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Richardson Lucy TV (PolusAI#258)
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‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/Dockerfile‎

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FROM labshare/polus-bfio-util:2.1.9-imagej
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# environment variables defined in labshare/polus-bfio-util
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# ENV EXEC_DIR="/opt/executables"
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# ENV DATA_DIR="/data"
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# ENV POLUS_EXT=".ome.tif"
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# ENV POLUS_LOG="INFO" # Change to WARNING for fewer logs, and DEBUG for debugging
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# Work directory defined in the base container
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# WORKDIR ${EXEC_DIR}
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COPY VERSION ${EXEC_DIR}
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COPY src ${EXEC_DIR}/
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RUN pip3 install -r ${EXEC_DIR}/requirements.txt --no-cache-dir
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RUN mkdir /.jgo && chmod 777 /.jgo
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# Default command. Additional arguments are provided through the command line
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ENTRYPOINT ["python3", "main.py"]

‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/README.md‎

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# ImageJ deconvolve richardsonLucyTV
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This plugin applies the [Richardson-Lucy Deconvolution](https://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution) to input collection with
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a total variation regluariztion factor as described in (Richardson-Lucy
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algorithm with total variation regularization for 3D confocal microscope
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deconvolution Microsc Res Rech 2006 Apr; 69(4)- 260-6). This is an iterative
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process that can recover an underlying blurred image if the psf
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(point spread function) mask of the image is known or can be estimated.
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This WIPP plugin was automatically generated by a utility that searches for
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ImageJ plugins and generates code to run them. For more information on what this
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plugin does, contact one of the authors: Nick Schaub (nick.schaub@nih.gov),
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Anjali Taneja or Benjamin Houghton (benjamin.houghton@axleinfo.com).
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For more information on WIPP, visit the [official WIPP page](https://isg.nist.gov/deepzoomweb/software/wipp).
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## Building
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Bump the verison in the `VERSION` file.
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Then oo build the Docker image for the conversion plugin, run
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`./build-docker.sh`.
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## Install WIPP Plugin
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If WIPP is running, navigate to the plugins page and add a new plugin.
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Paste the contents of `plugin.json` into the pop-up window and submit.
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## Options
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This plugin takes one input argument and one output argument:
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| Name | Description | I/O | Type |
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|---------------|-------------------------|--------|--------|
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| `--inpDir` | Collection to be processed by this plugin | Input | collection |
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| `--psf` | The point spread function mask to be used | Input | collection |
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| `--maxIterations` | The maximum number of algorithm iterations | Input | number |
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| `--opName` | Op overloading method to perform | Input | enum |
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| `--regularizationFactor` | The total variation regularization factor | Input | number |
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| `--outDir` | Output collection | Output | collection |
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‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/VERSION‎

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0.4.0

‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/build-docker.sh‎

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#!/bin/bash
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version=$(<VERSION)
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docker build . -t labshare/polus-imagej-deconvolve-richardsonlucytv-plugin:${version}

‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/plugin.json‎

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{
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"name": "ImageJ deconvolve richardsonLucyTV",
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"version": "0.4.0",
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"title": "ImageJ deconvolve richardsonLucyTV",
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"description": "PadAndRichardsonLucyTV",
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"author": "Benjamin Houghton (benjamin.houghton@axleinfo.com), Anjali Taneja and Nick Schaub (nick.schaub@nih.gov)",
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"institution": "National Center for Advancing Translational Sciences, National Institutes of Health",
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"repository": "https://github.com/labshare/polus-plugins",
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"website": "https://ncats.nih.gov/preclinical/core/informatics",
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"citation": "",
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"containerId": "polusai/imagej-deconvolve-richardsonlucytv-plugin:0.4.0",
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"inputs": [
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{
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"name": "opName",
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"type": "enum",
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"options": {
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"values": ["PadAndRichardsonLucyTV"]
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},
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"description": "Op overloading method to perform",
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"required": true
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},
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{
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"name": "inpDir",
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"type": "collection",
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"description": "Collection to be processed by this plugin",
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"required": true
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},
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{
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"name": "psf",
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"type": "collection",
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"description": "The point spread function mask to be used",
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"required": true
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},
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{
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"name": "maxIterations",
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"type": "number",
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"description": "The maximum number of algorithm iterations",
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"required": true
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},
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{
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"name": "regularizationFactor",
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"type": "number",
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"description": "The total variation regularization factor",
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"required": true
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}
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],
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"outputs": [
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{
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"name": "outDir",
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"type": "collection",
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"description": "Output collection"
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}
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],
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"ui": [
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{
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"key": "inputs.opName",
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"title": "opName",
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"description": "Op overloading method to perform"
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},
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{
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"key": "inputs.inpDir",
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"title": "inpDir",
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"description": "Collection to be processed by this plugin"
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},
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{
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"key": "inputs.psf",
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"title": "psf",
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"description": "The point spread function mask to be used"
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},
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{
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"key": "inputs.maxIterations",
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"title": "maxIterations",
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"description": "The maximum number of algorithm iterations"
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},
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{
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"key": "inputs.regularizationFactor",
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"title": "regularizationFactor",
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"description": "The total variation regularization factor"
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}
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]
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}

‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/push-docker.sh‎

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#!/bin/bash
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version=$(<VERSION)
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docker push polusai/polus-imagej-deconvolve-richardsonlucytv-plugin:${version}

‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/run-plugin.sh‎

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#!/bin/bash
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version=$(<VERSION)
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datapath=$(readlink --canonicalize ../../../data)
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# Inputs
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opName=PadAndRichardsonLucyTV
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inpDir=/data/input
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psf=/data/kernels
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maxIterations=5
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regularizationFactor=1
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outDir=/data/output
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# Output paths
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out=/data/output
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docker run --mount type=bind,source=${datapath},target=/data/ \
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polusai/imagej-deconvolve-richardsonlucytv-plugin:${version} \
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--opName ${opName} \
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--inpDir ${inpDir} \
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--psf ${psf} \
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--maxIterations ${maxIterations} \
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--regularizationFactor ${regularizationFactor} \
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--outDir ${outDir}
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‎transforms/images/polus-imagej-deconvolve-richardsonlucytv-plugin/src/ij_converter.py‎

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"""
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A conversion utility built to convert abstract to primitive
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"""
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import logging
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import imglyb
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import jpype
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import scyjava
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import numpy as np
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# Initialize the logger
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logging.basicConfig(
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format="%(asctime)s - %(name)-8s - %(levelname)-8s - %(message)s",
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datefmt="%d-%b-%y %H:%M:%S",
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)
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logger = logging.getLogger("ij_converter")
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logger.setLevel(logging.INFO)
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# Define the various data types to convert
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ABSTRACT_ITERABLES = [
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"IterableInterval",
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"Iterable",
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]
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IMG_ARRAYS = ["ArrayImg"]
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ABSTRACT_SCALARS = [
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"RealType",
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]
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SCALARS = [
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"double",
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"float",
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"long",
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"int",
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"short",
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"char",
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"byte",
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"boolean",
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]
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FLOAT_SCALARS = ["double", "float", "long"]
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INT_SCALARS = [
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"int",
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"short",
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"char",
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"byte",
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"boolean",
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]
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CHAR_PRIMITIVES = ["char"]
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BYTE_PRIMITIVES = ["byte"]
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BOOL_PRIMITIVES = ["boolean"]
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# Recognize array objects as primitive objects + '[]'
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FLOAT_ARRAYS = [s + "[]" for s in FLOAT_SCALARS]
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INT_ARRAYS = [s + "[]" for s in INT_SCALARS]
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CHAR_ARRAYS = [s + "[]" for s in CHAR_PRIMITIVES]
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BYTE_ARRAYS = [s + "[]" for s in BYTE_PRIMITIVES]
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BOOL_ARRAYS = [s + "[]" for s in BOOL_PRIMITIVES]
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def _java_setup():
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global IMGLYB_PRIMITIVES, PRIMITIVES, PRIMITIVE_FLOAT_ARRAYS, PRIMITIVE_INT_ARRAYS
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global PRIMITIVE_CHAR_ARRAYS, PRIMITIVE_BYTE_ARRAYS, PRIMITIVE_BOOL_ARRAYS
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IMGLYB_PRIMITIVES = {
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"float32": imglyb.types.FloatType,
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"float64": imglyb.types.DoubleType,
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"int8": imglyb.types.ByteType,
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"int16": imglyb.types.ShortType,
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"int32": imglyb.types.IntType,
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"int64": imglyb.types.LongType,
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"uint8": imglyb.types.UnsignedByteType,
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"uint16": imglyb.types.UnsignedShortType,
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"uint32": imglyb.types.UnsignedIntType,
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"uint64": imglyb.types.UnsignedLongType,
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}
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PRIMITIVES = {
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"double": jpype.JDouble,
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"float": jpype.JFloat,
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"long": jpype.JLong,
