




"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_										=       logging.get_logger(__name__)

class 	_lowerCamelCase  (     SCREAMING_SNAKE_CASE__				):

      """simple docstring"""

      lowerCAmelCase__     :	Optional[int]      =   ["pixel_values"]







      def __init__(       self   :	Dict	,    snake_case   :	Tuple = True	,    snake_case   :	List[str] = None	,    snake_case   :	str = True	,    snake_case   :	str = None	,    snake_case   :	List[str] = PILImageResampling.BICUBIC	,    snake_case   :	str = True	,    snake_case   :	int = 1 / 255	,    snake_case   :	Tuple = True	,    snake_case   :	Any = None	,    snake_case   :	Tuple = None	,    **snake_case   :	Tuple	,    ):
          super().__init__(**_UpperCAmelCase			)
          __UpperCamelCase											=  crop_size if crop_size is not None else {"height": 256, "width": 256}
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase	,    param_name='''crop_size'''			)
          __UpperCamelCase											=  size if size is not None else {"height": 224, "width": 224}
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase			)

          __UpperCamelCase											=  do_center_crop
          __UpperCamelCase											=  crop_size
          __UpperCamelCase											=  do_resize
          __UpperCamelCase											=  size
          __UpperCamelCase											=  resample
          __UpperCamelCase											=  do_rescale
          __UpperCamelCase											=  rescale_factor
          __UpperCamelCase											=  do_normalize
          __UpperCamelCase											=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase											=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[Any]	,    snake_case   :	Union[str, Any]	,    snake_case   :	Optional[Any] = None	,    snake_case   :	Optional[Any] = None	,    **snake_case   :	int	,    ):
          __UpperCamelCase											=  self.size if size is None else size
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase			)
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase	,    param_name='''crop_size'''			)

          __UpperCamelCase											=  get_image_size(_UpperCAmelCase			)
          __UpperCamelCase											=  min(_UpperCAmelCase	,    _UpperCAmelCase			)
          __UpperCamelCase											=  (size["height"] / crop_size["height"]) * min_dim
          __UpperCamelCase											=  (size["width"] / crop_size["width"]) * min_dim
          return center_crop(_UpperCAmelCase	,    size=(cropped_height, cropped_width)	,    data_format=_UpperCAmelCase	,    **_UpperCAmelCase			)







      def  snake_case		(       self   :	Dict	,    snake_case   :	str	,    snake_case   :	Tuple	,    snake_case   :	str = PIL.Image.BICUBIC	,    snake_case   :	Union[str, Any] = None	,    **snake_case   :	str	,    ):
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase			)
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"			)
          return resize(
              _UpperCAmelCase	,    size=(size['''height'''], size['''width'''])	,    resample=_UpperCAmelCase	,    data_format=_UpperCAmelCase	,    **_UpperCAmelCase			)







      def  snake_case		(       self   :	int	,    snake_case   :	List[str]	,    snake_case   :	List[str]	,    snake_case   :	List[str] = None	,    **snake_case   :	int	,    ):
          return rescale(_UpperCAmelCase	,    scale=_UpperCAmelCase	,    data_format=_UpperCAmelCase	,    **_UpperCAmelCase			)







      def  snake_case		(       self   :	Dict	,    snake_case   :	Optional[int]	,    snake_case   :	Tuple	,    snake_case   :	int	,    snake_case   :	Optional[Any] = None	,    **snake_case   :	List[Any]	,    ):
          return normalize(_UpperCAmelCase	,    mean=_UpperCAmelCase	,    std=_UpperCAmelCase	,    data_format=_UpperCAmelCase	,    **_UpperCAmelCase			)







      def  snake_case		(       self   :	Dict	,    snake_case   :	List[Any]	,    snake_case   :	Dict = None	,    snake_case   :	Dict = None	,    snake_case   :	List[str] = None	,    snake_case   :	List[str] = None	,    snake_case   :	int = None	,    snake_case   :	Optional[Any] = None	,    snake_case   :	str = None	,    snake_case   :	Dict = None	,    snake_case   :	List[str] = None	,    snake_case   :	Optional[Any] = None	,    snake_case   :	Union[str, Any] = None	,    snake_case   :	Union[str, Any] = ChannelDimension.FIRST	,    **snake_case   :	Dict	,    ):
          __UpperCamelCase											=  do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCamelCase											=  crop_size if crop_size is not None else self.crop_size
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase	,    param_name='''crop_size'''			)
          __UpperCamelCase											=  do_resize if do_resize is not None else self.do_resize
          __UpperCamelCase											=  size if size is not None else self.size
          __UpperCamelCase											=  get_size_dict(_UpperCAmelCase			)
          __UpperCamelCase											=  resample if resample is not None else self.resample
          __UpperCamelCase											=  do_rescale if do_rescale is not None else self.do_rescale
          __UpperCamelCase											=  rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCamelCase											=  do_normalize if do_normalize is not None else self.do_normalize
          __UpperCamelCase											=  image_mean if image_mean is not None else self.image_mean
          __UpperCamelCase											=  image_std if image_std is not None else self.image_std

          __UpperCamelCase											=  make_list_of_images(_UpperCAmelCase			)

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

          if do_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''			)

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''			)

          # All transformations expect numpy arrays.
          __UpperCamelCase											=  [to_numpy_array(_UpperCAmelCase			) for image in images]

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

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

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

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

          __UpperCamelCase											=  [to_channel_dimension_format(_UpperCAmelCase	,    _UpperCAmelCase			) for image in images]

          __UpperCamelCase											=  {"pixel_values": images}
          return BatchFeature(data=_UpperCAmelCase	,    tensor_type=_UpperCAmelCase			)












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate - 1   ,       lowercase_ ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(lowercase_   ,       lowercase_ ) )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 a_										=       cva.imread(
     "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
 )

 # Create a numpy array as the same height and width of read image
 a_										=       np.zeros((image.shape[0], image.shape[1]))

 # Iterating through the image and calculating the
 # local binary pattern value for each pixel.
 for i in range(0, image.shape[0]):
  for j in range(0, image.shape[1]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






