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69 lines
2.5 KiB
69 lines
2.5 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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import hydra |
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import torch |
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from ultralytics.yolo.engine.predictor import BasePredictor |
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from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT |
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from ultralytics.yolo.utils.checks import check_imgsz |
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from ultralytics.yolo.utils.plotting import Annotator |
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class ClassificationPredictor(BasePredictor): |
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def get_annotator(self, img): |
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return Annotator(img, example=str(self.model.names), pil=True) |
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def preprocess(self, img): |
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img = torch.Tensor(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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return img |
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def write_results(self, idx, preds, batch): |
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p, im, im0 = batch |
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log_string = "" |
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if len(im.shape) == 3: |
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im = im[None] # expand for batch dim |
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self.seen += 1 |
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im0 = im0.copy() |
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if self.webcam: # batch_size >= 1 |
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log_string += f'{idx}: ' |
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frame = self.dataset.cound |
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else: |
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frame = getattr(self.dataset, 'frame', 0) |
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self.data_path = p |
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# save_path = str(self.save_dir / p.name) # im.jpg |
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') |
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log_string += '%gx%g ' % im.shape[2:] # print string |
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self.annotator = self.get_annotator(im0) |
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prob = preds[idx].softmax(0) |
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if self.return_outputs: |
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self.output["prob"] = prob.cpu().numpy() |
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# Print results |
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top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices |
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log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, " |
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# write |
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text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i) |
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if self.args.save or self.args.show: # Add bbox to image |
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self.annotator.text((32, 32), text, txt_color=(255, 255, 255)) |
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if self.args.save_txt: # Write to file |
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with open(f'{self.txt_path}.txt', 'a') as f: |
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f.write(text + '\n') |
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return log_string |
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) |
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def predict(cfg): |
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cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18" |
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cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size |
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cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" |
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predictor = ClassificationPredictor(cfg) |
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predictor.predict_cli() |
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if __name__ == "__main__": |
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predict()
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