Repository 'imagej2_bunwarpj_raw_transform'
hg clone https://toolshed.g2.bx.psu.edu/repos/imgteam/imagej2_bunwarpj_raw_transform

Changeset 3:69e951528a0b (2024-09-25)
Previous changeset 2:eabdcf6ad900 (2023-11-05)
Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 8f49f3c66b5a1de99ec15e65c2519a56792f1d56
modified:
imagej2_adjust_threshold_binary_jython_script.py
imagej2_analyze_particles_binary_jython_script.py
imagej2_analyze_skeleton_jython_script.py
imagej2_binary_to_edm_jython_script.py
imagej2_bunwarpj_raw_transform.xml
imagej2_enhance_contrast_jython_script.py
imagej2_find_edges_jython_script.py
imagej2_find_maxima_jython_script.py
imagej2_macros.xml
imagej2_make_binary_jython_script.py
imagej2_math_jython_script.py
imagej2_noise_jython_script.py
imagej2_shadows_jython_script.py
imagej2_sharpen_jython_script.py
imagej2_skeletonize3d_jython_script.py
imagej2_smooth_jython_script.py
imagej2_watershed_binary_jython_script.py
test-data/add_specified_noise.gif
test-data/analyze_particles_outlines.gif
test-data/blobs_threshold_default.gif
test-data/blobs_threshold_huang_dark.gif
test-data/elastic_trans_registered_source1.png
test-data/raw_trans_registered_source1.png
test-data/registered_source1.png
test-data/registered_source2.png
test-data/registered_target1.png
test-data/registered_target2.png
test-data/remove_outliers.gif
test-data/shortest_branch_all_yes.tabular
test-data/shortest_branch_basic.tabular
added:
imagej2_crop_jython_script.py
imagej2_filter_jython_script.py
test-data/analyze_particles_bareoutlines.gif
test-data/analyze_particles_overlaymasks.gif
test-data/analyze_particles_rois.tabular
test-data/blobs_crop_top50.gif
test-data/blobs_crop_width50.gif
test-data/blobs_threshold_0-8.gif
test-data/blobs_threshold_8-255.gif
test-data/blobs_threshold_percentile.gif
test-data/composed_raw_elastic_transformation_full.txt
test-data/composed_raw_transformation_full.txt
test-data/confocal-series-both-channels.tiff
test-data/confocal-series-both-channels_cropped_singleZ.tiff
test-data/confocal-series-first-channel.tif
test-data/confocal-series-first-channel_cropped.tiff
test-data/confocal-series-first-channel_cropped_singleZ.tiff
test-data/confocal-series-first-channel_default_threshold_watershed.tiff
test-data/confocal-series-first-channel_gaussian_blur.tiff
test-data/confocal-series-first-channel_threshold_default.tiff
test-data/confocal-series-first-channel_threshold_default_overlay_mask.tiff
test-data/confocal_analyze_particles.tabular
test-data/confocal_analyze_particles_rois.tabular
test-data/gaussian_blur.gif
test-data/median.gif
test-data/raw_transformation_full.txt
test-data/regenerate_outputs.sh
test-data/registered_source1_forced1cpu.png
test-data/registered_source2_forced1cpu.png
test-data/registered_target1_forced1cpu.png
test-data/registered_target2_forced1cpu.png
test-data/source_elastic_transformation_forced1cpu_out_full.txt
test-data/source_elastic_transformation_out_full.txt
test-data/target_elastic_transformation_forced1cpu_out_full.txt
test-data/target_elastic_transformation_out_full.txt
test-data/top_hat.gif
test-data/top_hat2.gif
test-data/unsharp_mask.gif
test-data/warping_index1_full.txt
test-data/warping_index_raw_full.txt
removed:
test-data/analyze_particles_masks.gif
test-data/blobs_threshold_ijiso.gif
test-data/dot_blot.jpg
test-data/dot_blot.png
test-data/dot_blot.tiff
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_adjust_threshold_binary_jython_script.py
--- a/imagej2_adjust_threshold_binary_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_adjust_threshold_binary_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -1,18 +1,15 @@
 import sys
 
-from ij import IJ
+from ij import IJ, Prefs
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-10]
-input_file = sys.argv[-9]
-threshold_min = float(sys.argv[-8])
-threshold_max = float(sys.argv[-7])
-method = sys.argv[-6]
-display = sys.argv[-5]
-black_background = sys.argv[-4] == "yes"
-# TODO: this is not being used.
-stack_histogram = sys.argv[-3] == "yes"
+input_file = sys.argv[-8]
+threshold_min = float(sys.argv[-7])
+threshold_max = float(sys.argv[-6])
+method = sys.argv[-5]
+display = sys.argv[-4]
+black_background = sys.argv[-3] == "yes"
 output_filename = sys.argv[-2]
 output_datatype = sys.argv[-1]
 
@@ -21,22 +18,34 @@
 
 # Create a copy of the image.
 input_image_plus_copy = input_image_plus.duplicate()
-image_processor_copy = input_image_plus_copy.getProcessor()
+
+bit_depth = input_image_plus_copy.getBitDepth()
+
+if black_background:
+    Prefs.blackBackground = True
+else:
+    Prefs.blackBackground = False
 
-# Convert image to binary if necessary.
-if not image_processor_copy.isBinary():
-    # Convert the image to binary grayscale.
-    IJ.run(
-        input_image_plus_copy,
-        "Make Binary",
-        "iterations=1 count=1 edm=Overwrite do=Nothing",
-    )
-# Set the options.
-if black_background:
-    method_str = "%s dark" % method
-else:
-    method_str = method
-IJ.setAutoThreshold(input_image_plus_copy, method_str)
+if method != "Manual":
+    # Set the options.
+    if black_background:
+        method_str = "%s dark" % method
+        suffix = "black"
+    else:
+        method_str = method
+        suffix = ""
+    threshold_min = 1
+    # Set threshold_max based on image bit-depth
+    # For 8-bit images, use 255; for 16-bit images, use 65535
+    if bit_depth == 8:
+        threshold_max = 255  # Default for 8-bit images
+    elif bit_depth == 16:
+        threshold_max = 65535  # Default for 16-bit images
+    else:
+        threshold_max = float('inf')  # General fallback if bit depth is unknown
+
+    IJ.setAutoThreshold(input_image_plus_copy, method_str)
+    IJ.run(input_image_plus_copy, "Convert to Mask", "calculate %s" % suffix)
 if display == "red":
     display_mode = "Red"
 elif display == "bw":
@@ -44,7 +53,5 @@
 elif display == "over_under":
     display_mode = "Over/Under"
 IJ.setThreshold(input_image_plus_copy, threshold_min, threshold_max, display_mode)
-# Run the command.
-IJ.run(input_image_plus_copy, "threshold", "")
 # Save the ImagePlus object as a new image.
 IJ.saveAs(input_image_plus_copy, output_datatype, output_filename)
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_analyze_particles_binary_jython_script.py
--- a/imagej2_analyze_particles_binary_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_analyze_particles_binary_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -1,27 +1,32 @@
 import sys
 
