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





class 						lowercase       :
     def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







        lowerCAmelCase__ :    Optional[int]				      =			0
        # max greyscale value for #FFFFFF
        lowerCAmelCase__ :    Tuple				      =			int(self.get_greyscale(255 ,	255 ,	255    )    )

        if not self.min_threshold < threshold < self.max_threshold:
           lowerCAmelCase__ :    List[Any]				      =			f'''Factor value should be from 0 to {self.max_threshold}'''
           raise ValueError(__a    )

        lowerCAmelCase__ :    Optional[int]				      =			input_img
        lowerCAmelCase__ :    str				      =			threshold
        lowerCAmelCase__ :    Any				      =			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__ :    Dict				      =			[
            [0 for _ in range(self.height + 4    )] for __ in range(self.width + 1    )
        ]
        lowerCAmelCase__ :    str				      =			np.ones((self.width, self.height, 3) ,	np.uinta    ) * 255
     @classmethod
     def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







        return 0.114 * blue + 0.587 * green + 0.2_126 * red
     def lowercase_			(		self    ):
        """simple docstring"""







        for y in range(self.height    ):
           for x in range(self.width    ):
              lowerCAmelCase__ :    Optional[int]				      =			int(self.get_greyscale(*self.input_img[y][x]    )    )
              if self.threshold > greyscale + self.error_table[y][x]:
                 lowerCAmelCase__ :    Tuple				      =			(0, 0, 0)
                 lowerCAmelCase__ :    List[Any]				      =			greyscale + self.error_table[x][y]
              else:
                 lowerCAmelCase__ :    Dict				      =			(255, 255, 255)
                 lowerCAmelCase__ :    Tuple				      =			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__       :   Union[str, Any]   						=					[
           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()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



