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f71ac1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | """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
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