Proof-of-concept class to run ilastik pixel classifier in headless mode from python (intermediate files are generated as temp files)

run_ilastik.py

# tools to call an external ilastik classifier and run in headless mode
# see
# https://www.ilastik.org/documentation/basics/headless.html
#
# Volker Hilsenstein, Monash Micro Imaging 2019
# License: BSD-3

import subprocess
from abc import ABC, abstractmethod
from typing import List
import pathlib
import warnings
import tempfile
import h5py
import numpy as np

def ilastik_h5_asarray(h5file: str):
    with h5py.File(h5file, mode="r") as f:
        tmp = f["exported_data"][:]
    return tmp

class ilastikClassifier(ABC):
    def __init__(self, executable: str, project: str):
        """initialize an ilastik classifier with the path to ilastik and the project
        
        Arguments:
            executable {str} -- Path to ilastik executable
            project {str} -- Path to ilastik projcet file
        """
        self.ilastik_exe = executable
        self.project = project
        if not pathlib.Path(self.project).is_file():
            warnings.warn(f"project file {self.project} does not exist")
        if not pathlib.Path(self.ilastik_exe).is_file():
            warnings.warn(f"ilastik executable path incorrect: {self.ilastik_exe} does not exist") 
        super().__init__()

    @abstractmethod
    def run(self, files: List[str], outfile_pattern: str=''):
        pass


class ilastikPixelClassifier(ilastikClassifier):
    def run(self, files: List[str], outfile_pattern: str=''):
        """run the ilastik classifier on image file
        
        Arguments:
            file {str} -- input file path
        
        Keyword Arguments:
            outfile {str} -- output file name (default: {''})
        """
        args = [self.ilastik_exe, "--headless", "--project", self.project]
        if outfile_pattern:
            args += ['--output_filename_format', outfile_pattern]
        args += files
        print(args)
        return subprocess.call(args)

    def get_predictions(self, files: List[str]):
        """returns a generator for predictions for files as numpy arrays. Ilastik output is directed to tmp files and deleted afterwards. 
        
        Arguments:
            files {List[str]} -- list of input files
        """
        with tempfile.TemporaryDirectory() as temp_dir:
            # run ilastik on all files
            print("temporary directory ******************** " + temp_dir)
            self.run(sorted(files), outfile_pattern=temp_dir+'/{nickname}_pred.h5')
            generated_files = list(pathlib.Path(temp_dir).rglob("*_pred.h5"))
            generated_files = list(map(str, generated_files))
            generated_files = sorted(generated_files)
            for gfile in generated_files:
                yield {"file" : gfile, "pred": ilastik_h5_asarray(gfile)}

def testPixelClassifier():
    ilastikexe = "c:/Program Files/ilastik-1.3.2post1/ilastik.exe"
    proj =  "C:/Users/Volker/Data/Natasha/podo.ilp"
    samplefiles = [r'c:\Users\Volker\Dropbox\Github\podocytes\CroppedPodos\1644 Cropped\1644 Glom 2.tif',
        r'c:\Users\Volker\Dropbox\Github\podocytes\CroppedPodos\1644 Cropped\1644  Glom 1.tif']   
    irunner = ilastikPixelClassifier(ilastikexe , proj)
    #print(irunner.run([samplefiles], "C:/Users/Volker/Data/Natasha/{nickname}_pred.h5"))
    preds = irunner.get_predictions(samplefiles)
    import time
    for p in preds:
        print(p.shape)

#testPixelClassifier()

Beware, there may still be a bug in the file sorting affecting the association of input files with output predictions in some cases. Beware !