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Merge pull request #18064 from akineeic:gsoc_2020_dnn
[GSoC] Develop OpenCV.js DNN modules for promising web use cases together with their tutorials * [Opencv.js doc] Init commit to add image classification example in opencv.js tutorial * [Opencv.js doc] Make the code snippet interactive and put the functions into code snippet. * Fix the utils.loadOpenCv for promise module * [Opencv.js doc] Code modify and fixed layout issue. * [Opencv.js doc] Add a JSON file to store parameters for models and show in the web page. * [Opencv.js doc] Change let to const. * [Opencv.js doc] Init commit to add image classification example with camera in opencv.js tutorial * [Opencv.js doc] Init commit to add semantic segmentation example in opencv.js tutorial * [Opencv.js doc] Add object detection example, supprot YOLOv2 * [Opencv.js doc] Support SSD model for object detection example * [Opencv.js doc] Add fast neural style transfer example with opencv.js * [Opencv.js doc] Add pose estimation example in opencv.js tutorial * Delete whitespace for code check * [Opencv.js doc] Add object detection example with camera * [Opencv.js doc] Add json files containing model information to each example * [Opencv.js doc] Add a js file for common function in dnn example * [Opencv.js doc] Create single function getBlobFromImage * [Opencv.js doc] Add url of model into webpage * [OpenCV.js doc] Update UI for running * [Opencv.js doc] Load dnn model by input button * [Opencv.js doc] Fix some UI issues * [Opencv.js doc] Change code format Co-authored-by: Ningxin Hu <ningxin.hu@intel.com>
This commit is contained in:
Zhiming-Zeng
GitHub
parent
7c78c59e64
commit
4e4458416d
119
doc/js_tutorials/js_assets/js_dnn_example_helper.js
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119
doc/js_tutorials/js_assets/js_dnn_example_helper.js
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getBlobFromImage = function(inputSize, mean, std, swapRB, image) {
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let mat;
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if (typeof(image) === 'string') {
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mat = cv.imread(image);
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} else {
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mat = image;
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}
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let matC3 = new cv.Mat(mat.matSize[0], mat.matSize[1], cv.CV_8UC3);
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cv.cvtColor(mat, matC3, cv.COLOR_RGBA2BGR);
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let input = cv.blobFromImage(matC3, std, new cv.Size(inputSize[0], inputSize[1]),
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new cv.Scalar(mean[0], mean[1], mean[2]), swapRB);
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matC3.delete();
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return input;
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}
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loadLables = async function(labelsUrl) {
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let response = await fetch(labelsUrl);
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let label = await response.text();
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label = label.split('\n');
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return label;
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}
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loadModel = async function(e) {
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return new Promise((resolve) => {
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let file = e.target.files[0];
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let path = file.name;
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let reader = new FileReader();
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reader.readAsArrayBuffer(file);
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reader.onload = function(ev) {
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if (reader.readyState === 2) {
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let buffer = reader.result;
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let data = new Uint8Array(buffer);
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cv.FS_createDataFile('/', path, data, true, false, false);
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resolve(path);
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}
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}
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});
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}
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getTopClasses = function(probs, labels, topK = 3) {
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probs = Array.from(probs);
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let indexes = probs.map((prob, index) => [prob, index]);
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let sorted = indexes.sort((a, b) => {
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if (a[0] === b[0]) {return 0;}
