Update version to 0.4.0

Signed-off-by: Justin Georgi <justin.georgi@gmail.com>
Clear file input on image load
2024-03-07 16:00:21 -07:00 · 2024-03-07 15:18:35 -07:00 · 2024-03-07 14:52:48 -07:00 · 2024-03-07 14:41:58 -07:00 · 2024-03-07 13:06:14 -07:00 · 2024-03-06 16:41:18 -07:00
37 changed files with 581 additions and 362 deletions
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@ Anatomy Lab Visual Identification Neural Net (A.L.V.I.N.N) is a f7 based app for
 ## Install
 * **Android:** Download the latest Android apk in [packages](https://gitea.azgeorgis.net/Georgi_Lab/ALVINN_f7/packages) and open the downloaded file to install.
 * **iOS:** To do
-* **Web app:** To do
+* **Web app:** Download the latest Web zip file in [packages](https://gitea.azgeorgis.net/Georgi_Lab/ALVINN_f7/packages) and extract the files to a folder location available via web access then visit that location in your web browser.
 * **Run from source:** Clone this repository and in the root directory run `npm install` followed by `npm start`.  For more information see [f7 info](f7_info.md).

 ## Quick Start
--- a/cordova/config.xml
+++ b/cordova/config.xml
@@ -1,5 +1,5 @@
 <?xml version='1.0' encoding='utf-8'?>
-<widget id="edu.midwestern.alvinn" version="0.3.0" 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="0.4.0" 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">
--- a/cordova/package-lock.json
+++ b/cordova/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "edu.midwestern.alvinn",
-  "version": "0.1.0-b",
+  "version": "0.3.0",
  "lockfileVersion": 2,
  "requires": true,
  "packages": {
    "": {
      "name": "edu.midwestern.alvinn",
-      "version": "0.1.0-b",
+      "version": "0.3.0",
      "license": "Apache-2.0",
      "devDependencies": {
        "cordova-android": "^12.0.1",
--- a/cordova/package.json
+++ b/cordova/package.json
@@ -1,7 +1,7 @@
 {
  "name": "edu.midwestern.alvinn",
  "displayName": "ALVINN",
-  "version": "0.3.0",
+  "version": "0.4.0",
  "description": "Anatomy Lab Visual Identification Neural Network.",
  "main": "index.js",
  "scripts": {
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "alvinn",
-  "version": "0.2.1",
+  "version": "0.3.0",
  "lockfileVersion": 2,
  "requires": true,
  "packages": {
    "": {
      "name": "alvinn",
-      "version": "0.2.1",
+      "version": "0.3.0",
      "hasInstallScript": true,
      "license": "UNLICENSED",
      "dependencies": {
--- a/package.json
+++ b/package.json
@@ -1,7 +1,7 @@
 {
  "name": "alvinn",
  "private": true,
-  "version": "0.3.0",
+  "version": "0.4.0",
  "description": "ALVINN",
  "repository": "",
  "license": "UNLICENSED",
--- a/src/components/app.vue
+++ b/src/components/app.vue
@@ -11,7 +11,7 @@
          <f7-list-item link="/contact/" view=".view-main" panel-close=".panel-left">Contact</f7-list-item>
        </f7-list>
        <f7-toolbar class="panel-bar" position="bottom">
-          <span>version 0.3.0</span>
+          <span>version 0.4.0</span>
        </f7-toolbar>
      </f7-page>
    </f7-view>
@@ -65,6 +65,7 @@
      return {
        rememberAgreement: false,
        siteAgreement: false,
+        dateAgreement: null,
        showDisclaimer: true
      }
    },
@@ -74,8 +75,11 @@
        var loadedSettings = JSON.parse(loadSiteSettings)
        this.siteAgreement = loadedSettings.siteAgreement
        this.rememberAgreement = loadedSettings.rememberAgreement
+        this.dateAgreement = loadedSettings.dateAgreement && new Date(loadedSettings.dateAgreement)
      }
-      if (this.siteAgreement && this.rememberAgreement) {
+      var curDate = new Date ()
+      var agreeStillValid = this.dateAgreement && (curDate < this.dateAgreement.setMonth(this.dateAgreement.getMonth() + 3)) 
+      if (this.siteAgreement && this.rememberAgreement && agreeStillValid) {
        this.showDisclaimer = false
        store().agree()
      }
@@ -91,7 +95,8 @@
        store().agree()
        let newSettings = {
          siteAgreement: this.siteAgreement,
-          rememberAgreement: this.rememberAgreement
+          rememberAgreement: this.rememberAgreement,
+          dateAgreement: new Date()
        }
        let saveSiteSettings = new Promise(
          (saved,failed) => {
@@ -141,7 +146,7 @@

        // Register service worker (only on production build)
        serviceWorker: process.env.NODE_ENV ==='production' ? {
-          path: '/service-worker.js',
+          path: './service-worker.js',
        } : {},

        // Input settings
--- a/src/css/detect.css
+++ b/src/css/detect.css
@@ -0,0 +1,188 @@
+/*Styles for the structure detection page*/
+/*Basic style*/
+.detect-grid {
+  display: grid;
+  grid-template-columns: 1fr;
+  grid-template-rows: 1fr minmax(calc(var(--f7-chip-height) + 33px), auto) auto min-content;
+  grid-template-areas: 
+    "image-view"
+    "result-view"
+    "detect-settings"
+    "menu-view";
+  justify-items: center;
+  height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
+  max-height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
+}
+
+.image-container {
+  grid-area: image-view;
