- 1 Introduction
- 2 When to use Jython
- 3 Jython basics for ImageJ
- 4 Self written Jython modules for ImageJ
- 5 Self written Jython packages for ImageJ
- 6 Bundle packages in a JAR file
- 7 Links
- 8 References
When to use Jython
All scripting language supported by ImageJ can be used to access the ImageJ API. There are only differences in how the imports are handled and in the syntax of the selected language. Jython has a syntax that differs from most other language as indentations instead of brackets are used to group code blocks.
The following list will help you to decide if Jython is the right choice to create scripts for ImageJ:
- If you have experience with Python you can easily use Jython for ImageJ scripting. But you have to keep in mind that tools commonly used in many Python projects (e.g. Numpy) are not available in Jython. By building your own modules you can create complex scripts that otherwise are only possible by writing ImageJ plugins in Java.
- If don't have any experience in programming, the Python language is a good choice to start with. If your only aim is to write scripts for ImageJ, there are other languages you should try first (e.g. Groovy).
- In Python many problems can be solved with less code than in other languages. Still the code is easy to read. Have a look at the examples on this page and decide if you want to start using Python for ImageJ scripting.
The Java implementation of Python is limited in functionality. One can use the standard library, but it's not possible to install additional Python modules. Moreover a growing number of projects build on Python3 which is not fully compatible with Python2 Jython is based on. If you want to start learning Python it's recommended to learn Python3.x instead of Python2.
Even with the given limitations Jython is a powerful language for ImageJ scripting. Hopefully the examples on this page can convince you of that.
Jython basics for ImageJ
The aim of this page is not to teach how to program in Python. This purpose is much better fulfilled by the documentation of Python2. The focus of this page is to show how features of the Python language can be useful for ImageJ scripting.
That is why more complex examples are used that are fully functional. Just copy the code to the Script Editor and try them by yourself. Extensive in-line documentation is used to explain the implementation.
Image selection using the GenericDialog class
This example script will create up to 10 new images and create a GenericDialog to select 3 of them. Finally the names of the selected images are printed to the Log window. It is recommended to copy the code to the Script Editor and run it by yourself.
The following list links to documentation of the used Python features:
- Future statement definitions
- Built-in Functions
- List Comprehensions
- Generator Expressions
- ** (double star) and * (star) parameters
- Top-level script environment (__main__)
- Purpose of the single underscore “_” variable
'''Image selection using the GenericDialog class This code is part of the Jython tutorial at the ImageJ wiki. http://imagej.net/Jython_Scripting#Image_selection_using_the_GenericDialog_class ''' # The module __future__ contains some useful functions: # https://docs.python.org/2/library/__future__.html from __future__ import with_statement, division # This imports the function random from the module random. from random import random # Next we import Java Classes into Jython. # This is how we can acces the ImageJ API: # https://imagej.nih.gov/ij/developer/api/allclasses-noframe.html from ij import IJ, WindowManager from ij.gui import GenericDialog # A function is created with the def keyword. # This function does not need any parameters. def create_test_image(): # Python uses indentation to create code blocks # Local variables are assigned. # We can assign the same value to more than one variable. image_width = image_height = 512 box_width = box_height = 128 offset_x = offset_y = 192 counts = 64 stdv = 16 # The build in function int() is used to convert float to int. # The variable random contains a function that is called by adding parentheses. offset_x = int(2 * random() * offset_x) offset_y = int(2 * random() * offset_y) # We can define a function inside a function. # Outside of create_test_image() this function is not available. # By adding an