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return a[0] < b[0] ? -1 : 1;
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});
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sorted.reverse();
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let classes = [];
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for (let i = 0; i < topK; ++i) {
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let prob = sorted[i][0];
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let index = sorted[i][1];
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let c = {
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label: labels[index],
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prob: (prob * 100).toFixed(2)
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}
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classes.push(c);
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}
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return classes;
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}
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loadImageToCanvas = function(e, canvasId) {
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let files = e.target.files;
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let imgUrl = URL.createObjectURL(files[0]);
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let canvas = document.getElementById(canvasId);
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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 = imgUrl;
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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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}
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drawInfoTable = async function(jsonUrl, divId) {
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let response = await fetch(jsonUrl);
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let json = await response.json();
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let appendix = document.getElementById(divId);
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for (key of Object.keys(json)) {
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let h3 = document.createElement('h3');
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h3.textContent = key + " model";
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appendix.appendChild(h3);
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let table = document.createElement('table');
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let head_tr = document.createElement('tr');
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for (head of Object.keys(json[key][0])) {
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let th = document.createElement('th');
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th.textContent = head;
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th.style.border = "1px solid black";
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head_tr.appendChild(th);
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}
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table.appendChild(head_tr)
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for (model of json[key]) {
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let tr = document.createElement('tr');
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for (params of Object.keys(model)) {
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let td = document.createElement('td');
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td.style.border = "1px solid black";
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if (params !== "modelUrl" && params !== "configUrl" && params !== "labelsUrl") {
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td.textContent = model[params];
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tr.appendChild(td);
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} else {
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let a = document.createElement('a');
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let link = document.createTextNode('link');
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a.append(link);
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a.href = model[params];
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td.appendChild(a);
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tr.appendChild(td);
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}
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}
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table.appendChild(tr);
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}
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table.style.width = "800px";
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table.style.borderCollapse = "collapse";
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appendix.appendChild(table);
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}
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}
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263
doc/js_tutorials/js_assets/js_image_classification.html
