tensorflow.js调用摄像头识别你的手势代码-html代码-BFW代码库

tensorflow.js调用摄像头识别你的手势代码

代码语言：html

所属分类：其他

代码描述：tensorflow.js调用摄像头识别你的手势代码

下面为部分代码预览，完整代码请点击下载或在bfwstudio webide中打开

<html>
    <head>
        <meta charset="UTF-8">
<script type="text/javascript" src="//repo.bfw.wiki/bfwrepo/js/tf.2.0.js"></script>
<script type="text/javascript" src="//repo.bfw.wiki/bfwrepo/js/tf-data.js"></script>
        <style>
            img, video {
                object-fit: cover;
            }
        </style>
    </head>
    <body>
        <video autoplay playsinline muted id="webcam" width="224" height="224"></video>
        <div id="buttons">
            <button onclick="captureSample(0)">None</button>
            <button onclick="captureSample(1)">✊ (Rock)</button>
            <button onclick="captureSample(2)">🖐 (Paper)</button>
            <button onclick="captureSample(3)">✌️ (Scissors)</button>
            <button onclick="captureSample(4)">👌 (Letter D)</button>
            <button onclick="captureSample(5)">👍 (Thumb Up)</button>
            <button onclick="captureSample(6)">🖖 (Vulcan)</button>
            <button onclick="captureSample(7)">🤟 (ILY - I Love You)</button>
            <button onclick="trainModel()">训练</button>
        </div>
        <h1 id="status">模型下载中...</h1>
        <script>
        let trainingData = [];
 
        const labels = [
            "None",
            "✊ (Rock)",
            "🖐 (Paper)",
            "✌️ (Scissors)",
            "👌 (Letter D)",
            "👍 (Thumb Up)",
            "🖖 (Vulcan)",
            "🤟 (ILY - I Love You)"
        ];
 
        function setText( text ) {
            document.getElementById( "status" ).innerText = text;
        }
 
        async function predictImage() {
            if( !hasTrained ) { return; } // Skip prediction until trained
            const img = await getWebcamImage();
            let result = tf.tidy( () => {
                const input = img.reshape( [ 1, 224, 224, 3 ] );
                return model.predict( input );
            });
            img.dispose();
            let prediction = await result.data();
            result.dispose();
            // Get the index of the highest value in the prediction
            let id = prediction.indexOf( Math.max( ...prediction ) );
            setText( labels[ id ] );
        }
 
        function createTransferModel( model ) {
            // Create the truncated base model (remove the "top" layers, classification + bottleneck layers)
            const bottleneck = model.getLayer( "dropout" ); // This is the final layer before the conv_pred pre-trained classification layer
            const baseModel = tf.model({
                inputs: model.inputs,
                outputs: bottleneck.output
            });
            // Freeze the convolutional base
            for( const layer of baseModel.layers ) {
                layer.trainable = false;
            }
            // Add a classification head
            const newHead = tf.sequential();
            newHead.add( tf.layers.flatten( {
                inputShape: baseModel.outputs[ 0 ].shape.slice( 1 )
            } ) );
            newHead.add( tf.layers.dense( { units: 100, activation: 'relu' } ) );
            newHead.add( tf.layers.dense( { units: 100, activation: 'relu' } ) );
            newHead.add( tf.layers.dense( { units: 10, activation: 'relu' } ) );
            newHead.add( tf.layers.dense( {
          .........完整代码请登录后点击上方下载按钮下载查看