



"""simple docstring"""







from collections import namedtuple

UpperCAmelCase:  Tuple			     =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase:  Union[str, Any]			     =				{
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.0_000_098, 101_325),
    """bar""": from_to(0.986_923, 1.01_325),
    """kilopascal""": from_to(0.00_986_923, 101.325),
    """megapascal""": from_to(9.86_923, 0.101_325),
    """psi""": from_to(0.068_046, 14.6_959),
    """inHg""": from_to(0.0_334_211, 29.9_213),
    """torr""": from_to(0.00_131_579, 760),
}


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		if from_type not in PRESSURE_CONVERSION:
				raise ValueError(
				    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
				    + """, """.join(__UpperCAmelCase      )      )
		if to_type not in PRESSURE_CONVERSION:
				raise ValueError(
				    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
				    + """, """.join(__UpperCAmelCase      )      )
		return (
		    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





'''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

_snake_case					: Union[str, Any]     			=					180



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



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

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

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

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

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

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

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: 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],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



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

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


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