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263
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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|
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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);
|
||||
}
|
||||
}
|
||||
|
||||
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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|
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</script>
|
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|
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</body>
|
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|
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</html>
|
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{
|
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"caffe": [
|
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{
|
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"model": "alexnet",
|
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"mean": "104, 117, 123",
|
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"std": "1",
|
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"swapRB": "false",
|
||||
"needSoftmax": "false",
|
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"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
|
||||
"modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
|
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"configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "densenet",
|
||||
"mean": "127.5, 127.5, 127.5",
|
||||
"std": "0.007843",
|
||||
"swapRB": "false",
|
||||
"needSoftmax": "true",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
|
||||
"modelUrl": "https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
|
||||
"configUrl": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "googlenet",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"needSoftmax": "false",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
|
||||
"modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "squeezenet",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"needSoftmax": "false",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
|
||||
"modelUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/deploy.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "VGG",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"needSoftmax": "false",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
|
||||
"modelUrl": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
|
||||
"configUrl": "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
|
||||
}
|
||||
],
|
||||
"tensorflow": [
|
||||
{
|
||||
"model": "inception",
|
||||
"mean": "123, 117, 104",
|
||||
"std": "1",
|
||||
"swapRB": "true",
|
||||
"needSoftmax": "false",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/imagenet_comp_graph_label_strings.txt",
|
||||
"modelUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/tensorflow_inception_graph.pb"
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -0,0 +1,281 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Image Classification Example with Camera</title>
|
||||
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<h2>Image Classification Example with Camera</h2>
|
||||
<p>
|
||||
This tutorial shows you how to write an image classification example with camera.<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>Start/Stop</b> button to start or stop the camera capture.<br>
|
||||
</p>
|
||||
|
||||
<div class="control"><button id="startAndStop" disabled>Start</button></div>
|
||||
<div>
|
||||
<table cellpadding="0" cellspacing="0" width="0" border="0">
|
||||
<tr>
|
||||
<td>
|
||||
<video id="videoInput" width="400" height="400"></video>
|
||||
</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">
|
||||
videoInput
|
||||
</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.The function to capture video from camera, and the main loop in which will do inference once.</p>
|
||||
<textarea class="code" rows="35" 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/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet1" type="text/code-snippet">
|
||||
let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
|
||||
let cap = new cv.VideoCapture(video);
|
||||
|
||||
main = async function(frame) {
|
||||
const labels = await loadLables(labelsUrl);
|
||||
const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
|
||||
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);
|
||||
setTimeout(processVideo, 0);
|
||||
input.delete();
|
||||
net.delete();
|
||||
result.delete();
|
||||
}
|
||||
|
||||
function processVideo() {
|
||||
try {
|
||||
if (!streaming) {
|
||||
return;
|
||||
}
|
||||
cap.read(frame);
|
||||
main(frame);
|
||||
} catch (err) {
|
||||
utils.printError(err);
|
||||
}
|
||||
}
|
||||
|
||||
setTimeout(processVideo, 0);
|
||||
</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 video = document.getElementById('videoInput');
|
||||
let streaming = false;
|
||||
let startAndStop = document.getElementById('startAndStop');
|
||||
startAndStop.addEventListener('click', () => {
|
||||
if (!streaming) {
|
||||
utils.clearError();
|
||||
utils.startCamera('qvga', onVideoStarted, 'videoInput');
