diff imagej_basic_ashlar_filepattern.py @ 0:cad3339b566b draft default tip

"planemo upload for repository https://github.com/ohsu-comp-bio/basic-illumination commit d06e0682d1847fae0d5a464d7aa9e47e40d31fe7-dirty"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej_basic_ashlar_filepattern.py	Tue Dec 08 20:57:09 2020 +0000
@@ -0,0 +1,135 @@
+# @String(label="Enter a filename pattern describing the TIFFs to process") pattern
+# @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
+
+import sys
+import os
+import re
+import collections
+from ij import IJ, WindowManager, ImagePlus, ImageStack
+from ij.io import Opener
+from ij.macro import Interpreter
+import BaSiC_ as Basic
+
+
+def enumerate_filenames(pattern):
+    """Return filenames matching pattern (a str.format pattern containing
+    {channel} and {tile} placeholders).
+
+    Returns a list of lists, where the top level is indexed by channel number
+    and the bottom level is sorted filenames for that channel.
+
+    """
+    (base, pattern) = os.path.split(pattern)
+    regex = re.sub(r'{([^:}]+)(?:[^}]*)}', r'(?P<\1>.*?)',
+                   pattern.replace('.', '\.'))
+    tiles = set()
+    channels = set()
+    num_images = 0
+    # Dict[channel: int, List[filename: str]]
+    filenames = collections.defaultdict(list)
+    for f in os.listdir(base):
+        match = re.match(regex, f)
+        if match:
+            gd = match.groupdict()
+            tile = int(gd['tile'])
+            channel = int(gd['channel'])
+            tiles.add(tile)
+            channels.add(channel)
+            filenames[channel].append(os.path.join(base, f))
+            num_images += 1
+    if len(tiles) * len(channels) != num_images:
+        raise Exception("Missing some image files")
+    filenames = [
+        sorted(filenames[channel])
+        for channel in sorted(filenames.keys())
+    ]
+    return filenames
+
+
+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"
+
+    #import pdb; pdb.set_trace()
+    print "Loading images..."
+    filenames = enumerate_filenames(pattern)
+    num_channels = len(filenames)
+    num_images = len(filenames[0])
+    image = Opener().openImage(filenames[0][0])
+    width = image.width
+    height = image.height
+    image.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 "==========================="
+
+        stack = ImageStack(width, height, num_images)
+        opener = Opener()
+        for i, filename in enumerate(filenames[channel]):
+            print "Loading image %d/%d" % (i + 1, num_images)
+            image = opener.openImage(filename)
+            stack.setProcessor(image.getProcessor(), i + 1)
+        input_image = ImagePlus("input", stack)
+
+        # 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()