+  width: 100%;
+  height: 100%;
+  min-width: 0;
+  min-height: 0;
+  position: relative;
+  display: flex;
+  align-self: stretch;
+}
+
+.popover-button-menu {
+  max-width: 90vw;
+  max-height: 90vh;
+  width: auto;
+}
+
+.segment-button-menu {
+  flex-wrap: nowrap;
+  flex-direction: column;
+  max-height: 100%;
+  min-height: 0px;
+}
+
+.chip-media {
+  background-color: var(--chip-media-background) !important;
+}
+
+.chip-results {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 5px;
+  padding: 10px;
+  --f7-chip-border-radius: 16px;
+  --f7-chip-media-size: 32px;
+  --f7-chip-font-weight: normal;
+  overflow-y: auto;
+  overflow-x: hidden;
+  max-height: 100%;
+}
+
+.chip-results .chip {
+  padding-left: 8px;
+}
+
+.progress-hide {
+  display: hidden;
+}
+
+.selected-chip {
+  font-weight: 500;
+  box-shadow: 4px 4px 1px var(--avn-theme-color);
+  transform: translate(-2px, -2px);
+}
+
+.detect-inputs {
+  display: flex;
+  align-items: center;
+  margin: 5px;
+  width: 100%;
+  max-width: 400px;
+  min-width: 192px;
+}
+
+.level-slide-vert {
+  display: none;
+}
+
+.image-menu {
+  grid-area: menu-view;
+  margin: 5px;
+  max-width: 400px;
+  min-width: 192px;
+}
+
+.image-menu .button {
+  aspect-ratio: 1;
+  height: auto;
+  padding: 5px;
+  flex: 1 1 0%;
+}
+
+.image-menu > .button > svg {
+  aspect-ratio: 1;
+  height: auto;
+  width: 100%;
+}
+
+.segment-button-menu .button {
+  padding: 8px;
+  aspect-ratio: 1;
+  width: auto;
+  flex: 1 1 0%;
+  max-height: 100px;
+  max-width: 100px;
+}
+
+/*Additional styles for small format landscape orientation*/
+@media (max-height: 450px) and (orientation: landscape) {
+  .detect-grid {
+    grid-template-columns: minmax(0,1fr) minmax(56px,max-content) auto auto;
+    grid-template-rows: calc(100vh - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom) - 64px);
+    grid-template-areas: 
+      "image-view result-view detect-settings menu-view";
+    justify-items: stretch;
+    align-items: stretch;
+    height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
+    max-height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
+    position: relative;
+  }
+
+  .chip-results {
+    flex-direction: column;
+    max-height: 100%;
+    justify-self: start;
+    flex-wrap: nowrap;
+  }
+  
+  .detect-inputs {
+    flex-direction: column;
+    min-width: 0;
+    max-width: 72px;
+  }
+
+  .level-slide-horz {
+    display: none;
+  }
+
+  .level-slide-vert {
+    display: block;
+  }
+
+
+  .image-container {
+    flex-direction: column;
+  }
+
+  .image-menu {
+    flex-direction: column;
+    aspect-ratio: .25;
+    width: auto;
+    min-width: 0;
+    height: 100%;
+  }
+
+  .image-menu .button {
+    aspect-ratio: 1;
+    width: auto;
+    height: 100%;
+    flex: 1 1 0%;
+    border-bottom: 1px solid var(--f7-segmented-raised-divider-color);
+    border-bottom-left-radius: 0px !important;
+  }
+
+  .segment-button-menu {
+    flex-direction: row;
+    max-height: 100%;
+    min-height: 0px;
+  }
+
+  .segment-button-menu .button {
+    height: auto;
+    flex: 1 1 0%;
+    max-height: 100px;
+    max-width: 100px;
+  }
+
+  .button > svg {
+    width: 100%;
+    height: auto;
+  }
+}
--- a/src/index.html
+++ b/src/index.html
@@ -10,7 +10,7 @@
    * Disables use of inline scripts in order to mitigate risk of XSS vulnerabilities. To change this:
      * Enable inline JS: add 'unsafe-inline' to default-src
  -->
-  <meta http-equiv="Content-Security-Policy" content="worker-src blob:;  child-src blob: gap:; img-src 'self' blob: data:; default-src * 'self' 'unsafe-inline' 'unsafe-eval' data: content:">
+  <meta http-equiv="Content-Security-Policy" content="worker-src 'self' blob:;  child-src blob: gap:; img-src 'self' blob: data:; default-src * 'self' 'unsafe-inline' 'unsafe-eval' data: content:">
  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, minimum-scale=1, user-scalable=no, viewport-fit=cover">

  <meta name="theme-color" content="#fff">
--- a/src/manifest.json
+++ b/src/manifest.json
@@ -9,32 +9,32 @@
  "theme_color": "#002f65",
  "icons": [
    {
-      "src": "/icons/128x128.png",
+      "src": "../icons/128x128.png",
      "sizes": "128x128",
      "type": "image/png"
    },
    {
-      "src": "/icons/144x144.png",
+      "src": "../icons/144x144.png",
      "sizes": "144x144",
      "type": "image/png"
    },
    {
-      "src": "/icons/152x152.png",
+      "src": "../icons/152x152.png",
      "sizes": "152x152",
      "type": "image/png"
    },
    {
-      "src": "/icons/192x192.png",
+      "src": "../icons/192x192.png",
      "sizes": "192x192",
      "type": "image/png"
    },
    {
-      "src": "/icons/256x256.png",
+      "src": "../icons/256x256.png",
      "sizes": "256x256",
      "type": "image/png"
    },
    {
-      "src": "/icons/512x512.png",
+      "src": "../icons/512x512.png",
      "sizes": "512x512",
      "type": "image/png"
    }
--- a/src/models/thorax/classes.json
+++ b/src/models/thorax/classes.json
@@ -0,0 +1,43 @@
+[
+  "Abdominal diaphragm",
+  "Aorta",
+  "Azygous vein",
+  "Brachiocephalic trunk",
