



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							=       180

def       A_   (						__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		k_size // 2
				a__   ,      a__					   =		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				a__					   =		(
				    1
				    / (2 * np.pi * sigma)
				    * np.exp(-(np.square(__a		) + np.square(__a		)) / (2 * np.square(__a		))		)
				)
				return g

def       A_   (						__a  :					Optional[int]			,		__a  :					Tuple			,		__a  :					List[str]		):

				"""simple docstring"""
				a__					   =		np.zeros(__a		)

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

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

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

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

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

				return destination

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]			,		__a  :					int			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

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

def       A_   (						__a  :					Tuple			,		__a  :					int			,		__a  :					List[str]			,		__a  :					Union[str, 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],
																):
																				a__					   =		strong
																else:
																				a__					   =		0

def       A_   (						__a  :					str			,		__a  :					str=15			,		__a  :					Optional[int]=30			,		__a  :					Dict=128			,		__a  :					List[str]=255		):

				"""simple docstring"""
				# gaussian_filter
				a__					   =		img_convolve(__a			,		gen_gaussian_kernel(9			,		sigma=1.4		)		)
				# get the gradient and degree by sobel_filter
				a__   ,      a__					   =		sobel_filter(__a		)
				a__					   =		PI + np.radadeg(__a		)

				a__					   =		suppress_non_maximum(image.shape			,		__a			,		__a		)

				detect_high_low_threshold(
				    image.shape			,		__a			,		__a			,		__a			,		__a			,		__a		)

				track_edge(image.shape			,		__a			,		__a			,		__a		)

				return destination


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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_A					:     Optional[int]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
