

from pathlib import Path

import numpy as np
from PIL import Image


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_,    snake_case_,    snake_case_   =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.2989 * r + 0.5870 * g + 0.1140 * b


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return (gray > 127) & (gray <= 255)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	np.zeros_like(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	np.zeros(
						    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

						# Copy image to padded image
						snake_case_   =	image

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


if __name__ == "__main__":
					# read original image
					lowerCAmelCase_       	=	Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
					lowerCAmelCase_       	=	np.array(Image.open(lena_path))
					# kernel to be applied
					lowerCAmelCase_       	=	np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
					lowerCAmelCase_       	=	dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
					# Save the output image
					lowerCAmelCase_       	=	Image.fromarray(output).convert('''RGB''')
					pil_img.save('''result_dilation.png''')
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


