From ddf149c5d80123c21c1ae3a092f8cc436ee48f04 Mon Sep 17 00:00:00 2001 From: Craig Russell Date: Thu, 16 Nov 2023 16:33:57 +0000 Subject: [PATCH 01/12] Updating hypha link (#365) --- public/plugins/bioengine-test-run.imjoy.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/public/plugins/bioengine-test-run.imjoy.html b/public/plugins/bioengine-test-run.imjoy.html index 79d82cbb..d111a270 100644 --- a/public/plugins/bioengine-test-run.imjoy.html +++ b/public/plugins/bioengine-test-run.imjoy.html @@ -496,7 +496,7 @@ async def setup(self): api.log("Connector plugin is ready.") server = await connect_to_server( - {"name": "client", "server_url": "https://ai.imjoy.io", "method_timeout": 30} + {"name": "client", "server_url": "https://hypha.bioimage.io", "method_timeout": 30} ) self.triton = await server.get_service("triton-client") self.image = None From f7d685e2a0d519eb8f7985e5cb1e0d8dd8d6a734 Mon Sep 17 00:00:00 2001 From: Joran Deschamps <6367888+jdeschamps@users.noreply.github.com> Date: Thu, 23 Nov 2023 09:35:07 +0100 Subject: [PATCH 02/12] Proposal for default tags update (#327) * Replace n2v (method) by napari (software) * Add method tag * Remove unet from method --------- Co-authored-by: Wei Ouyang --- site.config.json | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/site.config.json b/site.config.json index 8e5781de..609e96e1 100644 --- a/site.config.json +++ b/site.config.json @@ -33,12 +33,12 @@ }, { "label": "Source Code", "tooltip": "Github Repository", - "logo": "/static/img/github.png", + "logo": "/static/img/github.png", "url": "https://github.com/bioimage-io/bioimage.io" }, { "label": "Contact Us", "tooltip": "Send us a message", - "logo": "/static/img/contact.png", + "logo": "/static/img/contact.png", "url": "https://oeway.typeform.com/to/K3j2tJt7" }, { "label": "AI4Life", @@ -48,7 +48,7 @@ }, { "label": "Deploys By Netlify", "tooltip": "This site is powered by Netlify", - "logo": "https://www.netlify.com/img/global/badges/netlify-color-accent.svg", + "logo": "https://www.netlify.com/img/global/badges/netlify-color-accent.svg", "url": "https://www.netlify.com" } ], @@ -124,19 +124,19 @@ "outline_color": "rgb(36 0 236)", "tag_categories": { "modality": [ - "electron-microscopy", + "electron-microscopy", "cryo-electron-microscopy", "fluorescence-light-microscopy", "transmission-light-microscopy", - "super-resolution-microscopy", - "x-ray-microscopy", + "super-resolution-microscopy", + "x-ray-microscopy", "force-microscopy", "high-content-imaging", - "whole-slide-imaging", + "whole-slide-imaging", "other"], "dims": [ "2d", "3d", "2d-t", "3d-t"], - + "content": [ "cells", "nuclei", @@ -150,7 +150,15 @@ "whole-organism" ], "framework": ["tensorflow", "pytorch", "tensorflow.js"], - "software": ["ilastik", "imagej", "fiji", "imjoy", "deepimagej", "n2v"], + "software": ["ilastik", "imagej", "fiji", "imjoy", "deepimagej", "napari"], + "method": [ + "stardist", + "cellpose", + "yolo", + "care", + "n2v", + "denoiseg" + ], "network": [ "unet", "densenet", From d205fe05e53122bd179d89b3952fb346720bdceb Mon Sep 17 00:00:00 2001 From: Wei Ouyang Date: Tue, 5 Dec 2023 10:29:50 +0100 Subject: [PATCH 03/12] Change funding to European Union --- site.config.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site.config.json b/site.config.json index 609e96e1..512b6af5 100644 --- a/site.config.json +++ b/site.config.json @@ -42,7 +42,7 @@ "url": "https://oeway.typeform.com/to/K3j2tJt7" }, { "label": "AI4Life", - "tooltip": "BioImage.IO is funeded by the European Commission through the AI4Life project", + "tooltip": "BioImage.IO is funeded by the European Union through the AI4Life project", "logo": "/static/img/AI4Life-logo-giraffe.png", "url": "https://ai4life.eurobioimaging.eu/" }, From f91e42afe9c9570ac136974151a327854561a233 Mon Sep 17 00:00:00 2001 From: esgomezm <43643518+esgomezm@users.noreply.github.com> Date: Tue, 5 Dec 2023 12:03:34 +0000 Subject: [PATCH 04/12] modify european commission to union --- site.config.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site.config.json b/site.config.json index 512b6af5..8f57d4c4 100644 --- a/site.config.json +++ b/site.config.json @@ -112,7 +112,7 @@ "detailed_image": null, "detailed_body": "description" }, - "footnote": "BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. We receive funding support from the European Commission through the AI4Life project (https://ai4life.eurobioimaging.eu/)", + "footnote": "BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. We receive funding support from the European Union through the AI4Life project (https://ai4life.eurobioimaging.eu/)", "partners": [], "excluded_partners": ["bioimageio"], "excluded_tags": ["bioimage.io", "bioimage.io:model", "bioimage.io:application", "bioimage.io:workflow", "bioimage.io:notebook", "bioimage.io:dataset", "zenodo"], From d405121d32247c91a886581513fb79930f09833f Mon