| """Normalize Transform.""" |
|
|
| from __future__ import annotations |
|
|
| import torch |
|
|
| from vis4d.common.typing import NDArrayF32 |
|
|
| from ..const import CommonKeys as K |
| from .base import Transform |
|
|
|
|
| @Transform(K.images, K.images) |
| class NormalizeImages: |
| """Normalize a list of image tensor with given mean and std. |
| |
| Image tensor is of shape [N, H, W, C] and range (0, 255). |
| """ |
|
|
| def __init__( |
| self, |
| mean: tuple[float, float, float] = (123.675, 116.28, 103.53), |
| std: tuple[float, float, float] = (58.395, 57.12, 57.375), |
| epsilon: float = 1e-08, |
| ) -> None: |
| """Creates an instance of NormalizeImage. |
| |
| Args: |
| mean (Tuple[float, float, float], optional): Mean value. Defaults |
| to (123.675, 116.28, 103.53). |
| std (Tuple[float, float, float], optional): Standard deviation |
| value. Defaults to (58.395, 57.12, 57.375). |
| epsilon (float, optional): Epsilon for numerical stability of |
| division. Defaults to 1e-08. |
| """ |
| self.mean = mean |
| self.std = std |
| self.epsilon = epsilon |
|
|
| def __call__(self, images: list[NDArrayF32]) -> list[NDArrayF32]: |
| """Normalize image tensor.""" |
| for i, image in enumerate(images): |
| img = torch.from_numpy(image).permute(0, 3, 1, 2) |
| pixel_mean = torch.tensor(self.mean).view(-1, 1, 1) |
| pixel_std = torch.tensor(self.std).view(-1, 1, 1) |
| img = (img - pixel_mean) / (pixel_std + self.epsilon) |
|
|
| images[i] = img.permute(0, 2, 3, 1).numpy() |
|
|
| return images |
|
|
