Gaussian blur subtraction

In this example we will use SciJava Ops to open an image, apply a guassian blur and subract the blurred image from the input image.

This technique can be used to extract features, such as puncta, from a noisy background.

SciJava Ops via Fiji's sripting engine with `script parameters`_:

.. tabs::

.. code-tab:: groovy

#@ ImgPlus img

#@ Double (label="Sigma:", value=5.0) sigma

#@output ImgPlus result

import org.scijava.ops.api.OpEnvironment

import net.imglib2.type.numeric.real.FloatType

// build the Ops environment

ops = OpEnvironment.build();

// convert input ImgPlus image to float32

img = ops.op("convert.float32").arity1().input(img).apply();

// create gaussian blurred image

img_gauss = ops.op("filter.gauss").arity2().input(img, sigma).apply();

// subtract the input and blurred images

result = ops.op("create.img").arity2().input(img, new FloatType()).apply();

ops.op("math.sub").arity2().input(img, img_gauss).output(result).compute();

.. code-tab:: python

#@ ImgPlus img

#@ Double (label="Sigma:", value=5.0) sigma

#@output ImgPlus result

from org.scijava.ops.api import OpEnvironment

from net.imglib2.type.numeric.real import FloatType

# build the Ops environment

ops = OpEnvironment.build()

# convert input ImgPlus image to float32

img = ops.op("convert.float32").arity1().input(img).apply()

# create gaussian blurred image

img_gauss = ops.op("filter.gauss").arity2().input(img, sigma).apply()

# subtract the input and blurred images

result = ops.op("create.img").arity2().input(img, FloatType()).apply()

ops.op("math.sub").arity2().input(img, img_gauss).output(result).compute()

.. _`script parameters`: https://imagej.net/scripting/parameters