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| # Ultralytics YOLO 🚀, AGPL-3.0 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 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 | |