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| # Ultralytics YOLO 🚀, GPL-3.0 license | |
| import torch | |
| from ultralytics.yolo.engine.predictor import BasePredictor | |
| from ultralytics.yolo.engine.results import Results | |
| from ultralytics.yolo.utils import DEFAULT_CFG, ROOT | |
| from ultralytics.yolo.utils.plotting import Annotator | |
| class ClassificationPredictor(BasePredictor): | |
| def get_annotator(self, img): | |
| return Annotator(img, example=str(self.model.names), pil=True) | |
| def preprocess(self, img): | |
| 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() # uint8 to fp16/32 | |
| def postprocess(self, preds, img, orig_imgs): | |
| results = [] | |
| for i, pred in enumerate(preds): | |
| orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs | |
| path, _, _, _, _ = self.batch | |
| 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 write_results(self, idx, results, batch): | |
| p, im, im0 = batch | |
| log_string = '' | |
| if len(im.shape) == 3: | |
| im = im[None] # expand for batch dim | |
| self.seen += 1 | |
| im0 = im0.copy() | |
| if self.source_type.webcam or self.source_type.from_img: # batch_size >= 1 | |
| log_string += f'{idx}: ' | |
| frame = self.dataset.count | |
| else: | |
| frame = getattr(self.dataset, 'frame', 0) | |
| self.data_path = p | |
| # save_path = str(self.save_dir / p.name) # im.jpg | |
| self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') | |
| log_string += '%gx%g ' % im.shape[2:] # print string | |
| self.annotator = self.get_annotator(im0) | |
| result = results[idx] | |
| if len(result) == 0: | |
| return log_string | |
| prob = result.probs | |
| # Print results | |
| n5 = min(len(self.model.names), 5) | |
| top5i = prob.argsort(0, descending=True)[:n5].tolist() # top 5 indices | |
| log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, " | |
| # write | |
| text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i) | |
| if self.args.save or self.args.show: # Add bbox to image | |
| self.annotator.text((32, 32), text, txt_color=(255, 255, 255)) | |
| if self.args.save_txt: # Write to file | |
| with open(f'{self.txt_path}.txt', 'a') as f: | |
| f.write(text + '\n') | |
| return log_string | |
| def predict(cfg=DEFAULT_CFG, use_python=False): | |
| model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" | |
| 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() | |