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53 lines
2.0 KiB
53 lines
2.0 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import torch
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from ultralytics.yolo.engine.predictor import BasePredictor
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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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class DetectionPredictor(BasePredictor):
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def preprocess(self, img):
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img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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return img
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def postprocess(self, preds, img, orig_imgs):
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preds = ops.non_max_suppression(preds,
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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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classes=self.args.classes)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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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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path, _, _, _, _ = self.batch
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img_path = path[i] if isinstance(path, list) else path
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
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return results
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def predict(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or 'yolov8n.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 = DetectionPredictor(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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