Viewer for hyperspectral Numpy cubes and (2d/3d/4d)-tensors.
This viewer opens practically all numpy files, that has at least two dimensions. Using a slider it is possible to scroll through the channels. It features an envi to numpy converter, that converts raw ENVI shots to numpy (even white/black-ref support).
It features full support for 4d arrays, such as Tensors. During debugging, a Tensor can be saved using np.save() and analyzed by this tool. The tool allows to slice through batches and channels, and can merge channels into RGB/BGR into one rendered view.
This tool might also be useful for people working with DICOM. If you convert an array of DICOM images to a numpy array, and then save it, then this tool is useful to scroll through slices.
Can convert BIL ENVI files from e.g. "Lumo scanner", Specim FX17, Specim FX10, etc. You can also compare the spectrum of a pixel by left/right clicking in the canvas.
Also available as AppImage. Checkout the GitHub page for more info.
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Snaps are applications packaged with all their dependencies to run on all popular Linux distributions from a single build. They update automatically and roll back gracefully.
Snaps are discoverable and installable from the Snap Store, an app store with an audience of millions.
Snap is available for CentOS 7.6+, and Red Hat Enterprise Linux 7.6+, from the Extra Packages for Enterprise Linux (EPEL) repository. The EPEL repository can be added to your system with the following command:
sudo yum install epel-release
Snap can now be installed as follows:
sudo yum install snapd
Once installed, the systemd unit that manages the main snap communication socket needs to be enabled:
sudo systemctl enable --now snapd.socket
To enable classic snap support, enter the following to create a symbolic link between /var/lib/snapd/snap and /snap:
sudo ln -s /var/lib/snapd/snap /snap
Either log out and back in again, or restart your system, to ensure snap’s paths are updated correctly.
To install Numpy viewer, simply use the following command:
sudo snap install numpyviewer
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