TensorFlowJS-Standalone-Image-Classifier/index.html at master · CapnDeCode/TensorFlowJS-Standalone-Image-Classifier · GitHub

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<head>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.2.7"> </script>
    <script src="https://unpkg.com/@tensorflow-models/mobilenet"></script>
</head>
<body>
    <div>
        <div>
            <input id ="image-selector" class="form-control" type="file"/>
        </div>
        <div id="prediction-list"></div>
        <div>
            <canvas id="canvas" width="400" height="300" style="border:1px solid #000000;"></canvas>
        </div>
    </div>
    <div id="training-images">
        <img width="400" height="300" class="train-image cat" src="training-images/cat2.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat3.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat4.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat5.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat6.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat7.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat8.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat9.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat10.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat11.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat12.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat13.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat14.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat15.jpeg" />
        <img width="400" height="300" class="train-image cat" src="training-images/cat16.jpeg" />

        <img width="400" height="300" class="train-image dog" src="training-images/dog2.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog3.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog4.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog5.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog6.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog7.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog8.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog9.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog10.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog11.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog12.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog13.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog14.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog15.jpeg" />
        <img width="400" height="300" class="train-image dog" src="training-images/dog16.jpeg" />
    </div>
</body>

<script>
    const modelType = "mobilenet";
    const model = tf.sequential();
    var labels = ['cat', 'dog'];

    //-------------------------- Training: --------------------------------
    window.addEventListener('load', (event) => {
        console.log(labels)

        // Prepare model :
        prepareModel();

        // Prepare training images and labels
        var X = [];
        var Y = [];
        for(var i = 0; i < document.getElementsByClassName('train-image').length; i++) {
            X.push(preprocessImage(document.getElementsByClassName('train-image')[i]));
            if (document.getElementsByClassName('train-image')[i].classList.contains("cat")){
                Y.push(0)
            } else {
                Y.push(1)
            }
        }

        /*
            tensor X and Y need to have the same structure.
                [0,0,0,1,1,1, ...] => Tensor[0,0,0,1,1,1, ...]
                [tensorImg1, tensorImg2, tensorImg3, ...] => Tensor[Img1, Img2, Img3, ...]
            so that the index X[0] contains the data of the image and Y[0] contains the class/label reference to the same image
        */
        Y = tf.oneHot(Y, 2);
        X = tf.concat(X, 0)
        console.log('ys:::' + Y);
        console.log(X);

        trainModel(X, Y);
    });

    async function trainModel(X, Y) {
        await model.fit(X, Y, {batchSize: 64, epochs: 150, shuffle:true}).then((loss) => {

            // shows the error rate for every iteration
            console.log(loss)
            }).catch((e) => {
                console.log(e.message);
            })
        console.log("Training done!");
    }

    //-------------------------- Predict: --------------------------------
    document.getElementById("image-selector").addEventListener("change", function() {
        var canvas = document.getElementById("canvas");
        var context = canvas.getContext('2d');

        var reader = new FileReader();
        reader.addEventListener("loadend", function(arg) {
            var src_image = new Image();
            src_image.onload = function() {
                canvas.height = src_image.height;
                canvas.width = src_image.width;
                context.drawImage(src_image, 0, 0);
                var imageData = canvas.toDataURL("image/png");
                runPrediction(src_image)
            }
            src_image.src = this.result;
        });
        var res = reader.readAsDataURL(this.files[0]);
    });

    async function runPrediction(imageData){
        let tensor = preprocessImage(imageData);
        const resize_image = tf.reshape(tensor, [1, 224, 224, 3],'resize');
        let prediction = await model.predict(tensor).data();
        console.log('prediction:::'+ prediction);


        var labelIndexOfHighestProbability = -1;
        var tempVal = -1;
        for(i = 0; i < prediction.length; i++) {
            if(prediction[i] > tempVal) {
                tempVal = prediction[i]
                labelIndexOfHighestProbability = i
            }
        }
        $("#prediction-list").empty();
        $("#prediction-list").append(`<h3>Prediction: ${labels[labelIndexOfHighestProbability]} (${prediction[labelIndexOfHighestProbability]})</h3>`);
    }

    //-------------------------- Helpers: --------------------------------

    function prepareModel(){
        model.add(tf.layers.conv2d({
            inputShape: [224, 224 , 3],
            kernelSize: 5,
            // filters: 8,
            filters: 4,
            strides: 2,
            activation: 'relu',
            kernelInitializer: 'VarianceScaling'
        }));
        model.add(tf.layers.maxPooling2d({poolSize: 2, strides: 2}));
        model.add(tf.layers.maxPooling2d({poolSize: 2, strides: 2}));
        model.add(tf.layers.flatten({}));
        model.add(tf.layers.dense({units: 64, activation: 'relu'}));
        model.add(tf.layers.dense({units: 2, activation: 'softmax'}));
        model.compile({
            loss: 'meanSquaredError',
            optimizer : 'sgd'
        });
        model.summary()
    }

    function preprocessImage(image)
    {
        let tensor = tf.browser.fromPixels(image)
        .resizeNearestNeighbor([224,224])
        .toFloat();

        let offset=tf.scalar(127.5);

        return tensor.sub(offset)
        .div(offset)
        .expandDims();
    }
</script>