asterisk to a parameter, all given parameters are combined to a tuple. def make_title(*to_concat): prefix = 'TestImage' # To create a tuple with a single entry the comma is necessary. # The 2 tuples are concatenated by using the + operator. to_join = (prefix,) + to_concat # We create a generator that converts every singe entry of the tuple to a string. strings_to_join = (str(arg) for arg in to_join) # The string ',' has a join method to concatenate values of a tuple with the string as seperator. # The result is a string. return ','.join(strings_to_join) def check_existence(title): if WindowManager.getIDList() is None: return False image_titles = (WindowManager.getImage(id).getTitle() for id in WindowManager.getIDList()) return title in image_titles # To negate an expression put not in front of it. if not check_existence(make_title(offset_x, offset_y)): # The following code has been created by using the Recorder of ImageJ, set to output Java code. # By removing the semicolons, the code can be used in Jython. # The parameters can be modified by using variables and string concatenation. imp = IJ.createImage(make_title(offset_x, offset_y), "8-bit black", image_width, image_height, 1) # The build in function str() is used to convert int to string. IJ.run(imp, "Add...", "value=" + str(counts)) imp.setRoi(offset_x , offset_y, box_width, box_height) IJ.run(imp, "Add...", "value=" + str(counts)) IJ.run(imp, "Select None", "") IJ.run(imp, "Add Specified Noise...", "standard=" + str(stdv)); # We don't want to confirm when closing one of the newly created images. imp.changes = False imp.show() # This function uses parameters. # A default value is given to the third parameter. def create_selection_dialog(image_titles, defaults, title='Select images for processing'): gd = GenericDialog(title) # The build in function enumerate() returns two values: # The index and the value stored in the tuple/list. for index, default in enumerate(defaults): # for each loop we add a new choice element to the dialog. gd.addChoice('Image_'+ str(index + 1), image_titles, image_titles[default]) gd.showDialog() if gd.wasCanceled(): return None # This function returns a list. # _ is used as a placeholder for values we don't use. # The for loop is used to call gd.getNextChoiceIndex() len(defaults) times. return [gd.getNextChoiceIndex() for _ in defaults] # It's best practice to create a function that contains the code that is executed when running the script. # This enables us to stop the script by just calling return. def run_script(): while WindowManager.getImageCount() < 10: create_test_image() image_titles = [WindowManager.getImage(id).getTitle() for id in WindowManager.getIDList()] # range(3) will create the list [0, 1, 2]. selected_indices = create_selection_dialog(image_titles, range(3)) # The script stops if the dialog has ben canceld (None was returned from create_selection_dialog). if selected_indices is None: print('Script was canceld.') return # We have to get the corresponding IMagePlus objects. selected_imps = [WindowManager.getImage(id) for id in [WindowManager.getIDList()[index] for index in selected_indices]] # The previous line can be split into 2 lines: # selected_ids = [WindowManager.getIDList()[index] for index in selected_indices] # selected_imps = [WindowManager.getImage(id) for id in selected_ids] for imp in selected_imps: # Strings can be formated using the % operator: # http://www.learnpython.org/en/String_Formatting IJ.log('The image \'%s\' has been selected.' % imp.getTitle()) # If a Jython script is run, the variable __name__ contains the string '__main__'. # If a script is loaded as module, __name__ has a different value. if __name__ in ['__builtin__','__main__']: run_script()
Using Scripting Parameters
The second example is inspired by atomic resolution images recorded with an Transmission Electron Microscope (TEM). Such images show a regular structure (a crystal), but the images are noisy because of the low signal. By using a Fourier filter the contrast can be enhanced.
The script will create a periodic structure and add some random noise. The user can control the parameters of the created image. This is realized using Script parameters. The Fourier filtering has been created by using the Recorder. Finally a simple image calculator is used to show that functions can be passed as parameters.
