






'''simple docstring'''
from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a_ 		=       numpy.array([0, 0])
a_ 		=       numpy.array([0.5, 0.8660254])
a_ 		=       numpy.array([1, 0])
a_ 		=       [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]





def        _a(     UpperCamelCase__ :  list[numpy.ndarray],		UpperCamelCase__ :  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Optional[Any]													=initial_vectors
      for _ in range(A__							):
            SCREAMING_SNAKE_CASE__   : Tuple													=iteration_step(A__							)
      return vectors





def        _a(     UpperCamelCase__ :  list[numpy.ndarray]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Any													=[]
      for i, start_vector in enumerate(vectors[:-1]							):
            SCREAMING_SNAKE_CASE__   : int													=vectors[i + 1]
            new_vectors.append(A__							)
            SCREAMING_SNAKE_CASE__   : int													=end_vector - start_vector
            new_vectors.append(start_vector + difference_vector / 3							)
            new_vectors.append(
                start_vector + difference_vector / 3 + rotate(difference_vector / 3,		6_0							)							)
            new_vectors.append(start_vector + difference_vector * 2 / 3							)
      new_vectors.append(vectors[-1]							)
      return new_vectors





def        _a(     UpperCamelCase__ :  numpy.ndarray,		UpperCamelCase__ :  float							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Any													=numpy.radians(A__							)
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : str													=numpy.cos(A__							), numpy.sin(A__							)
      SCREAMING_SNAKE_CASE__   : Any													=numpy.array(((c, -s), (s, c))							)
      return numpy.dot(A__,		A__							)





def        _a(     UpperCamelCase__ :  list[numpy.ndarray]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Tuple													=plt.gca()
      axes.set_aspect('''equal'''							)

      # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
      # y-coordinates as inputs, which are constructed from the vector-list using
      # zip()
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : int													=zip(*A__							)
      plt.plot(A__,		A__							)
      plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_ 		=       iterate(INITIAL_VECTORS, 5)
  plot(processed_vectors)


import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


