



'''simple docstring'''

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[int]:



		if (ksize % 2) == 0:
				_a					:				str				=							ksize + 1
		_a					:				List[Any]				=							np.zeros((ksize, ksize)						,      dtype=np.floataa  )

		# each value
		for y in range(A__  ):
				for x in range(A__  ):
						# distance from center
						_a					:				List[Any]				=							x - ksize // 2
						_a					:				Any				=							y - ksize // 2

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

						# get kernel x
						_a					:				Optional[int]				=							cos_theta * px + sin_theta * py

						# get kernel y
						_a					:				Dict				=							-sin_theta * px + cos_theta * py

						# fill kernel
						_a					:				Optional[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
					__lowerCAmelCase						      =       imread('''../image_data/lena.jpg''')
					# turn image in gray scale value
					__lowerCAmelCase						      =       cvtColor(img, COLOR_BGR2GRAY)

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


__lowerCAmelCase						      =       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecAudioForAudioFrameClassification''',
							    '''Data2VecAudioForCTC''',
							    '''Data2VecAudioForSequenceClassification''',
							    '''Data2VecAudioForXVector''',
							    '''Data2VecAudioModel''',
							    '''Data2VecAudioPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecTextForCausalLM''',
							    '''Data2VecTextForMaskedLM''',
							    '''Data2VecTextForMultipleChoice''',
							    '''Data2VecTextForQuestionAnswering''',
							    '''Data2VecTextForSequenceClassification''',
							    '''Data2VecTextForTokenClassification''',
							    '''Data2VecTextModel''',
							    '''Data2VecTextPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecVisionForImageClassification''',
							    '''Data2VecVisionForMaskedImageModeling''',
							    '''Data2VecVisionForSemanticSegmentation''',
							    '''Data2VecVisionModel''',
							    '''Data2VecVisionPreTrainedModel''',
							]

if is_tf_available():
							__lowerCAmelCase						      =       [
							    '''TFData2VecVisionForImageClassification''',
							    '''TFData2VecVisionForSemanticSegmentation''',
							    '''TFData2VecVisionModel''',
							    '''TFData2VecVisionPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
							from .configuration_dataavec_text import (
							    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecTextConfig,
							    DataaVecTextOnnxConfig,
							)
							from .configuration_dataavec_vision import (
							    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecVisionConfig,
							    DataaVecVisionOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dataavec_audio import (
														    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecAudioForAudioFrameClassification,
														    DataaVecAudioForCTC,
														    DataaVecAudioForSequenceClassification,
														    DataaVecAudioForXVector,
														    DataaVecAudioModel,
														    DataaVecAudioPreTrainedModel,
														)
														from .modeling_dataavec_text import (
														    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecTextForCausalLM,
														    DataaVecTextForMaskedLM,
														    DataaVecTextForMultipleChoice,
														    DataaVecTextForQuestionAnswering,
														    DataaVecTextForSequenceClassification,
														    DataaVecTextForTokenClassification,
														    DataaVecTextModel,
														    DataaVecTextPreTrainedModel,
														)
														from .modeling_dataavec_vision import (
														    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecVisionForImageClassification,
														    DataaVecVisionForMaskedImageModeling,
														    DataaVecVisionForSemanticSegmentation,
														    DataaVecVisionModel,
														    DataaVecVisionPreTrainedModel,
														)
							if is_tf_available():
														from .modeling_tf_dataavec_vision import (
														    TFDataaVecVisionForImageClassification,
														    TFDataaVecVisionForSemanticSegmentation,
														    TFDataaVecVisionModel,
														    TFDataaVecVisionPreTrainedModel,
														)

else:
							import sys

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




