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70 lines
2.4 KiB
70 lines
2.4 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]
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self.all_outputs.append(prob)
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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 "squeezenet1_0"
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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()
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if __name__ == "__main__":
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predict()
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