-from ij import IJ
+from ij import IJ, Prefs
 from ij.plugin.filter import Analyzer
 
 OPTIONS = ["edm=Overwrite", "iterations=1", "count=1"]
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-14]
-input_file = sys.argv[-13]
-black_background = sys.argv[-12] == "yes"
-size = sys.argv[-11]
-circularity_min = float(sys.argv[-10])
-circularity_max = float(sys.argv[-9])
-show = sys.argv[-8]
-display_results = sys.argv[-7] == "yes"
-all_results = sys.argv[-6] == "yes"
+roi_coordinate_file = sys.argv[-13]
+input_file = sys.argv[-12]
+black_background = sys.argv[-11] == "yes"
+size = sys.argv[-10]
+circularity_min = float(sys.argv[-9])
+circularity_max = float(sys.argv[-8])
+show = sys.argv[-7]
+display_results = sys.argv[-6] == "yes"
 exclude_edges = sys.argv[-5] == "yes"
 include_holes = sys.argv[-4] == "yes"
 output_filename = sys.argv[-3]
 output_datatype = sys.argv[-2]
 results_path = sys.argv[-1]
 
+
+if black_background:
+    Prefs.blackBackground = True
+else:
+    Prefs.blackBackground = False
+
 # Open the input image file.
 input_image_plus = IJ.openImage(input_file)
 
@@ -42,9 +47,32 @@
     IJ.run(input_image_plus_copy, "Make Binary", "")
 
 # Set the options.
-options = ["size=%s" % size]
+options = ["size=%s" % size, "stack"]
 circularity_str = "%.3f-%.3f" % (circularity_min, circularity_max)
 options.append("circularity=%s" % circularity_str)
+if exclude_edges:
+    options.append("exclude")
+if include_holes:
+    options.append("include")
+
+# If you need the coordinates of ROIs we compute it twice
+if len(roi_coordinate_file) > 0:
+    options2 = list(options)
+    options2.append("show=Overlay")
+    IJ.run(input_image_plus_copy, "Analyze Particles...", " ".join(options2))
+    ov = input_image_plus_copy.getOverlay()
+    with open(roi_coordinate_file, 'w') as fo:
+        fo.write("shape\tpoints\tlabel\tt\tz\n")
+        for i, roi in enumerate(ov):
+            if roi.getName() is None:
+                roi.name = "ROI_%d" % i
+            poly = roi.getPolygon()
+            x_values = poly.xpoints
+            y_values = poly.ypoints
+            points_coo = ",".join(["(%d,%d)" % (x, y) for x, y in zip(x_values, y_values)])
+            fo.write("Polygon\t%s\t%s\t%d\t%d\n" % (points_coo, roi.getName(), roi.getTPosition(), roi.getZPosition()))
+    analyzer.resetCounter()
+
 if show.find("_") >= 0:
     show_str = "[%s]" % show.replace("_", " ")
 else:
@@ -52,12 +80,6 @@
 options.append("show=%s" % show_str)
 if display_results:
     options.append("display")
-    if not all_results:
-        options.append("summarize")
-if exclude_edges:
-    options.append("exclude")
-if include_holes:
-    options.append("include")
 # Always run "in_situ".
 options.append("in_situ")
 
@@ -68,6 +90,6 @@
 if len(output_filename) > 0:
     # Save the ImagePlus object as a new image.
     IJ.saveAs(input_image_plus_copy, output_datatype, output_filename)
-if display_results and len(results_path) > 0:
+if len(results_path) > 0:
     results_table = analyzer.getResultsTable()
     results_table.saveAs(results_path)
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_analyze_skeleton_jython_script.py
--- a/imagej2_analyze_skeleton_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_analyze_skeleton_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -121,7 +121,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-8]
 input = sys.argv[-7]
 black_background = sys.argv[-6] == "yes"
 prune_cycle_method = sys.argv[-5]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_binary_to_edm_jython_script.py
--- a/imagej2_binary_to_edm_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_binary_to_edm_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-8]
 input_file = sys.argv[-7]
 iterations = int(sys.argv[-6])
 count = int(sys.argv[-5])
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_bunwarpj_raw_transform.xml
--- a/imagej2_bunwarpj_raw_transform.xml Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_bunwarpj_raw_transform.xml Wed Sep 25 16:04:07 2024 +0000
[
@@ -1,4 +1,4 @@
-<tool id="imagej2_bunwarpj_raw_transform" name="Apply raw transformation" version="@WRAPPER_VERSION@.2">
+<tool id="imagej2_bunwarpj_raw_transform" name="Apply raw transformation" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
     <description>with ImageJ2</description>
     <macros>
         <import>imagej2_macros.xml</import>
@@ -18,10 +18,10 @@
 
