Merge pull request #145 from ml5js/removingNew

Removing class initialization for all methods

Author: shiffman

src/FeatureExtractor/index.js

@@ -10,10 +10,10 @@ General Feature Extractor Manager
 import Mobilenet from './Mobilenet';
 
 /* eslint max-len: ["error", { "code": 180 }] */
-const FeatureExtractor = (model, optionsOrCallback, cb = () => {}) => {
+const featureExtractor = (model, optionsOrCallback, cb = () => {}) => {
   let modelName;
   if (typeof model !== 'string') {
-    console.error('Please specify a model to use. E.g: "Mobilenet"');
+    throw new Error('Please specify a model to use. E.g: "MobileNet"');
   } else {
     modelName = model.toLowerCase();
   }
@@ -30,8 +30,7 @@ const FeatureExtractor = (model, optionsOrCallback, cb = () => {}) => {
   if (modelName === 'mobilenet') {
     return new Mobilenet(options, callback);
   }
-  console.error(`${modelName} is not a valid model.`);
-  return null;
+  throw new Error(`${modelName} is not a valid model.`);
 };
 
-export default FeatureExtractor;
+export default featureExtractor;

src/ImageClassifier/index.js

@@ -19,44 +19,21 @@ const DEFAULTS = {
 };
 
 class ImageClassifier {
-  constructor(modelName, videoOrOptionsOrCallback, optionsOrCallback, cb = null) {
-    let options = {};
-    let callback = cb;
-
-    if (videoOrOptionsOrCallback instanceof HTMLVideoElement) {
-      this.video = videoOrOptionsOrCallback;
-    } else if (typeof videoOrOptionsOrCallback === 'object' && videoOrOptionsOrCallback.elt instanceof HTMLVideoElement) {
-      this.video = videoOrOptionsOrCallback.elt; // Handle a p5.js video element
-    } else if (videoOrOptionsOrCallback === 'object') {
-      options = videoOrOptionsOrCallback;
-    } else if (videoOrOptionsOrCallback === 'function') {
-      callback = videoOrOptionsOrCallback;
-    }
-
-    if (typeof optionsOrCallback === 'object') {
-      options = optionsOrCallback;
-    } else if (typeof optionsOrCallback === 'function') {
-      callback = optionsOrCallback;
-    }
-
-    if (typeof modelName === 'string') {
-      this.modelName = modelName.toLowerCase();
-      this.version = options.version || DEFAULTS[this.modelName].version;
-      this.alpha = options.alpha || DEFAULTS[this.modelName].alpha;
-      this.topk = options.topk || DEFAULTS[this.modelName].topk;
-      this.modelLoaded = false;
-      this.model = null;
-      if (this.modelName === 'mobilenet') {
-        this.modelToUse = mobilenet;
-      } else {
-        this.modelToUse = null;
-      }
-
-      // Load the model
-      this.modelLoaded = this.loadModel(callback);
+  constructor(modelName, video, options, callback) {
+    this.modelName = modelName;
+    this.video = video;
+    this.version = options.version || DEFAULTS[this.modelName].version;
+    this.alpha = options.alpha || DEFAULTS[this.modelName].alpha;
+    this.topk = options.topk || DEFAULTS[this.modelName].topk;
+    this.modelLoaded = false;
+    this.model = null;
+    if (this.modelName === 'mobilenet') {
+      this.modelToUse = mobilenet;
     } else {
-      console.error('Please specify a model to use. E.g: "Mobilenet"');
+      this.modelToUse = null;
     }
+    // Load the model
+    this.modelLoaded = this.loadModel(callback);
   }
 
   async loadModel(callback) {
@@ -121,4 +98,35 @@ class ImageClassifier {
   }
 }
 
-export default ImageClassifier;
+const imageClassifier = (modelName, videoOrOptionsOrCallback, optionsOrCallback, cb = null) => {
+  let model;
+  let video;
+  let options = {};
+  let callback = cb;
+
+  if (typeof modelName === 'string') {
+    model = modelName.toLowerCase();
+  } else {
+    throw new Error('Please specify a model to use. E.g: "MobileNet"');
+  }
+
+  if (videoOrOptionsOrCallback instanceof HTMLVideoElement) {
+    video = videoOrOptionsOrCallback;
+  } else if (typeof videoOrOptionsOrCallback === 'object' && videoOrOptionsOrCallback.elt instanceof HTMLVideoElement) {
+    video = videoOrOptionsOrCallback.elt; // Handle a p5.js video element
+  } else if (videoOrOptionsOrCallback === 'object') {
+    options = videoOrOptionsOrCallback;
+  } else if (videoOrOptionsOrCallback === 'function') {
+    callback = videoOrOptionsOrCallback;
+  }
+
+  if (typeof optionsOrCallback === 'object') {
+    options = optionsOrCallback;
+  } else if (typeof optionsOrCallback === 'function') {
+    callback = optionsOrCallback;
+  }
+
+  return new ImageClassifier(model, video, options, callback);
+};
+
+export default imageClassifier;

src/ImageClassifier/index.test.js

@@ -11,9 +11,8 @@ const DEFAULTS = {
 };
 
 describe('Create an image classifier', () => {
-  //let classifier;
+  // let classifier;
 
