Skip to content

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

Note

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


ultralytics.models.yolo.segment.predict.SegmentationPredictor

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

Bases: DetectionPredictor

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

Example
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()
Source code in ultralytics/models/yolo/segment/predict.py
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"

postprocess

postprocess(preds, img, orig_imgs)

Applies non-max suppression and processes detections for each image in an input batch.

Source code in ultralytics/models/yolo/segment/predict.py
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 in enumerate(p):
        orig_img = orig_imgs[i]
        img_path = self.batch[0][i]
        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





Created 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1)