 ## ImageJ2 requires file extensions to be valid image data types.
 #set source_sans_ext = $os.path.splitext($os.path.basename($source_image.file_name))[0]
-#set source_with_ext = '.'.join([source_sans_ext, $source_image.ext])
+#set source_with_ext = './' + '.'.join([source_sans_ext, $source_image.ext])
 ln -s '$source_image.file_name' '$source_with_ext' &&
 #set target_sans_ext = $os.path.splitext($os.path.basename($target_image.file_name))[0]
-#set target_with_ext = '.'.join([target_sans_ext, $target_image.ext])
+#set target_with_ext = './' + '.'.join([target_sans_ext, $target_image.ext])
 ln -s '$target_image.file_name' '$target_with_ext' &&
 
 bunwarpj -raw_transform
@@ -29,10 +29,6 @@
 '$source_with_ext'
 '$raw_transformation'
 '$source_out'
-&>'$error_log';
-if [[ $? -ne 0 ]]; then
-    cat '$error_log' >&2;
-fi
 ]]></command>
     <inputs>
         <expand macro="param_target_image"/>
@@ -57,7 +53,7 @@
             <expand macro="test_target_source_images"/>
             <param name="raw_transformation" value="source_raw_transformation.txt"/>
             <param name="source_out_datatype" value="png"/>
-            <output name="source_out" file="raw_trans_registered_source1.png" compare="sim_size"/>
+            <output name="source_out" file="raw_trans_registered_source1.png" compare="image_diff"/>
         </test>
     </tests>
     <help>
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_crop_jython_script.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej2_crop_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -0,0 +1,55 @@
+import sys
+
+from ij import IJ
+from ij.plugin import Duplicator
+
+# Fiji Jython interpreter implements Python 2.5 which does not
+# provide support for argparse.
+input_file = sys.argv[-13]
+xleft = int(sys.argv[-12])
+width = int(sys.argv[-11])
+ytop = int(sys.argv[-10])
+height = int(sys.argv[-9])
+first_channel = int(sys.argv[-8])
+last_channel = int(sys.argv[-7])
+first_slice = int(sys.argv[-6])
+last_slice = int(sys.argv[-5])
+first_frame = int(sys.argv[-4])
+last_frame = int(sys.argv[-3])
+output_filename = sys.argv[-2]
+output_datatype = sys.argv[-1]
+
+# Open the input image file.
+input_image_plus = IJ.openImage(input_file)
+
+# Get image dimensions (width, height, nChannels, nSlices, nFrames)
+image_dims = input_image_plus.getDimensions()
+
+# Create a copy of the image.
+input_image_plus_copy = input_image_plus.duplicate()
+
+# Determine if crop in XY is needed:
+if xleft != 0 or width != 0 or ytop != 0 or height != 0:
+    # Need to define a ROI
+    if width == 0:
+        width = image_dims[0] - xleft
+    if height == 0:
+        height = image_dims[1] - ytop
+    input_image_plus_copy.setRoi(xleft, ytop, width, height)
+
+# Replace 0's with default:
+if last_channel == 0:
+    last_channel = image_dims[2]
+if last_slice == 0:
+    last_slice = image_dims[3]
+if last_frame == 0:
+    last_frame = image_dims[4]
+print("Original dimensions:")
+print(image_dims)
+# This will also crop in XY is a ROI has been set
+input_image_plus_copy = Duplicator().run(input_image_plus_copy, first_channel, last_channel, first_slice, last_slice, first_frame, last_frame)
+print("Final dimensions:")
+print(input_image_plus_copy.getDimensions())
+
+# Save the ImagePlus object as a new image.
+IJ.saveAs(input_image_plus_copy, output_datatype, output_filename)
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_enhance_contrast_jython_script.py
--- a/imagej2_enhance_contrast_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_enhance_contrast_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-7]
 input = sys.argv[-6]
 equalize_histogram = sys.argv[-5] == "yes"
 saturated_pixels = sys.argv[-4]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_filter_jython_script.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej2_filter_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -0,0 +1,39 @@
+import sys
+
+from ij import IJ
+
+# Fiji Jython interpreter implements Python 2.5 which does not
+# provide support for argparse.
+input_file = sys.argv[-8]
+image_datatype = sys.argv[-7]
+filter = sys.argv[-6]
+radius = sys.argv[-5]
+mask = sys.argv[-4]
+light_background = sys.argv[-3]
+dont_substract = sys.argv[-2]
+tmp_output_path = sys.argv[-1]
+
+# Open the input image file.
+image_plus = IJ.openImage(input_file)
+# Create an ImagePlus object for the image.
+image_plus_copy = image_plus.duplicate()
+
+# Perform the analysis on the ImagePlus object.
+try:
+    if filter == "gaussian_blur":
+        IJ.run(image_plus_copy, "Gaussian Blur...", "sigma=%s stack" % radius)
+    elif filter in ["median", "mean", "minimum", "maximum", "variance"]:
+        IJ.run(image_plus_copy, "%s..." % filter.title(), "radius=%s stack" % radius)
+    elif filter == "unsharp_mask":
+        IJ.run(image_plus_copy, "Unsharp Mask...", "radius=%s mask=%s stack" % (radius, mask))
+    elif filter == "top_hat":
+        print("radius=%s %s %s" % (radius, light_background, dont_substract.replace('dont', "don't")))
+        IJ.run(image_plus_copy, "Top Hat...", "radius=%s %s %s stack" % (radius, light_background, dont_substract.replace('dont', "don't")))
+except Exception as e:
+    # This is due to some operations like gaussian_blur which block the script
+    print(e)
+    exit(1)
+# Save the ImagePlus object as a new image.
+IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)
+# This is due to some operations like gaussian_blur which block the script
+exit(0)
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_find_edges_jython_script.py
--- a/imagej2_find_edges_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_find_edges_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-4]
 input = sys.argv[-3]
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_find_maxima_jython_script.py
--- a/imagej2_find_maxima_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_find_maxima_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -6,7 +6,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-10]
 input_file = sys.argv[-9]
 scale_when_converting = sys.argv[-8] == "yes"
 weighted_rgb_conversions = sys.argv[-7] == "yes"
@@ -35,7 +34,7 @@
     if weighted_rgb_conversions:
         options.append("weighted")
 # Perform conversion - must happen even if no options are set.
-IJ.run(input_image_plus_copy, "Conversions...", "%s" % " ".join(options))
+IJ.run(input_image_plus_copy, "Conversions...", " %s" % " ".join(options))
 if output_type in ["List", "Count"]:
     # W're  generating a tabular file for the output.
     # Set the Find Maxima options.
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_macros.xml
--- a/imagej2_macros.xml Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_macros.xml Wed Sep 25 16:04:07 2024 +0000
b
@@ -1,19 +1,12 @@
 <macros>
-    <token name="@WRAPPER_VERSION@">3.0</token>
+    <token name="@TOOL_VERSION@">20240614</token>
+    <token name="@VERSION_SUFFIX@">0</token>
     <xml name="fiji_requirements">
         <requirements>
-            <requirement type="package" version="20170530">fiji</requirement>
-            <requirement type="package" version="3.4">grep</requirement>
+            <requirement type="package" version="@TOOL_VERSION@">fiji</requirement>
+            <requirement type="package" version="3.11">grep</requirement>
         </requirements>
     </xml>
-    <xml name="stdio">
-        <stdio>
-            <exit_code range="1:"/>
-            <exit_code range=":-1"/>
-            <regex match="Error:"/>
-            <regex match="Exception:"/>
-        </stdio>
-    </xml>
     <xml name="image_type">
         <param name="image_type" type="select" label="Image type">
             <option value="8-bit_white" selected="true">8-bit white</option>
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_make_binary_jython_script.py
--- a/imagej2_make_binary_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_make_binary_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-8]
 input = sys.argv[-7]
 iterations = int(sys.argv[-6])
 count = int(sys.argv[-5])
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_math_jython_script.py
--- a/imagej2_math_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_math_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -3,8 +3,7 @@
 from ij import IJ
 
