| | |
| |
|
| | import torch |
| |
|
| | from ultralytics.yolo.engine.predictor import BasePredictor |
| | from ultralytics.yolo.engine.results import Results |
| | from ultralytics.yolo.utils import DEFAULT_CFG, ROOT |
| |
|
| |
|
| | class ClassificationPredictor(BasePredictor): |
| |
|
| | def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
| | super().__init__(cfg, overrides, _callbacks) |
| | self.args.task = 'classify' |
| |
|
| | def preprocess(self, img): |
| | """Converts input image to model-compatible data type.""" |
| | if not isinstance(img, torch.Tensor): |
| | img = torch.stack([self.transforms(im) for im in img], dim=0) |
| | img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) |
| | return img.half() if self.model.fp16 else img.float() |
| |
|
| | def postprocess(self, preds, img, orig_imgs): |
| | """Postprocesses predictions to return Results objects.""" |
| | results = [] |
| | for i, pred in enumerate(preds): |
| | orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs |
| | path = self.batch[0] |
| | img_path = path[i] if isinstance(path, list) else path |
| | results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred)) |
| |
|
| | return results |
| |
|
| |
|
| | def predict(cfg=DEFAULT_CFG, use_python=False): |
| | """Run YOLO model predictions on input images/videos.""" |
| | model = cfg.model or 'yolov8n-cls.pt' |
| | source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ |
| | else 'https://ultralytics.com/images/bus.jpg' |
| |
|
| | args = dict(model=model, source=source) |
| | if use_python: |
| | from ultralytics import YOLO |
| | YOLO(model)(**args) |
| | else: |
| | predictor = ClassificationPredictor(overrides=args) |
| | predictor.predict_cli() |
| |
|
| |
|
| | if __name__ == '__main__': |
| | predict() |
| |
|