+  "Caudal vena cava",
+  "Cranial vena cava",
+  "Esophagus",
+  "External abdominal oblique",
+  "Iliocostalis",
+  "Latissimus dorsi",
+  "Left atrium",
+  "Left auricle",
+  "Left lung",
+  "Left subclavian artery",
+  "Left ventricle",
+  "Longissimus",
+  "Pectoralis profundus",
+  "Pectoralis superficialis",
+  "Pericardium",
+  "Phrenic nerve",
+  "Primary bronchus",
+  "Pulmonary artery",
+  "Pulmonary trunk",
+  "Pulmonary vein",
+  "Rectus abdominis",
+  "Rectus thoracis",
+  "Recurrent laryngeal nerve",
+  "Rhomboideus",
+  "Right atrium",
+  "Right auricle",
+  "Right lung",
+  "Right ventricle",
+  "Scalenus",
+  "Serratus dorsalis caudalis",
+  "Serratus dorsalis cranialis",
+  "Serratus ventralis",
+  "Spinalis",
+  "Sympathetic chain",
+  "Trachea",
+  "Trapezius",
+  "Vagus nerve"
+]
--- a/src/models/thorax/group1-shard10of11.bin
+++ b/src/models/thorax/group1-shard10of11.bin
--- a/src/models/thorax/group1-shard11of11.bin
+++ b/src/models/thorax/group1-shard11of11.bin
--- a/src/models/thorax/group1-shard1of11.bin
+++ b/src/models/thorax/group1-shard1of11.bin
--- a/src/models/thorax/group1-shard2of11.bin
+++ b/src/models/thorax/group1-shard2of11.bin
--- a/src/models/thorax/group1-shard3of11.bin
+++ b/src/models/thorax/group1-shard3of11.bin
--- a/src/models/thorax/group1-shard4of11.bin
+++ b/src/models/thorax/group1-shard4of11.bin
--- a/src/models/thorax/group1-shard5of11.bin
+++ b/src/models/thorax/group1-shard5of11.bin
--- a/src/models/thorax/group1-shard6of11.bin
+++ b/src/models/thorax/group1-shard6of11.bin
--- a/src/models/thorax/group1-shard7of11.bin
+++ b/src/models/thorax/group1-shard7of11.bin
--- a/src/models/thorax/group1-shard8of11.bin
+++ b/src/models/thorax/group1-shard8of11.bin
--- a/src/models/thorax/group1-shard9of11.bin
+++ b/src/models/thorax/group1-shard9of11.bin
--- a/src/models/thorax/metadata.yaml
+++ b/src/models/thorax/metadata.yaml
@@ -0,0 +1,53 @@
+description: Ultralytics best model trained on /data/ALVINN/Thorax/Thorax 0.1.0/thorax.yaml
+author: Ultralytics
+license: AGPL-3.0 https://ultralytics.com/license
+date: '2024-03-07T16:03:03.296997'
+version: 8.1.20
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: Abdominal diaphragm
+  1: Aorta
+  2: Azygous vein
+  3: Brachiocephalic trunk
+  4: Caudal vena cava
+  5: Cranial vena cava
+  6: Esophagus
+  7: External abdominal oblique
+  8: Iliocostalis
+  9: Latissimus dorsi
+  10: Left atrium
+  11: Left auricle
+  12: Left lung
+  13: Left subclavian artery
+  14: Left ventricle
+  15: Longissimus
+  16: Pectoralis profundus
+  17: Pectoralis superficialis
+  18: Pericardium
+  19: Phrenic nerve
+  20: Primary bronchus
+  21: Pulmonary artery
+  22: Pulmonary trunk
+  23: Pulmonary vein
+  24: Rectus abdominis
+  25: Rectus thoracis
+  26: Recurrent laryngeal nerve
+  27: Rhomboideus
+  28: Right atrium
+  29: Right auricle
+  30: Right lung
+  31: Right ventricle
+  32: Scalenus
+  33: Serratus dorsalis caudalis
+  34: Serratus dorsalis cranialis
+  35: Serratus ventralis
+  36: Spinalis
+  37: Sympathetic chain
+  38: Trachea
+  39: Trapezius
+  40: Vagus nerve
--- a/src/models/thorax/model.json
+++ b/src/models/thorax/model.json
--- a/src/models/thorax_tfwm/classes.json
+++ b/src/models/thorax_tfwm/classes.json
@@ -1,12 +0,0 @@
-[
-  "Right lung",
-  "Diaphragm",
-  "Heart",
-  "Caudal vena cava",
-  "Cranial vena cava",
-  "Phrenic nerve",
-  "Trachea",
-  "Vagus nerve",
-  "Left Lung",
-  "Aorta"
-]
--- a/src/models/thorax_tfwm/group1-shard1of7.bin
+++ b/src/models/thorax_tfwm/group1-shard1of7.bin
--- a/src/models/thorax_tfwm/group1-shard2of7.bin
+++ b/src/models/thorax_tfwm/group1-shard2of7.bin
--- a/src/models/thorax_tfwm/group1-shard3of7.bin
+++ b/src/models/thorax_tfwm/group1-shard3of7.bin
--- a/src/models/thorax_tfwm/group1-shard4of7.bin
+++ b/src/models/thorax_tfwm/group1-shard4of7.bin
--- a/src/models/thorax_tfwm/group1-shard5of7.bin
+++ b/src/models/thorax_tfwm/group1-shard5of7.bin
--- a/src/models/thorax_tfwm/group1-shard6of7.bin
+++ b/src/models/thorax_tfwm/group1-shard6of7.bin
--- a/src/models/thorax_tfwm/group1-shard7of7.bin
+++ b/src/models/thorax_tfwm/group1-shard7of7.bin
--- a/src/models/thorax_tfwm/model.json
+++ b/src/models/thorax_tfwm/model.json
--- a/src/pages/camera-mixin.js
+++ b/src/pages/camera-mixin.js
@@ -0,0 +1,45 @@
+export default {
+  methods: {
+    async openCamera() {
+      var cameraLoaded = false
+      const devicesList = await navigator.mediaDevices.enumerateDevices()
+      this.videoDeviceAvailable = devicesList.some( d => d.kind == "videoinput")
+      if (this.videoDeviceAvailable) {
+        navigator.mediaDevices.getUserMedia({video: true})
+        var vidConstraint = {
+          video: {
+            width: {
+              ideal: 1920
+            },
+            height: {
+              ideal: 1080
+            },
+            facingMode: 'environment'
+          },
+          audio: false
+        }
+        const stream = await navigator.mediaDevices.getUserMedia(vidConstraint);
+        cameraLoaded = true
+        this.cameraStream = stream
+      }