 # Fiji Jython interpreter implements Python 2.5 which does not
-# provide support for argparse.
-error_log = sys.argv[-8]
+# provide support for argparse.\
 input_file = sys.argv[-7]
 operation = sys.argv[-6]
 expression = sys.argv[-5]
@@ -19,7 +18,6 @@
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
 
-print("\nerror_log: %s\n" % str(error_log))
 print("\ninput_file: %s\n" % str(input_file))
 print("\noperation: %s\n" % str(operation))
 print("\nexpression: %s\n" % str(expression))
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_noise_jython_script.py
--- a/imagej2_noise_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_noise_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -1,85 +1,56 @@
 import sys
 
-from ij import IJ
+from ij import IJ, ImagePlus
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-19]
-input_file = sys.argv[-18]
-image_datatype = sys.argv[-17]
-noise = sys.argv[-16]
-standard_deviation = sys.argv[-15]
-radius = sys.argv[-14]
-threshold = sys.argv[-13]
-which_outliers = sys.argv[-12]
-randomj = sys.argv[-11]
-trials = sys.argv[-10]
-probability = sys.argv[-9]
-# Note the spelling - so things don't get confused due to Python lambda function.
-lammbda = sys.argv[-8]
-order = sys.argv[-7]
-mean = sys.argv[-6]
-sigma = sys.argv[-5]
-min = sys.argv[-4]
-max = sys.argv[-3]
-insertion = sys.argv[-2]
+input_file = sys.argv[-8]
+image_datatype = sys.argv[-7]
+noise = sys.argv[-6]
+standard_deviation = sys.argv[-5]
+radius = sys.argv[-4]
+threshold = sys.argv[-3]
+which_outliers = sys.argv[-2]
 tmp_output_path = sys.argv[-1]
 
 # Open the input image file.
 image_plus = IJ.openImage(input_file)
-bit_depth = image_plus.getBitDepth()
 image_type = image_plus.getType()
+is32BITS_GREY = image_type == ImagePlus.GRAY32
 # Create an ImagePlus object for the image.
 image_plus_copy = image_plus.duplicate()
-# Make a copy of the image.
-image_processor_copy = image_plus_copy.getProcessor()
 
 # Perform the analysis on the ImagePlus object.
-if noise == "add_noise":
-    IJ.run(image_plus_copy, "Add Noise", "")
-elif noise == "add_specified_noise":
-    IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation")
-elif noise == "salt_and_pepper":
-    IJ.run(image_plus_copy, "Salt and Pepper", "")
-elif noise == "despeckle":
-    IJ.run(image_plus_copy, "Despeckle", "")
-elif noise == "remove_outliers":
-    IJ.run(
-        image_plus_copy,
-        "Remove Outliers",
-        "radius=&radius threshold=&threshold which=&which_outliers",
-    )
-elif noise == "remove_nans":
-    IJ.run(image_plus_copy, "Remove NaNs", "")
-elif noise == "rof_denoise":
-    IJ.run(image_plus_copy, "ROF Denoise", "")
-elif noise == "randomj":
-    if randomj == "randomj_binomial":
+try:
+    if noise == "add_noise":
+        IJ.run(image_plus_copy, "Add Noise", "")
+    elif noise == "add_specified_noise":
+        IJ.run(image_plus_copy, "Add Specified Noise...", "standard=%s" % standard_deviation)
+    elif noise == "salt_and_pepper":
+        IJ.run(image_plus_copy, "Salt and Pepper", "")
+    elif noise == "despeckle":
+        IJ.run(image_plus_copy, "Despeckle", "")
+    elif noise == "remove_outliers":
         IJ.run(
             image_plus_copy,
-            "RandomJ Binomial",
-            "trials=&trials probability=&probability insertion=&insertion",
-        )
-    elif randomj == "randomj_exponential":
-        IJ.run(
-            image_plus_copy,
-            "RandomJ Exponential",
-            "lambda=&lammbda insertion=&insertion",
+            "Remove Outliers...",
+            "radius=%s threshold=%s which=%s" % (radius, threshold, which_outliers)
         )
-    elif randomj == "randomj_gamma":
-        IJ.run(image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion")
-    elif randomj == "randomj_gaussian":
-        IJ.run(
-            image_plus_copy,
-            "RandomJ Gaussian",
-            "mean=&mean sigma=&sigma insertion=&insertion",
-        )
-    elif randomj == "randomj_poisson":
-        IJ.run(image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion")
-    elif randomj == "randomj_uniform":
-        IJ.run(
-            image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion"
-        )
-
+    elif noise == "remove_nans":
+        if is32BITS_GREY:
+            IJ.run(image_plus_copy, "Remove NaNs...", "")
+        else:
+            raise Exception("Remove NaNs can only be applied to 32bits grey images.")
+    elif noise == "rof_denoise":
+        if is32BITS_GREY:
+            IJ.run(image_plus_copy, "ROF Denoise", "")
+        else:
+            raise Exception("ROF Denoise can only be applied to 32bits grey images.")
+except Exception as e:
+    # This is due to some operations like remove_outliers and despeckle which block the script
+    print(e)
+    exit(1)
 # Save the ImagePlus object as a new image.
 IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)
+# This is due to some operations like remove_outliers and despeckle which block the script
+exit(0)
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_shadows_jython_script.py
--- a/imagej2_shadows_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_shadows_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-5]
 input_file = sys.argv[-4]
 direction = sys.argv[-3]
 tmp_output_path = sys.argv[-2]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_sharpen_jython_script.py
--- a/imagej2_sharpen_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_sharpen_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-4]
 input_file = sys.argv[-3]
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_skeletonize3d_jython_script.py
--- a/imagej2_skeletonize3d_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_skeletonize3d_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-5]
 input_file = sys.argv[-4]
 black_background = sys.argv[-3] == "yes"
 tmp_output_path = sys.argv[-2]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_smooth_jython_script.py
--- a/imagej2_smooth_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_smooth_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-4]
 input = sys.argv[-3]
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
b
diff -r eabdcf6ad900 -r 69e951528a0b imagej2_watershed_binary_jython_script.py
--- a/imagej2_watershed_binary_jython_script.py Sun Nov 05 10:50:30 2023 +0000
+++ b/imagej2_watershed_binary_jython_script.py Wed Sep 25 16:04:07 2024 +0000
[
@@ -4,7 +4,6 @@
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[-5]
 input = sys.argv[-4]
 black_background = sys.argv[-3] == "yes"
 tmp_output_path = sys.argv[-2]
@@ -30,7 +29,7 @@
     IJ.run(input_image_plus_copy, "Make Binary", "")
 
