



'''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,
							)


SCREAMING_SNAKE_CASE__     : Tuple				=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     : Optional[int]				=   Dict[str, Any]
SCREAMING_SNAKE_CASE__     : List[str]				=   List[Prediction]




@add_end_docstrings(lowerCAmelCase__      )
class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							Optional[int]      ,       *SCREAMING_SNAKE_CASE  :							int      ,       **SCREAMING_SNAKE_CASE  :							Dict	):




					"""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 				_lowercase	(   self  :							Dict      ,       **SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      {}
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {}
					if "subtask" in kwargs:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      kwargs['subtask']
							SCREAMING_SNAKE_CASE_						:str										=      kwargs['subtask']
					if "threshold" in kwargs:
							SCREAMING_SNAKE_CASE_						:List[str]										=      kwargs['threshold']
					if "mask_threshold" in kwargs:
							SCREAMING_SNAKE_CASE_						:str										=      kwargs['mask_threshold']
					if "overlap_mask_area_threshold" in kwargs:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      kwargs['overlap_mask_area_threshold']

					return preprocess_kwargs, {}, postprocess_kwargs






			def __call__(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       **SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					return super().__call__(SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      model_inputs.pop('target_size'	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.model(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      target_size
					return model_outputs






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=None      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.9      ,       SCREAMING_SNAKE_CASE  :							List[str]=0.5      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=0.5	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      None
					if subtask in {"panoptic", None} and hasattr(self.image_processor      ,       'post_process_panoptic_segmentation'	):
							SCREAMING_SNAKE_CASE_						:Any										=      self.image_processor.post_process_panoptic_segmentation
					elif subtask in {"instance", None} and hasattr(self.image_processor      ,       'post_process_instance_segmentation'	):
							SCREAMING_SNAKE_CASE_						:List[str]										=      self.image_processor.post_process_instance_segmentation

					if fn is not None:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      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]

							SCREAMING_SNAKE_CASE_						:str										=      []
							SCREAMING_SNAKE_CASE_						:Dict										=      outputs['segmentation']

							for segment in outputs["segments_info"]:
									SCREAMING_SNAKE_CASE_						:Any										=      (segmentation == segment['id']) * 255
									SCREAMING_SNAKE_CASE_						:Any										=      Image.fromarray(mask.numpy().astype(np.uinta	)      ,       mode='L'	)
									SCREAMING_SNAKE_CASE_						:int										=      self.model.config.idalabel[segment['label_id']]
									SCREAMING_SNAKE_CASE_						:Dict										=      segment['score']
									annotation.append({'score': score, 'label': label, 'mask': mask}	)

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

							SCREAMING_SNAKE_CASE_						:Dict										=      []
							SCREAMING_SNAKE_CASE_						:str										=      outputs.numpy()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.unique(SCREAMING_SNAKE_CASE	)

							for label in labels:
									SCREAMING_SNAKE_CASE_						:Tuple										=      (segmentation == label) * 255
									SCREAMING_SNAKE_CASE_						:Optional[Any]										=      Image.fromarray(mask.astype(np.uinta	)      ,       mode='L'	)
									SCREAMING_SNAKE_CASE_						:Any										=      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




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 0
								# max greyscale value for #FFFFFF
								lowercase_			 = int(self.get_greyscale(255						,       255						,       255  )  )

								if not self.min_threshold < threshold < self.max_threshold:
									lowercase_			 = F'Factor value should be from 0 to {self.max_threshold}'
									raise ValueError(UpperCAmelCase  )

								lowercase_			 = input_img
								lowercase_			 = threshold
								lowercase_ ,	lowercase_			 = self.input_img.shape[1], self.input_img.shape[0]

								# error table size (+4 columns and +1 row) greater than input image because of
								# lack of if statements
								lowercase_			 = [
								    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
								]
								lowercase_			 = np.ones((self.width, self.height, 3)						,       np.uinta  ) * 255




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       float:






								'''simple docstring'''






								return 0.114 * blue + 0.587 * green + 0.2126 * red




							def 				A__      (  self  )     ->       None:






								'''simple docstring'''






								for y in range(self.height  ):
									for x in range(self.width  ):
										lowercase_			 = int(self.get_greyscale(*self.input_img[y][x]  )  )
										if self.threshold > greyscale + self.error_table[y][x]:
											lowercase_			 = (0, 0, 0)
											lowercase_			 = greyscale + self.error_table[x][y]
										else:
											lowercase_			 = (255, 255, 255)
											lowercase_			 = greyscale + self.error_table[x][y] - 255
										self.error_table[y][x + 1] += int(8 / 32 * current_error  )
										self.error_table[y][x + 2] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x] += int(8 / 32 * current_error  )
										self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
										self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							SCREAMING_SNAKE_CASE__       			=				[
							    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f"""Original image with dithering threshold: {burkes.threshold}""",
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

