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As you may know ShowJ is our default viewer for most of Scipion objects and Particle Picker allows us to visualize coordinates or pick particles either manually or automatically. Scipion source packages related to ShowJ and Particle Picking include:
xmipp.ij.commmons : Contains ImageJ-related classes (In ShowJ we use and extend ImageJ to display and process images), such as Xmipp canvas with customized events, Xmipp image window with our menu and imageplus loader.
xmipp.jni : Contains Java bindings with Xmipp C++ code
xmipp.viewer.models : Contains the different table models used to display data in ShowJ
xmipp.viewer.windows : Contains ShowJ main frame and associated dialogs: columns editor, plotter, CTF Profile, etc.
xmipp.viewer.scipion : Contains Scipion extensions of Metadata, GalleryData and GalleryJFrame classes
xmipp.viewer.particlepicker: Contains base classes for particle picking: frame, canvas, micrograph, particle dialog, etc.
xmipp.viewer.particlepicker.training: Contains supervised picker implementation
xmipp.viewer.particlepicker.tiltpair: Contains tilt pair picker implementation
xmipp.jni package contains java versions of Xmipp core classes for image processing, such as MetaData or ImageGeneric. A Metadata represents a star file and it is used to read and store information in Xmipp. ImageGeneric represents an image in memory. It is used to load and write image files, such as .stk, .mrc, .vol, etc.
xmipp.viewer.particlepicker.training and xmipp.viewer.particlepicker.tiltpair packages in turn contain model and GUI packages with data model and windows respectively.
In the following class diagram, we display ShowJ main classes and its relations:
GalleryJFrame and table models are used for data visualization, while GalleryData is in charge of data manipulation and business logic. To visualize most data objects in Scipion, DataViews are used. DataViews can display data in desktop and web using ShowJ or ShowJ Web. These classes are defined in pyworkflow/em/viewer.py. The developer can specify column visibility, order, type of viewer (gallery or table), etc. In turn, this configuration is given as input to ShowJ from the command line.
In the following class diagram, we display particle picker main classes and its relations:
Base classes ParticlePicker, ParticlePickerJFrame, ParticlePickerCanvas, etc., are extended to implement supervised and tilt pair picker. Images are loaded using ImagePlusLoader and displayed using XmippImageWindow, as in ShowJ. While ParticlePickerJFrame and ParticlePickerCanvas are used to visualize and edit coordinates, ParticlePicker contains all the business logic related to the picking process.
ShowJ and Particle Picker use sockets to communicate with Scipion. The port used for communication is specified as an input parameter from the command line. There are a number of operations that require this functionality:
creating subsets: We can create subsets of classes, of particles from classes, of representatives, of images, of volumes, etc. These subsets are created through Scipion buttons added in ScipionGalleryJFrame at the bottom of the window. They invoke the creation of the corresponding ProtUserSubset output implemented in the protocol_batch module.
registering coordinates: This socket is implemented in pyworkflow/em/showj.py to register picker coordinates. It invokes picker protocol method "registerCoords" in order to convert its coordinates into the Scipion format.
executing object commands: In ShowJ the developer can specify a set of commmands to be linked with the object menu (popup menu displayed when the user does right click). Eg: Display CTF Fitting, Plot Distance Profile (Normal Modes), etc. Python code related to these actions is executed using sockets. The socket handler and the functions related to these actions are implemented in pyworkflow/gui/project/project.py
Particle Picker has also been extended to use a generic classifier if provided with a configuration file specifying classifier autopick and convert commands and input parameters. This allows us to use it as a wizard to configure automatic pickers such as Relion autopick, Appion dogpicker or gaussian picker. A classifier configuration should look like this:
parameters = diameter,threshold diameter.value = 100 diameter.label = Diameter diameter.help = some help threshold.value = 0.5 threshold.label = Threshold threshold.help = some help autopickCommand = /path/to/scipion/software/em/dogpicker/ApDogPicker.py --thresh=%(threshold) --diam=%(diameter) --apix=3.54 --image=%(micrograph) --outfile=Tmp/348.outputMicrographs04/%(micrographName).txt convertCommand = /path/to/scipion/pyworkflow/apps/pw_convert.py --coordinates --from dogpicker --to xmipp --input Runs/000348_ProtSplitSet/micrographs4.sqlite --output Tmp/348.outputMicrographs04
Autopick protocols implement wizards that generate this file and launch the particle picker to adjust parameters (considering the results in several micrographs) before running an automatic picking for the whole set of micrographs.