-  
   it('true', () => {
     expect(true).toBe(true);
   });

src/LSTM/index.js

@@ -18,8 +18,8 @@ const DEFAULTS = {
   temperature: 0.5,
 };
 
-class LSTMGenerator {
-  constructor(modelPath = './', callback = () => {}) {
+class LSTM {
+  constructor(modelPath, callback) {
     this.modelPath = modelPath;
     this.ready = false;
     this.indices_char = {};
@@ -58,7 +58,7 @@ class LSTMGenerator {
       const indexTensor = tf.tidy(() => {
         const input = this.convert(seed);
         const prediction = this.model.predict(input).squeeze();
-        return LSTMGenerator.sample(prediction, this.temperature);
+        return LSTM.sample(prediction, this.temperature);
       });
       const index = await indexTensor.data();
       indexTensor.dispose();
@@ -99,4 +99,6 @@ class LSTMGenerator {
   }
 }
 
+const LSTMGenerator = (modelPath = './', callback = () => {}) => new LSTM(modelPath, callback);
+
 export default LSTMGenerator;

src/Crepe/index.js renamed to src/PitchDetection/index.js

@@ -3,33 +3,59 @@
 // This software is released under the MIT License.
 // https://opensource.org/licenses/MIT
 
-// Crepe Pitch Detection model
-// https://github.com/marl/crepe/tree/gh-pages
-// https://marl.github.io/crepe/crepe.js
+/*
+  Crepe Pitch Detection model
+  https://github.com/marl/crepe/tree/gh-pages
+  https://marl.github.io/crepe/crepe.js
+*/
 
 import * as tf from '@tensorflow/tfjs';
 
-class Crepe {
-  // in here are the functions to make exposed
-  constructor(audioContext, stream) {
+class PitchDetection {
+  constructor(modelName, audioContext, stream) {
+    this.modelName = modelName;
     this.audioContext = audioContext;
     this.stream = stream;
-    this.initTF();
+    this.loadModel();
   }
 
-  async initTF() {
-    try {
-      console.log('Loading Keras model...');
-      this.model = await tf.loadModel('model/model.json');
-      console.log('Model loading complete');
-    } catch (e) {
-      console.error(e);
-    }
+  async loadModel() {
+    this.model = await tf.loadModel('model/model.json');
     this.initAudio();
   }
 
-  // perform resampling the audio to 16000 Hz, on which the model is trained.
-  // setting a sample rate in AudioContext is not supported by most browsers at the moment.
+  initAudio() {
+    if (this.audioContext) {
+      try {
+        this.processStream(this.stream);
+      } catch (e) {
+        throw new Error(`Error: Could not access microphone - ${e}`);
+      }
+    } else {
+      throw new Error('Could not access microphone - getUserMedia not available');
+    }
+  }
+
+  processStream(stream) {
+    const mic = this.audioContext.createMediaStreamSource(stream);
+    const minBufferSize = (this.audioContext.sampleRate / 16000) * 1024;
+    let bufferSize = 4;
+    while (bufferSize < minBufferSize) bufferSize *= 2;
+
+    const scriptNode = this.audioContext.createScriptProcessor(bufferSize, 1, 1);
+    scriptNode.onaudioprocess = this.processMicrophoneBuffer.bind(this);
+    const gain = this.audioContext.createGain();
+    gain.gain.setValueAtTime(0, this.audioContext.currentTime);
+
+    mic.connect(scriptNode);
+    scriptNode.connect(gain);
+    gain.connect(this.audioContext.destination);
+
+    if (this.audioContext.state !== 'running') {
+      console.warn('User gesture needed to start AudioContext, please click');
+    }
+  }
+
   static resample(audioBuffer, onComplete) {
     const interpolate = (audioBuffer.sampleRate % 16000 !== 0);
     const multiplier = audioBuffer.sampleRate / 16000;
@@ -39,7 +65,6 @@ class Crepe {
       if (!interpolate) {
         subsamples[i] = original[i * multiplier];
       } else {
-        // simplistic, linear resampling
         const left = Math.floor(i * multiplier);
         const right = left + 1;
         const p = (i * multiplier) - left;
@@ -51,42 +76,32 @@ class Crepe {
 
