






"""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

_UpperCamelCase   :						Tuple     						= 1_80




def 				a      (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Dict		)  ->		str:
 __A	:       str       =	k_size // 2
 __A    ,					__A	:       Any       =	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 __A	:       Optional[int]       =	(
     1
     / (2 * np.pi * sigma)
     * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
 )
 return g




def 				a      (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				Optional[Any]		)  ->		List[Any]:
 __A	:       Optional[int]       =	np.zeros(UpperCamelCase__		)

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

   if (
       0 <= direction < PI / 8
       or 1_5 * PI / 8 <= direction <= 2 * PI
       or 7 * PI / 8 <= direction <= 9 * PI / 8
   ):
    __A	:       str       =	sobel_grad[row, col - 1]
    __A	:       Optional[int]       =	sobel_grad[row, col + 1]
    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
     __A	:       Dict       =	sobel_grad[row, col]

   elif (
       PI / 8 <= direction < 3 * PI / 8
       or 9 * PI / 8 <= direction < 1_1 * PI / 8
   ):
    __A	:       List[str]       =	sobel_grad[row + 1, col - 1]
    __A	:       str       =	sobel_grad[row - 1, col + 1]
    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
     __A	:       Any       =	sobel_grad[row, col]

   elif (
       3 * PI / 8 <= direction < 5 * PI / 8
       or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
   ):
    __A	:       Union[str, Any]       =	sobel_grad[row - 1, col]
    __A	:       List[str]       =	sobel_grad[row + 1, col]
    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
     __A	:       str       =	sobel_grad[row, col]

   elif (
       5 * PI / 8 <= direction < 7 * PI / 8
       or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
   ):
    __A	:       str       =	sobel_grad[row - 1, col - 1]
    __A	:       Tuple       =	sobel_grad[row + 1, col + 1]
    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
     __A	:       str       =	sobel_grad[row, col]

 return destination




def 				a      (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Dict		)  ->		Any:






 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	:       int       =	strong
   elif destination[row, col] <= threshold_low:
    __A	:       List[str]       =	0
   else:
    __A	:       Any       =	weak




def 				a      (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				Optional[Any]		)  ->		int:






 for row in range(1     ,	image_shape[0]		):
  for col in range(1     ,	image_shape[1]		):
   if destination[row, col] == weak:
    if 2_5_5 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	:       Any       =	strong
    else:
     __A	:       Tuple       =	0




def 				a      (     UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Tuple=1_5     ,	UpperCamelCase_							:				Dict=3_0     ,	UpperCamelCase_							:				str=1_2_8     ,	UpperCamelCase_							:				Dict=2_5_5		)  ->		Tuple:
 __A	:       Union[str, Any]       =	img_convolve(UpperCamelCase__     ,	gen_gaussian_kernel(9     ,	sigma=1.4		)		)
 # get the gradient and degree by sobel_filter
 __A    ,					__A	:       int       =	sobel_filter(UpperCamelCase__		)
 __A	:       Dict       =	PI + np.radadeg(UpperCamelCase__		)

 __A	:       int       =	suppress_non_maximum(image.shape     ,	UpperCamelCase__     ,	UpperCamelCase__		)

 detect_high_low_threshold(
     image.shape     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__		)

 track_edge(image.shape     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__		)

 return destination


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













"""simple docstring"""


from string import ascii_uppercase




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				bool = False     ,	UpperCamelCase_							:				str = ascii_uppercase		)  ->		str:
 __A	:       List[str]       =	keyword.upper()
 __A	:       str       =	plaintext.upper()
 __A	:       int       =	set(UpperCamelCase_		)

 # create a list of unique characters in the keyword - their order matters
 # it determines how we will map plaintext characters to the ciphertext
 __A	:       int       =	[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(UpperCamelCase_		)
    # the number of those unique characters will determine the number of rows
 __A	:       Any       =	len(UpperCamelCase_		)

 # create a shifted version of the alphabet
 __A	:       str       =	unique_chars + [
     char for char in alphabet if char not in unique_chars
 ]

 # create a modified alphabet by splitting the shifted alphabet into rows
 __A	:       int       =	[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0     ,	2_6     ,	UpperCamelCase_		)
 ]

 # map the alphabet characters to the modified alphabet characters
 # going 'vertically' through the modified alphabet - consider columns first
 __A	:       Any       =	{}
 __A	:       Union[str, Any]       =	0
 for column in range(UpperCamelCase_		):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(UpperCamelCase_		) <= column:
    break

   # map current letter to letter in modified alphabet
   __A	:       str       =	row[column]
   letter_index += 1

 if verbose:
  print(UpperCamelCase_		)
 # create the encrypted text by mapping the plaintext to the modified alphabet
 return "".join(mapping[char] if char in mapping else char for char in plaintext		)


if __name__ == "__main__":
   # example use
   print(mixed_keyword("""college""", """UNIVERSITY"""))





