






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


A_       :Any       =						logging.get_logger(__name__)




class 				__A     (			a ):





				"""simple docstring"""







				UpperCamelCase__ :      int			 =["""pixel_values"""]


				def __init__( self							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = PIL.Image.BICUBIC							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 1 / 255							,  lowerCamelCase__ = True							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									super().__init__(**lowerCamelCase__    )
									__UpperCamelCase      :					Optional[Any]       		=size if size is not None else {'height': 256, 'width': 256}
									__UpperCamelCase      :					List[str]       		=get_size_dict(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		=crop_size if crop_size is not None else {'height': 224, 'width': 224}
									__UpperCamelCase      :					Optional[int]       		=get_size_dict(lowerCamelCase__							,  param_name='crop_size'    )

									__UpperCamelCase      :					Tuple       		=do_resize
									__UpperCamelCase      :					Any       		=size
									__UpperCamelCase      :					Dict       		=resample
									__UpperCamelCase      :					Tuple       		=do_center_crop
									__UpperCamelCase      :					int       		=crop_size
									__UpperCamelCase      :					Optional[int]       		=do_rescale
									__UpperCamelCase      :					List[Any]       		=rescale_factor
									__UpperCamelCase      :					List[Any]       		=do_normalize
									__UpperCamelCase      :					Union[str, Any]       		=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__UpperCamelCase      :					Optional[Any]       		=image_std if image_std is not None else IMAGENET_STANDARD_STD


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = PIL.Image.BICUBIC							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=get_size_dict(lowerCamelCase__    )
									if "height" not in size or "width" not in size:
														raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'    )
									return resize(
									    lowerCamelCase__							,  size=(size['height'], size['width'])							,  resample=lowerCamelCase__							,  data_format=lowerCamelCase__							,  **lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[int]       		=get_size_dict(lowerCamelCase__    )
									if "height" not in size or "width" not in size:
														raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'    )
									return center_crop(lowerCamelCase__							,  size=(size['height'], size['width'])							,  data_format=lowerCamelCase__							,  **lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									return rescale(lowerCamelCase__							,  scale=lowerCamelCase__							,  data_format=lowerCamelCase__							,  **lowerCamelCase__    )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									return normalize(lowerCamelCase__							,  mean=lowerCamelCase__							,  std=lowerCamelCase__							,  data_format=lowerCamelCase__							,  **lowerCamelCase__    )







				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__=None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = ChannelDimension.FIRST							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=do_resize if do_resize is not None else self.do_resize
									__UpperCamelCase      :					Union[str, Any]       		=resample if resample is not None else self.resample
									__UpperCamelCase      :					Optional[Any]       		=do_center_crop if do_center_crop is not None else self.do_center_crop
									__UpperCamelCase      :					Any       		=do_rescale if do_rescale is not None else self.do_rescale
									__UpperCamelCase      :					Dict       		=rescale_factor if rescale_factor is not None else self.rescale_factor
									__UpperCamelCase      :					Optional[Any]       		=do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase      :					Optional[Any]       		=image_mean if image_mean is not None else self.image_mean
									__UpperCamelCase      :					Any       		=image_std if image_std is not None else self.image_std

									__UpperCamelCase      :					Union[str, Any]       		=size if size is not None else self.size
									__UpperCamelCase      :					List[str]       		=get_size_dict(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		=crop_size if crop_size is not None else self.crop_size
									__UpperCamelCase      :					Tuple       		=get_size_dict(lowerCamelCase__							,  param_name='crop_size'    )

									__UpperCamelCase      :					List[Any]       		=make_list_of_images(lowerCamelCase__    )

									if not valid_images(lowerCamelCase__    ):
														raise ValueError(
														    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
														    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

									if do_resize and size is None or resample is None:
														raise ValueError('Size and resample must be specified if do_resize is True.'    )

									if do_center_crop and crop_size is None:
														raise ValueError('Crop size must be specified if do_center_crop is True.'    )

									if do_rescale and rescale_factor is None:
														raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

									if do_normalize and (image_mean is None or image_std is None):
														raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

									# All transformations expect numpy arrays.
									__UpperCamelCase      :					Optional[int]       		=[to_numpy_array(lowerCamelCase__    ) for image in images]

									if do_resize:
														__UpperCamelCase      :					Optional[Any]       		=[self.resize(image=lowerCamelCase__							,  size=lowerCamelCase__							,  resample=lowerCamelCase__    ) for image in images]

									if do_center_crop:
														__UpperCamelCase      :					int       		=[self.center_crop(image=lowerCamelCase__							,  size=lowerCamelCase__    ) for image in images]

									if do_rescale:
														__UpperCamelCase      :					int       		=[self.rescale(image=lowerCamelCase__							,  scale=lowerCamelCase__    ) for image in images]

									if do_normalize:
														__UpperCamelCase      :					Optional[int]       		=[self.normalize(image=lowerCamelCase__							,  mean=lowerCamelCase__							,  std=lowerCamelCase__    ) for image in images]

									__UpperCamelCase      :					Optional[Any]       		=[to_channel_dimension_format(lowerCamelCase__							,  lowerCamelCase__    ) for image in images]

									__UpperCamelCase      :					List[str]       		={'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__							,  tensor_type=lowerCamelCase__    )








import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				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)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



