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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>
|
