"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase__					       =	imread(r'digital_image_processing/image_data/lena_small.jpg')
lowercase__					       =	cvtColor(img, COLOR_BGR2GRAY)







def 						__a   (	)	->List[str]:
  a__:     Optional[int]										=	cn.convert_to_negative(_SCREAMING_SNAKE_CASE   )
  # assert negative_img array for at least one True
  assert negative_img.any()







def 						__a   (	)	->List[str]:
  with Image.open('digital_image_processing/image_data/lena_small.jpg'   ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(_SCREAMING_SNAKE_CASE		,     110   )   ).startswith(
        '<PIL.Image.Image image mode=RGB size=100x100 at'   )







def 						__a   (	)	->Optional[int]:
  a__:     Dict										=	canny.gen_gaussian_kernel(9		,     sigma=1.4   )
  # Assert ambiguous array
  assert resp.all()







def 						__a   (	)	->int:
  a__:     Tuple										=	imread('digital_image_processing/image_data/lena_small.jpg'		,     0   )
  # assert ambiguous array for all == True
  assert canny_img.all()
  a__:     Optional[Any]										=	canny.canny(_SCREAMING_SNAKE_CASE   )
  # assert canny array for at least one True
  assert canny_array.any()







def 						__a   (	)	->List[Any]:
  assert gg.gaussian_filter(_SCREAMING_SNAKE_CASE		,     5		,     sigma=0.9   ).all()







def 						__a   (	)	->Optional[int]:
  # laplace diagonals
  a__:     int										=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]   )
  a__:     Union[str, Any]										=	conv.img_convolve(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).astype(_SCREAMING_SNAKE_CASE   )
  assert res.any()







def 						__a   (	)	->List[str]:
  assert med.median_filter(_SCREAMING_SNAKE_CASE		,     3   ).any()







def 						__a   (	)	->List[str]:
  a__							,    a__:     int										=	sob.sobel_filter(_SCREAMING_SNAKE_CASE   )
  assert grad.any() and theta.any()







def 						__a   (	)	->Optional[int]:
  a__:     int										=	sp.make_sepia(_SCREAMING_SNAKE_CASE		,     20   )
  assert sepia.all()







def 						__a   (	_SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"   )	->List[Any]:
  a__:     Tuple										=	bs.Burkes(imread(_SCREAMING_SNAKE_CASE		,     1   )		,     120   )
  burkes.process()
  assert burkes.output_img.any()







def 						__a   (	_SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"		,     )	->Optional[Any]:
  a__:     Any										=	rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE		,     1   )		,     400		,     200   )
  nn.process()
  assert nn.output.any()







def 						__a   (	)	->List[str]:
  a__:     Union[str, Any]										=	'digital_image_processing/image_data/lena.jpg'

  # Reading the image and converting it to grayscale.
  a__:     Any										=	imread(_SCREAMING_SNAKE_CASE		,     0   )

  # Test for get_neighbors_pixel function() return not None
  a__:     List[str]										=	0
  a__:     Optional[int]										=	0
  a__:     str										=	image[x_coordinate][y_coordinate]

  a__:     Any										=	lbp.get_neighbors_pixel(
      _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  a__:     Dict										=	np.zeros((image.shape[0], image.shape[1])   )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,     image.shape[0]   ):
    for j in range(0		,     image.shape[1]   ):
      a__:     List[str]										=	lbp.local_binary_value(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  assert lbp_image.any()



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


