preprocess_sequence plugin : added the ablilty to smooth the dark image to avoid infinite pixel values in the result

infinite pixel values happen when a pixel in the dark image has the same value as its counterpart in the white image (in a dark area, this can happen)

src/ij-plugins/Ipr/Lipase/Preprocess_Sequence.py

@@ -1,10 +1,22 @@
+#@ String (choices={"no smooth", "gaussian blur"}, style="listBox", value="gaussian blur") DARK_SMOOTH_METHOD
+#@ Float (label="dark image gaussian blur sigma", style="slider", min=0.0, max=10.0, stepSize=0.1, value=3.0) DARK_GAUSSIAN_BLUR_SIGMA
 #@ ImagePlus (label="the input image stack") INPUT_STACK
 #@ ImagePlus (label="the input white image") INPUT_WHITE
 #@ ImagePlus (label="the input dark image") INPUT_DARK
 #@output ImagePlus PREPROCESSED_STACK
+
+# failed attempt to use alternative input parameters for headless testing but visibility=INVISIBLE doesn't make their widget disappear, unfortunately (see https://imagej.net/Script_parameters.html#Default_values)
+## String (label="the input image stack", visibility=INVISIBLE) INPUT_STACK_FILE
+## String (label="the input white image", visibility=INVISIBLE) INPUT_WHITE_FILE
+## String (label="the input dark image", visibility=INVISIBLE) INPUT_DARK_FILE
+
 """This script is supposed to be launched from fiji's jython interpreter
 """
 
+# to use log.info (see 3d_analytics_CH.py sample)
+#@LogService log
+
+
 #from ij import IJ  # pylint: disable=import-error
 
 #WHITE_ESTIMATE = IJ.openImage('/Users/graffy/ownCloud/ipr/lipase/lipase.git/white_estimate.tiff')
@@ -32,17 +44,9 @@ from lipase.catalog import ImageCatalog
 
 from ij import IJ
 from ij.gui import GenericDialog, DialogListener
+from ij.plugin.filter import GaussianBlur
 from java.awt.event import ItemListener
 
-# class MyListener(DialogListener):
-#     def dialogItemChanged(self, gd, event):
-#         IJ.log("Something was changed (event = %s)" % event)
-#         IJ.log("event's attributes : %s" % str(dir(event)))
-        
-#         # Something was changed (event = java.awt.event.ItemEvent[ITEM_STATE_CHANGED,item=res_soleil2018/DARK/DARK_40X_60min_1 im pae min_1/Pos0,stateChange=SELECTED] on choice0)
-
-#         # event's attributes : ['ACTION_EVENT_MASK', 'ADJUSTMENT_EVENT_MASK', 'COMPONENT_EVENT_MASK', 'CONTAINER_EVENT_MASK', 'DESELECTED', 'FOCUS_EVENT_MASK', 'HIERARCHY_BOUNDS_EVENT_MASK', 'HIERARCHY_EVENT_MASK', 'ID', 'INPUT_METHOD_EVENT_MASK', 'INVOCATION_EVENT_MASK', 'ITEM_EVENT_MASK', 'ITEM_FIRST', 'ITEM_LAST', 'ITEM_STATE_CHANGED', 'KEY_EVENT_MASK', 'MOUSE_EVENT_MASK', 'MOUSE_MOTION_EVENT_MASK', 'MOUSE_WHEEL_EVENT_MASK', 'PAINT_EVENT_MASK', 'RESERVED_ID_MAX', 'SELECTED', 'TEXT_EVENT_MASK', 'WINDOW_EVENT_MASK', 'WINDOW_FOCUS_EVENT_MASK', 'WINDOW_STATE_EVENT_MASK', '__class__', '__copy__', '__deepcopy__', '__delattr__', '__doc__', '__ensure_finalizer__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', '__subclasshook__', '__unicode__', 'class', 'equals', 'getClass', 'getID', 'getItem', 'getItemSelectable', 'getSource', 'getStateChange', 'hashCode', 'item', 'itemSelectable', 'notify', 'notifyAll', 'paramString', 'setSource', 'source', 'stateChange', 'toString', 'wait']
-
 
 def run_script():
     user_settings = UserSettings()
@@ -63,19 +67,26 @@ def run_script():
     if src_dark.get_pixel_type() != PixelType.F32:
         src_dark = src_dark.clone(clone_pixel_type=PixelType.F32)
 
+    global DARK_SMOOTH_METHOD
+    global DARK_GAUSSIAN_BLUR_SIGMA
+    log.info('DARK_SMOOTH_METHOD=%s' % DARK_SMOOTH_METHOD)
+    if DARK_SMOOTH_METHOD == 'gaussian blur':
+        log.info('smoothing dark image with gaussian blur filter (sigma=%f)' % DARK_GAUSSIAN_BLUR_SIGMA)
+        # ij.plugin.filter.GaussianBlur
+        # blurGaussian
+        gaussian_blur = GaussianBlur()
+        accuracy = 0.0002  # comes from https://imagej.nih.gov/ij/developer/source/ij/plugin/filter/GaussianBlur.java.html
+        gaussian_blur.blurFloat(src_dark.ij_image.getProcessor(), DARK_GAUSSIAN_BLUR_SIGMA, DARK_GAUSSIAN_BLUR_SIGMA, accuracy)
+
     dst_preproc_stack = IImageEngine.get_instance().create_hyperstack(width=src_hyperstack.get_width(), height=src_hyperstack.get_height(), num_channels=src_hyperstack.num_channels(), num_slices=src_hyperstack.num_slices(), num_frames=src_hyperstack.num_frames(), pixel_type=PixelType.F32)
 
     src_image_feeder = StackImageFeeder(src_hyperstack)
-    # dst_image_feeder = StackImageFeeder(dst_preproc_stack)
 
     src_it = iter(src_image_feeder)
-    # dst_it = iter(dst_image_feeder)
     frame_index = 0
     for src_image in src_it:
         nomin_image = ie.subtract(src_image, src_dark)
         denom_image = ie.subtract(src_white, src_dark)
-        # preproc_image = IImageEngine.get_instance().create_image(width=src_hyperstack.get_width(), height=src_hyperstack.get_height(), pixel_type=PixelType.F32)
-        # preproc_image = dst_it.next()
         preproc_image = ie.divide(nomin_image, denom_image)
         dst_preproc_stack.set_image(preproc_image, frame_index=frame_index, slice_index=0, channel_index=0)  # TODO: use frame_index, slice_index and channel_index from feeder
         frame_index += 1