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"int": jpype.JInt,
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"short": jpype.JShort,
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"char": jpype.JChar,
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"byte": jpype.JByte,
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"boolean": jpype.JBoolean,
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}
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PRIMITIVE_FLOAT_ARRAYS = {"double[]": jpype.JDouble[:], "float[]": jpype.JFloat[:]}
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PRIMITIVE_INT_ARRAYS = {
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"int[]": jpype.JInt[:],
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"short[]": jpype.JShort[:],
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"long[]": jpype.JLong[:],
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}
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PRIMITIVE_CHAR_ARRAYS = {
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"char[]": jpype.JChar[:],
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}
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PRIMITIVE_BYTE_ARRAYS = {
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"byte[]": jpype.JByte[:],
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}
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PRIMITIVE_BOOL_ARRAYS = {
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"boolean[]": jpype.JBoolean[:],
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}
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scyjava.when_jvm_starts(_java_setup)
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# Define empty dictionary to store the data type conversion functions
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JAVA_CONVERT = {}
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# Update the dictionary with conversion functions
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: IMGLYB_PRIMITIVES[str(st)](st.type(s))
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for t in ABSTRACT_SCALARS
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}
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)
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVES[t](float(s)) for t in SCALARS
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}
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)
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# JAVA_CONVERT.update(
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# {
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# t: lambda s, t, st: IMGLYB_PRIMITIVES[str(st)](s) for t in SCALARS}
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# )
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVE_FLOAT_ARRAYS[t](
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[float(si) for si in s.split(",")]
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)
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for t in FLOAT_ARRAYS
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}
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)
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVE_INT_ARRAYS[t]([int(si) for si in s.split(",")])
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for t in INT_ARRAYS
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}
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)
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVE_CHAR_ARRAYS[t]([si for si in s.split(",")])
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for t in CHAR_ARRAYS
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}
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)
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# TODO: Test funciton(s) with imagej op that requires byte array
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVE_BYTE_ARRAYS[t](
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[np.int8(si) for si in s.split(",")]
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)
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for t in BYTE_ARRAYS
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}
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)
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JAVA_CONVERT.update(
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{
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t: lambda s, t, st: PRIMITIVE_BOOL_ARRAYS[t]([bool(si) for si in s.split(",")])
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for t in BOOL_ARRAYS
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}
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)
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JAVA_CONVERT.update(
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{
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t: lambda s, ij: imglyb.util.Views.iterable(ij.py.to_java(s))
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for t in ABSTRACT_ITERABLES
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}
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)
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JAVA_CONVERT.update({t: lambda s, ij: imglyb.util._to_imglib(s) for t in IMG_ARRAYS})
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def to_java(ij, np_array, imagej_type, java_dtype=None):
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if ij is None:
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raise ValueError("No imagej instance found.")
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if isinstance(np_array, type(None)):
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return jpype.JObject(None, type)
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# TODO: Define how null objects should be converted from python to java
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# if java_type == "null":
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# return jpype.JObject(None, type)
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if imagej_type in JAVA_CONVERT.keys():
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if str(java_dtype) != "None":
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out_array = JAVA_CONVERT[imagej_type](np_array, imagej_type, java_dtype)
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else:
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out_array = JAVA_CONVERT[imagej_type](np_array, ij)
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else:
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logger.warning(
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"Did not recognize type, {}, will pass default.".format(imagej_type)
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)
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# Converts to RandomAccesibleInterval if imagej type not recognized
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out_array = ij.py.to_java(np_array)
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return out_array
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def from_java(ij, java_array, java_type):
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if ij is None:
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raise ValueError("No imagej instance found.")
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if ij.py.dtype(java_array) == bool:
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java_array = ij.op().convert().uint8(java_array)
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return ij.py.from_java(java_array)

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