
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 			UpperCamelCase_			(						lowerCAmelCase__   ):




       '''simple docstring'''

       UpperCamelCase_					   =							["""image_processor""", """tokenizer"""]
       UpperCamelCase_					   =							"""AutoImageProcessor"""
       UpperCamelCase_					   =							"""AutoTokenizer"""
       def __init__(				self     ,						UpperCamelCase=None     ,						UpperCamelCase=None     ,						**UpperCamelCase) ->	Optional[Any]:
          UpperCamelCase__    :		Tuple     =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'     ,						lowerCAmelCase_     ,						)
             UpperCamelCase__    :		Optional[int]     =				kwargs.pop('feature_extractor')

          UpperCamelCase__    :		Any     =				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.')
          if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.')

          super().__init__(lowerCAmelCase_     ,						lowerCAmelCase_)
          UpperCamelCase__    :		Tuple     =				self.image_processor
          UpperCamelCase__    :		str     =				False
       def __call__(				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	str:






          if self._in_target_context_manager:
             return self.current_processor(*lowerCAmelCase_     ,						**lowerCAmelCase_)

          UpperCamelCase__    :		List[Any]     =				kwargs.pop('images'     ,						lowerCAmelCase_)
          UpperCamelCase__    :		Union[str, Any]     =				kwargs.pop('text'     ,						lowerCAmelCase_)
          if len(lowerCAmelCase_) > 0:
             UpperCamelCase__    :		List[str]     =				args[0]
             UpperCamelCase__    :		List[Any]     =				args[1:]

          if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.')

          if images is not None:
             UpperCamelCase__    :		Dict     =				self.image_processor(lowerCAmelCase_     ,						*lowerCAmelCase_     ,						**lowerCAmelCase_)
          if text is not None:
             UpperCamelCase__    :		List[Any]     =				self.tokenizer(lowerCAmelCase_     ,						**lowerCAmelCase_)

          if text is None:
             return inputs
          elif images is None:
             return encodings
          else:
             UpperCamelCase__    :		Any     =				encodings['input_ids']
             return inputs
       def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	Dict:
          return self.tokenizer.batch_decode(*lowerCAmelCase_     ,						**lowerCAmelCase_)
       def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	int:
          return self.tokenizer.decode(*lowerCAmelCase_     ,						**lowerCAmelCase_)
       @contextmanager
       def        lowerCAmelCase__ (				self) ->	List[Any]:
          warnings.warn(
              '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
              'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
              'your images inputs, or in a separate call.')
          UpperCamelCase__    :		Tuple     =				True
          UpperCamelCase__    :		str     =				self.tokenizer
          yield
          UpperCamelCase__    :		Optional[Any]     =				self.image_processor
          UpperCamelCase__    :		Any     =				False
       @property
       def        lowerCAmelCase__ (				self) ->	Optional[int]:
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,						lowerCAmelCase_     ,						)
          return self.image_processor_class






       @property
       def        lowerCAmelCase__ (				self) ->	Tuple:
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,						lowerCAmelCase_     ,						)
          return self.image_processor





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__				:  str       					=				180



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Dict     =				k_size // 2
			UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCamelCase__    :		int     =				(
			    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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Dict     =				np.zeros(__SCREAMING_SNAKE_CASE )

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

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

									elif (
									    3 * PI / 8 <= direction < 5 * PI / 8
									    or 11 * PI / 8 <= direction < 13 * PI / 8
									):
												UpperCamelCase__    :		Optional[Any]     =				sobel_grad[row - 1, col]
												UpperCamelCase__    :		Dict     =				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__    :		Tuple     =				sobel_grad[row - 1, col - 1]
												UpperCamelCase__    :		Optional[int]     =				sobel_grad[row + 1, col + 1]
												if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
															UpperCamelCase__    :		Optional[Any]     =				sobel_grad[row, col]

			return destination



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			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__    :		str     =				strong
									elif destination[row, col] <= threshold_low:
												UpperCamelCase__    :		List[str]     =				0
									else:
												UpperCamelCase__    :		int     =				weak



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			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],
												):
															UpperCamelCase__    :		List[str]     =				strong
												else:
															UpperCamelCase__    :		Union[str, Any]     =				0



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=15				,    __SCREAMING_SNAKE_CASE=30				,    __SCREAMING_SNAKE_CASE=128				,    __SCREAMING_SNAKE_CASE=255 )	->     Any:
			# gaussian_filter
			UpperCamelCase__    :		Optional[int]     =				img_convolve(__SCREAMING_SNAKE_CASE				,    gen_gaussian_kernel(9				,    sigma=1.4 ) )
			# get the gradient and degree by sobel_filter
			UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				sobel_filter(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Tuple     =				PI + np.radadeg(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Any     =				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__				:  Tuple       					=				canny(lena)
							cva.imshow('''canny''', canny_destination)
							cva.waitKey(0)



