



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if (ksize % 2) == 0:
								SCREAMING_SNAKE_CASE_   :	int													=   ksize + 1
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   np.zeros((ksize, ksize)    ,    dtype=np.floataa      )

				# each value
				for y in range(lowerCAmelCase__      ):
								for x in range(lowerCAmelCase__      ):
												# distance from center
												SCREAMING_SNAKE_CASE_   :	int													=   x - ksize // 2
												SCREAMING_SNAKE_CASE_   :	Tuple													=   y - ksize // 2

												# degree to radiant
												SCREAMING_SNAKE_CASE_   :	Tuple													=   theta / 180 * np.pi
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   np.cos(_theta      )
												SCREAMING_SNAKE_CASE_   :	Dict													=   np.sin(_theta      )

												# get kernel x
												SCREAMING_SNAKE_CASE_   :	Dict													=   cos_theta * px + sin_theta * py

												# get kernel y
												SCREAMING_SNAKE_CASE_   :	str													=   -sin_theta * px + cos_theta * py

												# fill kernel
												SCREAMING_SNAKE_CASE_   :	Any													=   np.exp(
												    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)      ) * np.cos(2 * np.pi * _x / lambd + psi      )

				return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				A        =			imread('../image_data/lena.jpg')
				# turn image in gray scale value
				A        =			cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				A        =			np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								A        =			gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				A        =			out / out.max() * 255
				A        =			out.astype(np.uinta)

				imshow('Original', gray)
				imshow('Gabor filter with 20x20 mask and 6 directions', out)

				waitKey(0)







import colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   x
				SCREAMING_SNAKE_CASE_   :	Tuple													=   y
				for step in range(lowerCAmelCase__      ):  # noqa: B007
								SCREAMING_SNAKE_CASE_   :	str													=   a * a - b * b + x
								SCREAMING_SNAKE_CASE_   :	Any													=   2 * a * b + y
								SCREAMING_SNAKE_CASE_   :	int													=   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 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''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 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   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
												SCREAMING_SNAKE_CASE_   :	List[str]													=   figure_width / image_width * image_height
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   figure_center_x + (image_x / image_width - 0.5) * figure_width
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   figure_center_y + (image_y / image_height - 0.5) * figure_height

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

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

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			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()