PyImageJ _API_Summary

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• The overall API calling of PyImageJ is similar to ImageJ API calling in headless mode

# Create an ImageJ2 gateway with the newest available version of ImageJ2.
import imagej
ij = imagej.init()

# Load an image.
image_url = 'https://imagej.net/images/clown.png'
jimage = ij.io().open(image_url)

# Convert the image from ImageJ2 to xarray, a package that adds
# labeled datasets to numpy (http://xarray.pydata.org/en/stable/).
image = ij.py.from_java(jimage)

# Display the image (backed by matplotlib).
ij.py.show(image, cmap='gray')

• This is a complex case we could test, this code snippet requires:

  • Node ij_load_image
  • Knoweledge Graph ij_dataset->numpy
  • Node show_image

• As first step, we could consider putting the show_image and the process_image into one node

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1. Initialization:

imagej.init(ij_dir_or_version_or_endpoint=None, mode: Mode| str = Mode.HEADLESS, add_legacy=True, headless=None)

• This part in PyImageJ is in charge of connecting to ImageJ2 gateway

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• Important Parameters:

1. ij_dir_or_version_or_endpoint: defines the source of ImageJ functions, could be from

   • Maven
   • local_installation
   • we can also define the specific version of ImageJ, default is the latest version of net.imagej:imagej

2. mode: defines how the environment will behave

   • HEADLESS: this is the default mode, to use ImageJ as a library
   • GUI: to start ImageJ2 as a GUI application
   • INTERACTIVE: to start ImageJ2 with GUI support but not displaying GUI

• add_legacy: whether to include support for original ImageJ1 functionality.

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2.Convenience methods in imagej.ImageJPython :

---

1. Get the active image as certain datatype : Dataset, ImagePlus, xarray

These functions have some built-in converters:

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2. Get the shape and dtype of Image:

• image.shape provide shape information of image
• dtype of all types of imageJ or numpy array input are in format of numpy.dtype
• dtype are image data types as:

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3. Arguments conversion:

• jargs: Convert Python arguments into a Java Object[]

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4. ImageJ from_to_Python ImageJ type conversion:

• from_java: convert supported java data, objects into Python equivalents

• 

• to_java converts backwards:

• 

- to specific image type of ImageJ:

• 

• RandomAccessibleInterval--> numpy array:

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5. Run plugins

run_marco:

• Run an ImageJ macro by providing the macro code/script in a string and the arguments in a dictionary.

  macro = """
  #@ String name
  #@ int age
  output = name + " is " + age " years old."
  """
  args = {
  "name": "Sean",
  "age": 26
  }
  macro_result = ij.py.run_macro(macro, args)
  print(macro_result.getOutput("output"))

• run_plugin:

• run an ImageJ 1.x plugin

• ImageJ 1.x plugin by specifying the plugin name as a string, and the plugin arguments as a dictionary.

plugin = "Mean"
args = {
    "block_radius_x": 10,
    "block_radius_y": 10
}
ij.py.run_plugin(plugin, args)

---

run_script:

• run imageJ2 script

language = "ijm"
script = """
#@ String name
#@ int age
output = name + " is " + age " years old."
"""
args = {
    "name": "Sean",
    "age": 26
}
script_result = ij.py.run_script(language, script, args)
print(script_result.getOutput("output"))

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RandomAccesibleInterval operators

• This class offers operation methods for RAI:
• such as add, squeeze, transpose, tec

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Addons:

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ImageJ2 gateway addons :

• In this class, we add
• some check functions for importing ImageJ legacy check_legacy_active
• and give respective feedback when running headless mode: Operating in headless mode - the {class_name} class will not be fully functional.

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ImagePlusAddons

• In this class, we offer dims and shape methods of get the dimensional axis labels and shape of the image

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Interval addons

• shape method to get the shape of the interval

Euclidean space

• ndim method to get the number of dimensions

Typed space addons

• dims to get the axis labels of the dimensional space

Anntoted space addons

• dim axes to get the axes of the dimensional space.

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• i think the extra addons for different data structure is to provide information of image metadata

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PyImageJ submodules:

imagej.dims

• The dims utility functions are for manipulating dimensional axis metadata.

• Because the dimension order is different in imageJ's data type and some other data type in python

• We need dims.reorganize and dims.prioritize_rai_axes_order to change the dimension order as we need

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imagej.stack

• stack_rai_slice to slice ImageLib2 images in desired slice range

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imagej.doctor

• doctor.checkup for diagnosing the environment and offer advice for correcting problems