



'''simple docstring'''






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     str  )		->Union[str, Any]:






							'''simple docstring'''
							_lowercase			, _lowercase							:		List[str]				=   image.shape
							_lowercase							:		List[Any]				=   cols - block_size[1] + 1
							_lowercase							:		str				=   rows - block_size[0] + 1
							_lowercase							:		str				=   zeros((dst_height * dst_width, block_size[1] * block_size[0])  )
							_lowercase							:		Tuple				=   0
							for i in range(0 ,       snake_case_  ):
														for j in range(0 ,       snake_case_  ):
																					_lowercase							:		Tuple				=   ravel(image[i : i + block_size[0], j : j + block_size[1]]  )
																					_lowercase							:		Union[str, Any]				=   window
																					row += 1

							return image_array





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     int  )		->Optional[Any]:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Any				=   image.shape[0], image.shape[1]
							_lowercase							:		List[Any]				=   filter_kernel.shape[0]
							_lowercase							:		Optional[Any]				=   k_size // 2
							# Pads image with the edge values of array.
							_lowercase							:		Union[str, Any]				=   pad(snake_case_ ,       snake_case_ ,       mode='''edge'''  )

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							_lowercase							:		str				=   imacol(snake_case_ ,       (k_size, k_size)  )

							#  turn the kernel into shape(k*k, 1)
							_lowercase							:		Tuple				=   ravel(snake_case_  )
							# reshape and get the dst image
							_lowercase							:		Any				=   dot(snake_case_ ,       snake_case_  ).reshape(snake_case_ ,       snake_case_  )
							return dst


if __name__ == "__main__":
						# read original image
						lowerCamelCase__	  =		imread(R'../image_data/lena.jpg')
						# turn image in gray scale value
						lowerCamelCase__	  =		cvtColor(img, COLOR_BGR2GRAY)
						# Laplace operator
						lowerCamelCase__	  =		array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
						lowerCamelCase__	  =		img_convolve(gray, Laplace_kernel).astype(uinta)
						imshow('Laplacian', out)
						waitKey(0)








'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


lowerCamelCase__	  =		TypeVar('T', bound='TaskTemplate')

@dataclass(frozen=__A       )
class 	_lowerCAmelCase     (    abc.ABC       ):






		'''simple docstring'''
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= 42
		def 						__lowercase      (						self							:       T      ,		UpperCamelCase_							:       Features		)      ->			T:







									'''simple docstring'''





									return copy.deepcopy(self		)
		@property
		def 						__lowercase      (						self							:       int		)      ->			Features:







									'''simple docstring'''





									return Features(**self.input_schema      ,		**self.label_schema		)
		@property
		@abc.abstractmethod
		def 						__lowercase      (						self							:       List[str]		)      ->			Dict[str, str]:







									'''simple docstring'''





									raise NotImplementedError





		@classmethod
		def 						__lowercase      (						cls							:       Type[T]      ,		UpperCamelCase_							:       dict		)      ->			T:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {f.name for f in dataclasses.fields(cls		)}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)