 # Run the command.
-IJ.run(input_image_plus_copy, "Watershed", "")
+IJ.run(input_image_plus_copy, "Watershed", "stack")
 
 # Save the ImagePlus object as a new image.
 IJ.saveAs(input_image_plus_copy, output_datatype, tmp_output_path)
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/add_specified_noise.gif
b
Binary file test-data/add_specified_noise.gif has changed
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/analyze_particles_bareoutlines.gif
b
Binary file test-data/analyze_particles_bareoutlines.gif has changed
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/analyze_particles_masks.gif
b
Binary file test-data/analyze_particles_masks.gif has changed
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/analyze_particles_outlines.gif
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Binary file test-data/analyze_particles_outlines.gif has changed
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/analyze_particles_overlaymasks.gif
b
Binary file test-data/analyze_particles_overlaymasks.gif has changed
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/analyze_particles_rois.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/analyze_particles_rois.tabular Wed Sep 25 16:04:07 2024 +0000
b
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/composed_raw_elastic_transformation_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/composed_raw_elastic_transformation_full.txt Wed Sep 25 16:04:07 2024 +0000
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b'@@ -0,0 +1,294 @@\n+Width=144\n+Height=144\n+\n+X Trans -----------------------------------\n+   -8.356784649818302    -7.197224454983437    -6.007291128456463     -4.78857035976083    -3.542648755368276     -2.27111292175059   -0.9755494653795207   0.34245500727314293    1.6813138897356357    3.0394405755361955     4.415248458203067     5.807150931264452      7.21356138824859     6.722616523231703     7.982832965851492     9.241901922762699    10.500157904929564    11.755439850906992    13.007881998138114    14.257818796273467    15.503597042436024      16.7441831444607    17.979203521782882    19.209729191621175    20.434924351265607    21.653927817871562    22.868018271403987    24.077212618583225     25.28220858222662    26.482822213238034     27.68108269563495    28.876976885081163    30.071735843481154    31.267312088467172    32.461884574486334      33.6540253250871     34.84451070010306    36.032159052340404     37.21731193936262     38.40098887470948     39.58514301848773      40.7697991777691     41.95621659878733     43.14336916337468    44.332762755666245     45.52368427263207     46.71616063628192      47.9102587132078     49.10447011578231     50.29945996745416     51.49280946178481     52.68452392595436     53.87243480117253      55.0558589967881     56.23259866253449     57.40168688627666    58.560586048253406     59.70814582308569    60.842020356312204     61.96028478681958     63.06129999594824     64.14282865164152     65.20294849643733     66.23977708520655       67.251514074432      68.2364787499741     69.19379132689076     70.12368273237351     71.02688237947935     71.90443109666018     72.75759402782955     73.58797464576547     74.39754123580246     75.18880500856693     75.96371546546276      76.7252296396663     77.47583769656181     78.21957975177521     78.95905016293707     79.69712587468834      80.4370485162662     81.18210364892593     81.93429837927215     82.69597230924478      83.4691472921311     84.25544962565074     85.05614376573314     85.87215879473054     86.70392313153135     87.55146827026859     88.41445157658053     89.29218897542948     90.18369572122522     91.08773317913175    102.68235537543303     103.7042131782845    104.71196201385267    105.70449758967617    106.68071561329351    107.63951179224333    108.57978183406415    109.50042144629455    110.40032633647319     111.2783922121385    112.13351478082917    112.96458975008372    113.77051282744074    114.55017972043879    115.30267699164074    116.02873972512184    116.72995672023089    117.40792272643284     118.0642324931926    118.70048076997512    119.31826230624539    119.91917185146836    120.50480415510899    121.07675396663221    121.63661603550297    122.18598511118623    122.72645594314696     123.2596232808501    123.78708187376054    124.31042647134338    124.83125182306345    125.35115267838574    125.87172378677525    126.39455989769684    126.92125576061551    127.45340612499622    127.99260574030393    128.54044935600356     129.0985317215601    129.66844758643848    130.25179170010364    130.85015881202057     131.4651436716542     132.0983410284695    132.75134563193137    133.42575223150482    134.12315557665482    134.84515041684625     135.5933315015441    136.36929118355562 \n+   -8.374127336215409    -7.203348638731092   -6.0013206733761475   -4.7696961635913855   -3.5101287650461663   -2.2242721334099076   -0.9137799243519846   -0.5333284543446105    0.7142201362261558    1.9682741321748811     3.226898907276631     4.490136297753633    5.7547173142655375      7.02110257013391     8.287464876867245     9.554439102493117    10.819507996584367     12.08146847941271    13.340004085970438    14.594002156841423    15.845047001475221    17.090850593827977    18.331057062691976     19.56565481083514     20.79582989838611     22.02100274835715    23.240238683228082    24.456286411511062    25.668091576968095    26.876624360066277    28.083988655203306     29.28904913535645    30.496027139017805    31.70274927'..b'4268085129334     148.6469194029473    148.75112793673117    148.85494266869821    148.95804026817186    149.06009758700367    149.16079147704525    149.25979879014827    149.35679637816423    149.45146109294487    149.54346978634177     149.6324993102064    149.71822651639053    149.80032825674573    149.87848138312359     149.9523627473757     150.0216492013537     150.0860175969092    150.14514478589376    150.19870762015907    150.24638295155668    150.28784763193826    150.32277851315536    150.35085244705957     150.3717462855025    150.38513688033586     150.3907010834112     150.3881157465801    150.37705772169414    150.35720386060504     150.3282310151643    150.28981603722363    150.24163577863453    150.18336709124873    150.11468682691773    150.03527183749316    149.94479897482674    149.84294213626143 \n+    145.4017522591916    145.68728041991523    145.98694201220394    146.29952872192806    146.62382901867576    146.95863137203523    147.30272425159467    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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/composed_raw_transformation_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/composed_raw_transformation_full.txt Wed Sep 25 16:04:07 2024 +0000
b
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/confocal-series-both-channels.tiff
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/confocal-series-first-channel_threshold_default_overlay_mask.tiff
