PCKAccuracy

class mmeval.metrics.PCKAccuracy(thr: float = 0.2, norm_item: Union[str, Sequence[str]] = 'bbox', **kwargs)

PCK accuracy evaluation metric, which is widely used in pose estimation.

Calculate the pose accuracy of Percentage of Correct Keypoints (PCK) for each individual keypoint and the averaged accuracy across all keypoints. PCK metric measures the 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 0.2.

• 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 'bbox'.

• **kwargs – Keyword parameters passed to BaseMetric.

Examples

>>> from mmeval import PCKAccuracy
>>> import numpy as np
>>> num_keypoints = 15
>>> keypoints = np.random.random((1, num_keypoints, 2)) * 10
>>> predictions = [{'coords': keypoints}]
>>> keypoints_visible = np.ones((1, num_keypoints)).astype(bool)
>>> bbox_size = np.random.random((1, 2)) * 10
>>> groundtruths = [{
...     'coords': keypoints,
...     'mask': keypoints_visible,
...     'bbox_size': bbox_size,
... }]
>>> pck_metric = PCKAccuracy(thr=0.5, norm_item='bbox')
>>> pck_metric(predictions, groundtruths)
OrderedDict([('PCK@0.5', 1.0)])

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

  • bbox_size (np.ndarray, optional, [1, 2]): ground truth bbox size

  • head_size (np.ndarray, optional, [1, 2]): ground truth head size

  • torso_size (np.ndarray, optional, [1, 2]): ground truth torso size

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 the corresponding results.

Return type

Dict[str, float]