Sep 17 00:00:00 2001 From: weize Date: Tue, 5 Dec 2023 16:19:31 +0100 Subject: [PATCH 05/12] Using ImageJ.JS as the front-end for model test run (#364) * fix the bug caused by bioimage * try to use ij * fix bugs * changes for submit image * allow run * change plugin to component * fix bug * format * change axes infer function * fix bugs * support show bool result * use test_input instead of sample_input as default * format * fix bug in axes infer * change image show name * Fix info panel color; endian error * Add Tile and Tiler classes for image tiling * delete old things * format * Fix test run form visibility based on type * Add waitForImjoy function to loadImJoy * fix Endianness issue * Refactor * Add model availability check for test-run-form component * Add ImgPadder class for image padding and cropping * Fix error input type of processForShow * change hypha server url * Refactor image processing logic in runModel * Add tiler for tile large image * allow tiler,merger,show support non-img2img output * fix bug * change Object.groupBy to lodash.groupBy * add advance menu allow user setting tile size * change the default of z-overlap to 0 * Support show the result of classification model --- package-lock.json | 723 ++++++++++++++++++- package.json | 6 + public/plugins/bioengine-test-run.imjoy.html | 666 ----------------- src/components/ResourceItemInfo.vue | 97 +-- src/components/TestRun.vue | 671 +++++++++++++++++ src/imgProcess.js | 614 ++++++++++++++++ 6 files changed, 2015 insertions(+), 762 deletions(-) delete mode 100644 public/plugins/bioengine-test-run.imjoy.html create mode 100644 src/components/TestRun.vue create mode 100644 src/imgProcess.js diff --git a/package-lock.json b/package-lock.json index ed9475d5..de0f8b3a 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1023,6 +1023,20 @@ "to-fast-properties": "^2.0.0" } }, + "@d4c/numjs": { + "version": "0.17.34", + "resolved": "https://registry.npmjs.org/@d4c/numjs/-/numjs-0.17.34.tgz", + "integrity": "sha512-wPA61nUOFR1S7a40m3m9Ko+eHyRaKKE4Cha7/JR53jVL9VN6ljDUZX8x4G1k6DW3TCbtu0emn3c8yJvlyckpoA==", + "requires": { + "@types/ndarray": "^1.0.11", + "cwise": "^1.0.10", + "ndarray": "^1.0.19", + "ndarray-fft": "^1.0.3", + "ndarray-gemm": "^1.0.0", + "ndarray-ops": "^1.2.2", + "typedarray-pool": "^1.2.0" + } + }, "@hapi/address": { "version": "2.1.4", "resolved": "https://registry.npmjs.org/@hapi/address/-/address-2.1.4.tgz", @@ -1088,6 +1102,11 @@ "glob-to-regexp": "^0.3.0" } }, + "@msgpack/msgpack": { + "version": "2.8.0", + "resolved": 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"https://registry.npmjs.org/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-1.0.4.tgz", @@ -12459,8 +13124,7 @@ "uniq": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/uniq/-/uniq-1.0.1.tgz", - "integrity": "sha1-sxxa6CVIRKOoKBVBzisEuGWnNP8=", - "dev": true + "integrity": "sha1-sxxa6CVIRKOoKBVBzisEuGWnNP8=" }, "uniqs": { "version": "2.0.0", @@ -13468,12 +14132,22 @@ } } }, + "window-size": { + "version": "0.1.0", + "resolved": "https://registry.npmjs.org/window-size/-/window-size-0.1.0.tgz", + "integrity": "sha512-1pTPQDKTdd61ozlKGNCjhNRd+KPmgLSGa3mZTHoOliaGcESD8G1PXhh7c1fgiPjVbNVfgy2Faw4BI8/m0cC8Mg==" + }, "word-wrap": { "version": "1.2.3", "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.3.tgz", "integrity": "sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==", "dev": true }, + "wordwrap": { + "version": "0.0.2", + "resolved": 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\"{src,tests}/**/**\"" }, "dependencies": { + "@d4c/numjs": "^0.17.34", "@mdi/font": "^5.3.45", + "@tensorflow/tfjs-backend-cpu": "^4.13.0", + "@tensorflow/tfjs-core": "^4.13.0", "axios": "^0.21.2", "buefy": "^0.8.0", "core-js": "^3.6.5", @@ -20,11 +23,14 @@ "file-saver": "^2.0.5", "github-markdown-css": "^4.0.0", "highlight.js": "^10.1.1", + "imjoy-rpc": "^0.5.46", "js-yaml": "^4.1.0", "json-stringify-safe": "^5.0.1", "jszip": "^3.7.0", + "lodash": "^4.17.21", "marked": "^2.1.3", "mathjs": "^7.5.1", + "npyjs": "^0.5.0", "ol": "^6.3.1", "register-service-worker": "^1.7.1", "sortablejs": "^1.10.2", diff --git a/public/plugins/bioengine-test-run.imjoy.html b/public/plugins/bioengine-test-run.imjoy.html deleted file mode 100644 index d111a270..00000000 --- a/public/plugins/bioengine-test-run.imjoy.html +++ /dev/null @@ -1,666 +0,0 @@ - -The panel for upload the image file. - - - -{ - "name": "BioEngine Model Runner", - "type": "window", - "tags": [], - "ui": "", - "version": "0.1.0", - "cover": "", - "description": "[TODO: describe this plugin with one sentence.]", - "icon": "extension", - "inputs": null, - "outputs": null, - "api_version": "0.1.8", - "env": "", - "permissions": [], - "requirements": [ - "https://cdn.tailwindcss.com", - "https://cdn.jsdelivr.net/npm/imjoy-rpc@0.5.6/dist/hypha-rpc-websocket.min.js" - ], - "dependencies": [] -} - - - - - -
-