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/confocal_analyze_particles.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confocal_analyze_particles.tabular Wed Sep 25 16:04:07 2024 +0000
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/confocal_analyze_particles_rois.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confocal_analyze_particles_rois.tabular Wed Sep 25 16:04:07 2024 +0000
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/dot_blot.jpg
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/dot_blot.png
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/dot_blot.tiff
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/elastic_trans_registered_source1.png
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/gaussian_blur.gif
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/raw_trans_registered_source1.png
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/raw_transformation_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/raw_transformation_full.txt Wed Sep 25 16:04:07 2024 +0000
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b'@@ -0,0 +1,294 @@\n+Width=144\n+Height=144\n+\n+X Trans -----------------------------------\n+    4.176715938136983     5.028059425766952     5.848163112344083     6.625286081420434     7.346949869450837     7.999993504600507     8.570638908853306     9.044622789513964     9.413506255387968     9.681218421492703     9.854312101993061     9.940614113498254     9.949133680349874     9.889960336823316     9.774150445497835     9.613603978540679     9.420928827203937     9.209297594621251      8.99229520815497     8.783758029488393     8.597605040212112     8.447661785513965     8.347477892926463     8.310139148227263     8.348075306899938     8.472865046334638     8.695039719264653      9.02388784955587     9.467262612735961    10.031394859423866    10.720714562420634    11.537683888429232    12.482635931423559    13.548553680644847    14.713909933407903    15.955437681044362    17.250615997032845     18.57910317041673    19.924588848162042    21.273131961240793     22.61287165006273    23.933978509626925    25.228599373795102     26.49079330268239      27.7164571781934     28.90324005022652    30.050446065539795     31.15892642005994     32.23096128077027     33.27013301056534     34.28119229030748    35.269918863348025     36.24297862912084     37.20777868670669    38.172321680760554     39.14506043617836    40.134753390327724     41.15032074914446     42.20070061327572     43.29470355140843     44.44086325080732     45.64727996412613    46.921452516062644     48.27009365995084     49.69892261951472     51.21242468486518     52.81308002996115      54.5006890705208     56.27295866194047     58.12547164246331     60.05155284704986    62.042126167374654      64.0853963435782     66.16300087019144     68.25096023630147     70.32434810515224     72.35799991286407     74.32708587789452     76.20772360258024     77.97761366869307     79.61667819048832     81.10767966466686     82.43679584207585     83.59412588149885     84.57410381192264     85.37579734320398      86.0030732794589     86.46461509691974     86.77378348443933     86.94831659732299     87.00987319114694     86.98342840026854     86.89653840770765     86.77849631690853     86.65940540750502     86.56678652831073     86.51894680093264     86.52962234401939      86.6091568313555     86.76444343256262     86.99795950541491     87.30767458029689      87.6887773859162      88.1343150744824     88.63573469192004     89.18339795340363     89.76705660855026     90.37627810648544     91.00098259921862     91.63313750582321     92.26649727091988     92.89592945322954     93.51736271382487     94.12771798912087     94.72482575123138     95.30733544608778     95.87462173881887     96.42669102951422      96.9640907767786     97.48782343666693     97.99926625358638     98.50009769694455     98.99223099750193     99.47775497987517     99.95888219552717    100.43790422009906    100.91715387889398    101.39897409567641     101.8856930154123    102.37960502529904    102.88295728576551    103.39794138037603    103.92668969786209    104.47127616862906    105.03372099039578    105.61599899198129     106.2200512999695    106.84779998979062    107.50116542083198     108.1820859751434    108.89253994223978    109.63456932007529    110.41030533674008    111.22199287092927 \n+    4.419557770482417      5.27081665328943     6.089884477973564     6.864617423433236      7.58211191931238     8.228765517486593     8.790349543698422     9.252193879414877     9.606403124097358       9.8574446972737    10.012252733626926    10.079097632567096    10.067484069087474     9.988036761486226     9.852374541479348      9.67297328108479      9.46301824968602     9.236246507632229     9.006779993191172     8.788950030940104     8.597114084633965     8.445465698532526     8.347838719688951     8.317507070534788     8.366981545976168     8.507805340755091     8.750350268395447     9.103615908671069     9.575034210805253    10.170282377665819    10.893107153686593    11.745163926271625    12.725511960542491     13.8214660'..b'7992200723283    137.56614934000743     137.6547920526498     137.7457099777814    137.83880347311558    137.93396490724638    138.03107866170456    138.13002145620862     138.2306627135688    138.33286495861378    138.43648424547092    138.54137060755113    138.64736852467817    138.75431740194736     138.8620520551169    138.97040319761135    139.07919792456093    139.18826018970407    139.29741127145186     139.4064702249358     139.5152543174491    139.62357944532855    139.73126053101353    139.83811189975918    139.94394763625732    140.04858192224066    140.15182935699426    140.25350526358187    140.35342598449185    140.45140917132812     140.5472563727466    140.63973674588829    140.72607849633303     140.8034530709757    140.86911184204715    140.92038668238638    140.95469057503053    140.96951446175183 \n+   136.47128086107668     136.7647212366112    137.09257955898585    137.45244030061366    137.84178817314864    138.25801861080936    138.69844448570618    139.16030362004625    139.64076716336413    140.13694888129027    140.64591537702537    141.16469723830411    141.69030107199748    142.21972235649292     142.7499590095462    143.27802553739224     143.8009676005144    144.31587680358044    144.81990549256307    145.31028132185114    145.78432133896519    146.23944532498265     146.6731881254606    147.08321070988862     147.4673097077118    147.82342518575646     148.1497532260637    148.44517260159793    148.70888980473805     148.9402693649705    149.13881819510797    149.30416655832204    149.43604583881495    149.53426341914945    149.59867502302262     149.6291549350847    149.62557137943307    149.58856731644082    149.52026608135293    149.42281660859848    149.29821829294679    149.14833393227818     148.9749036871724    148.77955958387102    148.56384015461515     148.3292048735639    148.07704810673272    147.80871235013433    147.52550058127326    147.22868759518644    146.91953023733674    146.59927648193474    146.26917333585519    145.93047357545453    145.58444134654107     