# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from __future__ import annotations

from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ops


class PosePredictor(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:
        args (namespace): Configuration arguments for the predictor.
        model (torch.nn.Module): The loaded YOLO pose model with keypoint detection capabilities.

    Methods:
        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="yolo26n-pose.pt", source=ASSETS)
        >>> predictor = PosePredictor(overrides=args)
        >>> predictor.predict_cli()
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks: dict | None = 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 (dict, optional): Dictionary of callback functions to be invoked during prediction.
        """
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "pose"

    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(pred.shape[0], *self.model.kpt_shape)
        # 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