Load an image file:

-

Example formats: PNG, JPG or TIFF

-

TODO: display expected tensor info

-
- No file selected. - - -
- - -

or you may load the image from an URL.

-

A test image is provided for this model. Click the Load button to run the model on the provided test image or provide the url to your own test image.

-

The URL to OME-Zarr are supported.

-
- - -
-
-

- -
- -

Input axes (optional, for example yxzc):

-

-

More documentation about expected axes in the documentation page.

- - -
-
- -

-
-
- - -
-

- -
- -
- - - -
-
-
- - - -{ - "name": "mi-core", - "type": "web-python", - "tags": [], - "flags": [], - "ui": "", - "version": "0.1.0", - "cover": "", - "description": "Connect to the bioengine server, and execute operations.", - "icon": "extension", - "inputs": null, - "outputs": null, - "api_version": "0.1.8", - "env": "", - "permissions": [], - "requirements": ["imageio", "xarray"], - "dependencies": [] -} - - - - diff --git a/src/components/ResourceItemInfo.vue b/src/components/ResourceItemInfo.vue index 93cd86b6..ecb04c06 100644 --- a/src/components/ResourceItemInfo.vue +++ b/src/components/ResourceItemInfo.vue @@ -103,12 +103,14 @@ >
- + + +
+ This model is not available for testing. +

