Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,216 Bytes
28c256d |
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 52 53 54 55 56 57 58 59 60 61 62 63 64 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import numpy as np
import mmcv
try:
import torch
except ImportError:
torch = None
def tensor2imgs(tensor,
mean: Optional[tuple] = None,
std: Optional[tuple] = None,
to_rgb: bool = True) -> list:
"""Convert tensor to 3-channel images or 1-channel gray images.
Args:
tensor (torch.Tensor): Tensor that contains multiple images, shape (
N, C, H, W). :math:`C` can be either 3 or 1.
mean (tuple[float], optional): Mean of images. If None,
(0, 0, 0) will be used for tensor with 3-channel,
while (0, ) for tensor with 1-channel. Defaults to None.
std (tuple[float], optional): Standard deviation of images. If None,
(1, 1, 1) will be used for tensor with 3-channel,
while (1, ) for tensor with 1-channel. Defaults to None.
to_rgb (bool, optional): Whether the tensor was converted to RGB
format in the first place. If so, convert it back to BGR.
For the tensor with 1 channel, it must be False. Defaults to True.
Returns:
list[np.ndarray]: A list that contains multiple images.
"""
if torch is None:
raise RuntimeError('pytorch is not installed')
assert torch.is_tensor(tensor) and tensor.ndim == 4
channels = tensor.size(1)
assert channels in [1, 3]
if mean is None:
mean = (0, ) * channels
if std is None:
std = (1, ) * channels
assert (channels == len(mean) == len(std) == 3) or \
(channels == len(mean) == len(std) == 1 and not to_rgb)
num_imgs = tensor.size(0)
mean = np.array(mean, dtype=np.float32)
std = np.array(std, dtype=np.float32)
imgs = []
for img_id in range(num_imgs):
img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
img = mmcv.imdenormalize(
img, mean, std, to_bgr=to_rgb).astype(np.uint8)
imgs.append(np.ascontiguousarray(img))
return imgs
|