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

a_					      = 180





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Any			):
 UpperCamelCase_      :		Dict				=      k_size // 2
 UpperCamelCase_,				UpperCamelCase_      :		Dict				=      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 UpperCamelCase_      :		int				=      (
     1
     / (2 * np.pi * sigma)
     * np.exp(-(np.square(lowerCamelCase			) + np.square(lowerCamelCase			)) / (2 * np.square(lowerCamelCase			))			)
 )
 return g





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Union[str, Any]				=      np.zeros(lowerCamelCase			)

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

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

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

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

 return destination





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Dict						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  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:
    UpperCamelCase_      :		str				=      strong
   elif destination[row, col] <= threshold_low:
    UpperCamelCase_      :		Tuple				=      0
   else:
    UpperCamelCase_      :		int				=      weak





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Optional[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],
    ):
     UpperCamelCase_      :		List[str]				=      strong
    else:
     UpperCamelCase_      :		Tuple				=      0





def      __lowercase  (		lowerCamelCase      :  int						,						lowerCamelCase      :  str=15						,						lowerCamelCase      :  List[str]=30						,						lowerCamelCase      :  Optional[int]=128						,						lowerCamelCase      :  List[Any]=255			):
 UpperCamelCase_      :		Optional[Any]				=      img_convolve(lowerCamelCase						,						gen_gaussian_kernel(9						,						sigma=1.4			)			)
 # get the gradient and degree by sobel_filter
 UpperCamelCase_,				UpperCamelCase_      :		List[str]				=      sobel_filter(lowerCamelCase			)
 UpperCamelCase_      :		List[Any]				=      PI + np.radadeg(lowerCamelCase			)

 UpperCamelCase_      :		List[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
   a_					      = cva.imread(r'../image_data/lena.jpg', 0)
   # canny edge detection
   a_					      = canny(lena)
   cva.imshow('canny', canny_destination)
   cva.waitKey(0)




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 print(F"{name:<35} finished {number} runs in {res:.5f} seconds"			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds



