KeypointAUC

class mmeval.metrics.KeypointAUC(norm_factor: float = 30, num_thrs: int = 20, **kwargs)

AUC evaluation metric.

Calculate the Area Under Curve (AUC) of keypoint PCK accuracy.

By altering the threshold percentage in the calculation of PCK accuracy, AUC can be generated to further evaluate the pose estimation algorithms.

Note

• length of dataset: N

• num_keypoints: K

• number of keypoint dimensions: D (typically D = 2)

Parameters

• norm_factor (float) – AUC normalization factor, Defaults to 30 (pixels).

• num_thrs (int) – Number of thresholds to calculate AUC. Defaults to 20.

• **kwargs – Keyword parameters passed to mmeval.BaseMetric. Must include dataset_meta in order to compute the metric.

Examples

>>> from mmeval import KeypointAUC
>>> import numpy as np
>>> auc_metric = KeypointAUC(norm_factor=20, num_thrs=4)
>>> output = np.array([[[10.,  4.],
...     [10., 18.],
...     [ 0.,  0.],
...     [40., 40.],
...     [20., 10.]]])
>>> target = np.array([[[10.,  0.],
...     [10., 10.],
...     [ 0., -1.],
...     [30., 30.],
...     [ 0., 10.]]])
>>> keypoints_visible = np.array([[True, True, False, True, True]])
>>> num_keypoints = 15
>>> prediction = {'coords': output}
>>> groundtruth = {'coords': target, 'mask': keypoints_visible}
>>> predictions = [prediction]
>>> groundtruths = [groundtruth]
>>> auc_metric(predictions, groundtruths)
OrderedDict([('AUC@4', 0.375)])

add(predictions: List[Dict], groundtruths: List[Dict]) → None

Process one batch of predictions and groundtruths and add the intermediate results to self._results.

Parameters

• predictions (Sequence[dict]) –

  Predictions from the model. Each prediction dict has the following keys:

  • coords (np.ndarray, [1, K, D]): predicted keypoints coordinates

• groundtruths (Sequence[dict]) –

  The ground truth labels. Each groundtruth dict has the following keys:

  • coords (np.ndarray, [1, K, D]): ground truth keypoints coordinates

  • mask (np.ndarray, [1, K]): ground truth keypoints_visible

compute_metric(results: list) → Dict[str, float]

Compute the metrics from processed results.

Parameters

results (list) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of the metrics, and the values are corresponding results.

Return type

Dict[str, float]