+      return cameraLoaded
+    },
+    closeCamera () {
+      this.cameraStream.getTracks().forEach( t => t.stop())
+      this.videoAvailable = false
+    },
+    captureVidFrame() {
+      const vidViewer = this.$refs.vid_viewer
+      vidViewer.pause()
+      let tempCVS = document.createElement('canvas')
+      tempCVS.height = vidViewer.videoHeight || parseInt(vidViewer.style.height)
+      tempCVS.width = vidViewer.videoWidth || parseInt(vidViewer.style.width)
+      const tempCtx = tempCVS.getContext('2d')
+      tempCtx.drawImage(vidViewer, 0, 0)
+      this.getImage(tempCVS.toDataURL())
+    },
+    async videoStream() {
+      //TODO
+    }
+  }
+}
--- a/src/pages/detect.vue
+++ b/src/pages/detect.vue
@@ -7,9 +7,13 @@
    <f7-block class="detect-grid">
      <div class="image-container">
        <canvas id="im-draw" ref="image_cvs" @click="structureClick" :style="`display: ${imageLoaded ? 'block' : 'none'}; flex: 1 1 0%; max-width: 100%; max-height: 100%; min-width: 0; min-height: 0; background-size: contain; background-position: center; background-repeat: no-repeat`" />
-        <SvgIcon v-if="!imageView"  :icon="f7route.params.region" fill-color="var(--avn-theme-color)" @click="selectImage" />
+        <SvgIcon v-if="!imageView && !videoAvailable" :icon="f7route.params.region" fill-color="var(--avn-theme-color)" @click="selectImage" />
+        <div class="vid-container" v-if="videoAvailable" style="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%);" fill large>Capture</f7-button>
+        </div>
      </div>
-      <div v-if="(resultData && resultData.detections) || detecting" class="chip-results" style="grid-area: result-view; flex: 0 0 auto; align-self: center;">
+      <div class="chip-results" style="grid-area: result-view; flex: 0 0 auto; align-self: center;">
        <f7-chip v-for="result in showResults.filter( r => { return r.aboveThreshold && r.isSearched && !r.isDeleted })"
          :class="(result.resultIndex == selectedChip) ? 'selected-chip' : ''"
          :text="result.label"
@@ -20,8 +24,8 @@
          @delete="deleteChip(result.resultIndex)"
          :style="chipGradient(result.confidence)"
        />
-        <span v-if="numResults == 0 && !detecting" style="height: var(--f7-chip-height); font-size: calc(var(--f7-chip-height) - 4px); font-weight: bolder; margin: 2px;">No results.</span>
-        <f7-preloader v-if="detecting || modelLoading" size="32" style="color: var(--avn-theme-color);" />
+        <div v-if="!numResults" style="height: var(--f7-chip-height); width: 100%; text-align: center; font-size: calc(var(--f7-chip-height) - 4px); font-weight: bolder; margin: 2px;">{{  message }}</div>
+        <f7-progressbar v-if="(detecting || modelLoading)" style="width: 100%;" :infinite="true" />
      </div>
      <div v-if="showDetectSettings" class="detect-inputs" style="grid-area: detect-settings;">
        <f7-range class="level-slide-horz" :min="0" :max="100" :step="1" @range:change="onLevelChange" v-model:value="detectorLevel" type="range" style="flex: 1 1 100%"/>
@@ -37,17 +41,21 @@
        <f7-button popover-open="#region-popover">
          <RegionIcon :region="activeRegion" />
        </f7-button>
-        <f7-button popover-open="#capture-popover">
+        <f7-button v-if="!videoAvailable" :class="(!modelLoading) ? '' : 'disabled'" popover-open="#capture-popover">
          <SvgIcon icon="camera_add"/>
        </f7-button>
+        <f7-button v-if="videoAvailable" @click="closeCamera()">
+          <SvgIcon icon="no_photography"/>
+        </f7-button>
        <f7-button @click="() => showDetectSettings = !showDetectSettings" :class="(imageLoaded) ? '' : 'disabled'">
          <SvgIcon icon="visibility"/>
+          <f7-badge v-if="numResults && (showResults.length != numResults)" color="red" style="position: absolute; right: 15%; top: 15%;">{{ showResults.length - numResults }}</f7-badge>
        </f7-button>
        <f7-button :class="(numResults && uploadDirty && viewedAll) ? '' : 'disabled'" @click="submitData">
          <SvgIcon :icon="(uploadUid) ? 'cloud_done' : 'cloud_upload'"/>
        </f7-button>
      </f7-segmented>
-      <input type="file" ref="image_chooser" @change="getImage()" accept="image/*" capture="environment" style="display: none;"/>
+      <input type="file" ref="image_chooser" @change="getImage()" accept="image/*" style="display: none;"/>
    </f7-block>

    <f7-panel :id="detectorName + '-settings'" right cover :backdrop="false" :container-el="`#${detectorName}-detect-page`">
@@ -86,7 +94,7 @@
        <f7-button style="height: auto; width: auto;" popover-close="#capture-popover" @click="selectImage('file')">
          <SvgIcon icon="photo_library" />
        </f7-button>
-        <f7-button style="height: auto; width: auto;" popover-close="#capture-popover" class="disabled" @click="videoStream">
+        <f7-button style="height: auto; width: auto;" popover-close="#capture-popover" @click="videoStream">
          <SvgIcon icon="videocam"/>
        </f7-button>
      </f7-segmented>
@@ -95,189 +103,7 @@
  </f7-page>
 </template>

-<style>
-  .detect-grid {
