





"""simple docstring"""





from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__					=   "\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"

SCREAMING_SNAKE_CASE__					=   "\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"

SCREAMING_SNAKE_CASE__					=   "\\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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Union[str, Any]							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      if label_map is not None:
            for old_id, new_id in label_map.items():
                  lowerCAmelCase  						=       new_id

    # turn into Numpy arrays
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)

      if reduce_labels:
            lowerCAmelCase  						=       2_55
            lowerCAmelCase  						=       label - 1
            lowerCAmelCase  						=       2_55

      lowerCAmelCase  						=       label != ignore_index
      lowerCAmelCase  						=       np.not_equal(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       pred_label[mask]
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)[mask]

      lowerCAmelCase  						=       pred_label[pred_label == label]

      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]
      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]
      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]

      lowerCAmelCase  						=       area_pred_label + area_label - area_intersect

      return area_intersect, area_union, area_pred_label, area_label


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			):
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       intersect_and_union(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
            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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[int] = None							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       total_intersect_and_union(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # compute metrics
      lowerCAmelCase  						=       {}

      lowerCAmelCase  						=       total_area_intersect.sum() / total_area_label.sum()
      lowerCAmelCase  						=       total_area_intersect / total_area_union
      lowerCAmelCase  						=       total_area_intersect / total_area_label

      lowerCAmelCase  						=       np.nanmean(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       np.nanmean(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       all_acc
      lowerCAmelCase  						=       iou
      lowerCAmelCase  						=       acc

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

      return metrics






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





      def     _snake_case			(       self )		->     Tuple:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = False      ,						)		->     Tuple:
            lowerCAmelCase  						=       mean_iou(
                results=lowercase      ,						gt_seg_maps=lowercase      ,						num_labels=lowercase      ,						ignore_index=lowercase      ,						nan_to_num=lowercase      ,						label_map=lowercase      ,						reduce_labels=lowercase      ,						)
            return iou_result












from math import asin, atan, cos, radians, sin, sqrt, tan

_snake_case										=       6_3_7_8_1_3_7.0
_snake_case										=       6_3_5_6_7_5_2.3_1_4_2_4_5
_snake_case										=       6378137







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						(AXIS_A - AXIS_B) / AXIS_A
      _A							:     Optional[int]			=						atan((1 - flattening) * tan(radians(snake_case_     )     )     )
      _A							:     List[str]			=						atan((1 - flattening) * tan(radians(snake_case_     )     )     )
      _A							:     Optional[Any]			=						radians(snake_case_     )
      _A							:     str			=						radians(snake_case_     )
      # Equation
      _A							:     Dict			=						sin((phi_a - phi_a) / 2     )
      _A							:     List[str]			=						sin((lambda_a - lambda_a) / 2     )
      # Square both values
      sin_sq_phi *= sin_sq_phi
      sin_sq_lambda *= sin_sq_lambda
      _A							:     Optional[int]			=						sqrt(sin_sq_phi + (cos(snake_case_     ) * cos(snake_case_     ) * sin_sq_lambda)     )
      return 2 * RADIUS * asin(snake_case_     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

