






"""simple docstring"""



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


A_      =							'''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

A_      =							'''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

A_      =							'''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''






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


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													return datasets.MetricInfo(
													    description=_DESCRIPTION     ,      citation=_CITATION     ,      inputs_description=_KWARGS_DESCRIPTION     ,      features=datasets.Features(
													        {
													            """predictions""": datasets.Value("""string"""     ,      id="""sequence"""							),
													            """references""": datasets.Value("""string"""     ,      id="""sequence"""							),
													        }							)     ,      codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""]     ,      reference_urls=[
													        """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
													        """https://github.com/google-research/google-research/tree/master/rouge""",
													    ]     ,      )


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  Optional[Any]     ,      snake_case :  List[Any]     ,      snake_case :  Union[str, Any]=None     ,      snake_case :  Dict=True     ,      snake_case :  List[str]=False							):
													'''simple docstring'''





													if rouge_types is None:
																			A__  :					int											=					["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

													A__  :					List[str]											=					rouge_scorer.RougeScorer(rouge_types=snake_case     ,      use_stemmer=snake_case							)
													if use_aggregator:
																			A__  :					Any											=					scoring.BootstrapAggregator()
													else:
																			A__  :					str											=					[]

													for ref, pred in zip(snake_case     ,      snake_case							):
																			A__  :					Tuple											=					scorer.score(snake_case     ,      snake_case							)
																			if use_aggregator:
																									aggregator.add_scores(snake_case							)
																			else:
																									scores.append(snake_case							)

													if use_aggregator:
																			A__  :					Optional[int]											=					aggregator.aggregate()
													else:
																			A__  :					Union[str, Any]											=					{}
																			for key in scores[0]:
																									A__  :					int											=					[score[key] for score in scores]

													return result














"""simple docstring"""



import cva
import numpy as np






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  Union[str, Any]     ,      snake_case :  float     ,      snake_case :  int							):
													'''simple docstring'''





													if k in (0.04, 0.06):
																			A__  :					Optional[int]											=					k
																			A__  :					int											=					window_size
													else:
																			raise ValueError("""invalid k value"""							)


							def __str__(     self :  List[Any]							):
													'''simple docstring'''





													return str(self.k							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  str							):
													'''simple docstring'''





													A__  :					List[str]											=					cva.imread(snake_case     ,      0							)
													A__					,					A__  :					Union[str, Any]											=					img.shape
													A__  :					list[list[int]]											=					[]
													A__  :					Optional[Any]											=					img.copy()
													A__  :					List[str]											=					cva.cvtColor(snake_case     ,      cva.COLOR_GRAY2RGB							)
													A__					,					A__  :					List[Any]											=					np.gradient(snake_case							)
													A__  :					List[Any]											=					dx**2
													A__  :					Any											=					dy**2
													A__  :					Dict											=					dx * dy
													A__  :					Any											=					0.04
													A__  :					Optional[Any]											=					self.window_size // 2
													for y in range(snake_case     ,      h - offset							):
																			for x in range(snake_case     ,      w - offset							):
																									A__  :					List[str]											=					ixx[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()
																									A__  :					Tuple											=					iyy[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()
																									A__  :					Optional[int]											=					ixy[
																									    y - offset : y + offset + 1, x - offset : x + offset + 1
																									].sum()

																									A__  :					int											=					(wxx * wyy) - (wxy**2)
																									A__  :					Any											=					wxx + wyy
																									A__  :					List[str]											=					det - k * (trace**2)
																									# Can change the value
																									if r > 0.5:
																															corner_list.append([x, y, r]							)
																															color_img.itemset((y, x, 0)     ,      0							)
																															color_img.itemset((y, x, 1)     ,      0							)
																															color_img.itemset((y, x, 2)     ,      255							)
													return color_img, corner_list


if __name__ == "__main__":
						A_      =							HarrisCorner(0.04, 3)
						A_      ,		A_      =							edge_detect.detect('''path_to_image''')
						cva.imwrite('''detect.png''', color_img)