-    display: grid;
-    grid-template-columns: 1fr;
-    grid-template-rows: 1fr 56px auto min-content;
-    grid-template-areas: 
-      "image-view"
-      "result-view"
-      "detect-settings"
-      "menu-view";
-    justify-items: center;
-    height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
-    max-height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
-  }
-
-  .image-container {
-    grid-area: image-view;
-    width: 100%;
-    height: 100%;
-    min-width: 0;
-    min-height: 0;
-    position: relative;
-    display: flex;
-    align-self: stretch;
-  }
-
-  .popover-button-menu {
-    max-width: 90vw;
-    max-height: 90vh;
-    width: auto;
-  }
-  
-  .segment-button-menu {
-    flex-wrap: nowrap;
-    flex-direction: column;
-    max-height: 100%;
-    min-height: 0px;
-  }
-
-  .chip-media {
-    background-color: var(--chip-media-background) !important;
-  }
-
-  .chip-results {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 5px;
-    padding: 10px;
-    --f7-chip-border-radius: 16px;
-    --f7-chip-media-size: 32px;
-    --f7-chip-font-weight: normal;
-    overflow-y: auto;
-    max-height: 100%;
-  }
-
-  .chip-results .chip {
-    padding-left: 8px;
-  }
-
-  .selected-chip {
-    font-weight: 500;
-    box-shadow: 4px 4px 1px var(--avn-theme-color);
-    transform: translate(-2px, -2px);
-  }
-
-  .detect-inputs {
-    display: flex;
-    align-items: center;
-    margin: 5px;
-    width: 100%;
-    max-width: 400px;
-    min-width: 192px;
-  }
-
-  .level-slide-vert {
-    display: none;
-  }
-
-  .image-menu {
-    grid-area: menu-view;
-    margin: 5px;
-    /*width: 90%;*/
-    max-width: 400px;
-    min-width: 192px;
-  }
-
-  .image-menu .button {
-    aspect-ratio: 1;
-    height: auto;
-    padding: 5px;
-    flex: 1 1 0%;
-  }
-
-  .image-menu > .button > svg {
-    aspect-ratio: 1;
-    height: auto;
-    width: 100%;
-  }
-
-  .segment-button-menu .button {
-    padding: 8px;
-    aspect-ratio: 1;
-    width: auto;
-    flex: 1 1 0%;
-    max-height: 100px;
-    max-width: 100px;
-  }
-
-  @media (max-height: 450px) and (orientation: landscape) {
-    .detect-grid {
-      grid-template-columns: minmax(0,1fr) minmax(56px,max-content) auto auto;
-      grid-template-rows: calc(100vh - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom) - 64px);
-      grid-template-areas: 
-        "image-view result-view detect-settings menu-view";
-      justify-items: stretch;
-      align-items: stretch;
-      height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
-      max-height: calc(100% - var(--f7-navbar-height) - var(--f7-safe-area-top) - var(--f7-safe-area-bottom));
-      position: relative;
-    }
-
-    .chip-results {
-      flex-direction: column;
-      max-height: 100%;
-      justify-self: start;
-      flex-wrap: nowrap;
-    }
-    
-    .detect-inputs {
-      flex-direction: column;
-      min-width: 0;
-      max-width: 72px;
-    }
-
-    .level-slide-horz {
-      display: none;
-    }
-
-    .level-slide-vert {
-      display: block;
-    }
-
-
-    .image-container {
-      flex-direction: column;
-    }
-
-    .image-menu {
-      flex-direction: column;
-      aspect-ratio: .25;
-      width: auto;
-      min-width: 0;
-      height: 100%;
-    }
-
-    .image-menu .button {
-      aspect-ratio: 1;
-      width: auto;
-      height: 100%;
-      flex: 1 1 0%;
-      border-bottom: 1px solid var(--f7-segmented-raised-divider-color);
-      border-bottom-left-radius: 0px !important;
-    }
-  
-    .segment-button-menu {
-      flex-direction: row;
-      max-height: 100%;
-      min-height: 0px;
-    }
-
-    .segment-button-menu .button {
-      height: auto;
-      flex: 1 1 0%;
-      max-height: 100px;
-      max-width: 100px;
-    }
-
-    .button > svg {
-      width: 100%;
-      height: auto;
-    }
-  }
-</style>
+<style src="../css/detect.css" />

 <script>
  import { f7 } from 'framework7-vue'
@@ -287,12 +113,13 @@
  import SvgIcon from '../components/svg-icon.vue'

  import submitMixin from './submit-mixin'
-  import detectMixin from './local-detect'
+  import detectionMixin from './detection-mixin'
+  import cameraMixin from './camera-mixin'

-  import thoraxClasses from '../models/thorax_tfwm/classes.json'
+  import thoraxClasses from '../models/thorax/classes.json'

  export default {
-    mixins: [submitMixin, detectMixin],
+    mixins: [submitMixin, detectionMixin, cameraMixin],
    props: {
      f7route: Object,
    },
@@ -321,7 +148,10 @@
        uploadUid: null,
        uploadDirty: false,
        modelLocation: '',
-        modelLoading: false
+        modelLoading: true,
+        videoDeviceAvailable: false,
+        videoAvailable: false,
+        cameraStream: null
      }
    },
    setup() {
@@ -334,10 +164,10 @@
          this.detectorName = 'thorax'
          this.classesList = thoraxClasses
          /* VITE setting */
-          this.modelLocation = '../models/thorax_tfwm/model.json'
+          this.modelLocation = '../models/thorax/model.json'
          /* PWA Build setting */
