Headless Mode Operation

The Pixel Classification Workflow contains an interactive graphical user interface for training a classifier and refining your results. However, once you’re happy with your classifier, you may wish to apply it to other images without bothering with the graphical user interface at all. For that use case, ilastik provides a command-line interface to the batch processing applet, a.k.a. “headless mode”.

Before you can use headless mode, create a project and train a classifier. Then save your project and quit ilastik.

To start ilastik in “headless” mode, use the --headless command-line flag.

For example, on Linux:

$ cd ilastik-1.1.7-Linux
$ ./run_ilastik.sh --headless

On Mac:

$ ./ilastik-1.1.7-OSX.app/Contents/ilastik-release/run_ilastik.sh --headless

And on Windows:

$ cd "\Program Files\ilastik-1.0"
$ .\ilastik.bat --headless

…but those commands will just start ilastik and immediately quit. To do something useful, you’ll need to load your project file, and provide some input data for batch predictions. The following examples use linux shell syntax, but the options are the same for all platforms.

For example this example command will run your classifier over 2 additional images. (Here we assume your classifier was trained on 2D images.)

$ ./run_ilastik.sh --headless --project=MyProject.ilp my_next_image.png my_next_image2.png

Using stack input

If you are dealing with 3D data in the form of an image sequence (e.g. a tiff stack), then use globstring syntax to tell ilastik which images to combine for each volume.

$ ./run_ilastik.sh --headless --project=MyProject.ilp "my_next_stack_*.png" "my_other_stack_*.png"

Note: The use of quotation marks in the above example is critical. The * in each input argument must be provided to ilastik, NOT auto-expanded by the shell before ilastik sees the command!

###Side note: Running your own Python scripts

For developers and power-users, you can run your own ilastik-dependent python scripts using the interpreter shipped within the ilastik install tree. The interpreter is located in the bin directory:

# Linux
$ ./ilastik-1.1.7-Linux/bin/python -c "import ilastik; print ilastik.__version__"

# Mac
$ ./ilastik-1.1.7-OSX.app/Contents/ilastik-release/bin/python -c "import ilastik; print ilastik.__version__"

Output Options

By default, ilastik will export the results in hdf5 format, stored to the same directory as the input image.
However, you can customize the output location and format with extra parameters. For example:

$ ./run_ilastik.sh --headless 
                    my_next_image.png my_next_image2.png

Here’s a quick summary of each command-line option provided by the headless interface.
For the most part, these map directly to the corresponding controls in the Data Export Settings Window. No matter what settings you use, the list of input files to process must come after all other items in the command (as shown in the example above).

Required settings:

  • --headless Invokes headless classification mode.
  • --project The path to your project file, which you have already used to train a classifier.

Optional settings:

  • --export_source The data to export. See the dropdown list on the Data Export GUI for choices. (For example, in pixel classification try --export_source="Simple Segmentation")
  • --output_format The file format to store your results in. Some formats are less flexible than others and therefore cannot be combined with every option here. Choices are: bmp, gif, hdr, jpeg, jpg, pbm, pgm, png, pnm, ppm, ras, tif, tiff, xv, bmp sequence, gif sequence, hdr sequence, jpeg sequence, jpg sequence, pbm sequence, pgm sequence, png sequence, pnm sequence, ppm sequence, ras sequence, tif sequence, tiff sequence, xv sequence, multipage tiff, multipage tiff sequence, hdf5, compressed hdf5, numpy, dvid.
  • --output_filename_format The path to the output file to write. A few “magic” placeholders can be used in these settings. These are useful when you are exporting multiple datasets:
    • {dataset_dir} - the directory containing the original raw dataset corresponding these export results
    • {nickname} - the ilastik nickname of the raw dataset corresponding to these export results
    • {roi} - The region-of-interest as specified in the --cutout_subregion setting.
    • {x_start}, {x_stop}, {y_start}, {y_stop}, etc - Specific axis start/stop boundaries for the region-of-interest
    • {slice_index} - The index of each slice in an exported image sequence (required for all image sequence formats, not allowed with any other format).
  • --output_internal_path (HDF5 only) Specifies the name of the HDF5 dataset to write to. (Default: /exported_data)
  • --cutout_subregion Subregion to export (start,stop), e.g. --cutout_subregion="[(0,0,0,0,0), (1,100,200,20,3)]". As well, when either (or both) start or stop are indicated as None, the subregion range is automatically expanded to the starting or to the ending index of the corresponding dimension, e.g --cutout_subregion="[(None,None,None), (None,None,10)]" would yield a tridimensional array of size 10 in its last dimension.
  • --export_dtype The pixel type to convert your results to. Choices are: uint8, uint16, uint32, int8, int16, int32, float32, float64. Note that some formats don’t support every pixel type.
  • --output_axis_order Transpose the storage order of the results. For example, this affects the sliced dimension for stack outputs.
  • --pipeline_result_drange Pipeline result data range (min,max) BEFORE normalization, e.g. "(0.0, 1.0)"
  • --export_drange Exported data range (min,max) AFTER normalization, e.g. "(0, 255)"

