

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

__UpperCAmelCase     					=  1_8_0







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     k_size // 2
     _lowerCAmelCase :       Union[str, Any]	   =     np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     _lowerCAmelCase :       Tuple	   =     (
         1
         / (2 * np.pi * sigma)
         * np.exp(-(np.square(_SCREAMING_SNAKE_CASE					) + np.square(_SCREAMING_SNAKE_CASE					)) / (2 * np.square(_SCREAMING_SNAKE_CASE					))					)
     )
     return g







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Union[str, Any]	   =     np.zeros(_SCREAMING_SNAKE_CASE					)

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

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

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

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

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

     return destination







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     for row in range(1		,				image_shape[0] - 1					):
          for col in range(1		,				image_shape[1] - 1					):
               if destination[row, col] >= threshold_high:
                    _lowerCAmelCase :       Union[str, Any]	   =     strong
               elif destination[row, col] <= threshold_low:
                    _lowerCAmelCase :       Dict	   =     0
               else:
                    _lowerCAmelCase :       int	   =     weak







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     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],
                    ):
                         _lowerCAmelCase :       Optional[int]	   =     strong
                    else:
                         _lowerCAmelCase :       List[str]	   =     0







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=15		,				SCREAMING_SNAKE_CASE=30		,				SCREAMING_SNAKE_CASE=128		,				SCREAMING_SNAKE_CASE=255					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       Union[str, Any]	   =     img_convolve(_SCREAMING_SNAKE_CASE		,				gen_gaussian_kernel(9		,				sigma=1.4					)					)
     # get the gradient and degree by sobel_filter
     _lowerCAmelCase :       Optional[int]	   =     sobel_filter(_SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[Any]	   =     PI + np.radadeg(_SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Tuple	   =     suppress_non_maximum(image.shape		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)

     detect_high_low_threshold(
         image.shape		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)

     track_edge(image.shape		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)

     return destination


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







"""simple docstring"""
from __future__ import annotations

import sys







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







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->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
  a__				,       a__:	Optional[int]     				=      _fib(n // 2  )
  a__:	List[str]     				=      a * (b * 2 - a)
  a__:	Dict     				=      a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


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


