


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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):

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

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

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

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

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

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

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

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

  waitKey(0)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AutoformerForPrediction',
      'AutoformerModel',
      'AutoformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