Headless Mode for Object Classification

The Object Classification Workflow can also be used in “headless” mode.
The command-line interface is similar to the Pixel Classification interface described above, but with a few minor changes.
Here’s an example command (explanation below):

$ ./run_ilastik.sh --headless
                   --export_source="Object Predictions"
                   --raw_data "my_grayscale_stack_1/*.png"
                   --segmentation_image my_unclassified_objects_1.h5/binary_segmentation_volume

Depending on which variant of the Object Classification Workflow you’re using, you may need to provide more than one input image for each volume of data you want to process (e.g. “Raw Data” and “Segmentation Image”, or “Raw Data” and “Prediction Maps”). Both files must be provided on the command-line. To specify which is which, prefix the list of input files with either --raw_data, --segmentation_image, or --prediction_maps accordingly. If you are processing more than one volume a single command, provide all inputs of a given type in sequence:

$ ./run_ilastik.sh --headless
--raw_data "my_grayscale_stack_1/*.png" "my_grayscale_stack_2/*.png" "my_grayscale_stack_3/*.png"
--segmentation_image my_unclassified_objects_1.h5/binary_segmentation_volume my_unclassified_objects_2.h5/binary_segmentation_volume my_unclassified_objects_3.h5/binary_segmentation_volume

Per default, ilastik exports a label image of the object class predictions. This is equivalent to setting --export_source="Object Predictions" in the command line. Use the --export_source flag to specify the output of the processing explicitly. If the flag is set to --export_source="Object Probabilities", ilastik will export a multi-channel image volume of object prediction probabilities instead of a label image (one channel for each prediction class). Finally, if you happen to be using the “Pixel Classification + Object Classification” workflow which combines pixel classification and object classification into a single workflow, you may export the pixel prediction images by providing the --export_source="Pixel Probabilities" flag.

If you provide path for the --table_filename output, ilastik will export a .csv file of the computed object features that were used during classification, indexed by object id.

So, the example command above produces 3 files:

  • a prediction image (a label image),
  • a floating-point “probability image” (with N channels – one for each class), and
  • a .csv file containing a row for each object and columns for each of the features your project uses. This file also contains the probability value for each object.

Important Notes:

  • Just as in Pixel Classification, 3D object classification inputs may be provided as image stacks, but quotes are required around the argument, as shown in the above example. (See the corresponding note above.)
  • For paths to hdf5 datasests (either input or output), ilastik uses the same conventions as the generic h5ls utility. That is, the hdf5 dataset name should be appended to the file path: /path/to/my_file.h5/internal/path/to/dataset.
  • In the current “headless” implementation of object classification, the entire image is loaded into RAM in one go, and then object classification is run on it. Therefore, there is a limit to how large your input image can be.
  • If you want to change the output format or output file locations, you can add some options to the command, just as described in the headless pixel classification documentation shown above.

Controlling CPU and RAM resources

By default, ilastik will use all available CPU cores (as detected by Python’s “multiprocessing” module), including “virtual” cores if your CPU supports hyperthreading (like most modern Intel processors).

If you want to explicitly specify the number of parallel threads ilastik should use, you can do so via a special environment variable recognized by ilastik:

LAZYFLOW_THREADS=4 run_ilastik.sh --headless ...

There’s an additional environment variable for specifying how much RAM to use during headless execution:

LAZYFLOW_THREADS=4 LAZYFLOW_TOTAL_RAM_MB=4000 run_ilastik.sh --headless ...

The RAM limit is not perfectly respected in all cases, so you may want to leave some buffer if your RAM budget is strict.