Clean up shared worker addition

Signed-off-by: Justin Georgi <justin.georgi@gmail.com>

cordova/config.xml

@@ -1,5 +1,5 @@
 <?xml version='1.0' encoding='utf-8'?>
-<widget id="edu.midwestern.alvinn" version="0.5.0-alpha" xmlns="http://www.w3.org/ns/widgets" xmlns:cdv="http://cordova.apache.org/ns/1.0" xmlns:android="http://schemas.android.com/apk/res/android">
+<widget id="edu.midwestern.alvinn" version="0.5.0-rc" xmlns="http://www.w3.org/ns/widgets" xmlns:cdv="http://cordova.apache.org/ns/1.0" xmlns:android="http://schemas.android.com/apk/res/android">
     <name>ALVINN</name>
     <description>Anatomy Lab Visual Identification Neural Network.</description>
     <author email="jgeorg@midwestern.edu" href="https://midwestern.edu">

cordova/package.json

@@ -1,7 +1,7 @@
 {
   "name": "edu.midwestern.alvinn",
   "displayName": "ALVINN",
-  "version": "0.5.0-alpha",
+  "version": "0.5.0-rc",
   "description": "Anatomy Lab Visual Identification Neural Network.",
   "main": "index.js",
   "scripts": {

package.json

@@ -1,7 +1,7 @@
 {
   "name": "alvinn",
   "private": true,
-  "version": "0.5.0-alpha",
+  "version": "0.5.0-rc",
   "description": "ALVINN",
   "repository": "",
   "license": "UNLICENSED",
@@ -14,8 +14,7 @@
     "cordova-ios": "cross-env TARGET=cordova cross-env NODE_ENV=production vite build && node ./build/build-cordova.js && cd cordova && cordova run ios",
     "build-cordova-android": "cross-env TARGET=cordova cross-env NODE_ENV=production vite build && node ./build/build-cordova.js && cd cordova && cordova build android",
     "cordova-android": "cross-env TARGET=cordova cross-env NODE_ENV=production vite build && node ./build/build-cordova.js && cd cordova && cordova run android",
-    "postinstall": "cpy --flat ./node_modules/framework7-icons/fonts/*.* ./src/fonts/",
+    "postinstall": "cpy --flat ./node_modules/framework7-icons/fonts/*.* ./src/fonts/"
-    "preview": "vite preview"
   },
   "browserslist": [
     "IOS >= 15",

src/assets/detect-worker.js

@@ -1,39 +1,45 @@
 import * as tf  from '@tensorflow/tfjs'
+import { f7 } from 'framework7-vue'
 
 let model = null
 
-onmessage = function (e) {
+self.onconnect = (e) => {
+  const port = e.ports[0];
+
+  port.onmessage = function (e) {
     switch (e.data.call) {
       case 'loadModel':
-      loadModel(e.data.weights,e.data.preload).then(() => {
+        loadModel('.' + e.data.weights,e.data.preload).then(() => {
-        postMessage({success: 'model'})
+          port.postMessage({success: 'model'})
         }).catch((err) => {
-        postMessage({error: true, message: err.message})
+          port.postMessage({error: true, message: err.message})
         })
         break
       case 'localDetect':
         localDetect(e.data.image).then((dets) => {
-        postMessage({success: 'detection', detections: dets})
+          port.postMessage({success: 'detection', detections: dets})
         }).catch((err) => {
-        //throw (err)
+          port.postMessage({error: true, message: err.message})
-        postMessage({error: true, message: err.message})
         })
         e.data.image.close()
         break
       case 'videoFrame':
         videoFrame(e.data.image).then((frameDet) =>{
-        postMessage({succes: 'frame', coords: frameDet.cds, modelWidth: frameDet.mW, modelHeight: frameDet.mH})
+          port.postMessage({succes: 'frame', coords: frameDet.cds, modelWidth: frameDet.mW, modelHeight: frameDet.mH})
         }).catch((err) => {
-        postMessage({error: true, message: err.message})
+          port.postMessage({error: true, message: err.message})
         })
         e.data.image.close()
         break
       default:
         console.log('Worker message incoming:')
         console.log(e)
-      postMessage({result1: 'First result', result2: 'Second result'})
+        port.postMessage({result1: 'First result', result2: 'Second result'})
         break
     }
+  }
+
+  port.start()
 }
 
 async function loadModel(weights, preload) {
@@ -57,7 +63,7 @@ async function loadModel(weights, preload) {
 }
 
 async function localDetect(imageData) {
-  console.time('sw: pre-process')
+  console.time('pre-process')
   const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
   let gTense = null
   const input = tf.tidy(() => {
@@ -65,15 +71,15 @@ async function localDetect(imageData) {
     return tf.concat([gTense,gTense,gTense],3)
   })
   tf.dispose(gTense)
-  console.timeEnd('sw: pre-process')
+  console.timeEnd('pre-process')
 
