




'''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,
    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


lowercase	:      int				=       logging.get_logger(__name__)


if is_vision_available():
      import PIL

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     str			   =      ["pixel_values"]



 def __init__(  self   ,		A = True   ,		A = None   ,		A = PILImageResampling.BICUBIC   ,		A = True   ,		A = None   ,		A = True   ,		A = 1 / 255   ,		A = True   ,		A = None   ,		A = None   ,		A = True   ,		**A   ,		):






        '''simple docstring'''




        super().__init__(**A )
        lowercase  :		Union[str, Any]						=      size if size is not None else {'''shortest_edge''': 224}
        lowercase  :		Union[str, Any]						=      get_size_dict(A   ,		default_to_square=A )
        lowercase  :		Any						=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowercase  :		str						=      get_size_dict(A )

        lowercase  :		Optional[Any]						=      do_resize
        lowercase  :		Optional[int]						=      size
        lowercase  :		Any						=      resample
        lowercase  :		Tuple						=      do_center_crop
        lowercase  :		Any						=      crop_size
        lowercase  :		int						=      do_rescale
        lowercase  :		str						=      rescale_factor
        lowercase  :		List[Any]						=      do_normalize
        lowercase  :		Tuple						=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase  :		Union[str, Any]						=      image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase  :		str						=      do_convert_rgb



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A = PILImageResampling.BICUBIC   ,		A = None   ,		**A   ,		):






        '''simple docstring'''




        lowercase  :		Dict						=      get_size_dict(A   ,		default_to_square=A )
        lowercase  :		int						=      get_resize_output_image_size(
            A   ,		size=(size['''height'''], size['''width'''])   ,		default_to_square=A )
        return resize(A   ,		size=A   ,		resample=A   ,		data_format=A   ,		**A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A = None   ,		**A   ,		):






        '''simple docstring'''




        lowercase  :		Tuple						=      get_size_dict(A )
        return center_crop(A   ,		size=(size['''height'''], size['''width'''])   ,		data_format=A   ,		**A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A = None   ,		**A   ,		):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A = None   ,		**A   ,		):






        '''simple docstring'''




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





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = ChannelDimension.FIRST   ,		**A   ,		):






        '''simple docstring'''




        lowercase  :		str						=      do_resize if do_resize is not None else self.do_resize
        lowercase  :		int						=      size if size is not None else self.size
        lowercase  :		Tuple						=      get_size_dict(A   ,		default_to_square=A )
        lowercase  :		Optional[int]						=      resample if resample is not None else self.resample
        lowercase  :		Dict						=      do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase  :		Optional[int]						=      crop_size if crop_size is not None else self.crop_size
        lowercase  :		str						=      get_size_dict(A )
        lowercase  :		Tuple						=      do_rescale if do_rescale is not None else self.do_rescale
        lowercase  :		Optional[int]						=      rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase  :		Tuple						=      do_normalize if do_normalize is not None else self.do_normalize
        lowercase  :		Optional[int]						=      image_mean if image_mean is not None else self.image_mean
        lowercase  :		Any						=      image_std if image_std is not None else self.image_std
        lowercase  :		Optional[Any]						=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase  :		Optional[int]						=      make_list_of_images(A )

        if not valid_images(A ):
               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:
               lowercase  :		Union[str, Any]						=      [convert_to_rgb(A ) for image in images]

        # All transformations expect numpy arrays.
        lowercase  :		int						=      [to_numpy_array(A ) for image in images]

        if do_resize:
               lowercase  :		int						=      [self.resize(image=A   ,		size=A   ,		resample=A ) for image in images]

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

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

        if do_normalize:
               lowercase  :		int						=      [self.normalize(image=A   ,		mean=A   ,		std=A ) for image in images]

        lowercase  :		Optional[Any]						=      [to_channel_dimension_format(A   ,		A ) for image in images]

        lowercase  :		Tuple						=      {'''pixel_values''': images}
        return BatchFeature(data=A   ,		tensor_type=A )











'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

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


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




