






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




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      List[str]:
 lowerCamelCase_							:    int							=     np.zeros(lowerCamelCase__				)

 for row in range(1 ,			image_shape[0] - 1				):
  for col in range(1 ,			image_shape[1] - 1				):
   lowerCamelCase_							:    Optional[Any]							=     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[str]							=     sobel_grad[row, col - 1]
    lowerCamelCase_							:    Tuple							=     sobel_grad[row, col + 1]
    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
     lowerCamelCase_							:    int							=     sobel_grad[row, col]

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

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

   elif (
       5 * PI / 8 <= direction < 7 * PI / 8
       or 13 * PI / 8 <= direction < 15 * PI / 8
   ):
    lowerCamelCase_							:    Union[str, Any]							=     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_							:    Dict							=     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:
    lowerCamelCase_							:    int							=     strong
   elif destination[row, col] <= threshold_low:
    lowerCamelCase_							:    int							=     0
   else:
    lowerCamelCase_							:    List[Any]							=     weak




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Optional[int]:
 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],
    ):
     lowerCamelCase_							:    List[str]							=     strong
    else:
     lowerCamelCase_							:    List[Any]							=     0




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=15 ,			lowerCamelCase__=30 ,			lowerCamelCase__=1_28 ,			lowerCamelCase__=2_55				)  ->      List[Any]:
 # gaussian_filter
 lowerCamelCase_							:    Union[str, Any]							=     img_convolve(lowerCamelCase__ ,			gen_gaussian_kernel(9 ,			sigma=1.4				)				)
 # get the gradient and degree by sobel_filter
 lowerCamelCase_		,      lowerCamelCase_							:    int							=     sobel_filter(lowerCamelCase__				)
 lowerCamelCase_							:    Union[str, Any]							=     PI + np.radadeg(lowerCamelCase__				)

 lowerCamelCase_							:    Union[str, Any]							=     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
       UpperCamelCase											=      cva.imread(r'''../image_data/lena.jpg''', 0)
       # canny edge detection
       UpperCamelCase											=      canny(lena)
       cva.imshow('''canny''', canny_destination)
       cva.waitKey(0)








UpperCamelCase											=      [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      bool:
 return 0 <= row < rows and 0 <= col < cols




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      int:
 lowerCamelCase_							:    Tuple							=     len(lowerCamelCase__				)
 lowerCamelCase_							:    Tuple							=     len(mat[0]				)
 if is_safe(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) and (row, col) not in seen and mat[row][col] == 1:
  seen.add((row, col)				)
  return (
      1
      + depth_first_search(row + 1 ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
      + depth_first_search(row - 1 ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
      + depth_first_search(lowerCamelCase__ ,			col + 1 ,			lowerCamelCase__ ,			lowerCamelCase__				)
      + depth_first_search(lowerCamelCase__ ,			col - 1 ,			lowerCamelCase__ ,			lowerCamelCase__				)
  )
 else:
  return 0




def       _a   (							lowerCamelCase__				)  ->      int:
 lowerCamelCase_							:    set							=     set()

 lowerCamelCase_							:    int							=     0
 for row, line in enumerate(lowerCamelCase__				):
  for col, item in enumerate(lowerCamelCase__				):
   if item == 1 and (row, col) not in seen:
    # Maximizing the area
    lowerCamelCase_							:    str							=     max(lowerCamelCase__ ,			depth_first_search(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)				)
 return max_area


if __name__ == "__main__":
       import doctest

       print(find_max_area(matrix))  # Output -> 6
       doctest.testmod()
