
"""simple docstring"""






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





def 		__A						(   a_ :							int		,		a_ :							int		,		a_ :							int		,		a_ :							int		,		a_ :							int		,		a_ :							int     )->							np.ndarray:




		'''simple docstring'''






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

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

						# degree to radiant
						SCREAMING_SNAKE_CASE       :     Dict				= theta / 1_80 * np.pi
						SCREAMING_SNAKE_CASE       :     Dict				= np.cos(_theta     )
						SCREAMING_SNAKE_CASE       :     str				= np.sin(_theta     )

						# get kernel x
						SCREAMING_SNAKE_CASE       :     List[str]				= cos_theta * px + sin_theta * py

						# get kernel y
						SCREAMING_SNAKE_CASE       :     Tuple				= -sin_theta * px + cos_theta * py

						# fill kernel
						SCREAMING_SNAKE_CASE       :     int				= 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__ :       int						     =			imread("../image_data/lena.jpg")
					# turn image in gray scale value
					lowerCamelCase__ :       int						     =			cvtColor(img, COLOR_BGR2GRAY)

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


lowerCamelCase__ :       Tuple						     =			{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MCTCTForCTC",
					    "MCTCTModel",
					    "MCTCTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       Tuple						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





