



"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A    =	1_8_0

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Any    ,		lowerCamelCase_:       int   ):
						"""simple docstring"""






						snake_case      : Optional[Any]							=   k_size // 2
						snake_case						, snake_case      : List[str]							=   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						snake_case      : List[Any]							=   (
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(__snake_case   ) + np.square(__snake_case   )) / (2 * np.square(__snake_case   ))   )
						)
						return g

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[str]    ,		lowerCamelCase_:       List[str]    ,		lowerCamelCase_:       Union[str, Any]   ):
						"""simple docstring"""






						snake_case      : List[Any]							=   np.zeros(__snake_case   )

						for row in range(1    ,		image_shape[0] - 1   ):
												for col in range(1    ,		image_shape[1] - 1   ):
																		snake_case      : int							=   gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 1_5 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								snake_case      : str							=   sobel_grad[row, col - 1]
																								snake_case      : Union[str, Any]							=   sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														snake_case      : List[str]							=   sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 1_1 * PI / 8
																		):
																								snake_case      : Optional[int]							=   sobel_grad[row + 1, col - 1]
																								snake_case      : Union[str, Any]							=   sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														snake_case      : Tuple							=   sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
																		):
																								snake_case      : Dict							=   sobel_grad[row - 1, col]
																								snake_case      : Optional[Any]							=   sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														snake_case      : Optional[int]							=   sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
																		):
																								snake_case      : Optional[Any]							=   sobel_grad[row - 1, col - 1]
																								snake_case      : List[str]							=   sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														snake_case      : Union[str, Any]							=   sobel_grad[row, col]

						return destination

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[int]    ,		lowerCamelCase_:       Optional[int]    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       Dict    ,		lowerCamelCase_:       List[Any]    ,		lowerCamelCase_:       List[Any]   ):
						"""simple docstring"""






						for row in range(1    ,		image_shape[0] - 1   ):
												for col in range(1    ,		image_shape[1] - 1   ):
																		if destination[row, col] >= threshold_high:
																								snake_case      : Any							=   strong
																		elif destination[row, col] <= threshold_low:
																								snake_case      : str							=   0
																		else:
																								snake_case      : Union[str, Any]							=   weak

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Dict    ,		lowerCamelCase_:       str    ,		lowerCamelCase_:       Optional[int]    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						for row in range(1    ,		image_shape[0]   ):
												for col in range(1    ,		image_shape[1]   ):
																		if destination[row, col] == weak:
																								if 2_5_5 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														snake_case      : str							=   strong
																								else:
																														snake_case      : str							=   0

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[Any]    ,		lowerCamelCase_:       Dict=1_5    ,		lowerCamelCase_:       Dict=3_0    ,		lowerCamelCase_:       Tuple=1_2_8    ,		lowerCamelCase_:       Any=2_5_5   ):
						"""simple docstring"""






						snake_case      : Optional[int]							=   img_convolve(__snake_case    ,		gen_gaussian_kernel(9    ,		sigma=1.4   )   )
						# get the gradient and degree by sobel_filter
						snake_case						, snake_case      : Dict							=   sobel_filter(__snake_case   )
						snake_case      : int							=   PI + np.radadeg(__snake_case   )

						snake_case      : Dict							=   suppress_non_maximum(image.shape    ,		__snake_case    ,		__snake_case   )

						detect_high_low_threshold(
						    image.shape    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case   )

						track_edge(image.shape    ,		__snake_case    ,		__snake_case    ,		__snake_case   )

						return destination


if __name__ == "__main__":
			# read original image in gray mode
			A    =	cva.imread(R'../image_data/lena.jpg', 0)
			# canny edge detection
			A    =	canny(lena)
			cva.imshow('canny', canny_destination)
			cva.waitKey(0)








"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


A    =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


A    =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


A    =	'\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class 			_a     (   datasets.Metric):



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32"		)		),
									            "references": datasets.Sequence(datasets.Value("int32"		)		),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32"		),
									            "references": datasets.Value("int32"		),
									        }		)      ,  reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,  )



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Union[str, Any]=True      ,  _lowercase       :							int=None		)       ->				Any:
									return {
									    "accuracy": float(
									        accuracy_score(_lowercase      ,  _lowercase      ,  normalize=_lowercase      ,  sample_weight=_lowercase		)		)
									}



