


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

__A :  List[Any]    							=				imread(R"digital_image_processing/image_data/lena_small.jpg")
__A :  Optional[Any]    							=				cvtColor(img, COLOR_BGR2GRAY)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       cn.convert_to_negative(A__ )
  # assert negative_img array for at least one True
  assert negative_img.any()







def     UpperCamelCase_      (   ):
  '''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(A__							,   1_10 ) ).startswith(
        """<PIL.Image.Image image mode=RGB size=100x100 at""" )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       canny.gen_gaussian_kernel(9							,   sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       imread("""digital_image_processing/image_data/lena_small.jpg"""							,   0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  lowerCAmelCase_      :				Tuple      =       canny.canny(A__ )
  # assert canny array for at least one True
  assert canny_array.any()







def     UpperCamelCase_      (   ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  lowerCAmelCase_      :				Optional[int]      =       conv.img_convolve(A__							,   A__ ).astype(A__ )
  assert res.any()







def     UpperCamelCase_      (   ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       sob.sobel_filter(A__ )
  assert grad.any() and theta.any()







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       sp.make_sepia(A__							,   20 )
  assert sepia.all()







def     UpperCamelCase_      (   A__ :       Optional[Any] = "digital_image_processing/image_data/lena_small.jpg" ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       bs.Burkes(imread(A__							,   1 )							,   1_20 )
  burkes.process()
  assert burkes.output_img.any()







def     UpperCamelCase_      (   A__ :       Tuple = "digital_image_processing/image_data/lena_small.jpg"							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       rs.NearestNeighbour(imread(A__							,   1 )							,   4_00							,   2_00 )
  nn.process()
  assert nn.output.any()







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       """digital_image_processing/image_data/lena.jpg"""

  # Reading the image and converting it to grayscale.
  lowerCAmelCase_      :				str      =       imread(A__							,   0 )

  # Test for get_neighbors_pixel function() return not None
  lowerCAmelCase_      :				Tuple      =       0
  lowerCAmelCase_      :				Tuple      =       0
  lowerCAmelCase_      :				Union[str, Any]      =       image[x_coordinate][y_coordinate]

  lowerCAmelCase_      :				List[str]      =       lbp.get_neighbors_pixel(
      A__							,   A__							,   A__							,   A__ )

  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
  lowerCAmelCase_      :				Any      =       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] ):
      lowerCAmelCase_      :				Dict      =       lbp.local_binary_value(A__							,   A__							,   A__ )

  assert lbp_image.any()







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



