





"""simple docstring"""





from typing import Dict, Optional

import numpy as np

import datasets


__snake_case				:Optional[Any]				   =       '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

__snake_case				:int				   =       '\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

__snake_case				:Tuple				   =       '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = None  ,			UpperCAmelCase_ = False  ,			)						->Optional[Any]:





  """simple docstring"""


  if label_map is not None:
    for old_id, new_id in label_map.items():
      __UpperCAmelCase    :						Union[str, Any]       =						new_id

       # turn into Numpy arrays
  __UpperCAmelCase    :						str       =						np.array(UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						np.array(UpperCAmelCase_  )

  if reduce_labels:
    __UpperCAmelCase    :						int       =						2_55
    __UpperCAmelCase    :						int       =						label - 1
    __UpperCAmelCase    :						Optional[Any]       =						2_55

  __UpperCAmelCase    :						Any       =						label != ignore_index
  __UpperCAmelCase    :						List[str]       =						np.not_equal(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						pred_label[mask]
  __UpperCAmelCase    :						Tuple       =						np.array(UpperCAmelCase_  )[mask]

  __UpperCAmelCase    :						str       =						pred_label[pred_label == label]

  __UpperCAmelCase    :						Dict       =						np.histogram(UpperCAmelCase_  ,			bins=UpperCAmelCase_  ,			range=(0, num_labels - 1)  )[0]
  __UpperCAmelCase    :						Dict       =						np.histogram(UpperCAmelCase_  ,			bins=UpperCAmelCase_  ,			range=(0, num_labels - 1)  )[0]
  __UpperCAmelCase    :						Optional[Any]       =						np.histogram(UpperCAmelCase_  ,			bins=UpperCAmelCase_  ,			range=(0, num_labels - 1)  )[0]

  __UpperCAmelCase    :						List[str]       =						area_pred_label + area_label - area_intersect

  return area_intersect, area_union, area_pred_label, area_label


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = None  ,			UpperCAmelCase_ = False  ,			)						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						np.zeros((num_labels,)  ,			dtype=np.floataa  )
  __UpperCAmelCase    :						Any       =						np.zeros((num_labels,)  ,			dtype=np.floataa  )
  __UpperCAmelCase    :						int       =						np.zeros((num_labels,)  ,			dtype=np.floataa  )
  __UpperCAmelCase    :						Union[str, Any]       =						np.zeros((num_labels,)  ,			dtype=np.floataa  )
  for result, gt_seg_map in zip(UpperCAmelCase_  ,			UpperCAmelCase_  ):
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						intersect_and_union(
        UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    total_area_intersect += area_intersect
    total_area_union += area_union
    total_area_pred_label += area_pred_label
    total_area_label += area_label
  return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = None  ,			UpperCAmelCase_ = None  ,			UpperCAmelCase_ = False  ,			)						->List[str]:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						total_intersect_and_union(
      UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  # compute metrics
  __UpperCAmelCase    :						Any       =						{}

  __UpperCAmelCase    :						List[str]       =						total_area_intersect.sum() / total_area_label.sum()
  __UpperCAmelCase    :						Optional[int]       =						total_area_intersect / total_area_union
  __UpperCAmelCase    :						int       =						total_area_intersect / total_area_label

  __UpperCAmelCase    :						Any       =						np.nanmean(UpperCAmelCase_  )
  __UpperCAmelCase    :						Dict       =						np.nanmean(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						all_acc
  __UpperCAmelCase    :						List[str]       =						iou
  __UpperCAmelCase    :						Optional[Any]       =						acc

  if nan_to_num is not None:
    __UpperCAmelCase    :						str       =						{metric: np.nan_to_num(UpperCAmelCase_  ,			nan=UpperCAmelCase_  ) for metric, metric_value in metrics.items()}

  return metrics







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

      '''simple docstring'''



      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        return datasets.MetricInfo(
            description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
                # 1st Seq - height dim, 2nd - width dim
                {
                    '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
                    '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
                } )						,				reference_urls=[
                '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
            ]						,				)







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      Dict						,				__lowercase       :      Optional[int]						,				__lowercase       :      List[str]						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Tuple = None						,				__lowercase       :      List[Any] = False						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						mean_iou(
            results=A__						,				gt_seg_maps=A__						,				num_labels=A__						,				ignore_index=A__						,				nan_to_num=A__						,				label_map=A__						,				reduce_labels=A__						,				)
        return iou_result






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()