
import colorsys

from PIL import Image  # type: ignore



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		x
					lowercase					=		y
					for step in range(lowerCAmelCase__	):  # noqa: B007
										lowercase					=		a * a - b * b + x
										lowercase					=		2 * a * b + y
										lowercase					=		a_new

										# divergence happens for all complex number with an absolute value
										# greater than 4
										if a * a + b * b > 4:
															break
					return step / (max_step - 1)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if distance == 1:
										return (0, 0, 0)
					else:
										return (255, 255, 255)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 255	) for i in colorsys.hsv_to_rgb(lowerCAmelCase__				,    1				,    1	)	)



def  UpperCamelCase					(							lowerCAmelCase__ = 800				,    lowerCAmelCase__ = 600				,    lowerCAmelCase__ = -0.6				,    lowerCAmelCase__ = 0				,    lowerCAmelCase__ = 3.2				,    lowerCAmelCase__ = 50				,    lowerCAmelCase__ = True				,    ):
					'''simple docstring'''
					lowercase					=		Image.new('''RGB'''				,    (image_width, image_height)	)
					lowercase					=		img.load()

					# loop through the image-coordinates
					for image_x in range(lowerCAmelCase__	):
										for image_y in range(lowerCAmelCase__	):
															# determine the figure-coordinates based on the image-coordinates
															lowercase					=		figure_width / image_width * image_height
															lowercase					=		figure_center_x + (image_x / image_width - 0.5) * figure_width
															lowercase					=		figure_center_y + (image_y / image_height - 0.5) * figure_height

															lowercase					=		get_distance(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				lowercase					=		get_color_coded_rgb(lowerCAmelCase__	)
															else:
																				lowercase					=		get_black_and_white_rgb(lowerCAmelCase__	)

					return img


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# colored version, full figure
							lowercase__	:Union[str, Any]										=					get_image()

							# uncomment for colored version, different section, zoomed in
							# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
							# figure_width = 0.8)

							# uncomment for black and white version, full figure
							# img = get_image(use_distance_color_coding = False)

							# uncomment to save the image
							# img.save("mandelbrot.png")

							img.show()






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

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


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