@@ -158,6 +160,7 @@ import TestSummary from "@/components/TestSummary.vue"; import CommentBox from "@/components/CommentBox.vue"; import { randId, concatAndResolveUrl } from "../utils"; import ResourceItemCard from "./ResourceItemCard"; +import TestRunForm from "./TestRun.vue"; async function fetchTestSummary(url) { const response = await fetch(url); @@ -204,13 +207,15 @@ export default { attachments: Attachments, "app-icons": AppIcons, "comment-box": CommentBox, - "resource-item-card": ResourceItemCard + "resource-item-card": ResourceItemCard, + "test-run-form": TestRunForm }, data() { return { maxDescriptionLetters: 100, maxDocsLetters: 500, - showSource: false + showSource: false, + modelAvailable: false }; }, @@ -236,9 +241,7 @@ export default { this.$forceUpdate(); }); } - this.gettestRunDocs(this.resourceItem).then(() => { - this.$forceUpdate(); - }); + this.getManifestList(); }, computed: { runButtonContext: function() { @@ -353,69 +356,12 @@ export default { } } }, - async gettestRunDocs(resourceItem) { - const response = await fetch( - "https://raw.githubusercontent.com/bioimage-io/bioengine-model-runner/gh-pages/manifest.bioengine.json" - ); - const manifest = await response.json(); - if (!manifest.collection.find(c => c.id === resourceItem.id)) { - resourceItem.testRunDocs = `## Model testing is not available for this model\n\nSee [conversion log](https://github.com/bioimage-io/bioengine-model-runner/blob/gh-pages/manifest.bioengine.yaml) for more details.`; - return; - } - const url = - window.location.origin + "/plugins/bioengine-test-run.imjoy.html"; - const docs = ` -## Quick model testing with your own data -By clicking the \`Test the model\` button, you can test the model with your own data. - - -\`\`\`js -api.createWindow({ - src: "${url}", - window_id: "test-run-form", - data: { - id: "${resourceItem.id}", - input_window_id: "image_input_window", - output_window_id: "image_output_window" - }} - ) -\`\`\` - - -
-
-
-
-
- `; - resourceItem.testRunDocs = docs; + async getManifestList() { + const manifestUrl = + "https://raw.githubusercontent.com/bioimage-io/bioengine-model-runner/gh-pages/manifest.bioengine.json"; + const list = await fetch(manifestUrl).then(r => r.json()); + const aviableModels = list.collection.map(m => m.id); + this.modelAvailable = aviableModels.includes(this.resourceItem.id); } } }; @@ -460,4 +406,7 @@ api.createWindow({ display: inline-block; margin-right: 5px; } +.not-available { + color: red; +} diff --git a/src/components/TestRun.vue b/src/components/TestRun.vue new file mode 100644 index 00000000..bd617a2f --- /dev/null +++ b/src/components/TestRun.vue @@ -0,0 +1,671 @@ + + + + + diff --git a/src/imgProcess.js b/src/imgProcess.js new file mode 100644 index 00000000..79858d1c --- /dev/null +++ b/src/imgProcess.js @@ -0,0 +1,614 @@ +/** + * Functions for image processing. + * Used in the test run form. + */ + +import "@tensorflow/tfjs-backend-cpu"; +import * as tf from "@tensorflow/tfjs-core"; +import lodash from "lodash"; + +export function inferImgAxes(shape, order = "bcz") { + /** + * Infer the axes of an image. + * + * @param {Array} shape Shape of the image. + * @returns {string} Axes string. + */ + if (shape.length === 2) { + return "yx"; + } else if (shape.length <= 5) { + let minDimIdx = shape.indexOf(Math.min(...shape)); + let lowDimShape = shape.slice(); // Clone the shape array + lowDimShape.splice(minDimIdx, 1); // Remove the smallest dimension + let lowDimAxes = inferImgAxes(lowDimShape, order.slice(1)); + const insert = order[0]; + return insertCharAtPosition(lowDimAxes, insert, minDimIdx); + } else { + throw new Error(`Image shape [${shape.join(", ")}] is not supported.`); + } +} + +export function inferImgAxesViaSpec(shape, specAxes, fromIJ = false) { + let imgAxes; + if (fromIJ) { + if (shape.length === 2) { + imgAxes = "yx"; + } else if (shape.length === 3) { + imgAxes = "yxc"; + } else if (shape.length === 4) { + if (specAxes.includes("z")) { + imgAxes = "zyxc"; + } else { + imgAxes = "cyxb"; + } + } else { + throw new Error(`Image shape [${shape.join(", ")}] is not supported.`); + } + } else { + let order = "bcz"; + if (!specAxes.includes("c")) { + order = "bz"; + } else if (!specAxes.includes("z")) { + order = "bc"; + } else if (!specAxes.includes("b")) { + order = "cz"; + } + imgAxes = inferImgAxes(shape, order); + } + return imgAxes; +} + +export function insertCharAtPosition(originalString, charToInsert, position) { + return ( + originalString.substring(0, position) + + charToInsert + + originalString.substring(position) + ); +} + +function getConstructor(tpstr) { + /** +Int8Array int8 int8 +Int16Array int16 int16 +Int32Array int32 int32 +Uint8Array uint8 uint8 +Uint16Array uint16 uint16 +Uint32Array uint32 uint32 +Float32Array float32 float32 +Float64Array float64 float64 + */ + let Constructor; + if (tpstr == "uint8") { + Constructor = Uint8Array; + } else if (tpstr == "int8") { + Constructor = Int8Array; + } else if (tpstr == "uint16") { + Constructor = Uint16Array; + } else if (tpstr == "int16") { + Constructor = Int16Array; + } else if (tpstr == "uint32") { + Constructor = Uint32Array; + } else if (tpstr == "int32") { + Constructor = Int32Array; + } else if (tpstr == "float32") { + Constructor = Float32Array; + } else if (tpstr == "float64") { + Constructor = Float64Array; + } else if (tpstr == "bool") { + Constructor = Uint8Array; + } else { + throw new Error("Unsupported dtype: " + tpstr); + } + return Constructor; +} + +function reverseEndianness(arrayBuffer, bytesPerElement) { + let uint8Array = new Uint8Array(arrayBuffer); + for (let i = 0; i < uint8Array.length; i += bytesPerElement) { + for (let j = i, k = i + bytesPerElement - 1; j < k; j++, k--) { + [uint8Array[j], uint8Array[k]] = [uint8Array[k], uint8Array[j]]; + } + } + return arrayBuffer; +} + +export function ImjoyToTfJs(arr) { + let buffer = new ArrayBuffer(arr._rvalue.length); + let bufferView = new Uint8Array(buffer); + bufferView.set(arr._rvalue); + const Constructor = getConstructor(arr._rdtype); + let tarr = new Constructor(buffer); + if (arr._rdtype === "bool") { + // convert 1 to 255 + for (let i = 0; i < tarr.length; i++) { + if (tarr[i] === 1) { + tarr[i] = 255; + } + } + } + const tensor = tf.tensor(Array.from(tarr), arr._rshape); + tensor._rdtype = arr._rdtype; + return tensor; +} + +export function toImJoyArr(tensor, reverseEnd = false) { + const data = tensor.dataSync(); + const Constructor = getConstructor(tensor._rdtype); + let casted = new Constructor(data.length); + for (let i = 0; i < data.length; i++) { + casted[i] = data[i]; + } + if (reverseEnd) { + casted = new Constructor( + reverseEndianness(casted.buffer, Constructor.BYTES_PER_ELEMENT) + ); + } + const value = new Uint8Array(casted.buffer); + const ijarr = { + _rtype: "ndarray", + _rdtype: tensor._rdtype, + _rshape: tensor.shape, + _rvalue: value + }; + return ijarr; +} + +export function pick(tensor, idxes) { + const sliceBegin = []; + for (let i = 0; i < tensor.shape.length; i++) { + if (idxes[i] === null) { + sliceBegin.push(0); + } else { + sliceBegin.push(idxes[i]); + } + } + const sliceSize = []; + for (let i = 0; i < tensor.shape.length; i++) { + if (idxes[i] === null) { + sliceSize.push(tensor.shape[i]); + } else { + sliceSize.push(1); + } + } + const subTensor = tf.slice(tensor, sliceBegin, sliceSize); + const newShape = []; + for (let i = 0; i < tensor.shape.length; i++) { + if (idxes[i] === null) { + newShape.push(tensor.shape[i]); + } + } + return tf.reshape(subTensor, newShape); +} + +export function mapAxes(inputArray, inputAxes, outputAxes) { + if (inputAxes.length !== inputArray.shape.length) { + throw new Error( + `Number of axes ${inputAxes.length} and dimension of input ${inputArray.shape.length} don't match` + ); + } + + const pickIdxes = []; + inputAxes.split("").forEach((axName, axIdx) => { + const axLen = inputArray.shape[axIdx]; + if (!outputAxes.includes(axName)) { + let pickIdx = 0; + if ("zyx".includes(axName)) { + pickIdx = Math.floor(axLen / 2); + } + pickIdxes.push(pickIdx); + } else { + pickIdxes.push(null); + } + }); + let axes = inputAxes.split("").filter((name, idx) => pickIdxes[idx] === null); + + let newArray = pick(inputArray, pickIdxes); + + outputAxes.split("").forEach(axName => { + if (!inputAxes.includes(axName)) { + newArray = tf.reshape(newArray, newArray.shape.concat([1])); + axes.push(axName); + } + }); + + const transposeIdxes = []; + for (let i = 0; i < outputAxes.length; i++) { + const axName = outputAxes[i]; + const axIdx = axes.indexOf(axName); + transposeIdxes.push(axIdx); + } + + newArray = tf.transpose(newArray, transposeIdxes); + newArray._rdtype = inputArray._rdtype; + + return newArray; +} + +export const splitBy = (tensor, by, specAxes) => { + const byIdx = specAxes.indexOf(by); + const byLen = tensor.shape[byIdx]; + const splited = []; + for (let i = 0; i < byLen; i++) { + const pickIdx = []; + for (let j = 0; j < tensor.shape.length; j++) { + if (j === byIdx) { + pickIdx.push(i); + } else { + pickIdx.push(null); + } + } + const subArr = pick(tensor, pickIdx); + splited.push(subArr); + } + return splited; +}; + +export function splitForShow(tensor, specAxes) { + if (!specAxes.includes("x") || !specAxes.includes("y")) { + throw new Error("Unsupported axes: " + specAxes); + } + const hasC = specAxes.includes("c"); + const lenC = tensor.shape[specAxes.indexOf("c")]; + const hasZ = specAxes.includes("z"); + const lenZ = tensor.shape[specAxes.indexOf("z")]; + let newImgs = []; + if (specAxes.length === 2) { + newImgs.push(tensor); + } else if (specAxes.length === 3) { + if (hasC) { + if (lenC === 3) { + if (tensor._rdtype === "uint8") { + newImgs.push(mapAxes(tensor, specAxes, "yxc")); + } else { + newImgs.push(mapAxes(tensor, specAxes, "cyx")); + } + } else if (lenC === 1) { + newImgs.push(mapAxes(tensor, specAxes, "yx")); + } else { + newImgs.push(mapAxes(tensor, specAxes, "cyx")); + } + } else if (hasZ) { + newImgs.push(mapAxes(tensor, specAxes, "zyx")); + } else { + // b, y, x + newImgs = splitBy(tensor, "b", specAxes); + } + } else if (specAxes.length === 4) { + if (hasC && hasZ) { + if (lenC == 3) { + newImgs.push(mapAxes(tensor, specAxes, "zyxc")); + } else if (lenC == 1) { + newImgs.push(mapAxes(tensor, specAxes, "zyx")); + } else if (lenZ == 1) { + newImgs.push(mapAxes(tensor, specAxes, "cyx")); + } else { + // split by c + splitBy(tensor, "c", specAxes).map(arrs => { + const subAxes = specAxes.replace("c", ""); + newImgs = newImgs.concat(splitForShow(arrs, subAxes)); + }); + } + } else { + // b,c,y,x or b,z,y,x + // split by b + splitBy(tensor, "b", specAxes).map(arrs => { + const subAxes = specAxes.replace("b", ""); + newImgs = newImgs.concat(splitForShow(arrs, subAxes)); + }); + } + } else if (specAxes.length === 5) { + // b,c,z,y,x + // split by b + splitBy(tensor, "b", specAxes).map(arrs => { + const subAxes = specAxes.replace("b", ""); + newImgs = newImgs.concat(splitForShow(arrs, subAxes)); + }); + } else { + throw new Error("Unsupported axes: " + specAxes); + } + return newImgs; +} + +export function processForShow(tensor, specAxes) { + /** + Process the image for showing. + ImageJ.JS only supports: + [height, width] + [height, width, 1] + [height, width, 3] (will show as RGB image) + [z-stack, height, width] + [z-stack, height, width, 1] + [z-stack, height, width, 3] (will show as a stack of RGB image) + */ + const isImg2Img = specAxes.includes("x") && specAxes.includes("y"); + let splitedArrs; + if (isImg2Img) { + splitedArrs = splitForShow(tensor, specAxes); + } else { + if (specAxes.length > 2 && specAxes.includes("b")) { + splitedArrs = splitBy(tensor, "b", specAxes); + } else { + splitedArrs = [tensor]; + } + } + return splitedArrs.map(arr => { + arr._rdtype = tensor._rdtype; + return toImJoyArr(arr); + }); +} + +function getNpyDtype(buffer) { + const headerLength = new DataView(buffer.slice(8, 10)).getUint8(0); + const hcontents = new TextDecoder("utf-8").decode( + new Uint8Array(buffer.slice(10, 10 + headerLength)) + ); + const header = JSON.parse( + hcontents + .toLowerCase() // True -> true + .replace(/'/g, '"') + .replace("(", "[") + .replace(/,*\),*/g, "]") + ); + return header.descr; +} + +export async function getNpyEndianness(url) { + const resp = await fetch(url, { + headers: { + Range: "bytes=0-999" + } + }); + if (!resp.ok) { + console.error(resp); + return null; + } + const arrayBuffer = await resp.arrayBuffer(); + const npyDtype = getNpyDtype(arrayBuffer); + return npyDtype[0]; +} + +export class ImgPadder { + constructor(inputSpec, outputSpec, padValue = 0) { + this.inputSpec = inputSpec; + this.outputSpec = outputSpec; + this.padValue = padValue; + } + + getPaddedShape(shape) { + const specShape = this.inputSpec.shape; + let paddedShape = []; + if (specShape instanceof Array) { + // Explicit shape + paddedShape = specShape; + } else { + // Implicit shape + // infer from the min and step + const