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Reference for ultralytics/models/yolo/pose/predict.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/predict.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.pose.predict.PosePredictor 

PosePredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: DetectionPredictor

A class extending the DetectionPredictor class for prediction based on a pose model.

This class specializes in pose estimation, handling keypoints detection alongside standard object detection capabilities inherited from DetectionPredictor.

Attributes:

Name Type Description
args namespace

Configuration arguments for the predictor.
model Module

The loaded YOLO pose model with keypoint detection capabilities.

Methods:

Name Description
construct_result

Construct the result object from the prediction, including keypoints.

Examples:

>>> from ultralytics.utils import ASSETS
>>> from ultralytics.models.yolo.pose import PosePredictor
>>> args = dict(model="yolo11n-pose.pt", source=ASSETS)
>>> predictor = PosePredictor(overrides=args)
>>> predictor.predict_cli()

Sets up a PosePredictor instance, configuring it for pose detection tasks and handling device-specific warnings for Apple MPS.

Parameters:

Name Type Description Default
cfg Any

Configuration for the predictor.

 DEFAULT_CFG
overrides dict

Configuration overrides that take precedence over cfg.

 None
_callbacks list

List of callback functions to be invoked during prediction.

 None

Examples:

>>> from ultralytics.utils import ASSETS
>>> from ultralytics.models.yolo.pose import PosePredictor
>>> args = dict(model="yolo11n-pose.pt", source=ASSETS)
>>> predictor = PosePredictor(overrides=args)
>>> predictor.predict_cli()
Source code in ultralytics/models/yolo/pose/predict.py 
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """
    Initialize PosePredictor for pose estimation tasks.

    Sets up a PosePredictor instance, configuring it for pose detection tasks and handling device-specific
    warnings for Apple MPS.

    Args:
        cfg (Any): Configuration for the predictor.
        overrides (dict, optional): Configuration overrides that take precedence over cfg.
        _callbacks (list, optional): List of callback functions to be invoked during prediction.

    Examples:
        >>> from ultralytics.utils import ASSETS
        >>> from ultralytics.models.yolo.pose import PosePredictor
        >>> args = dict(model="yolo11n-pose.pt", source=ASSETS)
        >>> predictor = PosePredictor(overrides=args)
        >>> predictor.predict_cli()
    """
    super().__init__(cfg, overrides, _callbacks)
    self.args.task = "pose"
    if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
        LOGGER.warning(
            "Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
            "See https://github.com/ultralytics/ultralytics/issues/4031."
        )

construct_result 

construct_result(pred, img, orig_img, img_path)

Construct the result object from the prediction, including keypoints.

Extends the parent class implementation by extracting keypoint data from predictions and adding them to the result object.

Parameters:

Name Type Description Default
pred Tensor

The predicted bounding boxes, scores, and keypoints with shape (N, 6+K*D) where N is the number of detections, K is the number of keypoints, and D is the keypoint dimension.

 required
img Tensor

The processed input image tensor with shape (B, C, H, W).

 required
orig_img ndarray

The original unprocessed image as a numpy array.

 required
img_path str

The path to the original image file.

 required

Returns:

Type Description
Results

The result object containing the original image, image path, class names, bounding boxes, and keypoints.
Source code in ultralytics/models/yolo/pose/predict.py 
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def construct_result(self, pred, img, orig_img, img_path):
    """
    Construct the result object from the prediction, including keypoints.

    Extends the parent class implementation by extracting keypoint data from predictions and adding them to the
    result object.

    Args:
        pred (torch.Tensor): The predicted bounding boxes, scores, and keypoints with shape (N, 6+K*D) where N is
            the number of detections, K is the number of keypoints, and D is the keypoint dimension.
        img (torch.Tensor): The processed input image tensor with shape (B, C, H, W).
        orig_img (np.ndarray): The original unprocessed image as a numpy array.
        img_path (str): The path to the original image file.

    Returns:
        (Results): The result object containing the original image, image path, class names, bounding boxes, and
            keypoints.
    """
    result = super().construct_result(pred, img, orig_img, img_path)
    # Extract keypoints from prediction and reshape according to model's keypoint shape
    pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
    # Scale keypoints coordinates to match the original image dimensions
    pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
    result.update(keypoints=pred_kpts)
    return result





📅 Created 1 year ago ✏️ Updated 8 months ago
glenn-jocher jk4e Burhan-Q