





"""simple docstring"""






from __future__ import annotations

import math
from collections.abc import Callable

def       __A (     a_  :Callable[[int | float], int | float]				,    a_  :int | float				,    a_  :int | float				,    a_  :int = 1_00				,    )						->						float:
				__a	:  Union[str, Any]         =					x_start
				__a	:  int         =					fnc(a_)
				__a	:  Optional[int]         =					0.0

				for _ in range(a_):
								# Approximates curve as a sequence of linear lines and sums their length
								__a	:  Any         =					(x_end - x_start) / steps + xa
								__a	:  str         =					fnc(a_)
								length += math.hypot(xa - xa				,    fxa - fxa)

								# Increment step
								__a	:  Dict         =					xa
								__a	:  Tuple         =					fxa

				return length


if __name__ == "__main__":

		def       __A (     a_  :Optional[Any])						->						Dict:
						return math.sin(10 * x)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		A	     =					10
		while i <= 100_000:
				print(F'With {i} steps: {line_length(f, -10, 10, i)}')
				i *= 10






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					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       __A (     a_  :np.ndarray)						->						np.ndarray:
				return (gray > 1_27) & (gray <= 2_55)

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					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	:  Optional[Any]         =					(
												    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
												).sum()
												__a	:  Any         =					int(summation > 0)
				return output


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