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51 lines
1.9 KiB
51 lines
1.9 KiB
# Ultralytics YOLO 🚀, AGPL-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 |
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class ClassificationPredictor(BasePredictor): |
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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 = 'classify' |
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def preprocess(self, img): |
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"""Converts input image to model-compatible data type.""" |
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if not isinstance(img, torch.Tensor): |
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img = torch.stack([self.transforms(im) for im in img], dim=0) |
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img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) |
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return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 |
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def postprocess(self, preds, img, orig_imgs): |
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"""Postprocesses predictions to return Results objects.""" |
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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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path = self.batch[0] |
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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, probs=pred)) |
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return results |
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def predict(cfg=DEFAULT_CFG, use_python=False): |
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"""Run YOLO model predictions on input images/videos.""" |
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model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" |
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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 = ClassificationPredictor(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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