NVIDIA Clara Viz is a platform for visualization of 2D/3D medical imaging data. It enables building applications that leverage powerful volumetric visualization using CUDA-based ray tracing.
Clara Viz offers a Python Wrapper for rapid experimentation. It also includes a collection of visual widgets for performing interactive medical image visualization in Jupyter Lab notebooks.
On Windows, starting with Chrome version 91 (also with Microsoft Edge) the interactive Jupyter widget is not working correctly. There is a delay in the interactive view after starting interaction. This is an issue with the default (D3D11) rendering backend of the browser. To fix this open chrome://flags/#use-angle
and switch the backend to OpenGL
.
- NVIDIA GPU: Pascal or newer, including Pascal, Volta, Turing and Ampere families
- NVIDIA driver: 450.36.06+
https://docs.nvidia.com/clara-viz/index.html
This will install all Clara Viz packages use pip:
$ pip install clara-viz
Clara Viz is using namespace packages. The main functionality is implemented in the 'clara-viz-core' package, Jupyter Notebook widgets are found in the 'clara-viz-widgets' package. So for example if you just need the renderer use
$ pip install clara-viz-core
Install the Jupyter notebook widgets.
$ pip install clara-viz-widgets
Start Jupyter Lab, open the notebooks in the notbooks
folder. Make sure Git LFS is installed when cloning the repo, else the data files are not downloaded correctly and you will see file open errors when using the example notebooks.
from clara.viz.widgets import Widget
from clara.viz.core import Renderer
import numpy as np
# load a RAW CT data file (volume is 512x256x256 voxels)
input = np.fromfile("CT.raw", dtype=np.int16)
input = input.reshape((512, 256, 256))
display(Widget(Renderer(input)))
from PIL import Image
import clara.viz.core
import numpy as np
# load a RAW CT data file (volume is 512x256x256 voxels)
input = np.fromfile("CT.raw", dtype=np.int16)
input = input.reshape((512, 256, 256))
# create the renderer
renderer = clara.viz.core.Renderer(input)
# render to a raw numpy array
output = renderer.render_image(1024, 768, image_type=clara.viz.core.RAW_RGB_U8_DEPTH_U8)
rgb_data = np.asarray(output)[:, :, :3]
# show with PIL
image = Image.fromarray(rgb_data)
image.show()
Clara Viz requires CUDA, use a base
container from https://hub.docker.com/r/nvidia/cuda
for example nvidia/cuda:11.4.2-base-ubuntu20.04
. By default the CUDA container exposes the compute
and utility
capabilities only. Clara Viz additionally needs the graphics
and video
capabilites. Therefore the docker container needs to be run with the NVIDIA_DRIVER_CAPABILITIES
env variable set:
$ docker run -it --rm -e NVIDIA_DRIVER_CAPABILITIES=graphics,compute,utility nvidia/cuda:11.4.2-base-ubuntu20.04
or add:
ENV NVIDIA_DRIVER_CAPABILITIES graphics,video,compute,utility
to your docker build file. See https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#driver-capabilities for more information.
Currently Clara Viz won't run under WSL because OptiX is not supported in that environment.
Without awesome third-party open source software, this project wouldn't exist.
Please find LICENSE-3rdparty.md
to see which third-party open source software
is used in this project.
Apache-2.0 License (see LICENSE
file).
Copyright (c) 2020-2021, NVIDIA CORPORATION.