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46 lines
1.6 KiB
46 lines
1.6 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 preprocess(self, img):
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"""Converts input image to model-compatible data type."""
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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
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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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