-  console.time('sw: run prediction')
+  console.time('run prediction')
   const res = model.predict(input)
   const tRes = tf.transpose(res,[0,2,1])
   const rawRes = tRes.arraySync()[0]
-  console.timeEnd('sw: run prediction')
+  console.timeEnd('run prediction')
 
-  console.time('sw: post-process')
+  console.time('post-process')
   const outputSize = res.shape[1]
   let rawBoxes = []
   let rawScores = []
@@ -138,14 +144,14 @@ async function localDetect(imageData) {
   }
   tf.dispose(res)
   tf.dispose(input)
-  console.timeEnd('sw: post-process')
+  console.timeEnd('post-process')
 
   return output || { detections: [] }
 }
 
 async function videoFrame (vidData) {
   const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
-  console.time('sw: frame-process')
+  console.time('frame-process')
   let rawCoords = []
   try {
     const input = tf.tidy(() => {
@@ -171,6 +177,6 @@ async function videoFrame (vidData) {
   } catch (e) {
     console.log(e)
   }
-  console.timeEnd('sw: frame-process')
+  console.timeEnd('frame-process')
   return {cds: rawCoords, mW: modelWidth, mH: modelHeight}
 }

src/components/app.vue

@@ -79,7 +79,6 @@
           .then((mod) => { return mod.text() })
         this.siteConf = YAML.parse(confText)
       }
-      if (window.safari !== undefined) {store().safariDetected()}
       const loadSiteSettings = localStorage.getItem('siteSettings')
       if (loadSiteSettings) {
         let loadedSettings = JSON.parse(loadSiteSettings)

src/js/store.js

@@ -4,13 +4,12 @@ const state = reactive({
     disclaimerAgreement: false,
     enabledRegions: ['thorax','abdomen','limbs','head'],
     regionIconSet: Math.floor(Math.random() * 3) + 1,
-    version: '0.5.0-alpha',
+    version: '0.5.0-rc',
     fullscreen: false,
     useExternal: 'optional',
     siteDemo: false,
     externalServerList: [],
-    infoUrl: false,
+    infoUrl: false
-    safariBrowser: false
   })
 
 const set = (config, confObj) => {
@@ -22,10 +21,6 @@ const agree = () => {
   state.disclaimerAgreement = true
 }
 
-const safariDetected = () => {
-  state.safariBrowser = true
-}
-
 const getServerList = () => {
   if (state.useExternal == 'required') {
     return state.externalServerList[0]
@@ -55,10 +50,8 @@ export default () => ({
   getVersion: computed(() => state.version),
   getIconSet: computed(() => state.regionIconSet),
   getInfoUrl: computed(() => state.infoUrl),
-  isSafari: computed(() => state.safariBrowser),
   set,
   agree,
-  safariDetected,
   getServerList,
   toggleFullscreen
 })

src/pages/camera-mixin.js

@@ -41,20 +41,16 @@ export default {
       tempCtx.drawImage(vidViewer, 0, 0)
       this.getImage(tempCVS.toDataURL())
     },
-    async videoFrameDetectWorker (vidData) {
+    async videoFrameDetect (vidData) {
-      const startDetection = () => {
+      const vidWorker = new SharedWorker('../assets/detect-worker.js',{type: 'module'})
-        createImageBitmap(vidData).then(imVideoFrame => {
+      vidWorker.port.onmessage = (eVid) => {
-          this.vidWorker.postMessage({call: 'videoFrame', image: imVideoFrame}, [imVideoFrame])
+        self = this
-        })
-      }
-      vidData.addEventListener('resize',startDetection,{once: true})
-      this.vidWorker.onmessage = (eVid) => {
         if (eVid.data.error) {
           console.log(eVid.data.message)
           f7.dialog.alert(`ALVINN AI model error: ${eVid.data.message}`)
         } else if (this.videoAvailable) {
           createImageBitmap(vidData).then(imVideoFrame => {
-            this.vidWorker.postMessage({call: 'videoFrame', image: imVideoFrame}, [imVideoFrame])
+            vidWorker.port.postMessage({call: 'videoFrame', image: imVideoFrame}, [imVideoFrame])
           })
           if (eVid.data.coords) {
             imageCtx.clearRect(0,0,imCanvas.width,imCanvas.height)
@@ -69,6 +65,7 @@ export default {
         }
       }
 