This list links to the documentation of Python features that are introduced with this example:
#@ String(value='Please set some parameters.', visibility='MESSAGE') message #@ Short(label='Image size', value=512, min=128, max=2048, stepSize=128, style="slider") img_size #@ Double(label='Image amplitude', value=100) amplitude #@ Short(label='Spacing', value=16, min=8) spacing '''Using Scripting Parameters This code is part of the Jython tutorial at the ImageJ wiki. http://imagej.net/Jython_Scripting#Using_Scripting_Parameters ''' # The parameters in front of this comment are populated before the script runs. # Details on Script parameters can be found at # http://imagej.net/Script_parameters # The module __future__ contains some useful functions: # https://docs.python.org/2/library/__future__.html from __future__ import with_statement, division # It's best practice to create a function that contains the code that is executed when running the script. # This enables us to stop the script by just calling return. def run_script(): # We can use import inside of code blocks to limit the scope. import math from ij import IJ, ImagePlus from ij.process import FloatProcessor blank = IJ.createImage("Blank", "32-bit black", img_size, img_size, 1) # This create a list of lists. Each inner list represents a line. # pixel_matrix is the first line where y=0. pixel_matrix = split_list(blank.getProcessor().getPixels(), wanted_parts=img_size) # This swaps x and y coordinates. # http://stackoverflow.com/questions/8421337/rotating-a-two-dimensional-array-in-python # As zip() creates tuples, we have to convert each one by using list(). pixel_matrix = [list(x) for x in zip(*pixel_matrix)] for y in range(img_size): for x in range(img_size): # This function oszillates between 0 and 1. # The distance of 2 maxima in a row/column is given by spacing. val = (0.5 * (math.cos(2*math.pi/spacing*x) + math.sin(2*math.pi/spacing*y)))**2 # When assigning, we multiply the value by the amplitude. pixel_matrix[x][y] = amplitude * val # The constructor of FloatProcessor works fine with a 2D Python list. crystal = ImagePlus("Crystal", FloatProcessor(pixel_matrix)) # Crop without selection is used to duplicate an image. crystal_with_noise = crystal.crop() crystal_with_noise.setTitle("Crystal with noise") IJ.run(crystal_with_noise, "Add Specified Noise...", "standard=%d" % int(amplitude/math.sqrt(2))) # As this is a demo, we don't want to be ask to save an image on closing it. # In Python True and False start with capital letters. crystal_with_noise.changes = False crystal.show() crystal_with_noise.show() filtered = fft_filter(crystal_with_noise) # We create a lambda function to be used as a parameter of img_calc(). subtract = lambda values: values - values # This is a short form for: # def subtract(values): # return values - values # The first time we call img_calc with 2 images. difference = img_calc(subtract, crystal, filtered, title="Difference of 2") difference.show() # The first time we call img_calc with 3 images. minimum = img_calc(min, crystal, filtered, crystal_with_noise, title="Minimum of 3") minimum.show() for imp in (crystal, crystal_with_noise, filtered, difference, minimum): IJ.run(imp, "Measure", "") # Functions can be defined after they are used. # This is only possible if the main code is encapsulated into a function. # The main function has to be called at the end of the script. def img_calc(func, *imps, **kwargs): """Runs the given function on each pixel of the given list of images. An additional parameter, the title of the result, is passed as keyword parameter. We assume that each image has the same size. This is not checked by this function. """ # If the keyword parameter is not passed, it is set to a default value. if not kwargs['title']: kwargs['title'] = "Result" # This is a 2D list: list[number of images][pixels per image] . pixels = [imp.getProcessor().getPixels() for imp in imps] # The function is called pixel by pixel. # zip(*pixels) rotates the 2D list: list[pixels per image][mumber of images]. result = [func(vals) for vals in zip(*pixels)] # result is a 1D list and can be used to create an ImagePlus object. from ij import ImagePlus from ij.process import FloatProcessor return ImagePlus(kwargs['title'], FloatProcessor(img_size, img_size, result)) def split_list(alist, wanted_parts=1): """Split a list to the given number of parts.""" length = len(alist) # alist[a:b:step] is used to get only a subsection of the list 'alist'. # alist[a:b] is the same as [a:b:1]. # '//' is an integer division. # Without 'from __future__ import division' '/' would be an integer division. return [ alist[i*length // wanted_parts: (i+1)*length // wanted_parts] for i in range(wanted_parts) ] def fft_filter(imp): """ Removing noise from an image by using a FFT filter This are operations copied from the ImageJ macro recorder. Jython does not complain when you forget to remove the semicolons. """ from ij import IJ IJ.run(imp, "FFT", ""); # No ImagePlus is returned by the FFT function of ImageJ. # We need to use the WindowManager to select the newly created image. from ij import WindowManager as wm fft = wm.getImage("FFT of " + imp.getTitle()) IJ.run(fft, "Find Maxima...", "noise=64 output=[Point Selection] exclude"); # Enlarging the point selectins from Find Maxima. IJ.run(fft, "Enlarge...", "enlarge=2"); # Inverting the selection. IJ.run(fft, "Make Inverse", ""); IJ.run(fft, "Macro...", "code=v=0"); IJ.run(fft, "Inverse FFT", ""); fft.changes = False fft.close() imp_filtered = wm.getImage("Inverse FFT of " + imp.getTitle()) imp_filtered.setTitle("Filtered " + imp.getTitle()) imp_filtered.changes = False return imp_filtered # If a Jython script is run, the variable __name__ contains the string '__main__'. # If a script is loaded as module, __name__ has a different value. if __name__ in ['__builtin__','__main__']: run_script()
