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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Image Classification Example</title>
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<h2>Image Classification Example</h2>
<p>
This tutorial shows you how to write an image classification 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>
<table style="visibility: hidden;" id="result">
<thead>
<tr>
<th scope="col">#</th>
<th scope="col" width=300>Label</th>
<th scope="col">Probability</th>
</tr>
</thead>
<tbody>
<tr>
<th scope="row">1</th>
<td id="label0" align="center"></td>
<td id="prob0" align="center"></td>
</tr>
<tr>
<th scope="row">2</th>
<td id="label1" align="center"></td>
<td id="prob1" align="center"></td>
</tr>
<tr>
<th scope="row">3</th>
<td id="label2" align="center"></td>
<td id="prob2" align="center"></td>
</tr>
</tbody>
</table>
<p id='status' align="left"></p>
</td>
</tr>
<tr>
<td>
<div class="caption">
canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
</div>
</td>
<td></td>
</tr>
<tr>
<td>
<div class="caption">
modelFile <input type="file" id="modelFile">
</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="13" 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="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
<p>3.Load labels from txt file and process it into an array.</p>
<textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
<p>4.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="codeEditor3" spellcheck="false"></textarea>
<p>5.Fetch model file and save to emscripten file system once click the input button.</p>
<textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
<p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
<textarea class="code" rows="35" cols="100" id="codeEditor5" 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;
// record if need softmax function for post-processing
needSoftmax = false;
// url for label file, can from local or Internet
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt";
</script>
<script id="codeSnippet1" type="text/code-snippet">
main = async function() {
const labels = await loadLables(labelsUrl);
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 probs = softmax(result);
const classes = getTopClasses(probs, labels);
updateResult(classes, time);
input.delete();
net.delete();
result.delete();
}
</script>
<script id="codeSnippet5" type="text/code-snippet">
softmax = function(result) {
let arr = result.data32F;
if (needSoftmax) {
const maxNum = Math.max(...arr);
const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
return arr.map((value, index) => {
return Math.exp(value - maxNum) / expSum;
});
} else {
return arr;
}
}
</script>
<script type="text/javascript">
let jsonUrl = "js_image_classification_model_info.json";
drawInfoTable(jsonUrl, 'appendix');
let utils = new Utils('errorMessage');
utils.loadCode('codeSnippet', 'codeEditor');
utils.loadCode('codeSnippet1', 'codeEditor1');
let loadLablesCode = 'loadLables = ' + loadLables.toString();
document.getElementById('codeEditor2').value = loadLablesCode;
let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
document.getElementById('codeEditor3').value = getBlobFromImageCode;
let loadModelCode = 'loadModel = ' + loadModel.toString();
document.getElementById('codeEditor4').value = loadModelCode;
utils.loadCode('codeSnippet5', 'codeEditor5');
let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
document.getElementById('codeEditor5').value += '\n' + '\n' + getTopClassesCode;
let canvas = document.getElementById('canvasInput');
let ctx = canvas.getContext('2d');
let img = new Image();
img.crossOrigin = 'anonymous';
img.src = 'space_shuttle.jpg';
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 softmax = function(result){};
var getTopClasses = function(mat, labels, topK = 3){};
utils.executeCode('codeEditor1');
utils.executeCode('codeEditor2');
utils.executeCode('codeEditor3');
utils.executeCode('codeEditor4');
utils.executeCode('codeEditor5');
function updateResult(classes, time) {
try{
classes.forEach((c,i) => {
let labelElement = document.getElementById('label'+i);
let probElement = document.getElementById('prob'+i);
labelElement.innerHTML = c.label;
probElement.innerHTML = c.prob + '%';
});
let result = document.getElementById('result');
result.style.visibility = 'visible';
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('result').style.visibility = 'hidden';
utils.clearError();
}
</script>
</body>
</html>
|