+      vidWorker.port.postMessage({call: 'loadModel', weights: this.miniLocation, preload: true})
       const imCanvas = this.$refs.image_cvs
       const imageCtx = imCanvas.getContext("2d")
       const target = this.$refs.target_image

src/pages/detect.vue

@@ -10,7 +10,7 @@
     </f7-navbar>
     <f7-block class="detect-grid">
       <div class="image-container" ref="image_container">
-        <SvgIcon v-if="!imageView.src && !videoAvailable" :icon="f7route.params.region" fill-color="var(--avn-theme-color)"/>
+        <SvgIcon v-if="!imageView.src && !videoAvailable" :icon="f7route.params.region" fill-color="var(--avn-theme-color)" @click="selectImage" />
         <div class="vid-container" :style="`display: ${videoAvailable ? 'block' : 'none'}; position: absolute; width: 100%; height: 100%;`">
           <video id="vid-view" ref="vid_viewer" :srcObject="cameraStream" :autoPlay="true" style="width: 100%; height: 100%"></video>
           <f7-button @click="captureVidFrame()" style="position: absolute; bottom: 32px; left: 50%; transform: translateX(-50%); z-index: 3;" fill large>Capture</f7-button>
@@ -140,8 +140,6 @@
   import detectionMixin from './detection-mixin'
   import cameraMixin from './camera-mixin'
 
-  import detectionWorker from '@/assets/detect-worker.js?worker&inline'
-
   export default {
     mixins: [submitMixin, detectionMixin, cameraMixin],
     props: {
@@ -180,8 +178,7 @@
         videoAvailable: false,
         cameraStream: null,
         infoLinkPos: {},
-        detectWorker: null,
+        workerScript: null
-        vidWorker: null
       }
     },
     setup() {
@@ -190,6 +187,7 @@
     created () {
       let loadOtherSettings = localStorage.getItem('otherSettings')
       if (loadOtherSettings) this.otherSettings = JSON.parse(loadOtherSettings)
+      let modelRoot = this.isCordova ? 'https://localhost' : '.'
       this.detectorName = this.f7route.params.region
       switch (this.detectorName) {
         case 'thorax':
@@ -205,9 +203,9 @@
           this.activeRegion = 3
           break;
       }
-      this.modelLocation = URL.parse(`../models/${this.detectorName}${this.otherSettings.mini ? '-mini' : ''}/model.json`,import.meta.url).href
+      this.modelLocation = `${modelRoot}/models/${this.detectorName}${this.otherSettings.mini ? '-mini' : ''}/model.json`
-      this.miniLocation = URL.parse(`../models/${this.detectorName}-mini/model.json`,import.meta.url).href
+      this.miniLocation = `${modelRoot}/models/${this.detectorName}-mini/model.json`
-      fetch(URL.parse(`../models/${this.detectorName}/classes.json`,import.meta.url).href)
+      fetch(`${modelRoot}/models/${this.detectorName}/classes.json`)
         .then((mod) => { return mod.json() })
         .then((classes) => {
           this.classesList = classes
@@ -217,8 +215,8 @@
       if (loadServerSettings) this.serverSettings = JSON.parse(loadServerSettings)
     },
     mounted () {
-      this.detectWorker = new detectionWorker()
+      const mountWorker = new SharedWorker('../assets/detect-worker.js',{type: 'module'})
-      this.detectWorker.onmessage = (eMount) => {
+      mountWorker.port.onmessage = (eMount) => {
         self = this
         if (eMount.data.error) {
           console.log(eMount.data.message)
@@ -226,32 +224,13 @@
         }
         self.modelLoading = false
       }
-      this.vidWorker = new detectionWorker()
-      this.vidWorker.onmessage = (eMount) => {
-        self = this
-        if (eMount.data.error) {
-          console.log(eMount.data.message)
-          f7.dialog.alert(`ALVINN AI nano model error: ${eMount.data.message}`)
-        }
-      }
 
