Searching for Ops in the Environment

As the OpEnvironment is fully extensible, different OpEnvironments might contain different Ops, and it is important to be able to query an OpEnvironment about the available Ops.

The OpEnvironment.help() API allows you to query an OpEnvironment about the types of Ops it contains, as we show in the following sections. Note that the example printouts from the help API may not reflect the Ops available in your Op environment.

Searching for operations

The no-argument method OpEnvironment.help() is designed to give you a broad overview over the categories of Ops available within the OpEnvironment:

import org.scijava.ops.api.OpEnvironment
ops = OpEnvironment.getEnvironment()

print(ops.help())

This gives the following printout:

Namespaces:
	> coloc
	> convert
	> copy
	> create
	> deconvolve
	> expression
	> features
	> filter
	> geom
	> image
	> imageMoments
	> labeling
	> linalg
	> logic
	> map
	> math
	> morphology
	> segment
	> stats
	> thread
	> threshold
	> topology
	> transform
	> types

Interrogating a Namespace

You can choose one of the above namespaces, and ops.help() will give you information about the algorithms contained within:

import org.scijava.ops.api.OpEnvironment
ops = OpEnvironment.getEnvironment()

print(ops.help("filter"))

This gives the following printout:

Names:
	> filter.dog
	> filter.addNoise
	> filter.addPoissonNoise
	> filter.addUniformNoise
	> filter.applyCenterAware
	> filter.bilateral
	> filter.convolve
	> filter.convolveNaive
	> filter.correlate
	> filter.createFFTOutput
	> filter.derivativeGauss
	> filter.fft
	> filter.fftSize
	> filter.frangiVesselness
	> filter.gauss
	> filter.hessian
	> filter.ifft
	> filter.linearFilter
	> filter.max
	> filter.mean
	> filter.median
	> filter.min
	> filter.padInput
	> filter.padInputFFT
	> filter.padInputFFTMethods
	> filter.padIntervalCentered
	> filter.padIntervalOrigin
	> filter.padShiftFFTKernel
	> filter.padShiftKernel
	> filter.padShiftKernelFFTMethods
	> filter.partialDerivative
	> filter.sigma
	> filter.sobel
	> filter.tubeness
	> filter.variance

Signatures for Op Names

Finally, you can use OpEnvironment.help() on any Op name to see the list of signatures:

import org.scijava.ops.api.OpEnvironment
ops = OpEnvironment.getEnvironment()

print(ops.help("filter.gauss"))

filter.gauss:
	- (input, sigmas, @CONTAINER container1) -> None
	- (input, sigmas, outOfBounds = null, @CONTAINER container1) -> None
	- (input, sigma, @CONTAINER container1) -> None
	- (input, sigma, outOfBounds = null, @CONTAINER container1) -> None

Note that these descriptions are simple, and you can obtain more verbose descriptions by instead using the method OpEnvironment.helpVerbose():

import org.scijava.ops.api.OpEnvironment
ops = OpEnvironment.getEnvironment()

print(ops.helpVerbose("filter.gauss"))

filter.gauss:
	- org.scijava.ops.image.filter.gauss.Gaussians.defaultGaussRAI(net.imglib2.RandomAccessibleInterval<I>,double[],net.imglib2.outofbounds.OutOfBoundsFactory<I, net.imglib2.RandomAccessibleInterval<I>>,net.imglib2.RandomAccessibleInterval<O>)
		> input : net.imglib2.RandomAccessibleInterval<I>
			the input image
		> sigmas : double[]
			the sigmas for the gaussian
		> outOfBounds (optional) : net.imglib2.outofbounds.OutOfBoundsFactory<I, net.imglib2.RandomAccessibleInterval<I>>
			the {@link OutOfBoundsFactory} that defines how the
			calculation is affected outside the input bounds.
		> container1 : @CONTAINER net.imglib2.RandomAccessibleInterval<O>
			the output image
	- org.scijava.ops.image.filter.gauss.Gaussians.gaussRAISingleSigma(net.imglib2.RandomAccessibleInterval<I>,double,net.imglib2.outofbounds.OutOfBoundsFactory<I, net.imglib2.RandomAccessibleInterval<I>>,net.imglib2.RandomAccessibleInterval<O>)
		> input : net.imglib2.RandomAccessibleInterval<I>
			the input image
		> sigma : java.lang.Double
			the sigmas for the Gaussian
		> outOfBounds (optional) : net.imglib2.outofbounds.OutOfBoundsFactory<I, net.imglib2.RandomAccessibleInterval<I>>
			the {@link OutOfBoundsFactory} that defines how the
			calculation is affected outside the input bounds.
		> container1 : @CONTAINER net.imglib2.RandomAccessibleInterval<O>
			the preallocated output image