
'''simple docstring'''





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=SCREAMING_SNAKE_CASE__	)
class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : str       =field(default='''image-classification''' ,						metadata={'''include_in_asdict_even_if_is_default''': True}	)
							lowercase_ : Optional[Any]       =Features({'''image''': Image()}	)
							lowercase_ : Tuple       =Features({'''labels''': ClassLabel}	)
							lowercase_ : Optional[int]       ='''image'''
							lowercase_ : Tuple       ='''labels'''




							def 				A__   (       self     ,A__):
												if self.label_column not in features:
																	raise ValueError(f'Column {self.label_column} is not present in features.')
												if not isinstance(features[self.label_column]     ,A__):
																	raise ValueError(f'Column {self.label_column} is not a ClassLabel.')
												lowercase					=		copy.deepcopy(self)
												lowercase					=		self.label_schema.copy()
												lowercase					=		features[self.label_column]
												lowercase					=		label_schema
												return task_template





							@property
							def 				A__   (       self):
												return {
												    self.image_column: "image",
												    self.label_column: "labels",
												}






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")




