





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

__magic_name__   :						Dict    =			imread(r"""digital_image_processing/image_data/lena_small.jpg""")
__magic_name__   :						Tuple    =			cvtColor(img, COLOR_BGR2GRAY)




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				cn.convert_to_negative(SCREAMING_SNAKE_CASE__       )
  # assert negative_img array for at least one True
  assert negative_img.any()




def 				snake_case_      (		):







  '''simple docstring'''



  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__					,	1_10       )       ).startswith(
        "<PIL.Image.Image image mode=RGB size=100x100 at"       )




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				canny.gen_gaussian_kernel(9					,	sigma=1.4       )
  # Assert ambiguous array
  assert resp.all()




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				imread("digital_image_processing/image_data/lena_small.jpg"					,	0       )
  # assert ambiguous array for all == True
  assert canny_img.all()
  _snake_case     		=				canny.canny(SCREAMING_SNAKE_CASE__       )
  # assert canny array for at least one True
  assert canny_array.any()




def 				snake_case_      (		):







  '''simple docstring'''



  assert gg.gaussian_filter(SCREAMING_SNAKE_CASE__					,	5					,	sigma=0.9       ).all()




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]       )
  _snake_case     		=				conv.img_convolve(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ).astype(SCREAMING_SNAKE_CASE__       )
  assert res.any()




def 				snake_case_      (		):







  '''simple docstring'''



  assert med.median_filter(SCREAMING_SNAKE_CASE__					,	3       ).any()




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				sob.sobel_filter(SCREAMING_SNAKE_CASE__       )
  assert grad.any() and theta.any()




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				sp.make_sepia(SCREAMING_SNAKE_CASE__					,	20       )
  assert sepia.all()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = "digital_image_processing/image_data/lena_small.jpg"       ):







  '''simple docstring'''



  _snake_case     		=				bs.Burkes(imread(SCREAMING_SNAKE_CASE__					,	1       )					,	1_20       )
  burkes.process()
  assert burkes.output_img.any()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = "digital_image_processing/image_data/lena_small.jpg"					,	):







  '''simple docstring'''



  _snake_case     		=				rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE__					,	1       )					,	4_00					,	2_00       )
  nn.process()
  assert nn.output.any()




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				"digital_image_processing/image_data/lena.jpg"

  # Reading the image and converting it to grayscale.
  _snake_case     		=				imread(SCREAMING_SNAKE_CASE__					,	0       )

  # Test for get_neighbors_pixel function() return not None
  _snake_case     		=				0
  _snake_case     		=				0
  _snake_case     		=				image[x_coordinate][y_coordinate]

  _snake_case     		=				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
  _snake_case     		=				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]       ):
      _snake_case     		=				lbp.local_binary_value(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  assert lbp_image.any()









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


