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123 lines
2.8 KiB
JavaScript
123 lines
2.8 KiB
JavaScript
const CLASSES = {0:'zero', 1:'one', 2:'two', 3:'three', 4:'four',5:'five', 6:'six', 7:'seven', 8:'eight', 9:'nine'}
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//-----------------------
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// start button event
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//-----------------------
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$("#start-button").click(function(){
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loadModel() ;
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startWebcam();
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});
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//-----------------------
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// load model
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//-----------------------
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let model;
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async function loadModel() {
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console.log("model loading..");
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$("#console").html(`<li>model loading...</li>`);
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model=await tf.loadModel(`http://localhost:8080/sign_language_vgg16/model.json`);
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console.log("model loaded.");
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$("#console").html(`<li>VGG16 pre trained model loaded.</li>`);
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};
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//-----------------------
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// start webcam
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//-----------------------
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var video;
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function startWebcam() {
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console.log("video streaming start.");
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$("#console").html(`<li>video streaming start.</li>`);
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video = $('#main-stream-video').get(0);
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vendorUrl = window.URL || window.webkitURL;
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navigator.getMedia = navigator.getUserMedia ||
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navigator.webkitGetUserMedia ||
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navigator.mozGetUserMedia ||
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navigator.msGetUserMedia;
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navigator.getMedia({
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video: true,
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audio: false
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}, function(stream) {
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localStream = stream;
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video.srcObject = stream;
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video.play();
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}, function(error) {
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alert("Something wrong with webcam!");
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});
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}
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//-----------------------
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// predict button event
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//-----------------------
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$("#predict-button").click(function(){
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setInterval(predict, 1000/10);
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});
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//-----------------------
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// TensorFlow.js method
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// predict tensor
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//-----------------------
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async function predict(){
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let tensor = captureWebcam();
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let prediction = await model.predict(tensor).data();
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let results = Array.from(prediction)
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.map(function(p,i){
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return {
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probability: p,
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className: CLASSES[i]
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};
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}).sort(function(a,b){
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return b.probability-a.probability;
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}).slice(0,5);
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$("#console").empty();
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results.forEach(function(p){
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$("#console").append(`<li>${p.className} : ${p.probability.toFixed(6)}</li>`);
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console.log(p.className,p.probability.toFixed(6))
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});
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};
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//------------------------------
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// capture streaming video
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// to a canvas object
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//------------------------------
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function captureWebcam() {
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var canvas = document.createElement("canvas");
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var context = canvas.getContext('2d');
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canvas.width = video.width;
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canvas.height = video.height;
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context.drawImage(video, 0, 0, video.width, video.height);
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tensor_image = preprocessImage(canvas);
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return tensor_image;
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}
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//-----------------------
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// TensorFlow.js method
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// image to tensor
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//-----------------------
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function preprocessImage(image){
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let tensor = tf.fromPixels(image).resizeNearestNeighbor([100,100]).toFloat();
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let offset = tf.scalar(255);
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return tensor.div(offset).expandDims();
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}
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//-----------------------
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// clear button event
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//-----------------------
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$("#clear-button").click(function clear() {
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location.reload();
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}); |