
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     :				List[Any]             =       1_80
def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       k_size // 2
					lowerCAmelCase		,							lowerCAmelCase						:   Any							=       np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					lowerCAmelCase						:   Tuple							=       (
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(__magic_name__					) + np.square(__magic_name__					)) / (2 * np.square(__magic_name__					))					)
					)
					return g
def       UpperCAmelCase__							(    __magic_name__  :       Dict							,					__magic_name__  :       int							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       np.zeros(__magic_name__					)

					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						:   int							=       sobel_grad[row, col - 1]
																				lowerCAmelCase						:   Union[str, Any]							=       sobel_grad[row, col + 1]
																				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																									lowerCAmelCase						:   Optional[int]							=       sobel_grad[row, col]

															elif (
															    PI / 8 <= direction < 3 * PI / 8
															    or 9 * PI / 8 <= direction < 11 * PI / 8
															):
																				lowerCAmelCase						:   Optional[int]							=       sobel_grad[row + 1, col - 1]
																				lowerCAmelCase						:   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						:   Optional[int]							=       sobel_grad[row - 1, col]
																				lowerCAmelCase						:   Any							=       sobel_grad[row + 1, col]
																				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																									lowerCAmelCase						:   List[Any]							=       sobel_grad[row, col]

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

					return destination
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       Tuple							,					__magic_name__  :       Tuple							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       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:
																				lowerCAmelCase						:   Optional[int]							=       strong
															elif destination[row, col] <= threshold_low:
																				lowerCAmelCase						:   int							=       0
															else:
																				lowerCAmelCase						:   Union[str, Any]							=       weak
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					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						:   Dict							=       strong
																				else:
																									lowerCAmelCase						:   List[str]							=       0
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       str=15							,					__magic_name__  :       Dict=30							,					__magic_name__  :       int=1_28							,					__magic_name__  :       Optional[Any]=2_55					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       img_convolve(__magic_name__							,					gen_gaussian_kernel(9							,					sigma=1.4					)					)
					# get the gradient and degree by sobel_filter
					lowerCAmelCase		,							lowerCAmelCase						:   Union[str, Any]							=       sobel_filter(__magic_name__					)
					lowerCAmelCase						:   Any							=       PI + np.radadeg(__magic_name__					)

					lowerCAmelCase						:   Any							=       suppress_non_maximum(image.shape							,					__magic_name__							,					__magic_name__					)

					detect_high_low_threshold(
					    image.shape							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)

					track_edge(image.shape							,					__magic_name__							,					__magic_name__							,					__magic_name__					)

					return destination


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


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
