--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej_basic_ashlar.py	Fri Mar 12 00:13:46 2021 +0000
@@ -0,0 +1,135 @@
+# @File(label="Select a slide to process") filename
+# @File(label="Select the output location", style="directory") output_dir
+# @String(label="Experiment name (base name for output files)") experiment_name
+# @Float(label="Flat field smoothing parameter (0 for automatic)", value=0.1) lambda_flat
+# @Float(label="Dark field smoothing parameter (0 for automatic)", value=0.01) lambda_dark
+
+# Takes a slide (or other multi-series BioFormats-compatible file set) and
+# generates flat- and dark-field correction profile images with BaSiC. The
+# output format is two multi-series TIFF files (one for flat and one for dark)
+# which is the input format used by Ashlar.
+
+# Invocation for running from the commandline:
+#
+# ImageJ --ij2 --headless --run imagej_basic_ashlar.py "filename='input.ext',output_dir='output',experiment_name='my_experiment'"
+
+import sys
+from ij import IJ, WindowManager, Prefs
+from ij.macro import Interpreter
+from loci.plugins import BF
+from loci.plugins.in import ImporterOptions
+from loci.formats import ImageReader
+from loci.formats.in import DynamicMetadataOptions
+import BaSiC_ as Basic
+
+import pdb
+
+
+def main():
+
+    Interpreter.batchMode = True
+
+    if (lambda_flat == 0) ^ (lambda_dark == 0):
+        print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
+               " or both non-zero.")
+        return
+    lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
+
+    print "Loading images..."
+
+    # For multi-scene .CZI files, we need raw tiles instead of the
+    # auto-stitched mosaic and we don't want labels or overview images.  This
+    # only affects BF.openImagePlus, not direct use of the BioFormats reader
+    # classes which we also do (see below)
+    Prefs.set("bioformats.zeissczi.allow.autostitch",  "false")
+    Prefs.set("bioformats.zeissczi.include.attachments", "false")
+
+    # Use BioFormats reader directly to determine dataset dimensions without
+    # reading every single image. The series count (num_images) is the one value
+    # we can't easily get any other way, but we might as well grab the others
+    # while we have the reader available.
+    dyn_options = DynamicMetadataOptions()
+    # Directly calling a BioFormats reader will not use the IJ Prefs settings
+    # so we need to pass these options explicitly.
+    dyn_options.setBoolean("zeissczi.autostitch", False)
+    dyn_options.setBoolean("zeissczi.attachments", False)
+    bfreader = ImageReader()
+    bfreader.setMetadataOptions(dyn_options)
+    bfreader.id = str(filename)
+    num_images = bfreader.seriesCount
+    num_channels = bfreader.sizeC
+    width = bfreader.sizeX
+    height = bfreader.sizeY
+    bfreader.close()
+
+    # The internal initialization of the BaSiC code fails when we invoke it via
+    # scripting, unless we explicitly set a the private 'noOfSlices' field.
+    # Since it's private, we need to use Java reflection to access it.
+    Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
+    Basic_noOfSlices.setAccessible(True)
+    basic = Basic()
+    Basic_noOfSlices.setInt(basic, num_images)
+
+    # Pre-allocate the output profile images, since we have all the dimensions.
+    ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32);
+    df_image = IJ.createImage("Dark-field", width, height, num_channels, 32);
+
+    print("\n\n")
+
+    # BaSiC works on one channel at a time, so we only read the images from one
+    # channel at a time to limit memory usage.
+    for channel in range(num_channels):
+        print "Processing channel %d/%d..." % (channel + 1, num_channels)
+        print "==========================="
+
+        options = ImporterOptions()
+        options.id = str(filename)
+        options.setOpenAllSeries(True)
+        # concatenate=True gives us a single stack rather than a list of
+        # separate images.
+        options.setConcatenate(True)
+        # Limit the reader to the channel we're currently working on. This loop
+        # is mainly why we need to know num_images before opening anything.
+        for i in range(num_images):
+            options.setCBegin(i, channel)
+            options.setCEnd(i, channel)
+        # openImagePlus returns a list of images, but we expect just one (a
+        # stack).
+        input_image = BF.openImagePlus(options)[0]
+
+        # BaSiC seems to require the input image is actually the ImageJ
+        # "current" image, otherwise it prints an error and aborts.
+        WindowManager.setTempCurrentImage(input_image)
+        basic.exec(
+            input_image, None, None,
+            "Estimate shading profiles", "Estimate both flat-field and dark-field",
+            lambda_estimate, lambda_flat, lambda_dark,
+            "Ignore", "Compute shading only"
+        )
+        input_image.close()
+
+        # Copy the pixels from the BaSiC-generated profile images to the
+        # corresponding channel of our output images.
+        ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title)
+        ff_image.slice = channel + 1
+        ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0)
+        ff_channel.close()
+        df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title)
+        df_image.slice = channel + 1
+        df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0)
+        df_channel.close()
+
+        print("\n\n")
+
+    template = '%s/%s-%%s.tif' % (output_dir, experiment_name)
+    ff_filename = template % 'ffp'
+    IJ.saveAsTiff(ff_image, ff_filename)
+    ff_image.close()
+    df_filename = template % 'dfp'
+    IJ.saveAsTiff(df_image, df_filename)
+    df_image.close()
+
+    print "Done!"
+
+
+main()