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63 lines
2.8 KiB
63 lines
2.8 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import torch |
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from ultralytics.yolo.engine.results import Results |
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops |
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from ultralytics.yolo.v8.detect.predict import DetectionPredictor |
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class SegmentationPredictor(DetectionPredictor): |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
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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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"""TODO: filter by classes.""" |
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p = ops.non_max_suppression(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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results = [] |
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proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported |
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for i, pred in enumerate(p): |
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs |
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path = self.batch[0] |
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img_path = path[i] if isinstance(path, list) else path |
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if not len(pred): # save empty boxes |
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])) |
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continue |
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if self.args.retina_masks: |
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if not isinstance(orig_imgs, torch.Tensor): |
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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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if not isinstance(orig_imgs, torch.Tensor): |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) |
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results.append( |
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Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks)) |
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return results |
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def predict(cfg=DEFAULT_CFG, use_python=False): |
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"""Runs YOLO object detection on an image or video source.""" |
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model = cfg.model or 'yolov8n-seg.pt' |
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source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ |
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else 'https://ultralytics.com/images/bus.jpg' |
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args = dict(model=model, source=source) |
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if use_python: |
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from ultralytics import YOLO |
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YOLO(model)(**args) |
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else: |
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predictor = SegmentationPredictor(overrides=args) |
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predictor.predict_cli() |
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if __name__ == '__main__': |
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predict()
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