       if (this.serverSettings && this.serverSettings.use) {
         this.getRemoteLabels()
         this.modelLoading = false
       } else {
         this.modelLoading = true
-        if (this.isSafari) {
+        mountWorker.port.postMessage({call: 'loadModel', weights: this.modelLocation, preload: true})
-          this.loadModel(this.modelLocation, true).then(() => {
-            this.modelLoading = false
-          }).catch((e) => {
-            console.log(e.message)
-            f7.dialog.alert(`ALVINN AI model error: ${e.message}`)
-            this.modelLoading = false
-          })
-        } else {
-          this.detectWorker.postMessage({call: 'loadModel', weights: this.modelLocation, preload: true})
-          this.vidWorker.postMessage({call: 'loadModel', weights: this.miniLocation, preload: true})
-        }
       }
       window.onresize = (e) => { if (this.$refs.image_cvs) this.selectChip('redraw') }
     },
@@ -304,7 +283,7 @@
       infoLinkTarget () {
         if (!this.getInfoUrl) return ''
         let structure = this.showResults.find( r => r.resultIndex == this.selectedChip)
-        return structure ? this.getInfoUrl + structure.label.replaceAll(' ','_') : ''
+        return this.getInfoUrl + structure.label.replaceAll(' ','_')
       }
     },
     methods: {
@@ -313,12 +292,12 @@
         return `--chip-media-gradient: conic-gradient(from ${270 - (confFactor * 360 / 2)}deg, hsl(${confFactor * 120}deg, 100%, 50%) ${confFactor}turn, hsl(${confFactor * 120}deg, 50%, 66%) ${confFactor}turn)`
       },
       async setData () {
-        this.detectWorker.onmessage = (eDetect) => {
+        const detectWorker = new SharedWorker('../assets/detect-worker.js',{type: 'module'})
+        detectWorker.port.onmessage = (eDetect) => {
           self = this
           if (eDetect.data.error) {
             self.detecting = false
             self.resultData = {}
-            loadFailure()
             f7.dialog.alert(`ALVINN structure finding error: ${eDetect.data.message}`)
           } else if (eDetect.data.success == 'detection') {
             self.detecting = false
@@ -328,45 +307,28 @@
             }
             self.uploadDirty = true
           } else if (eDetect.data.success == 'model') {
-            self.reloadModel = false
+            this.reloadModel = false
-            loadSuccess()
+            loadSuccess(true)
           }
         }
 
         let loadSuccess = null
         let loadFailure = null
-        let modelReloading = null
+        let modelReloading = new Promise((res, rej) => {
-        if (this.isSafari && this.reloadModel) {
-          await this.loadModel(this.modelLocation)
-          this.reloadModel = false          
-        } else {
-          modelReloading = new Promise((res, rej) => {
           loadSuccess = res
           loadFailure = rej
           if (this.reloadModel) {
-              this.detectWorker.postMessage({call: 'loadModel', weights: this.modelLocation})
+            detectWorker.port.postMessage({call: 'loadModel', weights: this.modelLocation})
           } else {
-              loadSuccess()
+            loadSuccess(true)
           }
         })
-        }
 
         if (this.serverSettings && this.serverSettings.use) {
           this.remoteDetect()
-        } else if (!this.isSafari) {
-          Promise.all([modelReloading,createImageBitmap(this.imageView)]).then(res => {
-            this.detectWorker.postMessage({call: 'localDetect', image: res[1]}, [res[1]])
-          })
         } else {
-          this.localDetect(this.imageView).then(dets => {
+          Promise.all([modelReloading,createImageBitmap(this.imageView)]).then(res => {
-            this.detecting = false
+            detectWorker.port.postMessage({call: 'localDetect', image: res[1]}, [res[1]])
-            this.resultData = dets
-            this.uploadDirty = true
-          }).catch((e) => {
-            console.log(e.message)
-            this.detecting = false
-            this.resultData = {}
-            f7.dialog.alert(`ALVINN structure finding error: ${e.message}`)
           })
         }
       },
@@ -383,10 +345,9 @@
           navigator.camera.getPicture(this.getImage, this.onFail, { quality: 50, destinationType: Camera.DestinationType.DATA_URL, correctOrientation: true });
           return
         }
-        if (mode == "camera" && !this.otherSettings.disableVideo) {
+        if (mode == "camera") {
           this.videoAvailable = await this.openCamera(this.$refs.image_container)
           if (this.videoAvailable) {
-            this.selectedChip = -1
             this.imageLoaded = false
             this.imageView.src = null
             this.$refs.image_cvs.style['background-image'] = 'none'
@@ -395,10 +356,8 @@
             var vidElement = this.$refs.vid_viewer
             vidElement.width = trackDetails.width
             vidElement.height = trackDetails.height
-            if (this.isSafari) {
+            if (!this.otherSettings.disableVideo) {
               this.videoFrameDetect(vidElement)
-            } else {
-              this.videoFrameDetectWorker(vidElement)
             }
             return
           }
@@ -512,9 +471,13 @@
         }).then( () => {
           const [imCanvas, _]  = this.resetView()
           imCanvas.style['background-image'] = `url(${this.imageView.src})`
-          f7.utils.nextFrame(() => {
+          /******
+           * setTimeout is not a good solution, but it's the only way
+           * I can find to not cut off drawing of the canvas background
+           ******/
+//          setTimeout(() => {
             this.setData()
-          })
+//          }, 1)
         }).catch((e) => {
           console.log(e.message)
           f7.dialog.alert(`Error loading image: ${e.message}`)