min = specShape.min; + const step = specShape.step; + for (let d = 0; d < shape.length; d++) { + if (step[d] === 0) { + paddedShape.push(shape[d]); + } else { + const pad = Math.max( + 0, + Math.ceil((shape[d] - min[d]) / step[d]) * step[d] + ); + paddedShape.push(pad + min[d]); + } + } + } + return paddedShape; + } + + pad(tensor, position = "center") { + const paddedShape = this.getPaddedShape(tensor.shape); + const pad = []; + for (let d = 0; d < tensor.shape.length; d++) { + if (paddedShape[d] < tensor.shape[d]) { + throw new Error( + `Invalid shape: ${tensor.shape} for ${this.inputSpec.shape}` + ); + } + const diff = paddedShape[d] - tensor.shape[d]; + if (position === "center") { + pad.push([Math.floor(diff / 2), Math.ceil(diff / 2)]); + } else if (position === "begin") { + pad.push([0, diff]); + } else if (position === "end") { + pad.push([diff, 0]); + } else { + throw new Error(`Invalid position: ${position}`); + } + } + const res = tf.pad(tensor, pad, this.padValue); + res._rdtype = tensor._rdtype; + return [res, pad]; + } + + crop(tensor, pad, halo = undefined) { + let res; + const isImg2Img = + this.outputSpec.axes.includes("x") && this.outputSpec.axes.includes("y"); + if (isImg2Img) { + // img-to-img model + if (halo) { + res = tf.slice( + tensor, + pad.map((p, i) => p[0] + halo[i]), + tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1] - halo[i] * 2) + ); + } else { + res = tf.slice( + tensor, + pad.map(p => p[0]), + tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1]) + ); + } + } else { + // other model, e.g. classification + // no crop + res = tensor; + } + res._rdtype = tensor._rdtype; + return res; + } +} + +export class ImgTile { + constructor(starts, ends, indexes) { + this.starts = starts; + this.ends = ends; + this.indexes = indexes; + this.shape = ends.map((e, i) => e - this.starts[i]); + this.data = null; + } + + slice(tensor) { + this.data = tf.slice(tensor, this.starts, this.shape); + this.data._rdtype = tensor._rdtype; + } + + merge(another, axis) { + const newStarts = this.starts.slice(); + const newEnds = this.ends.slice(); + newEnds[axis] = another.ends[axis]; + const overlap = this.ends[axis] - another.starts[axis]; + if (overlap < 0) { + throw new Error("Cannot merge tiles with negative overlap."); + } + let newData; + if (this.data === null || another.data === null) { + newData = null; + } else { + if (overlap === 0) { + newData = tf.concat([this.data, another.data], axis); + } else { + const size1 = this.data.shape.slice(); + size1[axis] -= Math.ceil(overlap / 2); + const starts1 = size1.map(() => 0); + const firstPart = tf.slice(this.data, starts1, size1); + const size2 = another.data.shape.slice(); + size2[axis] -= Math.floor(overlap / 2); + const starts2 = size2.map(() => 0); + starts2[axis] += Math.floor(overlap / 2); + const secondPart = tf.slice(another.data, starts2, size2); + newData = tf.concat([firstPart, secondPart], axis); + } + newData._rdtype = this.data._rdtype; + } + const newTile = new ImgTile(newStarts, newEnds, this.indexes); + newTile.data = newData; + return newTile; + } + + mergeMean(another) { + const newStarts = this.starts.slice(); + const newEnds = this.ends.slice(); + const newData = tf.add(this.data, another.data).div(2); + newData._rdtype = this.data._rdtype; + const newTile = new ImgTile(newStarts, newEnds, this.indexes); + newTile.data = newData; + return newTile; + } +} + +const cartesian = (...a) => + a.reduce((a, b) => a.flatMap(d => b.map(e => [d, e].flat()))); + +export class ImgTiler { + constructor(imgShape, tileShape, overlap = undefined) { + this.imgShape = imgShape; + this.tileShape = tileShape; + if (overlap === undefined) { + overlap = tileShape.map(() => 0); + } + this.overlap = overlap; + } + + getNTiles() { + const overlap = this.overlap; + const tileShape = this.tileShape; + const imgShape = this.imgShape; + const nTiles = tileShape.map((s, i) => { + const n = Math.ceil(imgShape[i] / (s - overlap[i])); + return n; + }); + return nTiles; + } + + getTiles() { + const overlap = this.overlap; + const tileShape = this.tileShape; + const imgShape = this.imgShape; + const nTiles = this.getNTiles(); + const tileIndexes = cartesian( + ...nTiles.map(n => Array.from(Array(n).keys())) + ); + const starts = tileIndexes.map(idx => { + return idx.map((i, j) => { + return i * (tileShape[j] - overlap[j]); + }); + }); + const ends = starts.map(s => { + return s.map((v, i) => { + return Math.min(v + tileShape[i], imgShape[i]); + }); + }); + const tiles = starts.map((s, i) => { + return new ImgTile(s, ends[i], tileIndexes[i]); + }); + return tiles; + } +} + +export class TileMerger { + constructor(imgShape) { + this.imgShape = imgShape; + } + + mergeTiles(tiles) { + for (let d = 0; d < this.imgShape.length; d++) { + const newTiles = []; + const key = t => { + const res = []; + t.indexes.map((idx, j) => { + if (j !== d) { + res.push(idx); + } + }); + return res.join("-"); + }; + const groups = lodash.groupBy(tiles, key); + for (let k in groups) { + const v = groups[k]; + v.sort((a, b) => a.indexes[d] - b.indexes[d]); + if (v.length > 1) { + let merged = v[0]; + for (let i = 1; i < v.length; i++) { + merged = merged.merge(v[i], d); + } + newTiles.push(merged); + } else { + newTiles.push(v[0]); + } + } + tiles = newTiles; + } + const res = tiles[0]; + return res; + } +} + +export class MeanTileMerger extends TileMerger { + constructor(imgShape) { + super(imgShape); + } + + mergeTiles(tiles) { + const merged = tiles[0]; + for (let i = 1; i < tiles.length; i++) { + merged.mergeMean(tiles[i]); + } + return merged; + } +} From cd64674fe09499be1ecbc9e2fbd186d6e1a71737 Mon Sep 17 00:00:00 2001 From: weize Date: Wed, 6 Dec 2023 16:09:05 +0100 Subject: [PATCH 06/12] Fix bug for run affable-shark model (#368) * fix bugs; add progress * npm format --- src/components/ResourceItemInfo.vue | 7 +- src/components/TestRun.vue | 114 ++++++++++++++++++++-------- src/imgProcess.js | 47 +++++------- 3 files changed, 108 insertions(+), 60 deletions(-) diff --git a/src/components/ResourceItemInfo.vue b/src/components/ResourceItemInfo.vue index ecb04c06..681977b3 100644 --- a/src/components/ResourceItemInfo.vue +++ b/src/components/ResourceItemInfo.vue @@ -104,11 +104,14 @@
-
+
This model is not available for testing.
diff --git a/src/components/TestRun.vue b/src/components/TestRun.vue index bd617a2f..25aa8fa0 100644 --- a/src/components/TestRun.vue +++ b/src/components/TestRun.vue @@ -37,20 +37,16 @@

