


import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):


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

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

   # degree to radiant
   __lowerCamelCase						:				int						   =		theta / 180 * np.pi
   __lowerCamelCase						:				Any						   =		np.cos(_theta		)
   __lowerCamelCase						:				Any						   =		np.sin(_theta		)

   # get kernel x
   __lowerCamelCase						:				Tuple						   =		cos_theta * px + sin_theta * py

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

   # fill kernel
   __lowerCamelCase						:				Union[str, Any]						   =		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)







'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

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