kip - Common {i}mage {p}rocessing with {K}otlin

A concise DSL to perform common image processing tasks using Kotlin

Features

Fully typed method signatures
Concise and easy to read
Excellent tooling, interactive REPL and IDE support
Extension methods only, no new types
Sensible default parameters where possible
Immutable (for now)
Ready for jupyter

Examples

import de.mpicbg.scicomp.kip.*
import net.imglib2.type.numeric.real.FloatType


val image = bubbles()
val other = bubbles()

// image arithmetics
val imageDiff = image + other
val imageProp = image / other

// number arithmethics
val imageDiff = image - 3

// display as usual
image.show()

// save to file
image.save("my_image.png")

// open from file
val blobs = openImage<FloatType>("images/blobs32.tif")
//val image2 = openImage<FloatType>("/Users/brandl/projects/kotlin/kip/images/blobs32.tif")

// little helpers to ease some of the API pain
image.dim()

// misc operators to transform images

// default parameters which are also named parameters
val medianImage = image.median(listOf(10f,10f), shape = Shape.disk)

// method chaining
image.gauss().show()
image.gauss().median().apply{ save("some.png") }.show()


// segmentation & labeling
 val labelImage = bubbles()
    .gauss(10)
    .showThen()
    .threshold(0.3f)
    .invert()
    .showThen()
    .label()

labelImage.show()

Methods

Filters

Median Filter
Gauss Filter
Inversion

Segmentation

Threshold
Labeling

Still missing but on our list (drop us a ticket if needed)

Morphologial Ops (Erode, Dilate, Open, Close)
Projections
Scalar Arithmetics
Slicing
Watershed
TopHat Filter

Meanwhile you can for sure mix ops or imglib2 api directly

Next steps

Add remaining operators from CIP
Provide kscript and jupyter example notebooks
port some examples from here to kip

Acknowledgments

We stole most bits and pieces from CIP