|
||||
} else {
|
||||
utils.stopCamera();
|
||||
onVideoStopped();
|
||||
}
|
||||
});
|
||||
|
||||
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(() => {
|
||||
startAndStop.removeAttribute('disabled');
|
||||
|
||||
});
|
||||
|
||||
var main = async function(frame) {};
|
||||
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 onVideoStarted() {
|
||||
streaming = true;
|
||||
startAndStop.innerText = 'Stop';
|
||||
videoInput.width = videoInput.videoWidth;
|
||||
videoInput.height = videoInput.videoHeight;
|
||||
utils.executeCode('codeEditor');
|
||||
utils.executeCode('codeEditor1');
|
||||
}
|
||||
|
||||
function onVideoStopped() {
|
||||
streaming = false;
|
||||
startAndStop.innerText = 'Start';
|
||||
initStatus();
|
||||
}
|
||||
|
||||
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>
|
||||
387
doc/js_tutorials/js_assets/js_object_detection.html
Normal file
387
doc/js_tutorials/js_assets/js_object_detection.html
Normal file
@ -0,0 +1,387 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Object Detection Example</title>
|
||||
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<h2>Object Detection Example</h2>
|
||||
<p>
|
||||
This tutorial shows you how to write an object detection 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="15" 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="16" 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 get boxes from output and draw boxes into the image.</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 = [300, 300];
|
||||
mean = [127.5, 127.5, 127.5];
|
||||
std = 0.007843;
|
||||
swapRB = false;
|
||||
confThreshold = 0.5;
|
||||
nmsThreshold = 0.4;
|
||||
|
||||
// The type of output, can be YOLO or SSD
|
||||
outType = "SSD";
|
||||
|
||||
// url for label file, can from local or Internet
|
||||
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.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 output = postProcess(result, labels);
|
||||
|
||||
updateResult(output, time);
|
||||
input.delete();
|
||||
net.delete();
|
||||
result.delete();
|
||||
}
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet5" type="text/code-snippet">
|
||||
postProcess = function(result, labels) {
|
||||
let canvasOutput = document.getElementById('canvasOutput');
|
||||
const outputWidth = canvasOutput.width;
|
||||
const outputHeight = canvasOutput.height;
|
||||
const resultData = result.data32F;
|
||||
|
||||
// Get the boxes(with class and confidence) from the output
|
||||
let boxes = [];
|
||||
switch(outType) {
|
||||
case "YOLO": {
|
||||
const vecNum = result.matSize[0];
|
||||
const vecLength = result.matSize[1];
|
||||
const classNum = vecLength - 5;
|
||||
|
||||
for (let i = 0; i < vecNum; ++i) {
|
||||
let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
|
||||
let scores = vector.slice(5, vecLength);
|
||||
let classId = scores.indexOf(Math.max(...scores));
|
||||
let confidence = scores[classId];
|
||||
if (confidence > confThreshold) {
|
||||
let center_x = Math.round(vector[0] * outputWidth);
|
||||
let center_y = Math.round(vector[1] * outputHeight);
|
||||
let width = Math.round(vector[2] * outputWidth);
|
||||
let height = Math.round(vector[3] * outputHeight);
|
||||
let left = Math.round(center_x - width / 2);
|
||||
let top = Math.round(center_y - height / 2);
|
||||
|
||||
let box = {
|
||||
scores: scores,
|
||||
classId: classId,
|
||||
confidence: confidence,
|
||||
bounding: [left, top, width, height],
|
||||
toDraw: true
|
||||
}
|
||||
boxes.push(box);
|
||||
}
|
||||
}
|
||||
|
||||
// NMS(Non Maximum Suppression) algorithm
|
||||
let boxNum = boxes.length;
|
||||
let tmp_boxes = [];
|
||||
let sorted_boxes = [];
|
||||
for (let c = 0; c < classNum; ++c) {
|
||||
for (let i = 0; i < boxes.length; ++i) {
|
||||
tmp_boxes[i] = [boxes[i], i];
|
||||
}
|
||||
sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
|
||||
for (let i = 0; i < boxNum; ++i) {
|
||||
if (sorted_boxes[i][0].scores[c] === 0) continue;
|
||||
else {
|
||||
for (let j = i + 1; j < boxNum; ++j) {
|
||||
if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
|
||||
boxes[sorted_boxes[j][1]].toDraw = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case "SSD": {
|
||||
const vecNum = result.matSize[2];
|
||||
const vecLength = 7;
|
||||
|
||||
for (let i = 0; i < vecNum; ++i) {
|
||||
let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
|
||||
let confidence = vector[2];
|
||||
if (confidence > confThreshold) {
|
||||
let left, top, right, bottom, width, height;
|
||||
left = Math.round(vector[3]);
|
||||
top = Math.round(vector[4]);
|
||||
right = Math.round(vector[5]);
|
||||
bottom = Math.round(vector[6]);
|
||||
width = right - left + 1;
|
||||
height = bottom - top + 1;
|
||||
if (width <= 2 || height <= 2) {
|
||||
left = Math.round(vector[3] * outputWidth);
|
||||
top = Math.round(vector[4] * outputHeight);
|
||||
right = Math.round(vector[5] * outputWidth);
|
||||
bottom = Math.round(vector[6] * outputHeight);
|
||||
width = right - left + 1;
|
||||
height = bottom - top + 1;
|
||||
}
|
||||
let box = {
|
||||
classId: vector[1] - 1,
|
||||
confidence: confidence,
|
||||
bounding: [left, top, width, height],
|
||||
toDraw: true
