
'''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   :      Union[str, Any] 						=  180







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      Union[str, Any]:
		_UpperCamelCase       : str    =      k_size // 2
		_UpperCamelCase       : str    =      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		_UpperCamelCase       : Any    =      (
		    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 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      str:
		_UpperCamelCase       : List[Any]    =      np.zeros(_SCREAMING_SNAKE_CASE	)

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

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

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

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

		return destination







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Optional[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:
								_UpperCamelCase       : Tuple    =      strong
						elif destination[row, col] <= threshold_low:
								_UpperCamelCase       : Optional[Any]    =      0
						else:
								_UpperCamelCase       : int    =      weak







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Tuple:
		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],
								):
										_UpperCamelCase       : List[Any]    =      strong
								else:
										_UpperCamelCase       : Optional[Any]    =      0







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=15			,UpperCamelCase=30			,UpperCamelCase=1_28			,UpperCamelCase=2_55	) ->      Tuple:
		# gaussian_filter
		_UpperCamelCase       : List[str]    =      img_convolve(_SCREAMING_SNAKE_CASE			,gen_gaussian_kernel(9			,sigma=1.4	)	)
		# get the gradient and degree by sobel_filter
		_UpperCamelCase       : Union[str, Any]    =      sobel_filter(_SCREAMING_SNAKE_CASE	)
		_UpperCamelCase       : Tuple    =      PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

		_UpperCamelCase       : Tuple    =      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
	_UpperCAmelCase   :      List[str] 						=  cva.imread(R"""../image_data/lena.jpg""", 0)
	# canny edge detection
	_UpperCAmelCase   :      int 						=  canny(lena)
	cva.imshow("""canny""", canny_destination)
	cva.waitKey(0)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase   :      int 						=  {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase   :      int 						=  [
	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTSanJapaneseForConditionalGeneration""",
	    """GPTSanJapaneseModel""",
	    """GPTSanJapanesePreTrainedModel""",
	]
	_UpperCAmelCase   :      str 						=  [
	    """GPTSanJapaneseTokenizer""",
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