src/pages/detection-mixin.js

@@ -1,114 +1,7 @@
-import * as tf  from '@tensorflow/tfjs'
 import { f7 } from 'framework7-vue'
 
-let model = null
-
 export default {
   methods: {
-    async loadModel(weights, preload) {
-      if (model && model.modelURL == weights) {
-        return model
-      } else if (model) {
-        tf.dispose(model)
-      }
-      model = await tf.loadGraphModel(weights)
-      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
-      /*****************
-       * If preloading then run model
-       * once on fake data to preload
-       * weights for a faster response
-       *****************/
-      if (preload) {
-        const dummyT = tf.ones([1,modelWidth,modelHeight,3])
-        model.predict(dummyT)
-      }
-      return model
-    },
-    async localDetect(imageData) {
-      console.time('mx: pre-process')
-      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
-      let gTense = null
-      const input = tf.tidy(() => {
-        gTense = tf.image.rgbToGrayscale(tf.image.resizeBilinear(tf.browser.fromPixels(imageData), [modelWidth, modelHeight])).div(255.0).expandDims(0)
-        return tf.concat([gTense,gTense,gTense],3)
-      })
-      tf.dispose(gTense)
-      console.timeEnd('mx: pre-process')
-
-      console.time('mx: run prediction')
-      const res = model.predict(input)
-      const tRes = tf.transpose(res,[0,2,1])
-      const rawRes = tRes.arraySync()[0]
-      console.timeEnd('mx: run prediction')
-
-      console.time('mx: post-process')
-      const outputSize = res.shape[1]
-      let rawBoxes = []
-      let rawScores = []
-
-      for (var i = 0; i < rawRes.length; i++) {
-        var getScores = rawRes[i].slice(4)
-        if (getScores.every( s => s < .05)) { continue }
-        var getBox = rawRes[i].slice(0,4)
-        var boxCalc = [
-          (getBox[0] - (getBox[2] / 2)) / modelWidth,
-          (getBox[1] - (getBox[3] / 2)) / modelHeight,
-          (getBox[0] + (getBox[2] / 2)) / modelWidth,
-          (getBox[1] + (getBox[3] / 2)) / modelHeight,
-        ]
-        rawBoxes.push(boxCalc)
-        rawScores.push(getScores)
-      }
-
-      if (rawBoxes.length > 0) {
-        const tBoxes = tf.tensor2d(rawBoxes)
-        let tScores = null
-        let resBoxes = null
-        let validBoxes = []
-        let structureScores = null
-        let boxes_data = []
-        let scores_data = []
-        let classes_data = []
-        for (var c = 0; c < outputSize - 4; c++) {
-          structureScores = rawScores.map(x => x[c])
-          tScores = tf.tensor1d(structureScores)
-          resBoxes = await tf.image.nonMaxSuppressionAsync(tBoxes,tScores,10,0.5,.05)
-          validBoxes = resBoxes.dataSync()
-          tf.dispose(resBoxes)
-          if (validBoxes) {
-            boxes_data.push(...rawBoxes.filter( (_, idx) => validBoxes.includes(idx)))
-            var outputScores = structureScores.filter( (_, idx) => validBoxes.includes(idx))
-            scores_data.push(...outputScores)
-            classes_data.push(...outputScores.fill(c))
-          }
-        }
-
-        validBoxes = []
-        tf.dispose(tBoxes)
-        tf.dispose(tScores)
-        tf.dispose(tRes)
-        const valid_detections_data = classes_data.length
-        var output = {
-          detections: []
-        }
-        for (var i =0; i < valid_detections_data; i++) {
-          var [dLeft, dTop, dRight, dBottom] = boxes_data[i]
-          output.detections.push({
-            "top": dTop,
-            "left": dLeft,
-            "bottom": dBottom,
-            "right": dRight,
