


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)










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


