b5e0cee943
Signed-off-by: Francesco Mattioli <Francesco.mttl@gmail.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Lakshantha Dissanayake <lakshantha@ultralytics.com> Co-authored-by: Burhan <Burhan-Q@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Co-authored-by: Lakshantha Dissanayake <lakshanthad@yahoo.com> Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from ultralytics.engine.results import Results
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from ultralytics.models.yolo.detect.predict import DetectionPredictor
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from ultralytics.utils import DEFAULT_CFG, ops
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class SegmentationPredictor(DetectionPredictor):
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"""
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A class extending the DetectionPredictor class for prediction based on a segmentation model.
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Example:
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```python
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.segment import SegmentationPredictor
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args = dict(model="yolo11n-seg.pt", source=ASSETS)
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predictor = SegmentationPredictor(overrides=args)
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predictor.predict_cli()
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```
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initializes the SegmentationPredictor with the provided configuration, overrides, and callbacks."""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = "segment"
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def postprocess(self, preds, img, orig_imgs):
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"""Applies non-max suppression and processes detections for each image in an input batch."""
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p = ops.non_max_suppression(
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preds[0],
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self.args.conf,
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self.args.iou,
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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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nc=len(self.model.names),
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classes=self.args.classes,
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)
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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results = []
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proto = preds[1][-1] if isinstance(preds[1], tuple) else preds[1] # tuple if PyTorch model or array if exported
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for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
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if not len(pred): # save empty boxes
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masks = None
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elif self.args.retina_masks:
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
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else:
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masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
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return results
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