


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






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):

							# 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 / 1_8_0 * 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
						snake_case							: Dict      =   imread('''../image_data/lena.jpg''')
						# turn image in gray scale value
						snake_case							: Optional[Any]      =   cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						snake_case							: List[str]      =   np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 1_20, 1_50]:
												snake_case							: Optional[Any]      =   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						snake_case							: List[str]      =   out / out.max() * 2_55
						snake_case							: Union[str, Any]      =   out.astype(np.uinta)

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

						waitKey(0)








from typing import TYPE_CHECKING

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


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

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''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

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




