Add tensorflow for local detection

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

package.json

@@ -23,6 +23,7 @@
     "last 5 Firefox versions"
   ],
   "dependencies": {
+    "@tensorflow/tfjs": "^4.17.0",
     "dom7": "^4.0.6",
     "framework7": "^8.3.0",
     "framework7-icons": "^5.0.5",

src/pages/detect.vue

@@ -286,9 +286,10 @@
   import SvgIcon from '../components/svg-icon.vue'
 
   import submitMixin from './submit-mixin'
+  import detectMixin from './local-detect'
 
   export default {
-    mixins: [submitMixin],
+    mixins: [submitMixin, detectMixin],
     props: {
       f7route: Object,
     },
@@ -433,6 +434,7 @@
         } else {
           //TODO
           f7.dialog.alert('Using built-in model')
+          this.localDetect(this.activeRegion,this.imageView)
         }
       },
       remoteTimeout () {

src/pages/local-detect.js

@@ -1,21 +1,48 @@
+import * as tf  from '@tensorflow/tfjs'
+
 export default {
   methods: {
-    localDetect(region, imageData) {
+    async localDetect(region, imageData) {
       switch (region) {
         case 0:
-          var modelFile = 'some/path/to/thorax'
+          var weights = '../models/thorax_tfwm/model.json'
           break;
         case 1:
-          var modelFile = 'some/path/to/abdomen'
+          var weights = '../models/abdomen_tfwm/model.json'
           break;
         case 2:
-          var modelFile = 'some/path/to/limbs'
+          var weights = '../models/limbs_tfwm/model.json'
           break;
         case 3:
-          var modelFile = 'some/path/to/head'
+          var weights = '../models/head_tfwm/model.json'
           break;
         }
-      return finalDetections
+
+      const model = await tf.loadGraphModel(weights).then(model => {
+        return model
+      })
+      let [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 = await 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)
+
+        console.log(boxes_data)
+        console.log(scores_data)
+        console.log(classes_data)
+        console.log(valid_detections_data)
+
+        return boxes_data
+      })
+
+      return results
     }
   }
 }