- Modularity for bigdataviewer! Being able to combine scripts or code for display and processing. This update site creates a way to access an existing BDV instance when scripting or writing java code.
- Provide a set of macro recordable commands for bigdataviewer. Makes it possible to script basic actions on BigDataViewer with the IJ1 recorder.
Enable the update site (link TO BE DONE) to activate bigdataviewer_scijava commands. All commands from this update site are present in the repository bigdataviewer_scijava.
What you can do with this update site
By using Scijava framework to store BigDataViewer windows through its
BdvHandle interface, it is possible to communicate bdv instances between scripts and commands (see script parameter page). This also facilitates the use of FIJI GUI because the commands of this repository can be chained easily. Any script or Command which declares a
BdvHandle parameter can retrieve or provide reference to existing BigDataViewer instances.
In practice, Bdv windows created via the commands from this update site are put by default in the
ObjectService. To get a reference to these windows:
- In groovy, add this at the beginning of your script
- In Java:
@Parameter BdvHandle bdvh;
How to make your bigdataviewer workflow compatible with Scijava
- Use the command
BdvWindowCreatefrom this repository and retrieve its reference through SciJava parameter annotation. This will make your Bdv Window accessible to other plugins / commands.
- Create your own Bdv window, but declare the associated
BdvHandleas an output of your Command:
- in Java:
@Parameter(type = ItemIO.OUTPUT); BdvHandle bdvh_out;
- in Java:
The type of the parameter annotation can also be
ItemIO.BOTH if your command is modifying an existing
This repo provide an implementation of all the command from the repository bigdataviewer_fiji to make it scijava compatible, so it should have the same commands.
- Because complex bdv dataset (metadata, multiple views, channels...) are usually organized into
SpimDataobjects, this update site / repository also provide tools to manipulate these objects, in a scijava compatible manner. Hence declaring in groovy
#@mpicbg.spim.data.generic.AbstractSpimData sdor in java
@Parameter AbstractSpimData asd;allows to manipulate and communicate such objects.
- Easy visualization of the bdv sources present in a Bdv Window (-> access to display options). Right clicking on selected source also enables to access basic display operations.
- Easy edition with "standard actions" on bdv sources held within a Bdv Handle:
1 - Simple
https://www.youtube.com/watch?v=-q5qIdH9Idw (1 minute)
- Open a sample image.
- Change display settings.
- Double clicking on the source in the bdv window translate the window to the origin of the source
2 - IJ Script example
https://youtu.be/IjIW5bOn4P8 (3 minutes)
- Create Bdv Window
- Start IJ1 recorder
- Open a voronoi image
- Create an affine transform
- Transform source with affine transform
- Make a loop and execute it
- Modify display settings
3 - Procedural + Warping + Export (xml hdf5 and ImagePlus)
https://youtu.be/uOYWn7tUsf0 (7 minutes)
- Open a procedural image ( mandelbrot )
- Create new Bdv Window
- Open voronoi in this new window
- Warp one into another using BigWarp
- Create a new bdv window
- Transfer initial Voronoi Image into this window
- Transfer transformed fractal into this window
- Resample transformed Mandelbrot like Voronoi
- Save initial voronoi and transformed resampled mandelbrot into a new dataset
- Close everything
- Reopen saved dataset
- Apply correct display settings
Resampling is necessary because XML/Hdf5 do not allow to save procedural images.