


"""simple docstring"""






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


UpperCamelCase__							:str       =  logging.get_logger(__name__)






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       ["pixel_values"]
		def __init__(   self				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = PIL.Image.NEAREST				,							SCREAMING_SNAKE_CASE__ = False				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = 1 / 2_55				,							SCREAMING_SNAKE_CASE__ = False				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = True				,							**SCREAMING_SNAKE_CASE__				,							)			->  None:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Optional[Any]				    =  size if size is not None else {'''height''': 3_46, '''width''': 3_46}
					_UpperCamelCase				:List[str]				    =  get_size_dict(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Any				    =  crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
					_UpperCamelCase				:List[str]				    =  get_size_dict(SCREAMING_SNAKE_CASE__				,							param_name='''crop_size'''      )

					_UpperCamelCase				:Any				    =  do_resize
					_UpperCamelCase				:int				    =  size
					_UpperCamelCase				:List[Any]				    =  resample
					_UpperCamelCase				:Optional[int]				    =  do_center_crop
					_UpperCamelCase				:List[str]				    =  crop_size
					_UpperCamelCase				:Dict				    =  do_rescale
					_UpperCamelCase				:int				    =  rescale_factor
					_UpperCamelCase				:Union[str, Any]				    =  rescale_offset
					_UpperCamelCase				:List[str]				    =  do_normalize
					_UpperCamelCase				:str				    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_UpperCamelCase				:Dict				    =  image_std if image_std is not None else IMAGENET_STANDARD_STD
					_UpperCamelCase				:Optional[int]				    =  include_top
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = PIL.Image.NEAREST				,							SCREAMING_SNAKE_CASE__ = None				,							**SCREAMING_SNAKE_CASE__				,							)			->  np.ndarray:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  get_size_dict(SCREAMING_SNAKE_CASE__      )
					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(
					    SCREAMING_SNAKE_CASE__				,							size=(size['''height'''], size['''width'''])				,							resample=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							**SCREAMING_SNAKE_CASE__				,							)			->  np.ndarray:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  get_size_dict(SCREAMING_SNAKE_CASE__      )
					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(SCREAMING_SNAKE_CASE__				,							size=(size['''height'''], size['''width'''])				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = None				,							**SCREAMING_SNAKE_CASE__				,							)			->  Optional[int]:

					"""simple docstring"""



					if offset:
								_UpperCamelCase				:Dict				    =  (image - 1_2_7.5) * scale
								if data_format is not None:
											_UpperCamelCase				:Any				    =  to_channel_dimension_format(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
								_UpperCamelCase				:str				    =  rescaled_image.astype(np.floataa      )
					else:
								_UpperCamelCase				:int				    =  rescale(SCREAMING_SNAKE_CASE__				,							scale=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
					return rescaled_image
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							**SCREAMING_SNAKE_CASE__				,							)			->  np.ndarray:

					"""simple docstring"""



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





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST				,							**SCREAMING_SNAKE_CASE__				,							)			->  PIL.Image.Image:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  do_resize if do_resize is not None else self.do_resize
					_UpperCamelCase				:str				    =  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				:Union[str, 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				:int				    =  rescale_offset if rescale_offset is not None else self.rescale_offset
					_UpperCamelCase				:List[Any]				    =  do_normalize if do_normalize is not None else self.do_normalize
					_UpperCamelCase				:int				    =  image_mean if image_mean is not None else self.image_mean
					_UpperCamelCase				:List[Any]				    =  image_std if image_std is not None else self.image_std
					_UpperCamelCase				:Any				    =  include_top if include_top is not None else self.include_top

					_UpperCamelCase				:str				    =  size if size is not None else self.size
					_UpperCamelCase				:List[Any]				    =  get_size_dict(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  crop_size if crop_size is not None else self.crop_size
					_UpperCamelCase				:Optional[Any]				    =  get_size_dict(SCREAMING_SNAKE_CASE__				,							param_name='''crop_size'''      )

					_UpperCamelCase				:List[Any]				    =  make_list_of_images(SCREAMING_SNAKE_CASE__      )

					if not valid_images(SCREAMING_SNAKE_CASE__      ):
								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				:Tuple				    =  [to_numpy_array(SCREAMING_SNAKE_CASE__      ) for image in images]

					if do_resize:
								_UpperCamelCase				:str				    =  [self.resize(image=SCREAMING_SNAKE_CASE__				,							size=SCREAMING_SNAKE_CASE__				,							resample=SCREAMING_SNAKE_CASE__      ) for image in images]

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

					if do_rescale:
								_UpperCamelCase				:List[Any]				    =  [self.rescale(image=SCREAMING_SNAKE_CASE__				,							scale=SCREAMING_SNAKE_CASE__				,							offset=SCREAMING_SNAKE_CASE__      ) for image in images]

					if do_normalize:
								_UpperCamelCase				:str				    =  [self.normalize(image=SCREAMING_SNAKE_CASE__				,							mean=SCREAMING_SNAKE_CASE__				,							std=SCREAMING_SNAKE_CASE__      ) for image in images]

					if include_top:
								_UpperCamelCase				:Optional[int]				    =  [self.normalize(image=SCREAMING_SNAKE_CASE__				,							mean=[0, 0, 0]				,							std=SCREAMING_SNAKE_CASE__      ) for image in images]

					_UpperCamelCase				:Optional[int]				    =  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) for image in images]

					_UpperCamelCase				:Optional[int]				    =  {'''pixel_values''': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE__				,							tensor_type=SCREAMING_SNAKE_CASE__      )





"""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)

