diff imagej2_noise_jython_script.py @ 3:50f2b961b192 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 8f49f3c66b5a1de99ec15e65c2519a56792f1d56

--- a/imagej2_noise_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
+++ b/imagej2_noise_jython_script.py	Wed Sep 25 16:09:42 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)