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main.js
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main.js
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import { Niivue, NVMeshUtilities } from "@niivue/niivue"
import { Niimath } from "@niivue/niimath"
// import {runInference } from './brainchop-mainthread.js'
import { inferenceModelsList, brainChopOpts } from "./brainchop-parameters.js"
import { isChrome, localSystemDetails } from "./brainchop-telemetry.js"
import MyWorker from "./brainchop-webworker.js?worker"
async function main() {
const niimath = new Niimath()
await niimath.init()
// const wrapper = await NiiMathWrapper.load()
/*smoothCheck.onchange = function () {
nv1.setInterpolation(!smoothCheck.checked)
}*/
aboutBtn.onclick = function () {
const url = "https://github.com/niivue/brain2print";
window.open(url, '_blank');
}
/*diagnosticsBtn.onclick = function () {
if (diagnosticsString.length < 1) {
window.alert('No diagnostic string generated: run a model to create diagnostics')
return
}
navigator.clipboard.writeText(diagnosticsString)
window.alert('Diagnostics copied to clipboard\n' + diagnosticsString)
}*/
opacitySlider0.oninput = function () {
nv1.setOpacity(0, opacitySlider0.value / 255)
nv1.updateGLVolume()
}
opacitySlider1.oninput = function () {
nv1.setOpacity(1, opacitySlider1.value / 255)
}
async function ensureConformed() {
let nii = nv1.volumes[0]
let isConformed = ((nii.dims[1] === 256) && (nii.dims[2] === 256) && (nii.dims[3] === 256))
if ((nii.permRAS[0] !== -1) || (nii.permRAS[1] !== 3) || (nii.permRAS[2] !== -2))
isConformed = false
if (isConformed)
return
let nii2 = await nv1.conform(nii, false)
await nv1.removeVolume(nv1.volumes[0])
await nv1.addVolume(nii2)
}
async function closeAllOverlays() {
while (nv1.volumes.length > 1) {
await nv1.removeVolume(nv1.volumes[1])
}
}
modelSelect.onchange = async function () {
if (this.selectedIndex < 0)
modelSelect.selectedIndex = 11
await closeAllOverlays()
await ensureConformed()
let model = inferenceModelsList[this.selectedIndex]
model.isNvidia = false
const rendererInfo = nv1.gl.getExtension('WEBGL_debug_renderer_info')
if (rendererInfo) {
model.isNvidia = nv1.gl.getParameter(rendererInfo.UNMASKED_RENDERER_WEBGL).includes('NVIDIA')
}
let opts = brainChopOpts
opts.rootURL = location.href
const isLocalhost = Boolean(
window.location.hostname === 'localhost' ||
// [::1] is the IPv6 localhost address.
window.location.hostname === '[::1]' ||
// 127.0.0.1/8 is considered localhost for IPv4.
window.location.hostname.match(
/^127(?:\.(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$/
)
)
if (isLocalhost)
opts.rootURL = location.protocol + '//' + location.host
if (workerCheck.checked) {
if(typeof(chopWorker) !== "undefined") {
console.log('Unable to start new segmentation: previous call has not completed')
return
}
chopWorker = await new MyWorker({ type: "module" })
let hdr = {datatypeCode: nv1.volumes[0].hdr.datatypeCode, dims: nv1.volumes[0].hdr.dims}
let msg = {opts:opts, modelEntry: model, niftiHeader: hdr, niftiImage: nv1.volumes[0].img}
chopWorker.postMessage(msg)
chopWorker.onmessage = function(event) {
let cmd = event.data.cmd
if (cmd === 'ui') {
if (event.data.modalMessage !== "") {
chopWorker.terminate()
chopWorker = undefined
}
callbackUI(event.data.message, event.data.progressFrac, event.data.modalMessage, event.data.statData)
}
if (cmd === 'img') {
chopWorker.terminate()
chopWorker = undefined
callbackImg(event.data.img, event.data.opts, event.data.modelEntry)
}
}
} else {
console.log('Only provided with webworker code, see main brainchop github repository for main thread code')
// runInference(opts, model, nv1.volumes[0].hdr, nv1.volumes[0].img, callbackImg, callbackUI)
}
}
saveBtn.onclick = function () {
nv1.volumes[1].saveToDisk("Custom.nii")
}
workerCheck.onchange = function () {
modelSelect.onchange()
}
clipCheck.onchange = function () {
if (clipCheck.checked) {
nv1.setClipPlane([0, 0, 90])
} else {
nv1.setClipPlane([2, 0, 90])
}
}
function doLoadImage() {
opacitySlider0.oninput()
}
async function fetchJSON(fnm) {
const response = await fetch(fnm)
const js = await response.json()
return js
}
async function callbackImg(img, opts, modelEntry) {
closeAllOverlays()
let overlayVolume = await nv1.volumes[0].clone()
overlayVolume.zeroImage()
overlayVolume.hdr.scl_inter = 0
overlayVolume.hdr.scl_slope = 1
overlayVolume.img = new Uint8Array(img)
if (modelEntry.colormapPath) {
let cmap = await fetchJSON(modelEntry.colormapPath)
overlayVolume.setColormapLabel(cmap)
// n.b. most models create indexed labels, but those without colormap mask scalar input
overlayVolume.hdr.intent_code = 1002 // NIFTI_INTENT_LABEL
} else {