A batch opener using
We have yet introduced some powerful functions build into Python. Another one is
walk() from the
os module. It can be used to go through a directory structure and process the contained files. In this example
walk() is used to batch open images with ImageJ's function
To read more about the used features, the following list provides links to additional information:
- The walk() function
- The documentation of os.path
- The listdir() function
- Javadoc on IJ.openImage()
- Testing the type of an object using isinstance()
- Identifying the type of an object using type()
- Using continue to control a loop
- Truth Value Testing
#@ File(label='Choose a directory', style='directory') import_dir #@ String(label='File types', value='tif;png') file_types #@ String(label='Filter', value='') filters #@ Boolean(label='Recursive search', value=True) do_recursive '''A batch opener using os.walk() This code is part of the Jython tutorial at the ImageJ wiki. http://imagej.net/Jython_Scripting#A_batch_opener_using_os.walk.28.29 ''' # We do only include the module os, # as we can use os.path.walk() # to access functions of the submodule. import os from java.io import File from ij import IJ def batch_open_images(path, file_type=None, name_filter=None, recursive=False): '''Open all files in the given folder. :param path: The path from were to open the images. String and java.io.File are allowed. :param file_type: Only accept files with the given extension (default: None). :param name_filter: Only accept files that contain the given string (default: None). :param recursive: Process directories recursively (default: False). ''' # Converting a File object to a string. if isinstance(path, File): path = path.getAbsolutePath() def check_type(string): '''This function is used to check the file type. It is possible to use a single string or a list/tuple of strings as filter. This function can access the variables of the surrounding function. :param string: The filename to perform the check on. ''' if file_type: # The first branch is used if file_type is a list or a tuple. if isinstance(file_type, (list, tuple)): for file_type_ in file_type: if string.endswith(file_type_): # Exit the function with True. return True else: # Next iteration of the for loop. continue # The second branch is used if file_type is a string. elif isinstance(file_type, string): if string.endswith(file_type): return True else: return False return False # Accept all files if file_type is None. else: return True def check_filter(string): '''This function is used to check for a given filter. It is possible to use a single string or a list/tuple of strings as filter. This function can access the variables of the surrounding function. :param string: The filename to perform the filtering on. ''' if name_filter: # The first branch is used if name_filter is a list or a tuple. if isinstance(name_filter, (list, tuple)): for name_filter_ in name_filter: if name_filter_ in string: # Exit the function with True. return True else: # Next iteration of the for loop. continue # The second branch is used if name_filter is a string. elif isinstance(name_filter, string): if name_filter in string: return True else: return False return False else: # Accept all files if name_filter is None. return True # We collect all files to open in a list. path_to_images =  # Replacing some abbreviations (e.g. $HOME on Linux). path = os.path.expanduser(path) path = os.path.expandvars(path) # If we don't want a recursive search, we can use os.listdir(). if not recursive: for file_name in os.listdir(path): full_path = os.path.join(path, file_name) if os.path.isfile(full_path): if check_type(file_name): if check_filter(file_name): path_to_images.append(full_path) # For a recursive search os.walk() is used. else: # os.walk() is iterable. # Each iteration of the for loop processes a different directory. # the first return value represents the current directory. # The second return value is a list of included directories. # The third return value is a list of included files. for directory, dir_names, file_names in os.walk(path): # We are only interested in files. for file_name in file_names: # The list contains only the file names. # The full path needs to be reconstructed. full_path = os.path.join(directory, file_name) # Both checks are performed to filter the files. if check_type(file_name): if check_filter(file_name): # Add the file to the list of images to open. path_to_images.append(full_path) # Create the list that will be returned by this function. images =  for img_path in path_to_images: # IJ.openImage() returns an ImagePlus object or None. imp = IJ.openImage(img_path) # An object equals True and None equals False. if imp: images.append(imp) return images def split_string(input_string): '''Split a string to a list and strip it :param input_string: A string that contains semicolons as separators. ''' string_splitted = input_string.split(';') # Remove whitespace at the beginning and end of each string strings_striped = [string.strip() for string in string_splitted] return strings_striped if __name__ in ['__builtin__','__main__']: # Run the batch_open_images() function using the Scripting Parameters. images = batch_open_images(import_dir, split_string(file_types), split_string(filters), do_recursive ) for image in images: # Call the toString() method of each ImagePlus object. print(image)
Importing Java module and classes
Another great feature of Jython is the possibility to use functions from Java jar package that resides in the jar folder of imageJ.
