




import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

SCREAMING_SNAKE_CASE:    Any											=   1_8_0


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       k_size // 2
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		SCREAMING_SNAKE_CASE_							=       (
		    1
		    / (2 * np.pi * sigma)
		    * np.exp(-(np.square(lowerCAmelCase   ) + np.square(lowerCAmelCase   )) / (2 * np.square(lowerCAmelCase   ))   )
		)
		return g


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Tuple:
		SCREAMING_SNAKE_CASE_							=       np.zeros(lowerCAmelCase   )

		for row in range(1						,	image_shape[0] - 1   ):
				for col in range(1						,	image_shape[1] - 1   ):
						SCREAMING_SNAKE_CASE_							=       gradient_direction[row, col]

						if (
						    0 <= direction < PI / 8
						    or 15 * PI / 8 <= direction <= 2 * PI
						    or 7 * PI / 8 <= direction <= 9 * PI / 8
						):
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col - 1]
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col + 1]
								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
										SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col]

						elif (
						    PI / 8 <= direction < 3 * PI / 8
						    or 9 * PI / 8 <= direction < 11 * PI / 8
						):
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row + 1, col - 1]
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row - 1, col + 1]
								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
										SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col]

						elif (
						    3 * PI / 8 <= direction < 5 * PI / 8
						    or 11 * PI / 8 <= direction < 13 * PI / 8
						):
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row - 1, col]
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row + 1, col]
								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
										SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col]

						elif (
						    5 * PI / 8 <= direction < 7 * PI / 8
						    or 13 * PI / 8 <= direction < 15 * PI / 8
						):
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row - 1, col - 1]
								SCREAMING_SNAKE_CASE_							=       sobel_grad[row + 1, col + 1]
								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
										SCREAMING_SNAKE_CASE_							=       sobel_grad[row, col]

		return destination


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Tuple:
		for row in range(1						,	image_shape[0] - 1   ):
				for col in range(1						,	image_shape[1] - 1   ):
						if destination[row, col] >= threshold_high:
								SCREAMING_SNAKE_CASE_							=       strong
						elif destination[row, col] <= threshold_low:
								SCREAMING_SNAKE_CASE_							=       0
						else:
								SCREAMING_SNAKE_CASE_							=       weak


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		for row in range(1						,	image_shape[0]   ):
				for col in range(1						,	image_shape[1]   ):
						if destination[row, col] == weak:
								if 255 in (
								    destination[row, col + 1],
								    destination[row, col - 1],
								    destination[row - 1, col],
								    destination[row + 1, col],
								    destination[row - 1, col - 1],
								    destination[row + 1, col - 1],
								    destination[row - 1, col + 1],
								    destination[row + 1, col + 1],
								):
										SCREAMING_SNAKE_CASE_							=       strong
								else:
										SCREAMING_SNAKE_CASE_							=       0


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=15						,	lowerCAmelCase=30						,	lowerCAmelCase=128						,	lowerCAmelCase=255   )->  Optional[int]:
		# gaussian_filter
		SCREAMING_SNAKE_CASE_							=       img_convolve(lowerCAmelCase						,	gen_gaussian_kernel(9						,	sigma=1.4   )   )
		# get the gradient and degree by sobel_filter
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       sobel_filter(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       PI + np.radadeg(lowerCAmelCase   )

		SCREAMING_SNAKE_CASE_							=       suppress_non_maximum(image.shape						,	lowerCAmelCase						,	lowerCAmelCase   )

		detect_high_low_threshold(
		    image.shape						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )

		track_edge(image.shape						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )

		return destination


if __name__ == "__main__":
						# read original image in gray mode
						SCREAMING_SNAKE_CASE:    Dict											=   cva.imread(r'''../image_data/lena.jpg''', 0)
						# canny edge detection
						SCREAMING_SNAKE_CASE:    List[str]											=   canny(lena)
						cva.imshow('''canny''', canny_destination)
						cva.waitKey(0)





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  tuple[int, int]:
		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    Optional[int]											=   int(sys.argv[1])
						print(f"""fibonacci({n}) is {fibonacci(n)}""")