTextConfig',
        'AlignVisionConfig',
    ],
    'processing_align': ['AlignProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[Any]       			=		[
         'ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AlignModel',
         'AlignPreTrainedModel',
         'AlignTextModel',
         'AlignVisionModel',
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     _UpperCamelCase				:		str       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