ImageJ and Fiji API
The following API documentation lists all available module and functions :
Those package are built-in with Fiji, but any package that resides in the jars folder can be imported provided you know the path to the class.
Let's show one example with the ImageJ package and the class RoiManager. According to the javadoc the RoiManager class resides in
ij.plugin.frame. Therefore the code will look like :
from ij.plugin.frame import RoiManager
RM = RoiManager() # we create an instance of the RoiManager class
rm = RM.getRoiManager() # "activate" the RoiManager otherwise it can behave strangely
Using openCV in Jython
It is even possible to use most of opencv functionalities within Jython: For that the most simple is to enable the IJopenCV update site that will automatically download the necessary packages.
A manual installation is also possible by putting the jar packages in the jar folder of imageJ. They are avalaible on the IJopenCV github, which even provides a maven option. NB : the version on Github and the update sites are not identical and not compatible
The 1st things to know about openCV is that most functions works with openCV matrix object. Hopefully, the IJopenCV provides a converter :
#@ ImagePlus ImP from ijopencv import ImageConverter Converter = ImageConverter() # create an instance of the converter # Convert to a matrice ImCv = Converter.convertTo(ImP) # Do some processing in openCV #... # Convert back to an ImagePlus ImP2 = Converter.convertFrom(ImCv)
Such kind of converter is also available for PointRoi to opencv keypoints...
Now to use opencv function, we use the JavaCPP API that contains almost all functions of opencv. An simple example : creating an identity matrix of size 3x3
from org.bytedeco.javacpp.opencv_core import Mat,CvMat Id = Mat().eye(3,3,0).asMat() print Id print CvMat(Id) # nice to visualise the matrix
NB : The
org.bytedeco.javacpp.opencv_core.Mat object is different than the
org.opencv.core.Mat !! They dont have exactly the same attributes and functions. In Fiji we always use the
Similarly there is some apparent redudancy for the function in the javacpp API. ex : Transform exists in 3 different places :
This one takes
org.opencv.core.Mat as input. It is currently challenging to have such object in Fiji.
CvArr as input, but even if you manage to convert your input as a
CvArr it crashes Fiji. Apparently it is a deprecated version.
That's the one to use ! It takes only
org.bytedeco.javacpp.opencv_core.Mat as input, which is the most approriate in Fiji/Jython
Self written Jython modules for ImageJ
In Jython you can write all commands line by line in a single file and execute it. To create a neat program, functions and classes can be used to structure code. To prevent using copy&past for regularly used functions and classes, modules are the way to choose. Modules are files that contain functions and classes to import into other files.
To load modules, one has to save them to a directory where Jython will find them. Two lines of code will reveal these directories to you:
from sys import path print(path)
When running this code the result is an output like
['/home/michael/Software/ImageJ.app/jars/Lib', '/home/michael/Software/ImageJ.app/jars/jython-shaded-2.7.0.jar/Lib', '__classpath__', '__pyclasspath__/']
This tells us that the folder
jars/Lib/ inside our ImageJ/Fiji directory is the right place to save modules. As
Lib/ does not exist by default, we have to create it.
