diff --git a/README.md b/README.md index 787ab78..026da43 100644 --- a/README.md +++ b/README.md @@ -9,23 +9,21 @@ This is an extension of [brainchop](https://github.com/neuroneural/brainchop) th 1. Open the [live demo](https://niivue.github.io/brain2print/). 2. **Option 1** The web page automatically loads with a default T1 MRI scan. If you want to use this scan, go to step 5. 3. **Option 2** If your T1 MRI scan is in NIfTI format, drag and drop the file onto the web page. -4. **Option 3** If your image is in DICOM format, it may load if you drag and drop the files. If this fails, convert your images with dcm2niix. -5. Segment your brain scan by choosing a model from the `Segmentation Model` pull-down menu. Not all models with with all graphics cards. The `Tissue GWM (High Acc, Low Mem)` is a good starting point. Hopefully, it will accurately segment your brain into gray matter, white matter and cerebral spinal fluid. +4. **Option 3** If your image is in DICOM format, it may load if you drag and drop the files. If this fails, convert your images with [dcm2niix](https://github.com/rordenlab/dcm2niix). +5. Segment your brain scan by choosing a model from the `Segmentation Model` pull-down menu. Not all models work with all graphics cards. The `Tissue GWM (High Acc, Low Mem)` is a good starting point. Hopefully, it will accurately segment your brain into gray matter, white matter and cerebral spinal fluid. 6. Press the `Create Mesh` button and select your preferred settings: - ![settings dialog](Settings.png) - - [Closing](https://en.wikipedia.org/wiki/Closing_(morphology)) removes small crevices and cavities in your mesh which can plague printing. - - Fill bubbles will remove any cavities, this includes large cavities for example the ventricles for a brain scan. - - The `Largest cluster only` will only extract a single mesh. - - You can choose `Smoothing` to make the surfaces less jagged (note this can create self intersecting triangles that can confound some printers). - - You can choose to `Simplify` [reduce the number of triangles](https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification) to create smaller files (note this can create self intersecting triangles that can confound some printers). + - You can choose `Smoothing` to make the surfaces less jagged at the expense of computation time. + - You can choose to `Simplify` to reduce the number of triangles and create smaller files. + 7. Once you have set your preferences, press `Apply`. 8. You will see the mesh appear and can interactively view it. If you are unhappy with the result, repeat step 6 with different settings. If you want to print the results, press the `Save Mesh` button. ## How it Works -This web application uses some of the latest browser technologies that allow the tissue segmentation model to run on your local GPU, regardless of the type of GPU. This is possible via the `WebGPU` browser API. Additionally, we leverage `WebAssembly` to run the `niimath` [WASM wrapper](https://www.npmjs.com/package/@niivue/niimath) to turn the tissue segmentation into a 3D mesh. No data ever leaves your machine. +This web application uses some of the latest browser technologies that allow the tissue segmentation model to run on your local GPU, regardless of the type of GPU. This is possible via the `WebGPU` browser API. Additionally, we leverage `WebAssembly` to run the `niimath` [WASM wrapper](https://www.npmjs.com/package/@niivue/niimath) and [ITK-Wasm](https://wasm.itk.org) to turn the tissue segmentation into a 3D mesh. No data ever leaves your machine. ### Developers - Running a Local Live Demo @@ -50,5 +48,4 @@ npm run build - [brainchop](https://github.com/neuroneural/brainchop) - [niivue](https://github.com/niivue/niivue) - [niimath](https://github.com/rordenlab/niimath) - - +- [ITK-Wasm](https://github.com/InsightSoftwareConsortium/ITK-Wasm) diff --git a/Settings.png b/Settings.png old mode 100644 new mode 100755 index 41a9cf6..95f00ce Binary files a/Settings.png and b/Settings.png differ diff --git a/index.html b/index.html index ba861d2..13f8b65 100644 --- a/index.html +++ b/index.html @@ -10,6 +10,8 @@
