




from pathlib import Path

import numpy as np
from PIL import Image







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       np.ndarray:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       np.ndarray:



       """simple docstring"""

       return (gray > 127) & (gray <= 255)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       np.ndarray:



       """simple docstring"""

       A__       =    np.zeros_like(lowercase_						)
       A__       =    np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

       # Copy image to padded image
       A__       =    image

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


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:		List[str]							       =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTMSNModel""",
      """ViTMSNForImageClassification""",
      """ViTMSNPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase	:		Tuple							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