When a module is imported for the first time, Jython will compile it to Java code. If there is a module named
myModule.py, Jython will create a file called
myModule$py.class. The next time the module is imported, Jython will use the class file instead of the py file. When modifying the module, it necessary to restart ImageJ/Fiji to use the modified one. A work around is the following code (found at stackoverflow) that will force Jython to recompile all modules:
# Use this to recompile Jython modules to class files. from sys import modules modules.clear() # Imports of Jython modules are placed below: import myModule
Adding a custom directory
If you don't want to use
jars/Lib/ to save your modules, you have to extend the array
from sys import path from java.lang.System import getProperty # extend the search path by $FIJI_ROOT/bin/ # 'fiji.dir' works for plain ImageJ, too. path.append(getProperty('fiji.dir') + '/bin') # an alternative can be the users home directory # path.append(getProperty('user.home') + '/JythonModules') # Now you can import $FIJI_ROOT/bin/myModule.py import myModule
getProperty() accepts many more strings. A list can be found at The Java Tutorials.
Self written Jython packages for ImageJ
On the way to perfectly organize Jython code, packages are the next step. In Jython, folders that contain modules are made packages by adding the file
__init__.py. This file can be empty. An folder structure can look like this:
Imagej.app/jars/Lib/ -- myModule.py -- myPackage/ -- __init__.py -- mathTools.py -- customFilters.py -- fftTools.py -- myPackage2/ -- __init__.py -- mathTools.py -- stackProcessing.py
There are two packages and one module. The first package contains three modules and the second package contains two modules. We can import the modules on different ways:
# Import the single module using the default name: import myModule # Import mathTools from the first package import myPackage.mathTools # Use a function from the imported module myPackage.mathTools.aFunction() # Import mathTools from the second package from myPackage2 import mathTools # Use a function from the imported module without prefixing the package mathTools.aFunction() # Import customFilters from the first package and rename it from myPackage import customFilters as filters # Use a function from customFilters.py filters.aFunction() # Importing all module from a package from myPackage2 import * # The next line will fail stackProcessing.aFunction()
The reason for the last import to fail is the empty
__init__.py. We have to define which modules of the package are imported when using
import *. This is done by setting the variable
myPackage2 this line of code is needed:
__all__ = ["mathTools", "stackProcessing"]
Besides setting this variable, the file can contain normal Jython code that is executed on import.
Bundle packages in a JAR file
An interesting feature of Jython is to search for packages and modules inside of JAR files. The folder structure from the last section can be modified by packing everything into a single
myPackages.jar. The name of the JAR file doesn't matter. All imports work the same as explained before.
Imagej.app/jars/Lib/ -- myPackages.jar -- myModule.py -- myPackage/ -- __init__.py -- mathTools.py -- customFilters.py -- fftTools.py -- myPackage2/ -- __init__.py -- mathTools.py -- stackProcessing.py
The advantage of this approach is that you can share your packages easily. For example you can upload the JAR file to an update site. It is possible to upload .py scripts to update sites too, without packaging into a jar. The advantage of jar are that they allow to define dependencies more systematically.
NB : Script implementing "ImageJ menu macro" and "utilitary scripts" that are used as imported modules in other macros should be packed in separate jar files ! Indeed, if not explicitly mentioned, the jython interpreter only looks in the Jars/Lib folder to import module, so the .jar containing the "utilitary scripts" should be put there, while the jar containing the "ImageJ menu macro" can be put either in the Plugin or script/Plugin folder in order to appear in the ImageJ menu.
Contrary to the scripts in Jars/Lib, the menu macro scripts are not compiled, and as explained above they can not be imported in other scripts since the Plugin folder do not reside in the Jython search path by default.
This is the reason why a given project is rather distributed in 2 different jar files as explained here.
Using maven to build packages
Using maven you can automate the packaging of Jython code into JAR files. This approach is only recommended if you already use maven, as installing and learning how to use maven is not worth the time saving of automated packaging.
At GitHub you will find an example project that you can use as a template. Just run
mvn package and maven will generate a JAR file at the
- Wikipedia entry on Jython. Accessed: 2016-08-30