


"""simple docstring"""






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					from ..models.auto.modeling_auto import (
					    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
					    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
					    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
					    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
					)


UpperCamelCase__							:Union[str, Any]       =  logging.get_logger(__name__)


UpperCamelCase__							:Any       =  Dict[str, Any]
UpperCamelCase__							:Optional[Any]       =  List[Prediction]






@add_end_docstrings(lowerCamelCase__		)
class     A(     lowerCamelCase__		):

		"""simple docstring"""





		def __init__(   self				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

					"""simple docstring"""



					super().__init__(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )

					if self.framework == "tf":
								raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

					requires_backends(self				,							'''vision'''      )
					self.check_model_type(
					    dict(
					        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  {}
					_UpperCamelCase				:Optional[Any]				    =  {}
					if "subtask" in kwargs:
								_UpperCamelCase				:Any				    =  kwargs['''subtask''']
								_UpperCamelCase				:str				    =  kwargs['''subtask''']
					if "threshold" in kwargs:
								_UpperCamelCase				:List[Any]				    =  kwargs['''threshold''']
					if "mask_threshold" in kwargs:
								_UpperCamelCase				:List[str]				    =  kwargs['''mask_threshold''']
					if "overlap_mask_area_threshold" in kwargs:
								_UpperCamelCase				:Dict				    =  kwargs['''overlap_mask_area_threshold''']

					return preprocess_kwargs, {}, postprocess_kwargs
		def __call__(   self				,							SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  Union[Predictions, List[Prediction]]:

					"""simple docstring"""



					return super().__call__(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  load_image(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  [(image.height, image.width)]
					if self.model.config.__class__.__name__ == "OneFormerConfig":
								if subtask is None:
											_UpperCamelCase				:str				    =  {}
								else:
											_UpperCamelCase				:int				    =  {'''task_inputs''': [subtask]}
								_UpperCamelCase				:Any				    =  self.image_processor(images=[image]				,							return_tensors='''pt'''				,							**SCREAMING_SNAKE_CASE__      )
								_UpperCamelCase				:Optional[Any]				    =  self.tokenizer(
								    inputs['''task_inputs''']				,							padding='''max_length'''				,							max_length=self.model.config.task_seq_len				,							return_tensors=self.framework				,							)['''input_ids''']
					else:
								_UpperCamelCase				:List[Any]				    =  self.image_processor(images=[image]				,							return_tensors='''pt'''      )
					_UpperCamelCase				:Union[str, Any]				    =  target_size
					return inputs
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  model_inputs.pop('''target_size'''      )
					_UpperCamelCase				:str				    =  self.model(**SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  target_size
					return model_outputs





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=0.9				,							SCREAMING_SNAKE_CASE__=0.5				,							SCREAMING_SNAKE_CASE__=0.5      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  None
					if subtask in {"panoptic", None} and hasattr(self.image_processor				,							'''post_process_panoptic_segmentation'''      ):
								_UpperCamelCase				:int				    =  self.image_processor.post_process_panoptic_segmentation
					elif subtask in {"instance", None} and hasattr(self.image_processor				,							'''post_process_instance_segmentation'''      ):
								_UpperCamelCase				:Dict				    =  self.image_processor.post_process_instance_segmentation

					if fn is not None:
								_UpperCamelCase				:Dict				    =  fn(
								    SCREAMING_SNAKE_CASE__				,							threshold=SCREAMING_SNAKE_CASE__				,							mask_threshold=SCREAMING_SNAKE_CASE__				,							overlap_mask_area_threshold=SCREAMING_SNAKE_CASE__				,							target_sizes=model_outputs['''target_size''']				,							)[0]

								_UpperCamelCase				:List[str]				    =  []
								_UpperCamelCase				:List[Any]				    =  outputs['''segmentation''']

								for segment in outputs["segments_info"]:
											_UpperCamelCase				:Dict				    =  (segmentation == segment['''id''']) * 2_55
											_UpperCamelCase				:Union[str, Any]				    =  Image.fromarray(mask.numpy().astype(np.uinta      )				,							mode='''L'''      )
											_UpperCamelCase				:int				    =  self.model.config.idalabel[segment['''label_id''']]
											_UpperCamelCase				:Optional[Any]				    =  segment['''score''']
											annotation.append({'''score''': score, '''label''': label, '''mask''': mask}      )

					elif subtask in {"semantic", None} and hasattr(self.image_processor				,							'''post_process_semantic_segmentation'''      ):
								_UpperCamelCase				:Optional[int]				    =  self.image_processor.post_process_semantic_segmentation(
								    SCREAMING_SNAKE_CASE__				,							target_sizes=model_outputs['''target_size''']      )[0]

								_UpperCamelCase				:List[str]				    =  []
								_UpperCamelCase				:int				    =  outputs.numpy()
								_UpperCamelCase				:List[Any]				    =  np.unique(SCREAMING_SNAKE_CASE__      )

								for label in labels:
											_UpperCamelCase				:int				    =  (segmentation == label) * 2_55
											_UpperCamelCase				:List[Any]				    =  Image.fromarray(mask.astype(np.uinta      )				,							mode='''L'''      )
											_UpperCamelCase				:str				    =  self.model.config.idalabel[label]
											annotation.append({'''score''': None, '''label''': label, '''mask''': mask}      )
					else:
								raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model      )}"      )
					return annotation





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