-            "label": this.detectorLabels[classes_data[i]].name,
-            "confidence": scores_data[i] * 100
-          })
-        }
-      }
-      tf.dispose(res)
-      tf.dispose(input)
-      console.timeEnd('mx: post-process')
-
-      return output || { detections: [] }
-    },
     getRemoteLabels() {
       var self = this
       var modelURL = `http://${this.serverSettings.address}:${this.serverSettings.port}/detectors`
@@ -172,65 +65,5 @@ export default {
       this.detecting = false
       f7.dialog.alert('No connection to remote ALVINN instance.  Please check app settings.')
     },
-    async videoFrameDetect (vidData) {
-      await this.loadModel(this.miniLocation)
-      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
-      const imCanvas = this.$refs.image_cvs
-      const imageCtx = imCanvas.getContext("2d")
-      const target = this.$refs.target_image
-      await tf.nextFrame();
-      imCanvas.width = imCanvas.clientWidth
-      imCanvas.height = imCanvas.clientHeight
-      imageCtx.clearRect(0,0,imCanvas.width,imCanvas.height)
-      var imgWidth
-      var imgHeight
-      const imgAspect = vidData.width / vidData.height
-      const rendAspect = imCanvas.width / imCanvas.height
-      if (imgAspect >= rendAspect) {
-        imgWidth = imCanvas.width
-        imgHeight = imCanvas.width / imgAspect
-      } else {
-        imgWidth = imCanvas.height * imgAspect
-        imgHeight = imCanvas.height
-      }
-      while (this.videoAvailable) {
-        console.time('mx: frame-process')
-        try {
-          const input = tf.tidy(() => {
-            return tf.image.resizeBilinear(tf.browser.fromPixels(vidData), [modelWidth, modelHeight]).div(255.0).expandDims(0)
-          })
-          const res = model.predict(input)
-          const rawRes = tf.transpose(res,[0,2,1]).arraySync()[0]
-
-          let rawCoords = []
-          if (rawRes) {
-            for (var i = 0; i < rawRes.length; i++) {
-              let getScores = rawRes[i].slice(4) 
-              if (getScores.some( s => s > .5)) {
-                let foundTarget = rawRes[i].slice(0,2)
-                foundTarget.push(Math.max(...getScores))
-                rawCoords.push(foundTarget)
-              }
-            }
-
-            imageCtx.clearRect(0,0,imCanvas.width,imCanvas.height)
-            for (var coord of rawCoords) {
-              console.log(`x: ${coord[0]}, y: ${coord[1]}`)
-              let pointX = (imCanvas.width - imgWidth) / 2 + (coord[0] / modelWidth) * imgWidth -5
-              let pointY = (imCanvas.height - imgHeight) / 2 + (coord[1] / modelHeight) * imgHeight -5
-              imageCtx.globalAlpha = coord[2]
-              imageCtx.drawImage(target, pointX, pointY, 20, 20)
-            }
-          }
-          tf.dispose(input)
-          tf.dispose(res)
-          tf.dispose(rawRes)
-        } catch (e) {
-          console.log(e)
-        }
-        console.timeEnd('mx: frame-process')
-        await tf.nextFrame();
-      }
-    }
   }
 }

src/pages/specs.vue

@@ -8,7 +8,6 @@
         <f7-block-title medium>Details</f7-block-title>
         <f7-list>
           <f7-list-item title="Version" :after="alvinnVersion"></f7-list-item>
-          <f7-list-item v-if="isSafari" title="Safari" after="Workers disabled"></f7-list-item>
         </f7-list>
         <f7-block-title medium>Models</f7-block-title>
         <f7-list style="width: 100%;">
@@ -53,7 +52,6 @@
         miniHeadneckDetails: {},
         alvinnVersion: store().getVersion,
         isCordova: !!window.cordova,
-        isSafari: store().isSafari,
         otherSettings: {}
       }
     },