luoyc
/
OpenCV


			
				
					
						
						
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							<!DOCTYPE html>
<html>

<head>
    <meta charset="utf-8">
    <title>Style Transfer Example</title>
    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>

<body>
<h2>Style Transfer Example</h2>
<p>
    This tutorial shows you how to write an style transfer example with OpenCV.js.<br>
    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
    Then You should change the parameters in the first code snippet according to the uploaded model.
    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
</p>

<div class="control"><button id="tryIt" disabled>Try it</button></div>
<div>
    <table cellpadding="0" cellspacing="0" width="0" border="0">
        <tr>
            <td>
                <canvas id="canvasInput" width="400" height="400"></canvas>
            </td>
            <td>
                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
            </td>
        </tr>
        <tr>
            <td>
                <div class="caption">
                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
                </div>
            </td>
            <td>
                <p id='status' align="left"></p>
            </td>
        </tr>
        <tr>
            <td>
                <div class="caption">
                    modelFile <input type="file" id="modelFile" name="file">
                </div>
            </td>
        </tr>
        <tr>
            <td>
                <div class="caption">
                    configFile <input type="file" id="configFile">
                </div>
            </td>
        </tr>
    </table>
</div>

<div>
    <p class="err" id="errorMessage"></p>
</div>

<div>
    <h3>Help function</h3>
    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
    <p>5.The post-processing, including scaling and reordering.</p>
    <textarea class="code" rows="21" cols="100" id="codeEditor4" spellcheck="false"></textarea>
</div>

<div id="appendix">
    <h2>Model Info:</h2>
</div>

<script src="utils.js" type="text/javascript"></script>
<script src="js_dnn_example_helper.js" type="text/javascript"></script>

<script id="codeSnippet" type="text/code-snippet">
inputSize = [224, 224];
mean = [104, 117, 123];
std = 1;
swapRB = false;
</script>

<script id="codeSnippet1" type="text/code-snippet">
main = async function() {
    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
    let net = cv.readNet(configPath, modelPath);
    net.setInput(input);
    const start = performance.now();
    const result = net.forward();
    const time  = performance.now()-start;
    const output = postProcess(result);

    updateResult(output, time);
    input.delete();
    net.delete();
    result.delete();
}
</script>

<script id="codeSnippet4" type="text/code-snippet">
postProcess = function(result) {
    const resultData = result.data32F;
    const C = result.matSize[1];
    const H = result.matSize[2];
    const W = result.matSize[3];
    const mean = [104, 117, 123];

    let normData = [];
    for (let h = 0; h < H; ++h) {
        for (let w = 0; w < W; ++w) {
            for (let c = 0; c < C; ++c) {
                normData.push(resultData[c*H*W + h*W + w] + mean[c]);
            }
            normData.push(255);
        }
    }

    let output = new cv.matFromArray(H, W, cv.CV_8UC4, normData);
    return output;
}
</script>

<script type="text/javascript">
    let jsonUrl = "js_style_transfer_model_info.json";
    drawInfoTable(jsonUrl, 'appendix');

    let utils = new Utils('errorMessage');
    utils.loadCode('codeSnippet', 'codeEditor');
    utils.loadCode('codeSnippet1', 'codeEditor1');

    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
    document.getElementById('codeEditor2').value = getBlobFromImageCode;
    let loadModelCode = 'loadModel = ' + loadModel.toString();
    document.getElementById('codeEditor3').value = loadModelCode;

    utils.loadCode('codeSnippet4', 'codeEditor4');

    let canvas = document.getElementById('canvasInput');
    let ctx = canvas.getContext('2d');
    let img = new Image();
    img.crossOrigin = 'anonymous';
    img.src = 'lena.png';
    img.onload = function() {
        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
    };

    let tryIt = document.getElementById('tryIt');
    tryIt.addEventListener('click', () => {
        initStatus();
        document.getElementById('status').innerHTML = 'Running function main()...';
        utils.executeCode('codeEditor');
        utils.executeCode('codeEditor1');
        if (modelPath === "") {
            document.getElementById('status').innerHTML = 'Runing failed.';
            utils.printError('Please upload model file by clicking the button first.');
        } else {
            setTimeout(main, 1);
        }
    });

    let fileInput = document.getElementById('fileInput');
    fileInput.addEventListener('change', (e) => {
        initStatus();
        loadImageToCanvas(e, 'canvasInput');
    });

    let configPath = "";
    let configFile = document.getElementById('configFile');
    configFile.addEventListener('change', async (e) => {
        initStatus();
        configPath = await loadModel(e);
        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
    });

    let modelPath = "";
    let modelFile = document.getElementById('modelFile');
    modelFile.addEventListener('change', async (e) => {
        initStatus();
        modelPath = await loadModel(e);
        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
        configPath = "";
        configFile.value = "";
    });

    utils.loadOpenCv(() => {
        tryIt.removeAttribute('disabled');
    });

    var main = async function() {};
    var postProcess = function(result) {};

    utils.executeCode('codeEditor1');
    utils.executeCode('codeEditor2');
    utils.executeCode('codeEditor3');
    utils.executeCode('codeEditor4');

    function updateResult(output, time) {
        try{
            let canvasOutput = document.getElementById('canvasOutput');
            canvasOutput.style.visibility = "visible";
            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
            cv.imshow('canvasOutput', resized);
            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
        } catch(e) {
            console.log(e);
        }
    }

    function initStatus() {
        document.getElementById('status').innerHTML = '';
        document.getElementById('canvasOutput').style.visibility = "hidden";
        utils.clearError();
    }

</script>

</body>

</html>