   processMicrophoneBuffer(event) {
     this.results = {};
-    // bin number -> cent value mapping
     const centMapping = tf.add(tf.linspace(0, 7180, 360), tf.tensor(1997.3794084376191));
-    Crepe.resample(event.inputBuffer, (resampled) => {
+    PitchDetection.resample(event.inputBuffer, (resampled) => {
       tf.tidy(() => {
         this.running = true;
-
-        // run the prediction on the model
         const frame = tf.tensor(resampled.slice(0, 1024));
         const zeromean = tf.sub(frame, tf.mean(frame));
         const framestd = tf.tensor(tf.norm(zeromean).dataSync() / Math.sqrt(1024));
         const normalized = tf.div(zeromean, framestd);
         const input = normalized.reshape([1, 1024]);
         const activation = this.model.predict([input]).reshape([360]);
-
-        // the confidence of voicing activity and the argmax bin
         const confidence = activation.max().dataSync()[0];
         const center = activation.argMax().dataSync()[0];
         this.results.confidence = confidence.toFixed(3);
 
-        // slice the local neighborhood around the argmax bin
         const start = Math.max(0, center - 4);
         const end = Math.min(360, center + 5);
         const weights = activation.slice([start], [end - start]);
         const cents = centMapping.slice([start], [end - start]);
 
-        // take the local weighted average to get the predicted pitch
         const products = tf.mul(weights, cents);
         const productSum = products.dataSync().reduce((a, b) => a + b, 0);
         const weightSum = weights.dataSync().reduce((a, b) => a + b, 0);
         const predictedCent = productSum / weightSum;
         const predictedHz = 10 * ((predictedCent / 1200.0) ** 2);
 
-        // update
         const result = (confidence > 0.5) ? `${predictedHz.toFixed(3)} +  Hz` : 'no voice';
-        // const strlen = result.length;
-        // for (let i = 0; i < 11 - strlen; i += 1) result = result;
         this.results.result = result;
       });
     });
@@ -95,55 +110,20 @@ class Crepe {
   getResults() {
     return this.results;
   }
+}
 
-  processStream(stream) {
-    console.log('Setting up AudioContext ...');
-    console.log(`Audio context sample rate =  + ${this.audioContext.sampleRate}`);
-    const mic = this.audioContext.createMediaStreamSource(stream);
-
-    // We need the buffer size that is a power of two
-    // and is longer than 1024 samples when resampled to 16000 Hz.
-    // In most platforms where the sample rate is 44.1 kHz or 48 kHz,
-    // this will be 4096, giving 10-12 updates/sec.
-    const minBufferSize = (this.audioContext.sampleRate / 16000) * 1024;
-    let bufferSize = 4;
-    while (bufferSize < minBufferSize) bufferSize *= 2;
-    console.log(`Buffer size = ${bufferSize}`);
-    const scriptNode = this.audioContext.createScriptProcessor(bufferSize, 1, 1);
-    scriptNode.onaudioprocess = this.processMicrophoneBuffer.bind(this);
-    // It seems necessary to connect the stream to a sink
-    // for the pipeline to work, contrary to documentataions.
-    // As a workaround, here we create a gain node with zero gain,
-    // and connect temp to the system audio output.
-    const gain = this.audioContext.createGain();
-    gain.gain.setValueAtTime(0, this.audioContext.currentTime);
-
-    mic.connect(scriptNode);
-    scriptNode.connect(gain);
-    gain.connect(this.audioContext.destination);
-
-    if (this.audioContext.state === 'running') {
-      console.log('Running ...');
-    } else {
-      console.error('User gesture needed to start AudioContext, please click');
-      // user gesture (like click) is required to start AudioContext, in some browser versions
-      // status('<a href="javascript:crepe.resume();" style="color:red;">*
-      // Click here to start the demo *</a>')
-    }
+const pitchDetection = (modelName, context, stream) => {
+  let model;
+  if (typeof modelName === 'string') {
+    model = modelName.toLowerCase();
+  } else {
+    throw new Error('Please specify a model to use. E.g: "Crepe"');
   }
 
-  initAudio() {
-    if (this.audioContext) {
-      console.log('Initializing audio');
-      try {
-        this.processStream(this.stream);
-      } catch (e) {
-        console.error('Error: Could not access microphone - ', e);
-      }
-    } else {
-      console.error('Could not access microphone - getUserMedia not available');
-    }
+  if (model === 'crepe') {
+    return new PitchDetection(model, context, stream);
   }
-}
+  throw new Error(`${model} is not a valid model to use in pitchDetection()`);
+};
 
-export default Crepe;
+export default pitchDetection;