'''simple docstring'''


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

from digital_image_processing.filters.convolve import img_convolve


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]  )
     UpperCAmelCase_				:  int  = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]  )

     UpperCAmelCase_				:  Optional[Any]  = np.abs(img_convolve(lowerCamelCase_      ,	lowerCamelCase_  )  )
     UpperCAmelCase_				:  Optional[int]  = np.abs(img_convolve(lowerCamelCase_      ,	lowerCamelCase_  )  )
     # modify the pix within [0, 255]
     UpperCAmelCase_				:  Tuple  = dst_x * 255 / np.max(lowerCamelCase_  )
     UpperCAmelCase_				:  str  = dst_y * 255 / np.max(lowerCamelCase_  )

     UpperCAmelCase_				:  Any  = np.sqrt((np.square(lowerCamelCase_  )) + (np.square(lowerCamelCase_  ))  )
     UpperCAmelCase_				:  Optional[Any]  = dst_xy * 255 / np.max(lowerCamelCase_  )
     UpperCAmelCase_				:  Tuple  = dst_xy.astype(np.uinta  )

     UpperCAmelCase_				:  Dict  = np.arctana(lowerCamelCase_      ,	lowerCamelCase_  )
     return dst, theta


if __name__ == "__main__":
 # read original image
 snake_case__	:     Optional[Any]      =    imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 snake_case__	:     List[str]      =    cvtColor(img, COLOR_BGR2GRAY)

 snake_case__     , snake_case__	:     int      =    sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)









'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\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"


lowercase__            =     "\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"


lowercase__            =     "\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				):






     '''simple docstring'''



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




