init - 初始化项目
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Lee Nony
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doc/js_tutorials/js_assets/js_image_classification.html
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doc/js_tutorials/js_assets/js_image_classification.html
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<title>Image Classification Example</title>
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<link href="js_example_style.css" rel="stylesheet" type="text/css" />
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</head>
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<body>
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<h2>Image Classification Example</h2>
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<p>
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This tutorial shows you how to write an image classification example with OpenCV.js.<br>
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To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
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You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
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Then You should change the parameters in the first code snippet according to the uploaded model.
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Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
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</p>
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<div class="control"><button id="tryIt" disabled>Try it</button></div>
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<div>
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<table cellpadding="0" cellspacing="0" width="0" border="0">
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<tr>
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<td>
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<canvas id="canvasInput" width="400" height="400"></canvas>
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</td>
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<td>
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<table style="visibility: hidden;" id="result">
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<thead>
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<tr>
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<th scope="col">#</th>
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<th scope="col" width=300>Label</th>
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<th scope="col">Probability</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th scope="row">1</th>
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<td id="label0" align="center"></td>
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<td id="prob0" align="center"></td>
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</tr>
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<tr>
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<th scope="row">2</th>
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<td id="label1" align="center"></td>
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<td id="prob1" align="center"></td>
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</tr>
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<tr>
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<th scope="row">3</th>
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<td id="label2" align="center"></td>
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<td id="prob2" align="center"></td>
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</tr>
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</tbody>
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</table>
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<p id='status' align="left"></p>
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</td>
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</tr>
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<tr>
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<td>
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<div class="caption">
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canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
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</div>
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</td>
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<td></td>
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</tr>
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<tr>
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<td>
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<div class="caption">
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modelFile <input type="file" id="modelFile">
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</div>
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</td>
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</tr>
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<tr>
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<td>
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<div class="caption">
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configFile <input type="file" id="configFile">
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</div>
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</td>
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</tr>
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</table>
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</div>
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<div>
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<p class="err" id="errorMessage"></p>
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</div>
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<div>
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<h3>Help function</h3>
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<p>1.The parameters for model inference which you can modify to investigate more models.</p>
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<textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
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<p>2.Main loop in which will read the image from canvas and do inference once.</p>
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<textarea class="code" rows="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
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<p>3.Load labels from txt file and process it into an array.</p>
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<textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
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<p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
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<textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
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<p>5.Fetch model file and save to emscripten file system once click the input button.</p>
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<textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
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<p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
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<textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
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</div>
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<div id="appendix">
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<h2>Model Info:</h2>
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</div>
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<script src="utils.js" type="text/javascript"></script>
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<script src="js_dnn_example_helper.js" type="text/javascript"></script>
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<script id="codeSnippet" type="text/code-snippet">
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inputSize = [224,224];
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mean = [104, 117, 123];
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std = 1;
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swapRB = false;
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// record if need softmax function for post-processing
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needSoftmax = false;
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// url for label file, can from local or Internet
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labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
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</script>
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<script id="codeSnippet1" type="text/code-snippet">
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main = async function() {
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const labels = await loadLables(labelsUrl);
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const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
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let net = cv.readNet(configPath, modelPath);
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net.setInput(input);
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const start = performance.now();
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const result = net.forward();
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const time = performance.now()-start;
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const probs = softmax(result);
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const classes = getTopClasses(probs, labels);
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updateResult(classes, time);
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input.delete();
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net.delete();
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result.delete();
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}
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</script>
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<script id="codeSnippet5" type="text/code-snippet">
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softmax = function(result) {
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let arr = result.data32F;
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if (needSoftmax) {
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const maxNum = Math.max(...arr);
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const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
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return arr.map((value, index) => {
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return Math.exp(value - maxNum) / expSum;
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});
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} else {
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return arr;
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}
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}
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</script>
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<script type="text/javascript">
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let jsonUrl = "js_image_classification_model_info.json";
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drawInfoTable(jsonUrl, 'appendix');
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let utils = new Utils('errorMessage');
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utils.loadCode('codeSnippet', 'codeEditor');
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utils.loadCode('codeSnippet1', 'codeEditor1');
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let loadLablesCode = 'loadLables = ' + loadLables.toString();
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document.getElementById('codeEditor2').value = loadLablesCode;
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let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
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document.getElementById('codeEditor3').value = getBlobFromImageCode;
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let loadModelCode = 'loadModel = ' + loadModel.toString();
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document.getElementById('codeEditor4').value = loadModelCode;
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utils.loadCode('codeSnippet5', 'codeEditor5');
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let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
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document.getElementById('codeEditor5').value += '\n' + '\n' + getTopClassesCode;
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let canvas = document.getElementById('canvasInput');
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let ctx = canvas.getContext('2d');
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let img = new Image();
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img.crossOrigin = 'anonymous';
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img.src = 'space_shuttle.jpg';
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img.onload = function() {
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ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
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};
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let tryIt = document.getElementById('tryIt');
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tryIt.addEventListener('click', () => {
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initStatus();
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document.getElementById('status').innerHTML = 'Running function main()...';
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utils.executeCode('codeEditor');
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utils.executeCode('codeEditor1');
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if (modelPath === "") {
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document.getElementById('status').innerHTML = 'Runing failed.';
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utils.printError('Please upload model file by clicking the button first.');
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} else {
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setTimeout(main, 1);
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}
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});
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let fileInput = document.getElementById('fileInput');
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fileInput.addEventListener('change', (e) => {
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initStatus();
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loadImageToCanvas(e, 'canvasInput');
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});
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let configPath = "";
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let configFile = document.getElementById('configFile');
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configFile.addEventListener('change', async (e) => {
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initStatus();
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configPath = await loadModel(e);
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document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
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});
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let modelPath = "";
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let modelFile = document.getElementById('modelFile');
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modelFile.addEventListener('change', async (e) => {
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initStatus();
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modelPath = await loadModel(e);
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document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
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configPath = "";
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configFile.value = "";
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});
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utils.loadOpenCv(() => {
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tryIt.removeAttribute('disabled');
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});
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var main = async function() {};
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var softmax = function(result){};
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var getTopClasses = function(mat, labels, topK = 3){};
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utils.executeCode('codeEditor1');
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utils.executeCode('codeEditor2');
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utils.executeCode('codeEditor3');
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utils.executeCode('codeEditor4');
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utils.executeCode('codeEditor5');
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function updateResult(classes, time) {
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try{
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classes.forEach((c,i) => {
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let labelElement = document.getElementById('label'+i);
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let probElement = document.getElementById('prob'+i);
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labelElement.innerHTML = c.label;
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probElement.innerHTML = c.prob + '%';
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});
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let result = document.getElementById('result');
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result.style.visibility = 'visible';
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document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
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<b>Inference time:</b> ${time.toFixed(2)} ms`;
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} catch(e) {
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console.log(e);
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}
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}
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function initStatus() {
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document.getElementById('status').innerHTML = '';
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document.getElementById('result').style.visibility = 'hidden';
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utils.clearError();
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}
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</script>
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</body>
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</html>
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