

"""simple docstring"""





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




class    _lowerCAmelCase    :







 """simple docstring"""







 def __init__(     self				:     str,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     int      ):
    __lowercase								=		0
    # max greyscale value for #FFFFFF
    __lowercase								=		int(self.get_greyscale(2_5_5,   2_5_5,   2_5_5      )      )

    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      ) * 2_5_5


 @classmethod
 def    _lowercase					(     cls				:     Union[str, Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     int      ):
    return 0.114 * blue + 0.587 * green + 0.2_126 * red



 def    _lowercase					(     self				:     Dict      ):
    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								=		(2_5_5, 2_5_5, 2_5_5)
             __lowercase								=		greyscale + self.error_table[x][y] - 2_5_5
          self.error_table[y][x + 1] += int(8 / 3_2 * current_error      )
          self.error_table[y][x + 2] += int(4 / 3_2 * current_error      )
          self.error_table[y + 1][x] += int(8 / 3_2 * current_error      )
          self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error      )
          self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error      )
          self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error      )
          self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error      )


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   _a					 =				[
       Burkes(imread('image_data/lena.jpg', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   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()







"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


if __name__ == "__main__":
   # read original image
   _a					 =				imread('../image_data/lena.jpg')
   # turn image in gray scale value
   _a					 =				cvtColor(img, COLOR_BGR2GRAY)

   _a ,		_a					 =				sobel_filter(gray)

   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)




