
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


_SCREAMING_SNAKE_CASE     :   Tuple							 =      datasets.utils.logging.get_logger(__name__)







class 			UpperCamelCase__		(     folder_based_builder.FolderBasedBuilderConfig ):







	'''simple docstring'''
	_lowerCAmelCase											=					None
	_lowerCAmelCase											=					None







class 			UpperCamelCase__		(     folder_based_builder.FolderBasedBuilder ):







	'''simple docstring'''
	_lowerCAmelCase											=					datasets.Image()
	_lowerCAmelCase											=					"image"
	_lowerCAmelCase											=					ImageFolderConfig
	_lowerCAmelCase											=					42  # definition at the bottom of the script
	_lowerCAmelCase											=					ImageClassification(image_column="image",      label_column="label" )




_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      [
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
_SCREAMING_SNAKE_CASE     :   int							 =      IMAGE_EXTENSIONS



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 0
								# max greyscale value for #FFFFFF
								lowercase_			 = int(self.get_greyscale(255						,       255						,       255  )  )

								if not self.min_threshold < threshold < self.max_threshold:
									lowercase_			 = F'Factor value should be from 0 to {self.max_threshold}'
									raise ValueError(UpperCAmelCase  )

								lowercase_			 = input_img
								lowercase_			 = threshold
								lowercase_ ,	lowercase_			 = self.input_img.shape[1], self.input_img.shape[0]

								# error table size (+4 columns and +1 row) greater than input image because of
								# lack of if statements
								lowercase_			 = [
								    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
								]
								lowercase_			 = np.ones((self.width, self.height, 3)						,       np.uinta  ) * 255




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       float:






								'''simple docstring'''






								return 0.114 * blue + 0.587 * green + 0.2126 * red




							def 				A__      (  self  )     ->       None:






								'''simple docstring'''






								for y in range(self.height  ):
									for x in range(self.width  ):
										lowercase_			 = int(self.get_greyscale(*self.input_img[y][x]  )  )
										if self.threshold > greyscale + self.error_table[y][x]:
											lowercase_			 = (0, 0, 0)
											lowercase_			 = greyscale + self.error_table[x][y]
										else:
											lowercase_			 = (255, 255, 255)
											lowercase_			 = greyscale + self.error_table[x][y] - 255
										self.error_table[y][x + 1] += int(8 / 32 * current_error  )
										self.error_table[y][x + 2] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x] += int(8 / 32 * current_error  )
										self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
										self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
										self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							SCREAMING_SNAKE_CASE__       			=				[
							    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f"""Original image with dithering threshold: {burkes.threshold}""",
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

