Normalize the image. Args: mean (sequence): Mean values of 3 channels. std (sequence): Std values of 3 channels. to_rgb (bool): Whether to convert the image from BGR to RGB, default is true.
| 679 | |
| 680 | @PIPELINES.register_module() |
| 681 | class Normalize(object): |
| 682 | """Normalize the image. |
| 683 | |
| 684 | Args: |
| 685 | mean (sequence): Mean values of 3 channels. |
| 686 | std (sequence): Std values of 3 channels. |
| 687 | to_rgb (bool): Whether to convert the image from BGR to RGB, |
| 688 | default is true. |
| 689 | """ |
| 690 | def __init__(self, mean, std, to_rgb=True): |
| 691 | self.mean = np.array(mean, dtype=np.float32) |
| 692 | self.std = np.array(std, dtype=np.float32) |
| 693 | self.to_rgb = to_rgb |
| 694 | |
| 695 | def __call__(self, results): |
| 696 | for key in results.get('img_fields', ['img']): |
| 697 | results[key] = mmcv.imnormalize(results[key], self.mean, self.std, |
| 698 | self.to_rgb) |
| 699 | results['img_norm_cfg'] = dict(mean=self.mean, |
| 700 | std=self.std, |
| 701 | to_rgb=self.to_rgb) |
| 702 | return results |
| 703 | |
| 704 | def __repr__(self): |
| 705 | repr_str = self.__class__.__name__ |
| 706 | repr_str += f'(mean={list(self.mean)}, ' |
| 707 | repr_str += f'std={list(self.std)}, ' |
| 708 | repr_str += f'to_rgb={self.to_rgb})' |
| 709 | return repr_str |
| 710 | |
| 711 | |
| 712 | @PIPELINES.register_module() |