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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_    		=				logging.get_logger(__name__)


if is_vision_available():
      import PIL





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		=["""pixel_values"""]



     def __init__(       self					,   lowerCAmelCase__ = True					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = PILImageResampling.BICUBIC					,   lowerCAmelCase__ = True					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = True					,   lowerCAmelCase__ = 1 / 255					,   lowerCAmelCase__ = True					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = True					,   **lowerCAmelCase__					,   ):
            super().__init__(**lowerCAmelCase__							)
            _A=						size if size is not None else {'shortest_edge': 224}
            _A=						get_size_dict(lowerCAmelCase__					,   default_to_square=lowerCAmelCase__							)
            _A=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _A=						get_size_dict(lowerCAmelCase__							)

            _A=						do_resize
            _A=						size
            _A=						resample
            _A=						do_center_crop
            _A=						crop_size
            _A=						do_rescale
            _A=						rescale_factor
            _A=						do_normalize
            _A=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            _A=						image_std if image_std is not None else OPENAI_CLIP_STD
            _A=						do_convert_rgb



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__ = PILImageResampling.BICUBIC					,   lowerCAmelCase__ = None					,   **lowerCAmelCase__					,   ):
            _A=						get_size_dict(lowerCAmelCase__					,   default_to_square=lowerCAmelCase__							)
            _A=						get_resize_output_image_size(
                lowerCAmelCase__					,   size=(size['height'], size['width'])					,   default_to_square=lowerCAmelCase__							)
            return resize(lowerCAmelCase__					,   size=lowerCAmelCase__					,   resample=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   **lowerCAmelCase__					,   ):
            _A=						get_size_dict(lowerCAmelCase__							)
            return center_crop(lowerCAmelCase__					,   size=(size['height'], size['width'])					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   **lowerCAmelCase__					,   ):
            return rescale(lowerCAmelCase__					,   scale=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   **lowerCAmelCase__					,   ):
            return normalize(lowerCAmelCase__					,   mean=lowerCAmelCase__					,   std=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__							)





     def       a__	(       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__ = None					,   lowerCAmelCase__ = ChannelDimension.FIRST					,   **lowerCAmelCase__					,   ):
            _A=						do_resize if do_resize is not None else self.do_resize
            _A=						size if size is not None else self.size
            _A=						get_size_dict(lowerCAmelCase__					,   default_to_square=lowerCAmelCase__							)
            _A=						resample if resample is not None else self.resample
            _A=						do_center_crop if do_center_crop is not None else self.do_center_crop
            _A=						crop_size if crop_size is not None else self.crop_size
            _A=						get_size_dict(lowerCAmelCase__							)
            _A=						do_rescale if do_rescale is not None else self.do_rescale
            _A=						rescale_factor if rescale_factor is not None else self.rescale_factor
            _A=						do_normalize if do_normalize is not None else self.do_normalize
            _A=						image_mean if image_mean is not None else self.image_mean
            _A=						image_std if image_std is not None else self.image_std
            _A=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _A=						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:
                   raise ValueError('Size 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.'							)

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                   _A=						[convert_to_rgb(lowerCAmelCase__							) for image in images]

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

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

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

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

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

            _A=						[to_channel_dimension_format(lowerCAmelCase__					,   lowerCAmelCase__							) for image in images]

            _A=						{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__					,   tensor_type=lowerCAmelCase__							)
import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')