let colormap = opts.atlasSelectedColorTable.toLowerCase()
const cmaps = nv1.colormaps()
if (!cmaps.includes(colormap)) {
colormap = 'actc'
}
overlayVolume.colormap = colormap
}
overlayVolume.opacity = opacitySlider1.value / 255
await nv1.addVolume(overlayVolume)
}
async function reportTelemetry(statData) {
if (typeof statData === 'string' || statData instanceof String) {
function strToArray(str) {
const list = JSON.parse(str)
const array = []
for (const key in list) {
array[key] = list[key]
}
return array
}
statData = strToArray(statData)
}
statData = await localSystemDetails(statData, nv1.gl)
diagnosticsString = ':: Diagnostics can help resolve issues https://github.com/neuroneural/brainchop/issues ::\n'
for (var key in statData){
diagnosticsString += key + ': ' + statData[key]+'\n'
}
}
function callbackUI(message = "", progressFrac = -1, modalMessage = "", statData = []) {
if (message !== "") {
console.log(message)
document.getElementById("location").innerHTML = message
}
if (isNaN(progressFrac)) { //memory issue
memstatus.style.color = "red"
memstatus.innerHTML = "Memory Issue"
} else if (progressFrac >= 0) {
modelProgress.value = progressFrac * modelProgress.max
}
if (modalMessage !== "") {
window.alert(modalMessage)
}
if (Object.keys(statData).length > 0) {
reportTelemetry(statData)
}
}
function handleLocationChange(data) {
document.getElementById("location").innerHTML = " " + data.string
}
let defaults = {
backColor: [0.4, 0.4, 0.4, 1],
show3Dcrosshair: true,
onLocationChange: handleLocationChange,
}
createMeshBtn.onclick = function () {
if (nv1.meshes.length > 0)
nv1.removeMesh(nv1.meshes[0])
if (nv1.volumes.length < 1) {
window.alert("Image not loaded. Drag and drop an image.")
} else {
remeshDialog.show()
}
}
applyBtn.onclick = async function () {
const niiBuffer = await nv1.saveImage({volumeByIndex: nv1.volumes.length - 1}).buffer
const niiBlob = new Blob([niiBuffer], { type: 'application/octet-stream' })
const niiFile = new File([niiBlob], 'input.nii')
// get an ImageProcessor instance from niimath
// so we can build up the operations we want to perform
// based on the UI controls
let image = niimath.image(niiFile)
loadingCircle.classList.remove('hidden')
// initialize the operations object for the niimath mesh function
let ops = {
i: 0.5,
}
//const largestCheckValue = largestCheck.checked
if (largestCheck.checked) {
ops.l = 1
}
let reduce = Math.min(Math.max(Number(shrinkPct.value) / 100, 0.01), 1)
ops.r = reduce
if (bubbleCheck.checked) {
ops.b = 1
}
let hollowInt = Number(hollowSelect.value )
if (hollowInt < 0){
// append the hollow operation to the image processor
// but dont run it yet.
image = image.hollow(0.5, hollowInt)
}
let closeFloat = Number(closeMM.value)
if ((isFinite(closeFloat)) && (closeFloat > 0)){
// append the close operation to the image processor
// but dont run it yet.
image = image.close(0.5, closeFloat, 2 * closeFloat)
}
// add the mesh operations
image = image.mesh(ops)
console.log('niimath mesh operation', image.commands)
// finally, run the full set of operations
const outFile = await image.run('output.mz3')
const arrayBuffer = await outFile.arrayBuffer()
loadingCircle.classList.add('hidden')
if (nv1.meshes.length > 0)
nv1.removeMesh(nv1.meshes[0])
await nv1.loadFromArrayBuffer(arrayBuffer, 'output.mz3')
nv1.reverseFaces(0)
}
saveMeshBtn.onclick = function () {
if (nv1.meshes.length < 1) {
window.alert("No mesh open for saving. Use 'Create Mesh'.")
} else {
saveDialog.show()
}
}
applySaveBtn.onclick = function () {
if (nv1.meshes.length < 1) {
return
}
let format = 'obj'
if (formatSelect.selectedIndex === 0) {
format = 'mz3'
}
if (formatSelect.selectedIndex === 2) {
format = 'stl'
}
const scale = 1 / Number(scaleSelect.value)
const pts = nv1.meshes[0].pts.slice()
for (let i = 0; i < pts.length; i++)
pts[i] *= scale;
NVMeshUtilities.saveMesh(pts, nv1.meshes[0].tris, `mesh.${format}`, true)
}
var diagnosticsString = ''
var chopWorker
let nv1 = new Niivue(defaults)
nv1.attachToCanvas(gl1)
nv1.opts.dragMode = nv1.dragModes.pan
nv1.opts.multiplanarForceRender = true
nv1.opts.yoke3Dto2DZoom = true
nv1.opts.crosshairGap = 11
await nv1.loadVolumes([{ url: "./t1_crop.nii.gz" }])
for (let i = 0; i < inferenceModelsList.length; i++) {
var option = document.createElement("option")
option.text = inferenceModelsList[i].modelName
option.value = inferenceModelsList[i].id.toString()
modelSelect.appendChild(option)
}
nv1.onImageLoaded = doLoadImage
modelSelect.selectedIndex = -1
workerCheck.checked = await isChrome() //TODO: Safari does not yet support WebGL TFJS webworkers, test FireFox
// uncomment next two lines to automatically run segmentation when web page is loaded
// modelSelect.selectedIndex = 11
// modelSelect.onchange()
}
main()