






'''simple docstring'''







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





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   int						,		__A							:   int						,		__A							:   int						,		__A							:   int						,		__A							:   int      ):

				# prepare kernel
				# the kernel size have to be odd
				if (ksize % 2) == 0:
								a_		:  int											=      ksize + 1
				a_		:  str											=      np.zeros((ksize, ksize)						,		dtype=np.floataa      )

				# each value
				for y in range(__A      ):
								for x in range(__A      ):
												# distance from center
												a_		:  Dict											=      x - ksize // 2
												a_		:  Any											=      y - ksize // 2

												# degree to radiant
												a_		:  Optional[int]											=      theta / 1_80 * np.pi
												a_		:  List[Any]											=      np.cos(_theta      )
												a_		:  Optional[int]											=      np.sin(_theta      )

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

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

												# fill kernel
												a_		:  List[str]											=      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'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




