





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





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    __a		)     ->		np.ndarray:







  """simple docstring"""





  if (ksize % 2) == 0:
    lowerCamelCase__:      Optional[int]			  =ksize + 1
  lowerCamelCase__:      Tuple			  =np.zeros((ksize, ksize)	,    dtype=np.floataa		)

  # each value
  for y in range(__a		):
    for x in range(__a		):
      # distance from center
      lowerCamelCase__:      Optional[int]			  =x - ksize // 2
      lowerCamelCase__:      str			  =y - ksize // 2

      # degree to radiant
      lowerCamelCase__:      str			  =theta / 180 * np.pi
      lowerCamelCase__:      str			  =np.cos(_theta		)
      lowerCamelCase__:      List[Any]			  =np.sin(_theta		)

      # get kernel x
      lowerCamelCase__:      int			  =cos_theta * px + sin_theta * py

      # get kernel y
      lowerCamelCase__:      Optional[int]			  =-sin_theta * px + cos_theta * py

      # fill kernel
      lowerCamelCase__:      Tuple			  =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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



