



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 :				Tuple							=180



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					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(_lowerCAmelCase) + np.square(_lowerCAmelCase)) / (2 * np.square(_lowerCAmelCase)))
					)
					return g



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= np.zeros(_lowerCAmelCase)

					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  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					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  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					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_			= strong
																				else:
																									UpperCamelCase_			= 0



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase=15 ,						_lowerCAmelCase=30 ,						_lowerCAmelCase=1_28 ,						_lowerCAmelCase=2_55):
					# gaussian_filter
					UpperCamelCase_			= img_convolve(_lowerCAmelCase ,						gen_gaussian_kernel(9 ,						sigma=1.4))
					# get the gradient and degree by sobel_filter
					UpperCamelCase_	,     UpperCamelCase_			= sobel_filter(_lowerCAmelCase)
					UpperCamelCase_			= PI + np.radadeg(_lowerCAmelCase)

					UpperCamelCase_			= suppress_non_maximum(image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase)

					detect_high_low_threshold(
					    image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					track_edge(image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					return destination


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