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diff --git a/main.js b/main.js index 732b2f7..8b01a2b 100644 --- a/main.js +++ b/main.js @@ -1,21 +1,34 @@ -import { Niivue, NVMeshUtilities } from "@niivue/niivue" -import { Niimath } from "@niivue/niimath" +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" +import { inferenceModelsList, brainChopOpts } from "./brainchop-parameters.js"; +import { isChrome, localSystemDetails } from "./brainchop-telemetry.js"; +import MyWorker from "./brainchop-webworker.js?worker"; +import { + antiAliasCuberille, + setPipelinesBaseUrl as setCuberillePipelinesUrl +} from "@itk-wasm/cuberille"; +import { + repair, + smoothRemesh, + keepLargestComponent, + setPipelinesBaseUrl as setMeshFiltersPipelinesUrl, +} from "@itk-wasm/mesh-filters"; +import { nii2iwi, iwm2meshCore } from "@niivue/cbor-loader"; + +// Use local, vendored WebAssembly module assets +const viteBaseUrl = import.meta.env.BASE_URL +const pipelinesBaseUrl = new URL(`${viteBaseUrl}pipelines`, document.location.origin).href +setCuberillePipelinesUrl(pipelinesBaseUrl) +setMeshFiltersPipelinesUrl(pipelinesBaseUrl) async function main() { - const niimath = new Niimath() - await niimath.init() - // const wrapper = await NiiMathWrapper.load() - /*smoothCheck.onchange = function () { - nv1.setInterpolation(!smoothCheck.checked) - }*/ + const niimath = new Niimath(); + await niimath.init(); aboutBtn.onclick = function () { const url = "https://github.com/niivue/brain2print"; - window.open(url, '_blank'); - } + window.open(url, "_blank"); + }; /*diagnosticsBtn.onclick = function () { if (diagnosticsString.length < 1) { window.alert('No diagnostic string generated: run a model to create diagnostics') @@ -25,278 +38,287 @@ async function main() { window.alert('Diagnostics copied to clipboard\n' + diagnosticsString) }*/ opacitySlider0.oninput = function () { - nv1.setOpacity(0, opacitySlider0.value / 255) - nv1.updateGLVolume() - } + nv1.setOpacity(0, opacitySlider0.value / 255); + nv1.updateGLVolume(); + }; opacitySlider1.oninput = function () { - if (nv1.volumes.length < 2) return - nv1.setOpacity(1, opacitySlider1.value / 255) - } + if (nv1.volumes.length < 2) return; + 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) + 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]) + 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 (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') - + model.isNvidia = nv1.gl + .getParameter(rendererInfo.UNMASKED_RENDERER_WEBGL) + .includes("NVIDIA"); } - - let opts = brainChopOpts - opts.rootURL = location.href + + 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( + 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 (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 + 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) + 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) + 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') + 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") - } + nv1.volumes[1].saveToDisk("Custom.nii"); + }; workerCheck.onchange = function () { - modelSelect.onchange() - } + modelSelect.onchange(); + }; clipCheck.onchange = function () { if (clipCheck.checked) { - nv1.setClipPlane([0, 0, 90]) + nv1.setClipPlane([0, 0, 90]); } else { - nv1.setClipPlane([2, 0, 90]) + nv1.setClipPlane([2, 0, 90]); } - } + }; function doLoadImage() { - opacitySlider0.oninput() + opacitySlider0.oninput(); } async function fetchJSON(fnm) { - const response = await fetch(fnm) - const js = await response.json() - return js + 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) + 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) + 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 + overlayVolume.hdr.intent_code = 1002; // NIFTI_INTENT_LABEL } else { - let colormap = opts.atlasSelectedColorTable.toLowerCase() - const cmaps = nv1.colormaps() + let colormap = opts.atlasSelectedColorTable.toLowerCase(); + const cmaps = nv1.colormaps(); if (!cmaps.includes(colormap)) { - colormap = 'actc' + colormap = "actc"; } - overlayVolume.colormap = colormap + overlayVolume.colormap = colormap; } - overlayVolume.opacity = opacitySlider1.value / 255 - await nv1.addVolume(overlayVolume) + overlayVolume.opacity = opacitySlider1.value / 255; + await nv1.addVolume(overlayVolume); + saveBtn.disabled = false + createMeshBtn.disabled = false } async function reportTelemetry(statData) { - if (typeof statData === 'string' || statData instanceof String) { + if (typeof statData === "string" || statData instanceof String) { function strToArray(str) { - const list = JSON.parse(str) - const array = [] + const list = JSON.parse(str); + const array = []; for (const key in list) { - array[key] = list[key] + array[key] = list[key]; } - return array + return array; } - statData = strToArray(statData) + 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' + 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 = []) { + function callbackUI( + message = "", + progressFrac = -1, + modalMessage = "", + statData = [] + ) { if (message !== "") { - console.log(message) - document.getElementById("location").innerHTML = message + console.log(message); + document.getElementById("location").innerHTML = message; } - if (isNaN(progressFrac)) { //memory issue - memstatus.style.color = "red" - memstatus.innerHTML = "Memory Issue" + if (isNaN(progressFrac)) { + //memory issue + memstatus.style.color = "red"; + memstatus.innerHTML = "Memory Issue"; } else if (progressFrac >= 0) { - modelProgress.value = progressFrac * modelProgress.max + modelProgress.value = progressFrac * modelProgress.max; } if (modalMessage !== "") { - window.alert(modalMessage) + window.alert(modalMessage); } if (Object.keys(statData).length > 0) { - reportTelemetry(statData) + reportTelemetry(statData); } } function handleLocationChange(data) { - document.getElementById("location").innerHTML = "  " + data.string + 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.meshes.length > 0) nv1.removeMesh(nv1.meshes[0]); if (nv1.volumes.length < 1) { - window.alert("Image not loaded. Drag and drop an image.") + window.alert("Image not loaded. Drag and drop an image."); } else { - remeshDialog.show() + 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 - let smooth = parseFloat(smoothSlide.value) - ops.s = smooth - if (bubbleCheck.checked) { - ops.b = 1 - } + const volIdx = nv1.volumes.length - 1 + loadingCircle.classList.remove("hidden"); + meshProcessingMsg.classList.remove("hidden") + meshProcessingMsg.textContent = "Generating mesh from segmentation" + const hdr = nv1.volumes[volIdx].hdr; + const img = nv1.volumes[volIdx].img; + const itkImage = nii2iwi(hdr, img, false); + itkImage.size = itkImage.size.map(Number); - 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) - } + const { mesh } = await antiAliasCuberille(itkImage, { noClosing: true }); + meshProcessingMsg.textContent = "Generating manifold" + const { outputMesh: repairedMesh } = await repair(mesh, { maximumHoleArea: 50.0 }); + meshProcessingMsg.textContent = "Keep largest mesh component" + const { outputMesh: largestOnly } = await keepLargestComponent(repairedMesh) + while (nv1.meshes.length > 0) { + nv1.removeMesh(nv1.meshes[0]); + } + const initialNiiMesh = iwm2meshCore(largestOnly); + const initialNiiMeshBuffer = NVMeshUtilities.createMZ3(initialNiiMesh.positions, initialNiiMesh.indices, false) + await nv1.loadFromArrayBuffer(initialNiiMeshBuffer, 'trefoil.mz3') + saveMeshBtn.disabled = false - 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) - } + meshProcessingMsg.textContent = "Smoothing and remeshing" + const smooth = parseInt(smoothSlide.value) + const shrink = parseFloat(shrinkPct.value) + const { outputMesh: smoothedMesh } = await smoothRemesh(largestOnly, { newtonIterations: smooth, numberPoints: shrink }); + const niiMesh = iwm2meshCore(smoothedMesh); + loadingCircle.classList.add("hidden"); + meshProcessingMsg.classList.add("hidden"); + while (nv1.meshes.length > 0) { + nv1.removeMesh(nv1.meshes[0]); + } + const meshBuffer = NVMeshUtilities.createMZ3(niiMesh.positions, niiMesh.indices, false) + await nv1.loadFromArrayBuffer(meshBuffer, 'trefoil.mz3') + }; saveMeshBtn.onclick = function () { if (nv1.meshes.length < 1) { - window.alert("No mesh open for saving. Use 'Create Mesh'.") + window.alert("No mesh open for saving. Use 'Create Mesh'."); } else { - saveDialog.show() + saveDialog.show(); } - } + }; applySaveBtn.onclick = function () { if (nv1.meshes.length < 1) { - return + return; } - let format = 'obj' + let format = "obj"; if (formatSelect.selectedIndex === 0) { - format = 'mz3' + format = "mz3"; } if (formatSelect.selectedIndex === 2) { - format = 'stl' + 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.${format}`, true) - } + const scale = 1 / Number(scaleSelect.value); + const pts = nv1.meshes[0].pts.slice(); + for (let i = 0; i < pts.length; i++) pts[i] *= scale; + NVMeshUtilities.saveMesh(pts, nv1.meshes[0].tris, `mesh.${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" }]) + 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) + 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 + 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() +main(); diff --git a/package-lock.json b/package-lock.json index 8b3f78a..f7d2c70 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8,15 +8,91 @@ "name": "niivue-brainchop", "version": "0.1.0", "dependencies": { + "@itk-wasm/cuberille": "^0.1.0", + "@itk-wasm/mesh-filters": "^0.1.0", + "@niivue/cbor-loader": "^1.1.0", "@niivue/niimath": "^0.1.1", "@niivue/niivue": "^0.44.2", "@tensorflow/tfjs": "^4.19.0", "gl-matrix": "^3.4.3" }, "devDependencies": { - "vite": "^5.4.2" + "vite": "^5.4.2", + "vite-plugin-static-copy": "^2.2.0" } }, + "node_modules/@cbor-extract/cbor-extract-darwin-arm64": { + 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"https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz", + "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==", + "dependencies": { + "buffer-crc32": "~0.2.3", + "fd-slicer": "~1.1.0" + } } } } diff --git a/package.json b/package.json index 3a21cad..04ef63d 100644 --- a/package.json +++ b/package.json @@ -9,12 +9,16 @@ "preview": "vite preview" }, "dependencies": { + "@itk-wasm/cuberille": "^0.1.0", + "@itk-wasm/mesh-filters": "^0.1.0", + "@niivue/cbor-loader": "^1.1.0", "@niivue/niimath": "^0.1.1", "@niivue/niivue": "^0.44.2", "@tensorflow/tfjs": "^4.19.0", "gl-matrix": "^3.4.3" }, "devDependencies": { - "vite": "^5.4.2" + "vite": "^5.4.2", + "vite-plugin-static-copy": "^2.2.0" } } diff --git a/vite.config.js b/vite.config.js index 25e5b81..8394a46 100644 --- a/vite.config.js +++ b/vite.config.js @@ -1,16 +1,26 @@ -import { defineConfig } from 'vite' +import { defineConfig } from "vite"; +import { viteStaticCopy } from 'vite-plugin-static-copy' export default defineConfig({ // root: '.', - base: './', + base: "./", server: { - open: 'index.html', + open: "index.html", }, worker: { - format: 'esm' + format: "esm", }, // exclude @niivue/niimath from optimization optimizeDeps: { - exclude: ['@niivue/niimath'] - } -}) \ No newline at end of file + exclude: ["@niivue/niimath", "@itk-wasm/cuberille", "@itk-wasm/mesh-filters"], + }, + plugins: [ + // put lazy loaded JavaScript and Wasm bundles in dist directory + viteStaticCopy({ + targets: [ + { src: 'node_modules/@itk-wasm/cuberille/dist/pipelines/*.{js,wasm,wasm.zst}', dest: 'pipelines' }, + { src: 'node_modules/@itk-wasm/mesh-filters/dist/pipelines/*.{js,wasm,wasm.zst}', dest: 'pipelines' }, + ], + }) + ], +});