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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import mmcv
import numpy as np
import torch
from torch import Tensor
def imrenormalize(img: Union[Tensor, np.ndarray], img_norm_cfg: dict,
new_img_norm_cfg: dict) -> Union[Tensor, np.ndarray]:
"""Re-normalize the image.
Args:
img (Tensor | ndarray): Input image. If the input is a Tensor, the
shape is (1, C, H, W). If the input is a ndarray, the shape
is (H, W, C).
img_norm_cfg (dict): Original configuration for the normalization.
new_img_norm_cfg (dict): New configuration for the normalization.
Returns:
Tensor | ndarray: Output image with the same type and shape of
the input.
"""
if isinstance(img, torch.Tensor):
assert img.ndim == 4 and img.shape[0] == 1
new_img = img.squeeze(0).cpu().numpy().transpose(1, 2, 0)
new_img = _imrenormalize(new_img, img_norm_cfg, new_img_norm_cfg)
new_img = new_img.transpose(2, 0, 1)[None]
return torch.from_numpy(new_img).to(img)
else:
return _imrenormalize(img, img_norm_cfg, new_img_norm_cfg)
def _imrenormalize(img: Union[Tensor, np.ndarray], img_norm_cfg: dict,
new_img_norm_cfg: dict) -> Union[Tensor, np.ndarray]:
"""Re-normalize the image."""
img_norm_cfg = img_norm_cfg.copy()
new_img_norm_cfg = new_img_norm_cfg.copy()
for k, v in img_norm_cfg.items():
if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
img_norm_cfg[k] = np.array(v, dtype=img.dtype)
# reverse cfg
if 'bgr_to_rgb' in img_norm_cfg:
img_norm_cfg['rgb_to_bgr'] = img_norm_cfg['bgr_to_rgb']
img_norm_cfg.pop('bgr_to_rgb')
for k, v in new_img_norm_cfg.items():
if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
new_img_norm_cfg[k] = np.array(v, dtype=img.dtype)
img = mmcv.imdenormalize(img, **img_norm_cfg)
img = mmcv.imnormalize(img, **new_img_norm_cfg)
return img
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