MpiiPCKAccuracy¶

class mmeval.metrics.MpiiPCKAccuracy(thr: float = 0.5, norm_item: Union[str, Sequence[str]] = 'head', **kwargs)[source]¶

PCKh accuracy evaluation metric for MPII dataset.

Calculate the pose accuracy of Percentage of Correct Keypoints (PCK) for each individual keypoint and the averaged accuracy across all keypoints. PCK metric measures accuracy of the localization of the body joints. The distances between predicted positions and the ground-truth ones are typically normalized by the person bounding box size. The threshold (thr) of the normalized distance is commonly set as 0.05, 0.1 or 0.2 etc.

Note

length of dataset: N
num_keypoints: K
number of keypoint dimensions: D (typically D = 2)

Parameters

thr (float) – Threshold of PCK calculation. Defaults to 0.5.
norm_item (str | Sequence[str]) – The item used for normalization. Valid items include ‘bbox’, ‘head’, ‘torso’, which correspond to ‘PCK’, ‘PCKh’ and ‘tPCK’ respectively. Defaults to 'head'.
**kwargs – Keyword parameters passed to BaseMetric.

Examples

>>> from mmeval import MpiiPCKAccuracy
>>> import numpy as np
>>> num_keypoints = 16
>>> keypoints = np.random.random((1, num_keypoints, 2)) * 10
>>> predictions = [{'coords': keypoints}]
>>> keypoints_visible = np.ones((1, num_keypoints)).astype(bool)
>>> head_size = np.random.random((1, 2)) * 10
>>> groundtruths = [{
...     'coords': keypoints + 1.0,
...     'mask': keypoints_visible,
...     'head_size': head_size,
... }]
>>> mpii_pckh_metric = MpiiPCKAccuracy(thr=0.3, norm_item='head')
>>> mpii_pckh_metric(predictions, groundtruths)
OrderedDict([('Head', 100.0), ('Shoulder', 100.0), ('Elbow', 100.0),
('Wrist', 100.0), ('Hip', 100.0), ('Knee', 100.0), ('Ankle', 100.0),
('PCKh', 100.0), ('PCKh@0.1', 100.0)])

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

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]