Settings for image tiling

- + - - -
- + - - - { @@ -262,12 +262,11 @@ export default { if (this.rdf) { const axes = this.rdf.inputs[0].axes; // something like "zyx" let maxShape; // something like [16, 64, 64] - const shape = this.rdf.inputs[0].shape; - if (shape instanceof Array) { - maxShape = shape; + if (this.fixedTileSize !== false) { + maxShape = this.fixedTileSize; } else { // array of undefined - maxShape = shape.min.map(() => undefined); + maxShape = this.rdf.inputs[0].shape.min.map(() => undefined); } return axes.split("").reduce((acc, cur, i) => { acc[cur] = maxShape[i]; @@ -278,6 +277,27 @@ export default { } } }, + watch: { + tileSizes: { + handler(oldObj, newObj) { + if (newObj.y !== newObj.x) { + this.tileSizes.y = newObj.x; // keep x and y the same + } + console.log(oldObj, newObj); + }, + deep: true + }, + + tileOverlap: { + handler(oldObj, newObj) { + if (newObj.y !== newObj.x) { + this.tileOverlap.y = newObj.x; // keep x and y the same + } + console.log(oldObj, newObj); + }, + deep: true + } + }, methods: { async turnOn() { this.switch = true; @@ -285,6 +305,7 @@ export default { await this.loadImJoy(); await this.loadTritonClient(); await this.loadRdf(); + await this.loadTritonConfig(); this.setDefaultTileSize(); this.setDefaultOverlap(); await this.detectInputEndianness(); @@ -308,10 +329,15 @@ export default { setDefaultTileSize() { const tileSizes = Object.assign({}, this.inputMinShape); - if (!this.fixedTileSize) { + const axes = this.rdf.inputs[0].axes; + if (this.fixedTileSize === false) { const xyFactor = 4; tileSizes.x = xyFactor * this.inputMinShape.x; tileSizes.y = xyFactor * this.inputMinShape.y; + } else { + axes.split("").map((a, i) => { + tileSizes[a] = this.fixedTileSize[i]; + }); } this.tileSizes = tileSizes; }, @@ -321,7 +347,7 @@ export default { const outputSpec = this.rdf.outputs[0]; const axes = inputSpec.axes; let overlap = {}; - if (outputSpec.halo) { + if (outputSpec.halo && this.fixedTileSize === false) { axes.split("").map((a, i) => { if (outputSpec.axes.includes(a) && a !== "z") { overlap[a] = 2 * outputSpec.halo[i]; @@ -386,12 +412,29 @@ export default { let outImg = await this.submitTensor(paddedTensor); await this.api.log("Output tile shape: " + outImg._rshape); const outTensor = ImjoyToTfJs(outImg); - const cropedTensor = padder.crop(outTensor, padArr); - return cropedTensor; + const isImg2Img = + this.rdf.outputs[0].axes.includes("x") && + this.rdf.outputs[0].axes.includes("y"); + let result = outTensor; + if (isImg2Img) { + const cropedTensor = padder.crop(outTensor, padArr); + result = cropedTensor; + } + return result; }, async runTiles(tensor, inputSpec, outputSpec) { - const padder = new ImgPadder(inputSpec, outputSpec, 0); + let padder; + if (this.fixedTileSize === false) { + padder = new ImgPadder( + undefined, + inputSpec.shape.min, + inputSpec.shape.step, + 0 + ); + } else { + padder = new ImgPadder(this.fixedTileSize, undefined, undefined, 0); + } const tileSize = inputSpec.axes.split("").map(a => this.tileSizes[a]); const overlap = inputSpec.axes.split("").map(a => this.tileOverlap[a]); console.log("tile size:", tileSize, "overlap:", overlap); @@ -402,6 +445,10 @@ export default { await this.api.log("Number of tiles: " + inTiles.length); const outTiles = []; for (let i = 0; i < inTiles.length; i++) { + this.setInfoPanel( + `Running the model... (${i + 1}/${inTiles.length})`, + true + ); const tile = inTiles[i]; console.log(tile); tile.slice(tensor); @@ -514,6 +561,13 @@ export default { this.triton = await server.get_service("triton-client"); }, + async loadTritonConfig() { + const nickname = this.resourceItem.nickname; + const url = `https://ai.imjoy.io/triton/v2/models/${nickname}/config`; + const config = await fetch(url).then(res => res.json()); + this.tritonConfig = config; + }, + async loadImJoy() { function waitForImjoy(timeout = 10000) { return new Promise((resolve, reject) => { diff --git a/src/imgProcess.js b/src/imgProcess.js index 79858d1c..619c9c9c 100644 --- a/src/imgProcess.js +++ b/src/imgProcess.js @@ -368,23 +368,23 @@ export async function getNpyEndianness(url) { } export class ImgPadder { - constructor(inputSpec, outputSpec, padValue = 0) { - this.inputSpec = inputSpec; - this.outputSpec = outputSpec; + constructor(fixedPaddedShape, padMin, padStep, padValue = 0) { + this.fixedPaddedShape = fixedPaddedShape; + this.padMin = padMin; + this.padStep = padStep; this.padValue = padValue; } getPaddedShape(shape) { - const specShape = this.inputSpec.shape; let paddedShape = []; - if (specShape instanceof Array) { + if (this.fixedPaddedShape) { // Explicit shape - paddedShape = specShape; + paddedShape = this.fixedPaddedShape; } else { // Implicit shape // infer from the min and step - const min = specShape.min; - const step = specShape.step; + const min = this.padMin; + const step = this.padStep; for (let d = 0; d < shape.length; d++) { if (step[d] === 0) { paddedShape.push(shape[d]); @@ -427,27 +427,18 @@ export class ImgPadder { crop(tensor, pad, halo = undefined) { let res; - const isImg2Img = - this.outputSpec.axes.includes("x") && this.outputSpec.axes.includes("y"); - if (isImg2Img) { - // img-to-img model - if (halo) { - res = tf.slice( - tensor, - pad.map((p, i) => p[0] + halo[i]), - tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1] - halo[i] * 2) - ); - } else { - res = tf.slice( - tensor, - pad.map(p => p[0]), - tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1]) - ); - } + if (halo) { + res = tf.slice( + tensor, + pad.map((p, i) => p[0] + halo[i]), + tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1] - halo[i] * 2) + ); } else { - // other model, e.g. classification - // no crop - res = tensor; + res = tf.slice( + tensor, + pad.map(p => p[0]), + tensor.shape.map((s, i) => s - pad[i][0] - pad[i][1]) + ); } res._rdtype = tensor._rdtype; return res; From db6b52bf8478c8d3f5338b410a3dca50e8888534 Mon Sep 17 00:00:00 2001 From: Wei Ouyang Date: Thu, 7 Dec 2023 14:39:36 +0100 Subject: [PATCH 07/12] Update TestRun.vue --- src/components/TestRun.vue | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/components/TestRun.vue b/src/components/TestRun.vue index 25aa8fa0..1bfe6608 100644 --- a/src/components/TestRun.vue +++ b/src/components/TestRun.vue @@ -554,7 +554,7 @@ export default { async loadTritonClient() { const server = await hyphaWebsocketClient.connectToServer({ - server_url: "https://hypha.bioimage.io", + server_url: "https://ai.imjoy.io", method_timeout: 30, name: "client" }); From 291634f5f29be6428b55824fbc6fdeb2898e9f02 Mon Sep 17 00:00:00 2001 From: Wei Ouyang Date: Thu, 7 Dec 2023 16:43:14 +0100 Subject: [PATCH 08/12] Update EU project acknowledgement --- site.config.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/site.config.json b/site.config.json index 8f57d4c4..7b2ffe9f 100644 --- a/site.config.json +++ b/site.config.json @@ -42,7 +42,7 @@ "url": "https://oeway.typeform.com/to/K3j2tJt7" }, { "label": "AI4Life", - "tooltip": "BioImage.IO is funeded by the European Union through the AI4Life project", + "tooltip": "We receive funding through the AI4Life project with support from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970", "logo": "/static/img/AI4Life-logo-giraffe.png", "url": "https://ai4life.eurobioimaging.eu/" }, @@ -112,7 +112,7 @@ "detailed_image": null, "detailed_body": "description" }, - "footnote": "BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. We receive funding support from the European Union through the AI4Life project (https://ai4life.eurobioimaging.eu/)", + "footnote": "BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. We receive funding support from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970 (https://ai4life.eurobioimaging.eu/)", "partners": [], "excluded_partners": ["bioimageio"], "excluded_tags": ["bioimage.io", "bioimage.io:model", "bioimage.io:application", "bioimage.io:workflow", "bioimage.io:notebook", "bioimage.io:dataset", "zenodo"], From 38fe95584e3cdb75e49e4730a35c9b0b64d02a99 Mon Sep 17 00:00:00 2001 From: weize Date: Wed, 13 Dec 2023 15:57:33 +0100 Subject: [PATCH 09/12] Add imjoy icon to bioengine runnable models (#369) * fix bugs; add progress * npm format * add imjoy icon to bioengine runnable model --- src/components/TestRun.vue | 2 +- src/views/Home.vue | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/src/components/TestRun.vue b/src/components/TestRun.vue index 1bfe6608..06d851a3 100644 --- a/src/components/TestRun.vue +++ b/src/components/TestRun.vue @@ -76,7 +76,7 @@