-          //this.modelLocation = './models/thorax_tfwm/model.json'
-          this.modelLocationCordova = 'https://localhost/models/thorax_tfwm/model.json'
+          //this.modelLocation = './models/thorax/model.json'
+          this.modelLocationCordova = 'https://localhost/models/thorax/model.json'
          break;
        case 'abdomen':
          this.activeRegion = 1
@@ -353,43 +183,36 @@
      }
      var loadServerSettings = localStorage.getItem('serverSettings')
      if (loadServerSettings) this.serverSettings = JSON.parse(loadServerSettings)
-      var self = this
+    },
+    mounted () {
      if (this.serverSettings && this.serverSettings.use) {
-        var modelURL = `http://${this.serverSettings.address}:${this.serverSettings.port}/detectors`
-        var xhr = new XMLHttpRequest()
-        xhr.open("GET", modelURL)
-        xhr.setRequestHeader('Content-Type', 'application/json')
-        xhr.timeout = 10000
-        xhr.ontimeout = this.remoteTimeout
-        xhr.onload = function () {
-          if (this.status !== 200) {
-            console.log(xhr.response)
-            const errorResponse = JSON.parse(xhr.response)
-            f7.dialog.alert(`ALVINN has encountered an error: ${errorResponse.error}`)
-            return;
-          }
-          var detectors = JSON.parse(xhr.response).detectors
-          var findLabel = detectors
-            .find( d => { return d.name == self.detectorName } )?.labels
-            .filter( l => { return l != "" } ).sort()
-            .map( l => { return {'name': l, 'detect': true} } )
-          self.detectorLabels = findLabel || []
-        }
-        
-        xhr.send()
+        this.getRemoteLabels()
+        this.modelLoading = false
      } else {
-        self.modelLoading = true
-        self.detectorLabels = self.classesList.map( l => { return {'name': l, 'detect': true} } )
-        self.loadModel(self.isCordova ? self.modelLocationCordova : self.modelLocation).then(() => {
-          self.modelLoading = false
+        this.modelLoading = true
+        this.detectorLabels = this.classesList.map( l => { return {'name': l, 'detect': true} } )
+        this.loadModel(this.isCordova ? this.modelLocationCordova : this.modelLocation).then(() => {
+          this.modelLoading = false
        }).catch((e) => {
          console.log(e.message)
          f7.dialog.alert(`ALVINN AI model error: ${e.message}`)
+          this.modelLoading = false
        })
      }
      window.onresize = (e) => { this.selectChip('redraw') }
    },
    computed: {
+      message () {
+        if (this.modelLoading) {
+          return "Preparing ALVINN..."
+        } else if (this.detecting) {
+          return "Finding structures..."
+        } else if (this.numResults == 0 && this.imageLoaded) {
+          return "No results."
+        } else {
+          return "ALVINN is ready."
+        }
+      },
      showResults () {
        var filteredResults = this.resultData.detections
        if (!filteredResults) return []
@@ -427,50 +250,21 @@
        return `--chip-media-background: hsl(${confVal / 100 * 120}deg 100% 50%)`
      },
      setData () {
-        var self = this
        if (this.serverSettings && this.serverSettings.use) {
-          var modelURL = `http://${this.serverSettings.address}:${this.serverSettings.port}/detect`
-          var xhr = new XMLHttpRequest()
-          xhr.open("POST", modelURL)
-          xhr.timeout = 10000
-          xhr.ontimeout = this.remoteTimeout
-          xhr.setRequestHeader('Content-Type', 'application/json')
-          xhr.onload = function () {
-            self.detecting = false
-            if (this.status !== 200) {
-              console.log(xhr.response)
-              const errorResponse = JSON.parse(xhr.response)
-              f7.dialog.alert(`ALVINN has encountered an error: ${errorResponse.error}`)
-              return;
-            }
-            self.resultData = JSON.parse(xhr.response)
-            self.uploadDirty = true
-          }
-          
-          var doodsData = {
-            "detector_name": this.detectorName,
-            "detect": {
-              "*": 1
-            },
-            "data": this.imageView.src.split(',')[1]
-          }
-
-          xhr.send(JSON.stringify(doodsData))
+          this.remoteDetect()
        } else {
          this.localDetect(this.imageView).then(dets => {
-            self.detecting = false
-            self.resultData = dets
-            self.uploadDirty = true
+            this.detecting = false
+            this.resultData = dets
+            this.uploadDirty = true
          }).catch((e) => {
-          console.log(e.message)
-          f7.dialog.alert(`ALVINN structure finding error: ${e.message}`)
-        })
+            console.log(e.message)
+            this.detecting = false
+            this.resultData = {}
+            f7.dialog.alert(`ALVINN structure finding error: ${e.message}`)
+          })
        }
      },
-      remoteTimeout () {
-        this.detecting = false
-        f7.dialog.alert('No connection to remote ALVINN instance.  Please check app settings.')
