Describe the bug
The output image of the Affine transform does not have the correct affine.
To Reproduce
- Load this example data:
image.nii.gz
labels.nii.gz
- Run the following script:
# %%
from pathlib import Path
from monai.data.meta_tensor import MetaTensor
from monai.transforms.spatial.array import Affine
import nibabel as nib
import numpy as np
import torch
# %%
folder = Path('./example_data')
def load(path: Path):
image_ni = nib.load(path)
image_np = np.asarray(image_ni.get_fdata(), dtype=np.float64)
image_affine = np.asarray(image_ni.affine, dtype=np.float64)
image_mt = MetaTensor(torch.from_numpy(image_np).unsqueeze(0), torch.from_numpy(image_affine))
return image_ni, image_np, image_affine, image_mt
source_ni, source_np, source_affine, source_mt = load(folder / 'labels.nii.gz')
target_ni, target_np, target_affine, target_mt = load(folder / 'image.nii.gz')
# %%#
resample_affine = np.linalg.inv(source_affine) @ target_affine
affine_transform = Affine(
affine=resample_affine,
image_only=True,
mode=0,
padding_mode="zeros",
dtype=np.float64,
)
resampled_mt = affine_transform(source_mt)
# %%
diff = target_affine - resampled_mt.affine.numpy()
with np.printoptions(precision=3, suppress=True):
print(target_affine)
print(source_affine @ resample_affine)
print(resampled_mt.affine.numpy())
print(diff)target_affine
[[ 1. -0. 0. -130.245]
[ -0. 1. 0. -163.11 ]
[ 0. 0. 1. -112.138]
[ 0. 0. 0. 1. ]]
source_affine @ resample_affine
[[ 1. -0. -0. -130.245]
[ -0. 1. 0. -163.11 ]
[ 0. 0. 1. -112.138]
[ 0. 0. 0. 1. ]]
resampled_mt.affine.numpy()
[[ 1. -0. -0. -139.079]
[ -0. 1. 0. -231.945]
[ 0. 0. 1. -62.873]
[ 0. 0. 0. 1. ]]
diff
[[ 0. 0. 0. 8.835]
[ -0. -0. -0. 68.835]
[ 0. -0. -0. -49.265]
[ 0. 0. 0. 0. ]]
As we can see, source_affine @ resample_affine is equal to target_affine.
But resampled_mt.affine.numpy() is different and there is a diff.
Expected behavior
I expected resampled_mt.affine.numpy() to be equal to source_affine @ resample_affine (or target_affine).
Environment
================================
Printing MONAI config...
================================
MONAI version: 1.2.0
Numpy version: 1.22.3
Pytorch version: 2.1.0+cu121
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: c33f1ba588ee00229a309000e888f9817b4f1934
MONAI __file__: /home/spe/.pyenv/versions/datascience/lib/python3.10/site-packages/monai/__init__.py
Optional dependencies:
Pytorch Ignite version: 0.4.11
ITK version: 5.3.0
Nibabel version: 5.1.0
scikit-image version: 0.22.0
Pillow version: 10.0.1
Tensorboard version: 2.14.1
gdown version: 4.7.1
TorchVision version: 0.16.0+cu121
tqdm version: 4.66.1
lmdb version: 1.4.1
psutil version: 5.9.5
pandas version: 2.0.3
einops version: 0.7.0
transformers version: 4.21.3
mlflow version: 2.7.1
pynrrd version: 1.0.0
For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
================================
Printing system config...
================================
System: Linux
Linux version: Ubuntu 20.04.6 LTS
Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
Processor: x86_64
Machine: x86_64
Python version: 3.10.12
Process name: python
Command: ['/home/spe/.pyenv/versions/datascience/bin/python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: [popenfile(path='/home/spe/.vscode-server/data/logs/20231005T155144/ptyhost.log', fd=19, position=0, mode='a', flags=33793), popenfile(path='/home/spe/.vscode-server/data/logs/20231005T155144/remoteagent.log', fd=21, position=13979, mode='a', flags=33793), popenfile(path='/home/spe/.vscode-server/data/logs/20231005T155144/network.log', fd=23, position=0, mode='a', flags=33793)]
Num physical CPUs: 6
Num logical CPUs: 12
Num usable CPUs: 12
CPU usage (%): [10.5, 4.5, 5.8, 2.1, 5.6, 2.6, 5.9, 3.1, 6.1, 2.1, 11.1, 90.8]
CPU freq. (MHz): 3492
Load avg. in last 1, 5, 15 mins (%): [5.2, 3.4, 3.8]
Disk usage (%): 53.4
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 15.6
Available memory (GB): 8.5
Used memory (GB): 6.8
================================
Printing GPU config...
================================
Num GPUs: 1
Has CUDA: True
CUDA version: 12.1
cuDNN enabled: True
cuDNN version: 8902
Current device: 0
Library compiled for CUDA architectures: ['sm_50', 'sm_60', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'sm_90']
GPU 0 Name: Quadro K2200
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 5
GPU 0 Total memory (GB): 4.0
GPU 0 CUDA capability (maj.min): 5.0
Describe the bug
The output image of the Affine transform does not have the correct affine.
To Reproduce
image.nii.gz
labels.nii.gz
target_affine
source_affine @ resample_affine
resampled_mt.affine.numpy()
diff
As we can see,
source_affine @ resample_affineis equal totarget_affine.But
resampled_mt.affine.numpy()is different and there is adiff.Expected behavior
I expected
resampled_mt.affine.numpy()to be equal tosource_affine @ resample_affine(ortarget_affine).Environment