


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
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_VISUAL_QUESTION_ANSWERING_MAPPING

a					  =     logging.get_logger(__name__)







@add_end_docstrings(__magic_name__   )
class        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   List[Any]							,					*UpperCamelCase__       :   List[Any]							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										super().__init__(*UpperCamelCase__							,					**UpperCamelCase__       )
										self.check_model_type(UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   str=None							,					UpperCamelCase__       :   Optional[int]=None							,					UpperCamelCase__       :   Optional[Any]=None							,					**UpperCamelCase__       :   List[str]       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			{}, {}
										if padding is not None:
															lowercase_  							=			padding
										if truncation is not None:
															lowercase_  							=			truncation
										if top_k is not None:
															lowercase_  							=			top_k
										return preprocess_params, {}, postprocess_params

					def __call__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union["Image.Image", str]							,					UpperCamelCase__       :   str = None							,					**UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										if isinstance(UpperCamelCase__							,					(Image.Image, str)       ) and isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
															lowercase_  							=			{"""image""": image, """question""": question}
										else:
															lowercase_  							=			image
										lowercase_  							=			super().__call__(UpperCamelCase__							,					**UpperCamelCase__       )
										return results

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str=False							,					UpperCamelCase__       :   List[str]=False       ):






										'''simple docstring'''






										lowercase_  							=			load_image(inputs["""image"""]       )
										lowercase_  							=			self.tokenizer(
										    inputs["""question"""]							,					return_tensors=self.framework							,					padding=UpperCamelCase__							,					truncation=UpperCamelCase__       )
										lowercase_  							=			self.image_processor(images=UpperCamelCase__							,					return_tensors=self.framework       )
										model_inputs.update(UpperCamelCase__       )
										return model_inputs

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.model(**UpperCamelCase__       )
										return model_outputs







					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Union[str, Any]=5       ):






										'''simple docstring'''






										if top_k > self.model.config.num_labels:
															lowercase_  							=			self.model.config.num_labels

										if self.framework == "pt":
															lowercase_  							=			model_outputs.logits.sigmoid()[0]
															lowercase_							,					lowercase_  							=			probs.topk(UpperCamelCase__       )
										else:
															raise ValueError(F'''Unsupported framework: {self.framework}'''       )

										lowercase_  							=			scores.tolist()
										lowercase_  							=			ids.tolist()
										return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__							,					UpperCamelCase__       )]






import cva
import numpy as np







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										if k in (0.04, 0.06):
															lowercase_  							=			k
															lowercase_  							=			window_size
										else:
															raise ValueError("""invalid k value"""       )

					def __str__(     self       :   Optional[int]       ):






										'''simple docstring'''






										return str(self.k       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			cva.imread(UpperCamelCase__							,					0       )
										lowercase_							,					lowercase_  							=			img.shape
										lowercase_  							=			[]
										lowercase_  							=			img.copy()
										lowercase_  							=			cva.cvtColor(UpperCamelCase__							,					cva.COLOR_GRAY2RGB       )
										lowercase_							,					lowercase_  							=			np.gradient(UpperCamelCase__       )
										lowercase_  							=			dx**2
										lowercase_  							=			dy**2
										lowercase_  							=			dx * dy
										lowercase_  							=			0.04
										lowercase_  							=			self.window_size // 2
										for y in range(UpperCamelCase__							,					h - offset       ):
															for x in range(UpperCamelCase__							,					w - offset       ):
																				lowercase_  							=			ixx[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			iyy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			ixy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()

																				lowercase_  							=			(wxx * wyy) - (wxy**2)
																				lowercase_  							=			wxx + wyy
																				lowercase_  							=			det - k * (trace**2)
																				# Can change the value
																				if r > 0.5:
																									corner_list.append([x, y, r]       )
																									color_img.itemset((y, x, 0)							,					0       )
																									color_img.itemset((y, x, 1)							,					0       )
																									color_img.itemset((y, x, 2)							,					255       )
										return color_img, corner_list


if __name__ == "__main__":
							a					  =     HarrisCorner(0.04, 3)
							a  ,					a					  =     edge_detect.detect('path_to_image')
							cva.imwrite('detect.png', color_img)