-      },
      selectAll (ev) {
        if (ev.target.checked) {
          this.detectorLabels.forEach( s => s.detect = true )
@@ -478,18 +272,17 @@
          this.detectorLabels.forEach( s => s.detect = false )
        }
      },
-      selectImage (mode) {
+      async selectImage (mode) {
        this.imageLoadMode = mode
-        if (mode == "camera") {
-          this.$refs.image_chooser.setAttribute("capture","environment")
-        } else {
-          this.$refs.image_chooser.removeAttribute("capture")
-        }
        if (this.isCordova && mode == "camera") {
-            navigator.camera.getPicture(this.getImage, this.onFail, { quality: 50, destinationType: Camera.DestinationType.DATA_URL, correctOrientation: true });
-        } else {
-          var loadResult = this.$refs.image_chooser.click()
+          navigator.camera.getPicture(this.getImage, this.onFail, { quality: 50, destinationType: Camera.DestinationType.DATA_URL, correctOrientation: true });
+          return
        }
+        if (mode == "camera") {
+          this.videoAvailable = await this.openCamera()
+          if (this.videoAvailable) { return }
+        }
+        this.$refs.image_chooser.click()
      },
      onFail (message) {
        alert(`Camera fail: ${message}`)
@@ -536,13 +329,19 @@
        return [imCanvas, imageCtx]
      },
      getImage (searchImage) {
-        let loadImage =new Promise(resolve => {
-          if (this.isCordova && this.imageLoadMode == "camera") {
+        let loadImage = new Promise(resolve => {
+          if (this.videoAvailable) {
+            this.closeCamera()
+            this.detecting = true
+            resolve(searchImage)
+          } else if (this.isCordova && this.imageLoadMode == "camera") {
+            this.detecting = true
            resolve('data:image/jpg;base64,' + searchImage)
          } else {
            const searchImage = this.$refs.image_chooser.files[0]
+            this.$refs.image_chooser.value=[]
            var reader = new FileReader()
-            reader.addEventListener("loadend", () => {
+            reader.addEventListener("load", () => {
              this.detecting = true
              resolve(reader.result)
            })
@@ -552,6 +351,7 @@
        loadImage.then((imgData) => {
          this.imageLoaded = true
          this.resultData = {}
+          this.selectedChip = -1
          this.imageView = new Image()
          this.imageView.src = imgData
          return(this.imageView.decode())
@@ -559,10 +359,8 @@
          const [imCanvas, _]  = this.resetView()
          imCanvas.style['background-image'] = `url(${this.imageView.src})`
          /******
-           * setTimeout is not a good solution,
-           * but it's the only way I can find to
-           * not cut off drawing of of the progress
-           * spinner
+           * setTimeout is not a good solution, but it's the only way
+           * I can find to not cut off drawing of the progress spinner
           ******/
          setTimeout(() => {
            this.setData()
@@ -572,10 +370,6 @@
          f7.dialog.alert(`Error loading image: ${e.message}`)
        })
      },
-      videoStream() {
-        //TODO
-        return null
-      },
      async submitData () {
        var uploadData = this.showResults
          .filter( d => { return d.aboveThreshold && d.isSearched && !d.isDeleted })
--- a/src/pages/detection-mixin.js
+++ b/src/pages/detection-mixin.js
@@ -0,0 +1,151 @@
+import * as tf  from '@tensorflow/tfjs'
+import { f7 } from 'framework7-vue'
+import { nextTick } from 'vue'
+
+var model = null
+
+export default {
+  methods: {
+    async loadModel(weights) {
+      await nextTick()
+      model = await tf.loadGraphModel(weights)
+      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
+      const dummyT = tf.ones([1,modelWidth,modelHeight,3])
+      model.predict(dummyT) //Run model once to preload weights for better response time
+      return model
+    },
+    async localDetect(imageData) {
+      console.time('pre-process')
+      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3)
+      const input = tf.tidy(() => {
+        return tf.image.resizeBilinear(tf.browser.fromPixels(imageData), [modelWidth, modelHeight]).div(255.0).expandDims(0)
+      })
+      console.timeEnd('pre-process')
+
+      console.time('run prediction')
+      const res = model.predict(input)
+      console.timeEnd('run prediction')
+
+      console.time('post-process')
+      const detectAttempts = res.shape[2]
+      const outputSize = res.shape[1]
+      const rawRes = tf.transpose(res,[0,2,1]).dataSync()
+      let rawBoxes = []
+      let rawScores = []
+
+      for (var i = 0; i < detectAttempts; i++) {
+        var getBox = rawRes.slice((i * outputSize),(i * outputSize) + 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(rawRes.slice((i * outputSize) + 4,(i + 1) * outputSize))
+      }
+      const tBoxes = tf.tensor2d(rawBoxes)
+      let tScores = null
+      let boxes_data = []
+      let scores_data = []
+      let classes_data = []
+      for (var c = 0; c < outputSize - 4; c++) {
+        tScores = rawScores.map(x => x[c])
+        var validBoxes = await tf.image.nonMaxSuppressionAsync(tBoxes,tf.tensor1d(tScores),10,0.5,.05)
+        validBoxes = validBoxes.dataSync()
+        if (validBoxes) {
+          boxes_data.push(...rawBoxes.filter( (_, idx) => validBoxes.includes(idx)))
+          var outputScores = tScores.filter( (_, idx) => validBoxes.includes(idx))
+          scores_data.push(...outputScores)
+          classes_data.push(...outputScores.fill(c))
+        }
+      }
+
+      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(tBoxes)
+      console.timeEnd('post-process')
+
+      return output
+    },
+    getRemoteLabels() {
+      var self = this
+      var modelURL = `http://${this.serverSettings.address}:${this.serverSettings.port}/detectors`
+      var xhr = new XMLHttpRequest()
+      xhr.open("GET", modelURL)
+      xhr.setRequestHeader('Content-Type', 'application/json')
+      xhr.timeout = 10000
+      xhr.ontimeout = this.remoteTimeout
+      xhr.onload = function () {
+        if (this.status !== 200) {
+          console.log(xhr.response)
+          const errorResponse = JSON.parse(xhr.response)
+          f7.dialog.alert(`ALVINN has encountered an error: ${errorResponse.error}`)
+          return
+        }
+        var detectors = JSON.parse(xhr.response).detectors
+        var findLabel = detectors
+          .find( d => { return d.name == self.detectorName } )?.labels
+          .filter( l => { return l != "" } ).sort()
+          .map( l => { return {'name': l, 'detect': true} } )
+        self.detectorLabels = findLabel || []
+      }
+      xhr.onerror = function (e) {
+        f7.dialog.alert('ALVINN has encountered an unknown server error')
+        return
+      }
+      
+      xhr.send()
+    },
+    remoteDetect() {
+      var self = this
+      var modelURL = `http://${this.serverSettings.address}:${this.serverSettings.port}/detect`
+      var xhr = new XMLHttpRequest()
+      xhr.open("POST", modelURL)
+      xhr.timeout = 10000
+      xhr.ontimeout = this.remoteTimeout
+      xhr.setRequestHeader('Content-Type', 'application/json')
+      xhr.onload = function () {
+        self.detecting = false
+        if (this.status !== 200) {
+          console.log(xhr.response)
+          const errorResponse = JSON.parse(xhr.response)
+          f7.dialog.alert(`ALVINN has encountered an error: ${errorResponse.error}`)
+          return;
+        }
+        self.resultData = JSON.parse(xhr.response)
+        self.uploadDirty = true
+      }
+      
+      var doodsData = {
+        "detector_name": this.detectorName,
+        "detect": {
+          "*": 1
+        },
+        "data": this.imageView.src.split(',')[1]
+      }
+
+      xhr.send(JSON.stringify(doodsData))
+    },
+    remoteTimeout () {
+      this.detecting = false
+      f7.dialog.alert('No connection to remote ALVINN instance.  Please check app settings.')