145.2323566768063    144.87551896549692    144.51524952733178    144.15289327697025     143.7898196470608    143.42742283736894     143.0671124065675    142.71023528935442      142.358125394621     142.0121564744563    141.67373855933508    141.34431343702633     141.0253498984435    140.71833886434322    140.42478849526893      140.146219373479    139.88415982966046    139.64009894032506    139.41455311339675    139.20705111229447     139.0170612930036     138.8440366566717     138.6874177431907    138.54663513701868    138.42111162799583    138.31026406516824     138.2135049374692    138.13024371148586    138.05988795339218    138.00184425941066    137.95551901682046    137.92031901551144     137.8956519283472     137.8809266770911    137.87555369934367    137.87894513078197    137.89051491595822    137.90967885997148    137.93585463244526    137.96846173440005    138.00692143778676    138.05065670662592     138.0990921078634    138.15166213829139    138.20800012268245    138.26793582882118    138.33130309952594     138.3979301697609    138.46763896738594    138.54024451618767    138.61555443918937    138.69336855891274    138.77347859003171    138.85567906141773    138.93985170020116     139.0259197913776     139.1137989718017    139.20339721001417     139.2946151183563     139.3873462992249    139.48147772015398    139.57689011239864    139.67345838774514    139.77105206837518    139.86953572477677    139.96876941691374     140.0686091341469    140.16890722973517     140.2695128461409    140.37027232780892    140.47102961859267    140.57162664155132    140.67190365944197     140.7716996148759    140.87085244979193    140.96919940462283    141.06657729828675     141.1628227909147    141.25777263203346     141.3512638977444    141.44313422127163    141.53322120902277    141.62069316426732    141.70292577621072    141.77706154607338    141.84032292956982    141.89001272391792    141.92351448531556     141.9382892449537 \n'
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/regenerate_outputs.sh
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regenerate_outputs.sh Wed Sep 25 16:04:07 2024 +0000
b
b"@@ -0,0 +1,204 @@\n+source $(dirname $(dirname $(which conda)))/etc/profile.d/conda.sh\n+conda_env=mulled-v1-9e9e3735532d83dd44ff0f622055395263f964e81ca8113d4dfa7e133e3156bf\n+conda activate $conda_env\n+# Adjust threshold\n+# Test 1\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 0.0 255.0 'Default' 'red' 'no' 'tools/image_processing/imagej2/test-data/blobs_threshold_default.gif' 'gif'\n+# Test 2\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 0.0 255.0 'Percentile' 'over_under' 'no' 'tools/image_processing/imagej2/test-data/blobs_threshold_percentile.gif' 'gif'\n+# Test 3\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 0.0 255.0 'Huang' 'bw' 'yes' 'tools/image_processing/imagej2/test-data/blobs_threshold_huang_dark.gif' 'gif'\n+# Test 4\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 8.0 255.0 'Manual' 'bw' 'no' 'tools/image_processing/imagej2/test-data/blobs_threshold_8-255.gif' 'gif'\n+# Test 5\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 0.0 8.0 'Manual' 'bw' 'no' 'tools/image_processing/imagej2/test-data/blobs_threshold_0-8.gif' 'gif'\n+# Test 6\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_adjust_threshold_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel.tif' 0.0 255.0 'Default' 'bw' 'yes' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel_threshold_default.tiff' 'tiff'\n+\n+\n+# Analyze particles\n+# Test 1\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_analyze_particles_binary_jython_script.py' '' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' '0-Infinity' 0.0 1.0 'Nothing' 'yes' 'no' 'no' '' '' 'tools/image_processing/imagej2/test-data/analyze_particles_nothing.tabular'\n+# Test 2\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_analyze_particles_binary_jython_script.py' '' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' '0-Infinity' 0.0 1.0 'Outlines' 'no' 'no' 'no' 'tools/image_processing/imagej2/test-data/analyze_particles_outlines.gif' 'gif' ''\n+# Test 3\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_analyze_particles_binary_jython_script.py' '' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' '0-Infinity' 0.0 1.0 'Bare_Outlines' 'no' 'yes' 'yes' 'tools/image_processing/imagej2/test-data/analyze_particles_bareoutlines.gif' 'gif' ''\n+# Test 4\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_analyze_particles_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/analyze_particles_rois.tabular' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' '0-Infinity' 0.0 1.0 'Overlay_Masks' 'yes' 'no' 'no' 'tools/image_processing/imagej2/test-data/analyze_particles_overlaymasks.gif' 'gif' 'tools/image_processing/imagej2/test-data/analyze_particles_nothing.tabular'\n+# Test 5\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_analyze_particles_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/confocal_analyze_particles_rois.tabular' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel_threshold_default.tiff' 'yes' '0-Infinity' 0.0 1.0 'Overlay_Masks' 'yes' 'no' 'no' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel_threshold_default_"..b"imagej2/imagej2_math_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif'  'Min' 'None' 'None' 255.0 'tools/image_processing/imagej2/test-data/blobs_min.gif' 'gif'\n+# Test 3\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_math_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif'  'Log' 'None' 'None' 'None' 'tools/image_processing/imagej2/test-data/blobs_log.gif' 'gif'\n+# Test 4\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_math_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'Square' 'None' 'None' 'None' 'tools/image_processing/imagej2/test-data/blobs_square.gif' 'gif'\n+# Test 5\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_math_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'Macro' 'v=v+50*sin(d/17)' 'None' 'None' 'tools/image_processing/imagej2/test-data/blobs_macro.gif' 'gif'\n+\n+# Noise\n+# Test 1\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_noise_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'gif' 'add_specified_noise' 5.0 'None' 'None' 'None' 'tools/image_processing/imagej2/test-data/add_specified_noise.gif'\n+# Test 2\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_noise_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'gif' 'despeckle' 'None' 'None' 'None' 'None' 'tools/image_processing/imagej2/test-data/despeckle.gif'\n+# Test 3\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_noise_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif'  'gif' 'remove_outliers' 'None' '10.0' '1' 'Bright' 'tools/image_processing/imagej2/test-data/remove_outliers.gif'\n+# Test 4 fails\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_noise_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif'  'gif' 'remove_nans' 'None' 'None' 'None' 'None' 