




"""simple docstring"""



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							=			180







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







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 A__			=							np.zeros(lowercase_		)

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

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

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

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

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

 return destination







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						Optional[int]:
 for row in range(1     ,					image_shape[0] - 1		):
  for col in range(1     ,					image_shape[1] - 1		):
   if destination[row, col] >= threshold_high:
    A__			=							strong
   elif destination[row, col] <= threshold_low:
    A__			=							0
   else:
    A__			=							weak







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						List[Any]:
 for row in range(1     ,					image_shape[0]		):
  for col in range(1     ,					image_shape[1]		):
   if destination[row, col] == weak:
    if 2_55 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],
    ):
     A__			=							strong
    else:
     A__			=							0







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=15     ,					lowercase_=30     ,					lowercase_=1_28     ,					lowercase_=2_55		)		->						Optional[int]:
 # gaussian_filter
 A__			=							img_convolve(lowercase_     ,					gen_gaussian_kernel(9     ,					sigma=1.4		)		)
 # get the gradient and degree by sobel_filter
 A__, A__			=							sobel_filter(lowercase_		)
 A__			=							PI + np.radadeg(lowercase_		)

 A__			=							suppress_non_maximum(image.shape     ,					lowercase_     ,					lowercase_		)

 detect_high_low_threshold(
     image.shape     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)

 track_edge(image.shape     ,					lowercase_     ,					lowercase_     ,					lowercase_		)

 return destination


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











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


if __name__ == "__main__":
      print(f'{solution() = }')