+    }
+
+  }
+}
--- a/src/pages/local-detect.js
+++ b/src/pages/local-detect.js
@@ -1,48 +0,0 @@
-import * as tf  from '@tensorflow/tfjs'
-
-var model = null
-
-export default {
-  methods: {
-    async loadModel(weights) {
-      model = await tf.loadGraphModel(weights).then(graphModel => {
-        return graphModel
-      })
-    },
-    async localDetect(imageData) {
-      const [modelWidth, modelHeight] = model.inputs[0].shape.slice(1, 3);
-
-      const input = tf.tidy(() => {
-        return tf.image.resizeBilinear(tf.browser.fromPixels(imageData), [modelWidth, modelHeight]).div(255.0).expandDims(0)
-      })
-      var results = model.executeAsync(input).then(res => {
-        const [boxes, scores, classes, valid_detections] = res;
-        const boxes_data = boxes.dataSync();
-        const scores_data = scores.dataSync();
-        const classes_data = classes.dataSync();
-        const valid_detections_data = valid_detections.dataSync()[0];
-
-        tf.dispose(res)
-
-        var output = {
-          detections: []
-        }
-        for (var i =0; i < valid_detections_data; i++) {
-          var [dLeft, dTop, dRight, dBottom] = boxes_data.slice(i * 4, (i + 1) * 4);
-          output.detections.push({
-            "top": dTop,
-            "left": dLeft,
-            "bottom": dBottom,
-            "right": dRight,
-            "label": this.detectorLabels[classes_data[i]].name,
-            "confidence": scores_data[i] * 100
-          })
-        }
-
-        return output
-      })
-
-      return results
-    }
-  }
-}
Author	SHA1	Message	Date
Justin Georgi	1cc76c5950	Update version to 0.4.0 Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-07 16:00:21 -07:00
Justin Georgi	091a38f7ab	Clear file input on image load Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-07 15:18:35 -07:00
Justin Georgi	5ec51d9ad9	Add hidden structures badge Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-07 14:52:48 -07:00
Justin Georgi	4d356df02a	Add stop camera button Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-07 14:41:58 -07:00
Justin Georgi	4af06f0fe5	Update thorax model and improve model performance (#125 ) Closes: #117 This bumps the thorax model from the yolo nano to the yolo sm (64 x 640 size) but greatly improves model performance by running a fake detection event on page load to get the model parameters in memory. As a result of that change a new loading message was required so the f7 preloader was switched out for an f7 progress bar and more messaging was added during various stages. The progress bar fixes the previous issue with the preloader. Signed-off-by: Justin Georgi <justin.georgi@gmail.com> Reviewed-on: #125	2024-03-07 13:06:14 -07:00
Justin Georgi	0e99679e00	Fix PWA build errors Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-06 16:41:18 -07:00
Justin Georgi	1f25a75cec	Split detect.vue (#122 ) Closes: #112 New mixins for camera and detection (remote and local) and new css for detection page. Reviewed-on: #122	2024-03-06 14:36:27 -07:00
Justin Georgi	b00b5cf492	Add performance tracking to local detection Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-05 15:51:28 -07:00
Justin Georgi	5086a52849	Return to standard model folder locations Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-05 15:06:01 -07:00
Justin Georgi	77240fe9bf	Fix missing detection progress indicator on camera load Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-05 06:48:07 -07:00
Justin Georgi	7f9a7cb2d2	Cordova thorax model location update Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-04 17:18:15 -07:00
Justin Georgi	c452776689	Add better camera access for non-cordova deployments (#116 ) This adds direct javascript access to the camera (with permission requests). Capturing an image with the camera now happens right on the detect page and not a separate window. This also opens the possibility of live detection on the web app. Reviewed-on: Georgi_Lab/ALVINN_f7#116	2024-03-04 17:03:28 -07:00
Justin Georgi	239558194e	Cordova package upgrade Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-03-04 16:58:59 -07:00
Justin Georgi	069ad74e30	Add full yolov8 compatible post-processing (#115 ) This updates the local detection to yolov8-based models from the older yolov5 based models. This includes significant additional post-processing of the raw yolov8 output, but also means that the smaller nano yolo model is available as well as all of the updated ALVINN thorax training data. Signed-off-by: Justin Georgi <justin.georgi@gmail.com> Reviewed-on: Georgi_Lab/ALVINN_f7#115	2024-03-04 15:03:20 -07:00
Justin Georgi	7f1aa63f07	Add site agreement expiration Closes: #88 Reviewed-on: Georgi_Lab/ALVINN_f7#111	2024-02-23 22:00:05 -07:00
Justin Georgi	e644aa26f0	Add PWA to README Signed-off-by: Justin Georgi <justin.georgi@gmail.com>	2024-02-22 20:28:45 -07:00
Justin Georgi	19a669dc98	Fix decoding error on android image load (#108 ) Closes: #105 Signed-off-by: Justin Georgi <justin.georgi@gmail.com> Reviewed-on: Georgi_Lab/ALVINN_f7#108	2024-02-22 17:29:44 -07:00