





'''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

lowercase__		:    str =       1_80







def       a__       (       lowercase      :  Tuple,      lowercase      :  Any   )	->	Dict:





					"""simple docstring"""

					_UpperCamelCase       					= k_size // 2
					_UpperCamelCase					,       _UpperCamelCase       					= np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					_UpperCamelCase       					= (
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(__snake_case   ) + np.square(__snake_case   )) / (2 * np.square(__snake_case   ))   )
					)
					return g







def       a__       (       lowercase      :  int,      lowercase      :  Dict,      lowercase      :  Union[str, Any]   )	->	int:





					"""simple docstring"""

					_UpperCamelCase       					= np.zeros(__snake_case   )

					for row in range(1,      image_shape[0] - 1   ):
										for col in range(1,      image_shape[1] - 1   ):
															_UpperCamelCase       					= 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       					= sobel_grad[row, col - 1]
																				_UpperCamelCase       					= sobel_grad[row, col + 1]
																				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																									_UpperCamelCase       					= sobel_grad[row, col]

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

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

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

					return destination







def       a__       (       lowercase      :  Union[str, Any],      lowercase      :  Dict,      lowercase      :  int,      lowercase      :  Dict,      lowercase      :  Tuple,      lowercase      :  str   )	->	Optional[int]:





					"""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:
																				_UpperCamelCase       					= strong
															elif destination[row, col] <= threshold_low:
																				_UpperCamelCase       					= 0
															else:
																				_UpperCamelCase       					= weak







def       a__       (       lowercase      :  Dict,      lowercase      :  Tuple,      lowercase      :  Optional[int],      lowercase      :  int   )	->	List[Any]:





					"""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],
																				):
																									_UpperCamelCase       					= strong
																				else:
																									_UpperCamelCase       					= 0







def       a__       (       lowercase      :  Tuple,      lowercase      :  List[str]=15,      lowercase      :  Optional[int]=30,      lowercase      :  int=128,      lowercase      :  int=255   )	->	List[Any]:





					"""simple docstring"""

					_UpperCamelCase       					= img_convolve(__snake_case,      gen_gaussian_kernel(9,      sigma=1.4   )   )
					# get the gradient and degree by sobel_filter
					_UpperCamelCase					,       _UpperCamelCase       					= sobel_filter(__snake_case   )
					_UpperCamelCase       					= PI + np.radadeg(__snake_case   )

					_UpperCamelCase       					= suppress_non_maximum(image.shape,      __snake_case,      __snake_case   )

					detect_high_low_threshold(
					    image.shape,      __snake_case,      __snake_case,      __snake_case,      __snake_case,      __snake_case   )

					track_edge(image.shape,      __snake_case,      __snake_case,      __snake_case   )

					return destination


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










'''simple docstring'''

lowercase__		:    Tuple =       '\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowercase__		:    Union[str, Any] =       [{'type': 'code', 'content': INSTALL_CONTENT}]
lowercase__		:    str =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



