
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def  lowerCamelCase__ (  a__     :     int		,					a__     :     int		,					a__     :     int		,					a__     :     int		,					a__     :     int		,					a__     :     int   )						->		np.ndarray:

       # prepare kernel
       # the kernel size have to be odd
       if (ksize % 2) == 0:
              UpperCamelCase_											=				ksize + 1
       UpperCamelCase_											=				np.zeros((ksize, ksize)		,					dtype=np.floataa   )

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

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

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

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

                     # fill kernel
                     UpperCamelCase_											=				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
      _A		     =    imread('''../image_data/lena.jpg''')
      # turn image in gray scale value
      _A		     =    cvtColor(img, COLOR_BGR2GRAY)

      # Apply multiple Kernel to detect edges
      _A		     =    np.zeros(gray.shape[:2])
      for theta in [0, 30, 60, 90, 120, 150]:
            _A		     =    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
            out += filteraD(gray, CV_8UC3, kernel_aa)
      _A		     =    out / out.max() * 255
      _A		     =    out.astype(np.uinta)

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

      waitKey(0)








'''simple docstring'''






from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCamelCase__:   Tuple								=			numpy.array([0, 0])
UpperCamelCase__:   Union[str, Any]								=			numpy.array([0.5, 0.8660254])
UpperCamelCase__:   Dict								=			numpy.array([1, 0])
UpperCamelCase__:   int								=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]   ,      _lowerCAmelCase  :						int  )      ->   list[numpy.ndarray]:
							UpperCAmelCase :       Union[str, Any] 							=		initial_vectors
							for _ in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Union[str, Any] 							=		iteration_step(_lowerCAmelCase  )
							return vectors


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]  )      ->   list[numpy.ndarray]:
							UpperCAmelCase :       Tuple 							=		[]
							for i, start_vector in enumerate(vectors[:-1]  ):
														UpperCAmelCase :       List[str] 							=		vectors[i + 1]
														new_vectors.append(_lowerCAmelCase  )
														UpperCAmelCase :       Optional[Any] 							=		end_vector - start_vector
														new_vectors.append(start_vector + difference_vector / 3  )
														new_vectors.append(
														    start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,      60  )  )
														new_vectors.append(start_vector + difference_vector * 2 / 3  )
							new_vectors.append(vectors[-1]  )
							return new_vectors


def  snake_case_     (					_lowerCAmelCase  :						numpy.ndarray   ,      _lowerCAmelCase  :						float  )      ->   numpy.ndarray:
							UpperCAmelCase :       List[str] 							=		numpy.radians(_lowerCAmelCase  )
							UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		numpy.cos(_lowerCAmelCase  ), numpy.sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		numpy.array(((c, -s), (s, c))  )
							return numpy.dot(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]  )      ->   None:
							UpperCAmelCase :       List[Any] 							=		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()
							UpperCAmelCase							,		UpperCAmelCase :       str 							=		zip(*_lowerCAmelCase  )
							plt.plot(_lowerCAmelCase   ,      _lowerCAmelCase  )
							plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						UpperCamelCase__:   List[Any]								=			iterate(INITIAL_VECTORS, 5)
						plot(processed_vectors)





