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78 lines
2.9 KiB
78 lines
2.9 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.engine.results import Results
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from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory
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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 = (img if isinstance(img, torch.Tensor) else 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 postprocess(self, preds, img, orig_img):
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results = []
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for i, pred in enumerate(preds):
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shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
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results.append(Results(probs=pred.softmax(0), orig_shape=shape[:2]))
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return results
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def write_results(self, idx, results, 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 or self.from_img: # batch_size >= 1
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log_string += f'{idx}: '
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frame = self.dataset.count
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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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result = results[idx]
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if len(result) == 0:
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return log_string
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prob = result.probs
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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.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
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else "https://ultralytics.com/images/bus.jpg"
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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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