




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

class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		List[str],     _lowercase:		int):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  input_img
									__lowerCAmelCase					=  threshold
									__lowerCAmelCase	,				__lowerCAmelCase					=  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
									__lowerCAmelCase					=  [
									    [0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
									]
									__lowerCAmelCase					=  np.ones((self.width, self.height, 3),     np.uinta) * 255




					@classmethod
					def     _lowercase						(   cls:		List[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									return 0.114 * blue + 0.587 * green + 0.2_126 * red







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									for y in range(self.height):
													for x in range(self.width):
																	__lowerCAmelCase					=  int(self.get_greyscale(*self.input_img[y][x]))
																	if self.threshold > greyscale + self.error_table[y][x]:
																					__lowerCAmelCase					=  (0, 0, 0)
																					__lowerCAmelCase					=  greyscale + self.error_table[x][y]
																	else:
																					__lowerCAmelCase					=  (255, 255, 255)
																					__lowerCAmelCase					=  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
	__A						:		Optional[Any]          =						[
	    Burkes(imread("image_data/lena.jpg", 1), threshold)
	    for threshold in (1, 126, 130, 140)
	]

	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()






import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


