




import numpy as np
from PIL import Image




def      __magic_name__							(					A						:   int  ):






			'''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      __magic_name__							(					A						:   int  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   str,    A						:   str  ):






			'''simple docstring'''







			a       =     np.zeros_like(A  )
			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 == 5  )
			return output


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

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














"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




