

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

lowerCAmelCase__             =		1_8_0






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Tuple						)    ->					Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    k_size // 2
	_UpperCamelCase						,		_UpperCamelCase :		List[str]								=    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	_UpperCamelCase :		Union[str, Any]								=    (
	    1
	    / (2 * np.pi * sigma)
	    * np.exp(-(np.square(UpperCAmelCase_						) + np.square(UpperCAmelCase_						)) / (2 * np.square(UpperCAmelCase_						))						)
	)
	return g






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    np.zeros(UpperCAmelCase_						)

	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 1_5 * PI / 8 <= direction <= 2 * PI
			    or 7 * PI / 8 <= direction <= 9 * PI / 8
			):
				_UpperCamelCase :		Optional[Any]								=    sobel_grad[row, col - 1]
				_UpperCamelCase :		str								=    sobel_grad[row, col + 1]
				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
					_UpperCamelCase :		Optional[int]								=    sobel_grad[row, col]

			elif (
			    PI / 8 <= direction < 3 * PI / 8
			    or 9 * PI / 8 <= direction < 1_1 * PI / 8
			):
				_UpperCamelCase :		Optional[Any]								=    sobel_grad[row + 1, col - 1]
				_UpperCamelCase :		Tuple								=    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 1_1 * PI / 8 <= direction < 1_3 * PI / 8
			):
				_UpperCamelCase :		Optional[Any]								=    sobel_grad[row - 1, col]
				_UpperCamelCase :		List[Any]								=    sobel_grad[row + 1, col]
				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
					_UpperCamelCase :		int								=    sobel_grad[row, col]

			elif (
			    5 * PI / 8 <= direction < 7 * PI / 8
			    or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
			):
				_UpperCamelCase :		Any								=    sobel_grad[row - 1, col - 1]
				_UpperCamelCase :		Tuple								=    sobel_grad[row + 1, col + 1]
				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
					_UpperCamelCase :		Any								=    sobel_grad[row, col]

	return destination






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  Optional[Any]						)    ->					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:
				_UpperCamelCase :		Union[str, Any]								=    strong
			elif destination[row, col] <= threshold_low:
				_UpperCamelCase :		List[str]								=    0
			else:
				_UpperCamelCase :		str								=    weak






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  int						)    ->					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 2_5_5 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 :		Optional[Any]								=    strong
				else:
					_UpperCamelCase :		Optional[Any]								=    0






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  str=1_5				,							UpperCAmelCase_				:  List[Any]=3_0				,							UpperCAmelCase_				:  Any=1_2_8				,							UpperCAmelCase_				:  Optional[int]=2_5_5						)    ->					Dict:




	'''simple docstring'''




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

	_UpperCamelCase :		int								=    suppress_non_maximum(image.shape				,							UpperCAmelCase_				,							UpperCAmelCase_						)

	detect_high_low_threshold(
	    image.shape				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)

	track_edge(image.shape				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)

	return destination


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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__             =		{"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ViTHybridForImageClassification""",
							    """ViTHybridModel""",
							    """ViTHybridPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




