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

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


class SegmentationPredictor(DetectionPredictor):
    """
    A class extending the DetectionPredictor class for prediction based on a segmentation model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.segment import SegmentationPredictor

        args = dict(model="yolov8n-seg.pt", source=ASSETS)
        predictor = SegmentationPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initializes the SegmentationPredictor with the provided configuration, overrides, and callbacks."""
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "segment"

    def postprocess(self, preds, img, orig_imgs):
        """Applies non-max suppression and processes detections for each image in an input batch."""
        p = ops.non_max_suppression(
            preds[0],
            self.args.conf,
            self.args.iou,
            agnostic=self.args.agnostic_nms,
            max_det=self.args.max_det,
            nc=len(self.model.names),
            classes=self.args.classes,
        )

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        proto = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]  # tuple if PyTorch model or array if exported
        for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
            if not len(pred):  # save empty boxes
                masks = None
            elif self.args.retina_masks:
                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
                masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
            else:
                masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
        return results