|
||||
}
|
||||
boxes.push(box);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
console.error(`Unsupported output type ${outType}`)
|
||||
}
|
||||
|
||||
// Draw the saved box into the image
|
||||
let image = cv.imread("canvasInput");
|
||||
let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
|
||||
cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
|
||||
let boxNum = boxes.length;
|
||||
for (let i = 0; i < boxNum; ++i) {
|
||||
if (boxes[i].toDraw) {
|
||||
drawBox(boxes[i]);
|
||||
}
|
||||
}
|
||||
|
||||
return output;
|
||||
|
||||
|
||||
// Calculate the IOU(Intersection over Union) of two boxes
|
||||
function IOU(box1, box2) {
|
||||
let bounding1 = box1.bounding;
|
||||
let bounding2 = box2.bounding;
|
||||
let s1 = bounding1[2] * bounding1[3];
|
||||
let s2 = bounding2[2] * bounding2[3];
|
||||
|
||||
let left1 = bounding1[0];
|
||||
let right1 = left1 + bounding1[2];
|
||||
let left2 = bounding2[0];
|
||||
let right2 = left2 + bounding2[2];
|
||||
let overlapW = calOverlap([left1, right1], [left2, right2]);
|
||||
|
||||
let top1 = bounding2[1];
|
||||
let bottom1 = top1 + bounding1[3];
|
||||
let top2 = bounding2[1];
|
||||
let bottom2 = top2 + bounding2[3];
|
||||
let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
|
||||
|
||||
let overlapS = overlapW * overlapH;
|
||||
return overlapS / (s1 + s2 + overlapS);
|
||||
}
|
||||
|
||||
// Calculate the overlap range of two vector
|
||||
function calOverlap(range1, range2) {
|
||||
let min1 = range1[0];
|
||||
let max1 = range1[1];
|
||||
let min2 = range2[0];
|
||||
let max2 = range2[1];
|
||||
|
||||
if (min2 > min1 && min2 < max1) {
|
||||
return max1 - min2;
|
||||
} else if (max2 > min1 && max2 < max1) {
|
||||
return max2 - min1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Draw one predict box into the origin image
|
||||
function drawBox(box) {
|
||||
let bounding = box.bounding;
|
||||
let left = bounding[0];
|
||||
let top = bounding[1];
|
||||
let width = bounding[2];
|
||||
let height = bounding[3];
|
||||
|
||||
cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
|
||||
new cv.Scalar(0, 255, 0));
|
||||
cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
|
||||
new cv.Scalar(255, 255, 255), cv.FILLED);
|
||||
let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
|
||||
cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
|
||||
new cv.Scalar(0, 0, 0));
|
||||
}
|
||||
}
|
||||
</script>
|
||||
|
||||
<script type="text/javascript">
|
||||
let jsonUrl = "js_object_detection_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 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, labels) {};
|
||||
|
||||
utils.executeCode('codeEditor1');
|
||||
utils.executeCode('codeEditor2');
|
||||
utils.executeCode('codeEditor3');
|
||||
utils.executeCode('codeEditor4');
|
||||
utils.executeCode('codeEditor5');
|
||||
|
||||
|
||||
function updateResult(output, time) {
|
||||
try{
|
||||
let canvasOutput = document.getElementById('canvasOutput');
|
||||
canvasOutput.style.visibility = "visible";
|
||||
cv.imshow('canvasOutput', output);
|
||||
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>
|
||||
@ -0,0 +1,39 @@
|
||||
{
|
||||
"caffe": [
|
||||
{
|
||||
"model": "mobilenet_SSD",
|
||||
"inputSize": "300, 300",
|
||||
"mean": "127.5, 127.5, 127.5",
|
||||
"std": "0.007843",
|
||||
"swapRB": "false",
|
||||
"outType": "SSD",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
|
||||
"modelUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/mobilenet_iter_73000.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/deploy.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "VGG_SSD",
|
||||
"inputSize": "300, 300",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"outType": "SSD",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
|
||||
"modelUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download",
|
||||
"configUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download"
|
||||
}
|
||||
],
|
||||
"darknet": [
|
||||
{
|
||||
"model": "yolov2_tiny",
|
||||
"inputSize": "416, 416",
|
||||
"mean": "0, 0, 0",
|
||||
"std": "0.00392",
|
||||
"swapRB": "false",
|
||||
"outType": "YOLO",
|
||||
"labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_yolov3.txt",
|
||||
"modelUrl": "https://pjreddie.com/media/files/yolov2-tiny.weights",
|
||||
"configUrl": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg"
|
||||
}
|
||||
]
|
||||
}
|
||||
402
doc/js_tutorials/js_assets/js_object_detection_with_camera.html
Normal file
402
doc/js_tutorials/js_assets/js_object_detection_with_camera.html
Normal file
@ -0,0 +1,402 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Object Detection Example with Camera</title>
|
||||
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<h2>Object Detection Example with Camera </h2>
|
||||
<p>
|
||||
This tutorial shows you how to write an object detection example with camera.<br>
|
||||
To try the example you should click the <b>modelFile</b> button(and <b>configInput</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>Start/Stop</b> button to start or stop the camera capture.<br>
|
||||
</p>
|
||||
|
||||
<div class="control"><button id="startAndStop" disabled>Start</button></div>
