


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

__SCREAMING_SNAKE_CASE	:							Any           =  180

def 		snake_case_   (				lowercase__     :    str       ,     lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=k_size // 2
				_lowerCAmelCase						,							_lowerCAmelCase  						=np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_lowerCAmelCase  						=(
				    1
				    / (2 * np.pi * sigma)
				    * np.exp(-(np.square(lowercase__     ) + np.square(lowercase__     )) / (2 * np.square(lowercase__     ))     )
				)
				return g

def 		snake_case_   (				lowercase__     :    int       ,     lowercase__     :    List[str]       ,     lowercase__     :    Optional[Any]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=np.zeros(lowercase__     )

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

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

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

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

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

				return destination

def 		snake_case_   (				lowercase__     :    str       ,     lowercase__     :    Dict       ,     lowercase__     :    Any       ,     lowercase__     :    Dict       ,     lowercase__     :    List[str]       ,     lowercase__     :    str     ):






				'''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:
																_lowerCAmelCase  						=strong
												elif destination[row, col] <= threshold_low:
																_lowerCAmelCase  						=0
												else:
																_lowerCAmelCase  						=weak

def 		snake_case_   (				lowercase__     :    Optional[int]       ,     lowercase__     :    Any       ,     lowercase__     :    Tuple       ,     lowercase__     :    Optional[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_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],
																):
																				_lowerCAmelCase  						=strong
																else:
																				_lowerCAmelCase  						=0

def 		snake_case_   (				lowercase__     :    Dict       ,     lowercase__     :    List[Any]=15       ,     lowercase__     :    Optional[int]=30       ,     lowercase__     :    Optional[Any]=1_28       ,     lowercase__     :    Tuple=2_55     ):






				'''simple docstring'''






				_lowerCAmelCase  						=img_convolve(lowercase__       ,     gen_gaussian_kernel(9       ,     sigma=1.4     )     )
				# get the gradient and degree by sobel_filter
				_lowerCAmelCase						,							_lowerCAmelCase  						=sobel_filter(lowercase__     )
				_lowerCAmelCase  						=PI + np.radadeg(lowercase__     )

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

				detect_high_low_threshold(
				    image.shape       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__     )

				track_edge(image.shape       ,     lowercase__       ,     lowercase__       ,     lowercase__     )

				return destination


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





from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

