detect.js

const tf = require('@tensorflow/tfjs-node');
 
const cocossd = require('@tensorflow-models/coco-ssd');
const mobilenet = require('@tensorflow-models/mobilenet');
 
const toUint8Array = require('base64-to-uint8array');
 
 
export default class ObjectDetectors {
 
   constructor(image, type) {
       this.inputImage = image;
       this.type = type;
   }
  
   async loadCocoSsdModal() {
       const modal = await cocossd.load({
           base: 'mobilenet_v2'
       })
       return modal;
   }
 
   async loadMobileNetModal() {
       const modal = await mobilenet.load({
           version: 1,
           alpha: 0.25 | .50 | .75 | 1.0,
       })
       return modal;
   }
 
   getTensor3dObject(numOfChannels) {
 
       const imageData = this.inputImage.replace('data:image/jpeg;base64','')
                           .replace('data:image/png;base64','');
      
       const imageArray = toUint8Array(imageData);
      
       const tensor3d = tf.node.decodeJpeg( imageArray, numOfChannels );
 
       return tensor3d;
   }
 
   async process() {
        
       let predictions = null;
       const tensor3D = this.getTensor3dObject(3);
 
       if(this.type === "imagenet") {
 
           const model =  await this.loadMobileNetModal();
           predictions = await model.classify(tensor3D);
 
       } else {
 
           const model =  await this.loadCocoSsdModal();
           predictions = await model.detect(tensor3D);
       }
 
       tensor3D.dispose();
 
      return {data: predictions, type: this.type};
   }
}