


'''simple docstring'''







from pathlib import Path

import numpy as np
from PIL import Image


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray  )  ->			np.ndarray:
       _a					,    _a					,    _a    : Optional[int]     					=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray  )  ->			np.ndarray:
       return (gray > 127) & (gray <= 255)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray    ,_UpperCAmelCase	:	np.ndarray  )  ->			np.ndarray:
       _a    : Optional[int]     					=np.zeros_like(_UpperCAmelCase  )
       _a    : Tuple     					=np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)  )

       # Copy image to padded image
       _a    : int     					=image

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


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






'''simple docstring'''







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)


