Update webcam-transfer-learning with tf.data.webcam API

webcam-transfer-learning/index.js

@@ -16,17 +16,17 @@
  */
 
 import * as tf from '@tensorflow/tfjs';
+import * as tfd from '@tensorflow/tfjs-data';
 
 import {ControllerDataset} from './controller_dataset';
 import * as ui from './ui';
-import {Webcam} from './webcam';
 
 // The number of classes we want to predict. In this example, we will be
 // predicting 4 classes for up, down, left, and right.
 const NUM_CLASSES = 4;
 
-// A webcam class that generates Tensors from the images from the webcam.
-const webcam = new Webcam(document.getElementById('webcam'));
+// A webcam iterator that generates Tensors from the images from the webcam.
+let webcam;
 
 // The dataset object where we will store activations.
 const controllerDataset = new ControllerDataset(NUM_CLASSES);
@@ -48,15 +48,15 @@ async function loadTruncatedMobileNet() {
 // When the UI buttons are pressed, read a frame from the webcam and associate
 // it with the class label given by the button. up, down, left, right are
 // labels 0, 1, 2, 3 respectively.
-ui.setExampleHandler(label => {
-  tf.tidy(() => {
-    const img = webcam.capture();
-    controllerDataset.addExample(truncatedMobileNet.predict(img), label);
+ui.setExampleHandler(async label => {
+  let img = await getImage();
 
-    // Draw the preview thumbnail.
-    ui.drawThumb(img, label);
-  });
-});
+  controllerDataset.addExample(truncatedMobileNet.predict(img), label);
+
+  // Draw the preview thumbnail.
+  ui.drawThumb(img, label);
+  img.dispose();
+})
 
 /**
  * Sets up and trains the classifier.
@@ -129,32 +129,41 @@ let isPredicting = false;
 async function predict() {
   ui.isPredicting();
   while (isPredicting) {
-    const predictedClass = tf.tidy(() => {
-      // Capture the frame from the webcam.
-      const img = webcam.capture();
+    // Capture the frame from the webcam.
+    const img = await getImage();
 
-      // Make a prediction through mobilenet, getting the internal activation of
-      // the mobilenet model, i.e., "embeddings" of the input images.
-      const embeddings = truncatedMobileNet.predict(img);
+    // Make a prediction through mobilenet, getting the internal activation of
+    // the mobilenet model, i.e., "embeddings" of the input images.
+    const embeddings = truncatedMobileNet.predict(img);
 
-      // Make a prediction through our newly-trained model using the embeddings
-      // from mobilenet as input.
-      const predictions = model.predict(embeddings);
-
-      // Returns the index with the maximum probability. This number corresponds
-      // to the class the model thinks is the most probable given the input.
-      return predictions.as1D().argMax();
-    });
+    // Make a prediction through our newly-trained model using the embeddings
+    // from mobilenet as input.
+    const predictions = model.predict(embeddings);
 
+    // Returns the index with the maximum probability. This number corresponds
+    // to the class the model thinks is the most probable given the input.
+    const predictedClass = predictions.as1D().argMax();
     const classId = (await predictedClass.data())[0];
-    predictedClass.dispose();
+    img.dispose();
 
     ui.predictClass(classId);
     await tf.nextFrame();
   }
   ui.donePredicting();
 }
 
+/**
+ * Captures a frame from the webcam and normalizes it between -1 and 1.
+ * Returns a batched image (1-element batch) of shape [1, w, h, c].
+ */
+async function getImage() {
+  const img = await webcam.capture();
+  const processedImg =
+      tf.tidy(() => img.expandDims(0).toFloat().div(127).sub(1));
+  img.dispose();
+  return processedImg;
+}
+
 document.getElementById('train').addEventListener('click', async () => {
   ui.trainStatus('Training...');
   await tf.nextFrame();
@@ -170,18 +179,21 @@ document.getElementById('predict').addEventListener('click', () => {
 
 async function init() {
   try {
-    await webcam.setup();
+    webcam = await tfd.webcam(document.getElementById('webcam'));
   } catch (e) {
+    console.log(e);
     document.getElementById('no-webcam').style.display = 'block';
   }
   truncatedMobileNet = await loadTruncatedMobileNet();
 
+  ui.init();
+
   // Warm up the model. This uploads weights to the GPU and compiles the WebGL
   // programs so the first time we collect data from the webcam it will be
   // quick.
-  tf.tidy(() => truncatedMobileNet.predict(webcam.capture()));
-
-  ui.init();
+  const screenShot = await webcam.capture();
+  truncatedMobileNet.predict(screenShot.expandDims(0));
+  screenShot.dispose();
 }
 
 // Initialize the application.

webcam-transfer-learning/package.json

@@ -9,7 +9,7 @@
     "node": ">=8.9.0"
   },
   "dependencies": {
-    "@tensorflow/tfjs": "^1.0.4",
+    "@tensorflow/tfjs": "^1.1.0",
     "vega-embed": "^3.0.0"
   },
   "scripts": {