init - 初始化项目
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Lee Nony
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doc/js_tutorials/js_assets/js_semantic_segmentation.html
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doc/js_tutorials/js_assets/js_semantic_segmentation.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>Semantic Segmentation 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>Semantic Segmentation Example</h2>
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<p>
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This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
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To try the example you should click the <b>modelFile</b> button(and <b>configInput</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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<canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
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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>
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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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modelFile <input type="file" id="modelFile" name="file">
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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="5" 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="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
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<p>3.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="codeEditor2" spellcheck="false"></textarea>
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<p>4.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="codeEditor3" spellcheck="false"></textarea>
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<p>5.The post-processing, including gengerate colors for different classes and argmax to get the classes for each pixel.</p>
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<textarea class="code" rows="34" cols="100" id="codeEditor4" 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 = [513, 513];
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mean = [127.5, 127.5, 127.5];
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std = 0.007843;
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swapRB = false;
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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 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 colors = generateColors(result);
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const output = argmax(result, colors);
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updateResult(output, 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="codeSnippet4" type="text/code-snippet">
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generateColors = function(result) {
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const numClasses = result.matSize[1];
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let colors = [0,0,0];
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while(colors.length < numClasses*3){
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colors.push(Math.round((Math.random()*255 + colors[colors.length-3]) / 2));
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}
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return colors;
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}
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argmax = function(result, colors) {
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const C = result.matSize[1];
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const H = result.matSize[2];
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const W = result.matSize[3];
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const resultData = result.data32F;
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const imgSize = H*W;
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let classId = [];
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for (i = 0; i<imgSize; ++i) {
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let id = 0;
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for (j = 0; j < C; ++j) {
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if (resultData[j*imgSize+i] > resultData[id*imgSize+i]) {
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id = j;
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}
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}
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classId.push(colors[id*3]);
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classId.push(colors[id*3+1]);
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classId.push(colors[id*3+2]);
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classId.push(255);
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}
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output = cv.matFromArray(H,W,cv.CV_8UC4,classId);
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return output;
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}
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</script>
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<script type="text/javascript">
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let jsonUrl = "js_semantic_segmentation_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 getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
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document.getElementById('codeEditor2').value = getBlobFromImageCode;
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let loadModelCode = 'loadModel = ' + loadModel.toString();
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document.getElementById('codeEditor3').value = loadModelCode;
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utils.loadCode('codeSnippet4', 'codeEditor4');
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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 = 'roi.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 generateColors = function(result) {};
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var argmax = function(result, colors) {};
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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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function updateResult(output, time) {
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try{
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let canvasOutput = document.getElementById('canvasOutput');
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canvasOutput.style.visibility = "visible";
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let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
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cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
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cv.imshow('canvasOutput', resized);
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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('canvasOutput').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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