'tools/image_processing/imagej2/test-data/remove_nans.gif'\n+\n+# Shadows\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_shadows_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'Northwest' 'tools/image_processing/imagej2/test-data/blobs_northwest.gif' 'gif'\n+\n+# Sharpen\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_sharpen_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'tools/image_processing/imagej2/test-data/blobs_sharpen.gif' 'gif'\n+\n+# Skeletonized 3D\n+# Test 1\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_skeletonize3d_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' 'tools/image_processing/imagej2/test-data/skeletonized_blobs.gif' 'gif'\n+# Test 2\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_skeletonize3d_jython_script.py' 'tools/image_processing/imagej2/test-data/clown.jpg' 'no' 'tools/image_processing/imagej2/test-data/skeletonized_clown.jpg' 'jpg'\n+\n+# Smooth\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_smooth_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'tools/image_processing/imagej2/test-data/blobs_smooth.gif' 'gif'\n+\n+# Watershed\n+# Test 1\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_watershed_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/blobs.gif' 'no' 'tools/image_processing/imagej2/test-data/blobs_watershed_binary.gif' 'gif'\n+# Test 2\n+ImageJ --ij2 --headless --debug --jython 'tools/image_processing/imagej2/imagej2_watershed_binary_jython_script.py' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel_threshold_default.tiff' 'yes' 'tools/image_processing/imagej2/test-data/confocal-series-first-channel_default_threshold_watershed.tiff' 'tiff'\n"
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diff -r eabdcf6ad900 -r 69e951528a0b test-data/shortest_branch_all_yes.tabular
--- a/test-data/shortest_branch_all_yes.tabular Sun Nov 05 10:50:30 2023 +0000
+++ b/test-data/shortest_branch_all_yes.tabular Wed Sep 25 16:04:07 2024 +0000
b
@@ -2,7 +2,7 @@
 1 0 2 0 1 2.414 0 0 2.414 2.414 1 59 0
 1 0 2 0 0 1.000 0 0 1.000 1.000 4 91 0
 0 0 1 0 0 0.000 0 0 0.000 0.000 5 43 0
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/shortest_branch_basic.tabular
--- a/test-data/shortest_branch_basic.tabular Sun Nov 05 10:50:30 2023 +0000
+++ b/test-data/shortest_branch_basic.tabular Wed Sep 25 16:04:07 2024 +0000
b
@@ -2,7 +2,7 @@
 1 0 2 0 1 2.414 0 0 2.414
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/source_elastic_transformation_forced1cpu_out_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/source_elastic_transformation_forced1cpu_out_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,19 @@
+Intervals=4
+
+X Coeffs -----------------------------------
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+   -50.83339149878828   -18.681337906130345     37.17883919175264    12.345198999048506     24.77206071041781     97.97412790389771    155.78421869117355 
+  -27.929816443724082    49.276999890632126      36.1275114393808     14.75760213419734     9.594845999044615    154.04607396012295    222.05804605430535 
+   -9.439167783998862     37.12324241599431     48.73717535550337     63.59070238727008     6.551668091613555     119.5935291038009    158.64847980599265 
+   -8.415920987887967    19.182085970213276     89.61484235977517     88.26966501794124    32.788077608672424     132.5037156161929    160.37829930526004 
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+   -36.33357650876076    -2.997067006070702    24.224980747085667     52.94713777251041     90.78409317136875    128.45213257281344     164.4624484013269 
+
+Y Coeffs -----------------------------------
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/source_elastic_transformation_out_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/source_elastic_transformation_out_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,19 @@
+Intervals=4
+
+X Coeffs -----------------------------------
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+   -36.40440156764347    -5.950917668399802     43.12656676959308    129.82569373508457     79.23025904056314    101.33109010989303    167.76122399799928 
+   -31.99153804062628   -28.228263165717934    108.82281688201007    135.71408978799562    119.88724905944063    115.66219769238425    162.57204585958897 
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+
+Y Coeffs -----------------------------------
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+   112.30429273541326    -31.84451342578042    150.53549831852334     93.43230937892993    105.20787588035367     55.33493688123222     89.69326973226897 
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/target_elastic_transformation_forced1cpu_out_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/target_elastic_transformation_forced1cpu_out_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,19 @@
+Intervals=4
+
+X Coeffs -----------------------------------
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+  -25.399961768058898     78.11666674988406     47.11538040963914     53.34193146612398     48.19823124809444    51.578486863081025     62.97102702637055 
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+
+Y Coeffs -----------------------------------
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+   152.52702215817044    151.40906569438428    144.41347160527243    148.75680143207387    154.16175661350192    147.09841734177542     144.8860096284916 
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/target_elastic_transformation_out_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/target_elastic_transformation_out_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,19 @@
+Intervals=4
+
+X Coeffs -----------------------------------
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+   18.940100807668394     9.376702061674932     11.32031302209601     9.081414015093257    -19.34191364999764    24.182277174612462    27.994218490639234 
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+
+Y Coeffs -----------------------------------
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+   62.479566052595004      -197.59219058435     99.97143069942541     77.04940775782963   -2.4521487960395176    113.09283045524303     65.44319987491002 
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b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/top_hat.gif
b
Binary file test-data/top_hat.gif has changed
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/top_hat2.gif
b
Binary file test-data/top_hat2.gif has changed
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/unsharp_mask.gif
b
Binary file test-data/unsharp_mask.gif has changed
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/warping_index1_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/warping_index1_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,1 @@
+33.63863677975145
b
diff -r eabdcf6ad900 -r 69e951528a0b test-data/warping_index_raw_full.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/warping_index_raw_full.txt Wed Sep 25 16:04:07 2024 +0000
b
@@ -0,0 +1,1 @@
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