- Advance options + Advanced Options
diff --git a/src/views/Home.vue b/src/views/Home.vue index 073f57c5..5743a5b2 100644 --- a/src/views/Home.vue +++ b/src/views/Home.vue @@ -511,6 +511,9 @@ function normalizeItem(self, item, bioEngineConfigs) { } item.links = item.links || []; + if (item.id in bioEngineConfigs) { + item.links.push("imjoy/imjoy"); + } if (item.training_data) { item.links.push(item.training_data.id); } From 33421139a17f18196304018eac52cba8a98e32c1 Mon Sep 17 00:00:00 2001 From: esgomezm <43643518+esgomezm@users.noreply.github.com> Date: Thu, 14 Dec 2023 17:40:57 +0000 Subject: [PATCH 10/12] zenodo upload doc (#370) * Add files via upload * Update contribute_zenodo.md * Delete docs/contribute_models/zenodo_upload_02.png --- docs/contribute_models/contribute_zenodo.md | 28 +++++++++----------- docs/contribute_models/zenodo_upload_02.png | Bin 104087 -> 0 bytes docs/contribute_models/zenodo_upload_03.png | Bin 92113 -> 20364 bytes docs/contribute_models/zenodo_upload_04.png | Bin 169235 -> 162035 bytes 4 files changed, 12 insertions(+), 16 deletions(-) delete mode 100644 docs/contribute_models/zenodo_upload_02.png diff --git a/docs/contribute_models/contribute_zenodo.md b/docs/contribute_models/contribute_zenodo.md index 486df82f..ec498854 100644 --- a/docs/contribute_models/contribute_zenodo.md +++ b/docs/contribute_models/contribute_zenodo.md @@ -7,29 +7,25 @@ This tutorial will guide you through the process of uploading a model to the Bio ## Uploading a model -1. Open your web browser and navigate to the Zenodo website at [https://zenodo.org/](https://zenodo.org). +1. Open your web browser and navigate to the Zenodo website at [https://zenodo.org/](https://zenodo.org). You need to create a Zenodo account if you do not have one. Zenodo initial page - -2. In the Zenodo search bar, enter "bioimage.io" to find the BioImage.IO community. - - Find BioImage.IO community - -3. Click on the "BioImage.IO" community to access it. You will see the "New upload" button on this page. - - BioImage.IO community - -4. Click the "New upload" button to begin the model upload process within the BioImage.IO community. + +2. On the right, close to your username, click the "New upload" button to begin the model upload process. Make sure that the repository is set as public. +The files in the BioImage.io zip have to be uploaded one-by-one (See the example in the image below). Note that you can drag & drop all together at once. New upload -5. Follow the on-screen instructions to provide the required information about your model. Make sure to include a clear description, relevant tags, and any necessary documentation. See [this documentation](contribute_models/README.md) for more details on the required files and information. - -6. When prompted, upload your model files to Zenodo. +3. Add `bioimage.io` on Keywords and subjects. This is crucial for us to identify your submission. + + Find BioImage.IO community + +4. Follow the on-screen instructions to provide the required information about your model. Make sure to include a clear description, relevant tags, and any necessary documentation. See [this documentation](contribute_models/README.md) for more details on the required files and information. -7. Review the information you provided and confirm the upload. +5. Once finished, click on Submit. + +6. Your model will be proposed as a new contribution to the BioImage Model Zoo automatically. If the model passes all the tests, it will be automatically displayed in the Zoo. If the model does not pass the test, the GitHub users indicated in `maintainers` in the `rdf.yaml` file will be noitified through GitHub. This process can take 12-24h. -8. Your model will be published in the BioImage Model Zoo community on Zenodo. ## Conclusion You've successfully uploaded your model to the BioImage Model Zoo community on Zenodo. Thank you for your contribution to the BioImage Model Zoo project. Remember that this is a temporary solution while the upload feature on the BioImage.io website is being fixed. 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+15,7 @@ - [Supported weight formats](bioimageio_weights_spec) - [Supported preprocessing](bioimageio_preprocessing_spec) - [Supported postprocessing](bioimageio_postprocessing_spec) + - [Upload models through Zenodo](/contribute_models/contribute_zenodo.md) * [Other contributions](/contribute_others/README.md) * [Resources for developers](/resources_developers/README.md) - [Python BioImageIO core package](pythoncorepackage) From fe465e0cd1806c38a7b2230f4efa547ad71a98c7 Mon Sep 17 00:00:00 2001 From: Wei Ouyang Date: Thu, 29 Feb 2024 11:05:21 +0100 Subject: [PATCH 12/12] Integrate chatbot into the app menu (#374) * Integrate chatbot into the app menu * Restore order --- site.config.json | 2 +- src/App.vue | 2 +- src/bioEngine.js | 8 ++++++++ 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/site.config.json b/site.config.json index 7b2ffe9f..ffd34082 100644 --- a/site.config.json +++ b/site.config.json @@ -13,7 +13,7 @@ "og_image": "https://bioimage.io/static/img/bioimage-io-banner.png", "og_description": "Advanced AI models in one-click", "background_image": "static/img/zoo-background.svg", - "manifest_url": "https://bioimage-io.github.io/collection-bioimage-io/rdf.json", + "manifest_url": "https://bioimage-io.github.io/collection-bioimage-io/collection.json", "explore_button_text": "🚀 Explore the Zoo", "subscribe_url": "https://docs.google.com/forms/d/e/1FAIpQLSfQhTjXOuTXZNtalprbsXMPd4ct2ydiMhlPc2lhcs6WY4yo0w/viewform?embedded=true", "contact_form_url": "https://oeway.typeform.com/to/K3j2tJt7", diff --git a/src/App.vue b/src/App.vue index 4878de1a..888e91cf 100644 --- a/src/App.vue +++ b/src/App.vue @@ -73,7 +73,7 @@ export default { computed: { ...mapState({ showNavbar: state => state.showNavbar, - siteConfig: state => state.siteConfig + siteConfig: state => state.siteConfig, }) }, mounted() { diff --git a/src/bioEngine.js b/src/bioEngine.js index 71b6f10b..7d951cf0 100644 --- a/src/bioEngine.js +++ b/src/bioEngine.js @@ -27,6 +27,7 @@ const imjoyReady = new Promise((resolve, reject) => { resolveImJoy = resolve; rejectImJoy = reject; }); + export async function setupBioEngine() { const queryString = window.location.href.split("#")[1].split("?")[1]; const urlParams = new URLSearchParams(queryString); @@ -191,6 +192,13 @@ export async function setupBioEngine() { api.createWindow({ src: "https://jupyter.imjoy.io" }); } }); + app.addMenuItem({ + label: "🤖 BioImage.IO Chatbot", + async callback() { + await api.createWindow({"src": "https://staging.chat.bioimage.io/public/apps/bioimageio-chatbot-client/chat", name: "BioImage.IO Chatbot"}); + await app.loadPlugin("https://raw.githubusercontent.com/bioimage-io/bioimageio-chatbot/main/bioimageio_chatbot/static/bioimage-model-zoo-extension.imjoy.html"); + } + }); // expose global variables window.api = api; window.imjoy = app.imjoy;