
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


lowercase__	:List[str]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Dict       =['''pixel_values''']




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PIL.Image.BICUBIC     ,A__ = True     ,A__ = None     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = True     ,A__ = None     ,A__ = None     ,**A__     ,):
												super().__init__(**A__)
												lowercase					=		size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
												lowercase					=		get_size_dict(A__)
												lowercase					=		crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
												lowercase					=		get_size_dict(A__     ,param_name='''crop_size''')

												lowercase					=		do_resize
												lowercase					=		size
												lowercase					=		resample
												lowercase					=		do_center_crop
												lowercase					=		crop_size
												lowercase					=		do_rescale
												lowercase					=		rescale_factor
												lowercase					=		do_normalize
												lowercase					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowercase					=		image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 				A__   (       self     ,A__     ,A__     ,A__ = PIL.Image.BICUBIC     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__)
												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(
												    A__     ,size=(size['''height'''], size['''width'''])     ,resample=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__)
												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(A__     ,size=(size['''height'''], size['''width'''])     ,data_format=A__     ,**A__)




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




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





							def 				A__   (       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__ = ChannelDimension.FIRST     ,**A__     ,):
												lowercase					=		do_resize if do_resize is not None else self.do_resize
												lowercase					=		resample if resample is not None else self.resample
												lowercase					=		do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
												lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase					=		do_normalize if do_normalize is not None else self.do_normalize
												lowercase					=		image_mean if image_mean is not None else self.image_mean
												lowercase					=		image_std if image_std is not None else self.image_std

												lowercase					=		size if size is not None else self.size
												lowercase					=		get_size_dict(A__)
												lowercase					=		crop_size if crop_size is not None else self.crop_size
												lowercase					=		get_size_dict(A__     ,param_name='''crop_size''')

												lowercase					=		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 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.
												lowercase					=		[to_numpy_array(A__) for image in images]

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

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

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

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

												lowercase					=		[to_channel_dimension_format(A__     ,A__) for image in images]

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






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

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


if __name__ == "__main__":
							# read original image
							lowercase__	:str										=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
							lowercase__	:List[str]										=					np.array(Image.open(lena_path))
							# kernel to be applied
							lowercase__	:Union[str, Any]										=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
							lowercase__	:Optional[int]										=					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
							# Save the output image
							lowercase__	:str										=					Image.fromarray(output).convert("RGB")
							pil_img.save("result_dilation.png")




