adding method to use installed ilastik instance

REQUIREMENTS.txt

@@ -5,6 +5,7 @@
 python-dateutil < 2.8.1
 click
 dataclasses==0.6
+h5py
 jsonschema
 matplotlib
 numpy != 1.13.0

starfish/core/image/Filter/__init__.py

@@ -6,6 +6,7 @@
 from .element_wise_mult import ElementWiseMultiply
 from .gaussian_high_pass import GaussianHighPass
 from .gaussian_low_pass import GaussianLowPass
+from .ilastik_pre_trained_probability import IlastikPretrainedProbability
 from .laplace import Laplace
 from .linear_unmixing import LinearUnmixing
 from .map import Map

starfish/core/image/Filter/ilastik_pre_trained_probability.py

@@ -0,0 +1,121 @@
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Optional, Union
+
+import h5py
+import numpy as np
+import scipy.ndimage as ndi
+from skimage.filters import threshold_otsu
+
+from starfish.core.imagestack.imagestack import ImageStack
+from ._base import FilterAlgorithm
+
+
+class IlastikPretrainedProbability(FilterAlgorithm):
+    """
+    Use an existing ilastik pixel classifier to generate a probability image for a dapi image.
+    NOTE: This api may not be used without a downloaded and installed version of Ilastik. Visit
+    https://www.ilastik.org/download.html to download.
+
+    Parameters
+    ----------
+    ilastik_executable: Union[Path, str]
+        Path to run_ilastik.sh needed for running ilastik in headless mode. Typically the script is
+        located: "/Applications/{ILASTIK_VERSION}/Contents/ilastik-release/run_ilastik.sh"
+
+    ilastik_project: Union[Path, str]
+        path to ilastik project .ilp file
+
+    """
+
+    def __init__(self, ilastik_executable: Union[Path, str], ilastik_project: Union[Path, str]):
+        ilastik_executable = Path(ilastik_executable)
+        if not ilastik_executable.exists():
+            raise EnvironmentError("Can not find run_ilastik.sh. Make sure you've provided the "
+                                   "correct location. If you need to download ilastik please"
+                                   " visit: https://www.ilastik.org/download.html")
+        self.ilastik_executable = ilastik_executable
+        self.ilastik_project = ilastik_project
+
+    def run(
+            self,
+            stack: ImageStack,
+            in_place: bool = False,
+            verbose: bool = False,
+            n_processes: Optional[int] = None,
+            *args,
+    ) -> Optional[ImageStack]:
+        """
+        Use a pre trained probability pixel classification model to generate probabilites
+        for a dapi image
+
+        Parameters
+        ----------
+        stack : ImageStack
+            Dapi image to be run through ilastik.
+        in_place : bool
+            This parameter is ignored for this filter.
+        verbose : bool
+            This parameter is ignored for this filter.
+        n_processes : Optional[int]
+            This parameter is ignored for this filter.
+
+        Returns
+        -------
+        ImageStack :
+            A new ImageStack created from the cell probabilities provided by ilastik.
+        """
+        if stack.num_rounds != 1:
+            raise ValueError(
+                f"{IlastikPretrainedProbability.__name__} given an image with more than one round "
+                f"{stack.num_rounds}")
+        if stack.num_chs != 1:
+            raise ValueError(
+                f"{IlastikPretrainedProbability.__name__} given an image with more than one channel"
+                f"{stack.num_chs}")
+
+        # temp files
+        temp_dir = tempfile.TemporaryDirectory()
+        dapi_file = f"{temp_dir.name}_dapi.npy"
+        output_file = f"{temp_dir.name}_dapi_Probabilities.h5"
+        np.save(dapi_file, stack.xarray.values.squeeze())
+
+        # env {} is needed to fix the weird virtualenv stuff
+        subprocess.check_call(
+            [self.ilastik_executable,
+             '--headless',
+             '--project',
+             self.ilastik_project,
+             "--output_filename_format",
+             output_file,
+             dapi_file], env={})  # type: ignore
+
+        return self.import_ilastik_probabilities(output_file)
+
+    @classmethod
+    def import_ilastik_probabilities(cls, path_to_h5_file: Union[str, Path]
+                                     ) -> ImageStack:
+        """
+        Import cell probabilities provided by ilastik as an ImageStack.
+
+        Parameters
+        ----------
+        path_to_h5_file : Union[str, Path]
+            Path to the .h5 file outputted by ilastik
+
+        Returns
+        -------
+        ImageStack :
+            A new ImageStack created from the cell probabilities provided by ilastik.
+        """
+
+        h5 = h5py.File(path_to_h5_file)
+        probability_images = h5["exported_data"][:]
+        h5.close()
+        cell_probabilities, _ = probability_images[:, :, 0], probability_images[:, :, 1]
+        cell_threshold = threshold_otsu(cell_probabilities)
+        label_array = ndi.label(cell_probabilities > cell_threshold)[0]
+        # Add flat dims to make 5d tensor
+        label_array = label_array[np.newaxis, np.newaxis, np.newaxis, ...]
+        return ImageStack.from_numpy(label_array)