|
||||
<div>
|
||||
<table cellpadding="0" cellspacing="0" width="0" border="0">
|
||||
<tr>
|
||||
<td>
|
||||
<video id="videoInput" width="400" height="400"></video>
|
||||
</td>
|
||||
<td>
|
||||
<canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
<div class="caption">
|
||||
videoInput
|
||||
</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="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
|
||||
<p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
|
||||
<textarea class="code" rows="34" 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 get boxes from output and draw boxes into the image.</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 = [300, 300];
|
||||
mean = [127.5, 127.5, 127.5];
|
||||
std = 0.007843;
|
||||
swapRB = false;
|
||||
confThreshold = 0.5;
|
||||
nmsThreshold = 0.4;
|
||||
|
||||
// the type of output, can be YOLO or SSD
|
||||
outType = "SSD";
|
||||
|
||||
// url for label file, can from local or Internet
|
||||
labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt";
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet1" type="text/code-snippet">
|
||||
let frame = new cv.Mat(videoInput.height, videoInput.width, cv.CV_8UC4);
|
||||
let cap = new cv.VideoCapture(videoInput);
|
||||
|
||||
main = async function(frame) {
|
||||
const labels = await loadLables(labelsUrl);
|
||||
const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
|
||||
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, labels, frame);
|
||||
|
||||
updateResult(output, time);
|
||||
setTimeout(processVideo, 0);
|
||||
input.delete();
|
||||
net.delete();
|
||||
result.delete();
|
||||
}
|
||||
|
||||
function processVideo() {
|
||||
try {
|
||||
if (!streaming) {
|
||||
return;
|
||||
}
|
||||
cap.read(frame);
|
||||
main(frame);
|
||||
} catch (err) {
|
||||
utils.printError(err);
|
||||
}
|
||||
}
|
||||
|
||||
setTimeout(processVideo, 0);
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet5" type="text/code-snippet">
|
||||
postProcess = function(result, labels, frame) {
|
||||
let canvasOutput = document.getElementById('canvasOutput');
|
||||
const outputWidth = canvasOutput.width;
|
||||
const outputHeight = canvasOutput.height;
|
||||
const resultData = result.data32F;
|
||||
|
||||
// Get the boxes(with class and confidence) from the output
|
||||
let boxes = [];
|
||||
switch(outType) {
|
||||
case "YOLO": {
|
||||
const vecNum = result.matSize[0];
|
||||
const vecLength = result.matSize[1];
|
||||
const classNum = vecLength - 5;
|
||||
|
||||
for (let i = 0; i < vecNum; ++i) {
|
||||
let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
|
||||
let scores = vector.slice(5, vecLength);
|
||||
let classId = scores.indexOf(Math.max(...scores));
|
||||
let confidence = scores[classId];
|
||||
if (confidence > confThreshold) {
|
||||
let center_x = Math.round(vector[0] * outputWidth);
|
||||
let center_y = Math.round(vector[1] * outputHeight);
|
||||
let width = Math.round(vector[2] * outputWidth);
|
||||
let height = Math.round(vector[3] * outputHeight);
|
||||
let left = Math.round(center_x - width / 2);
|
||||
let top = Math.round(center_y - height / 2);
|
||||
|
||||
let box = {
|
||||
scores: scores,
|
||||
classId: classId,
|
||||
confidence: confidence,
|
||||
bounding: [left, top, width, height],
|
||||
toDraw: true
|
||||
}
|
||||
boxes.push(box);
|
||||
}
|
||||
}
|
||||
|
||||
// NMS(Non Maximum Suppression) algorithm
|
||||
let boxNum = boxes.length;
|
||||
let tmp_boxes = [];
|
||||
let sorted_boxes = [];
|
||||
for (let c = 0; c < classNum; ++c) {
|
||||
for (let i = 0; i < boxes.length; ++i) {
|
||||
tmp_boxes[i] = [boxes[i], i];
|
||||
}
|
||||
sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
|
||||
for (let i = 0; i < boxNum; ++i) {
|
||||
if (sorted_boxes[i][0].scores[c] === 0) continue;
|
||||
else {
|
||||
for (let j = i + 1; j < boxNum; ++j) {
|
||||
if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
|
||||
boxes[sorted_boxes[j][1]].toDraw = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case "SSD": {
|
||||
const vecNum = result.matSize[2];
|
||||
const vecLength = 7;
|
||||
|
||||
for (let i = 0; i < vecNum; ++i) {
|
||||
let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
|
||||
let confidence = vector[2];
|
||||
if (confidence > confThreshold) {
|
||||
let left, top, right, bottom, width, height;
|
||||
left = Math.round(vector[3]);
|
||||
top = Math.round(vector[4]);
|
||||
right = Math.round(vector[5]);
|
||||
bottom = Math.round(vector[6]);
|
||||
width = right - left + 1;
|
||||
height = bottom - top + 1;
|
||||
if (width <= 2 || height <= 2) {
|
||||
left = Math.round(vector[3] * outputWidth);
|
||||
top = Math.round(vector[4] * outputHeight);
|
||||
right = Math.round(vector[5] * outputWidth);
|
||||
bottom = Math.round(vector[6] * outputHeight);
|
||||
width = right - left + 1;
|
||||
height = bottom - top + 1;
|
||||
}
|
||||
let box = {
|
||||
classId: vector[1] - 1,
|
||||
confidence: confidence,
|
||||
bounding: [left, top, width, height],
|
||||
toDraw: true
|
||||
}
|
||||
boxes.push(box);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
console.error(`Unsupported output type ${outType}`)
|
||||
}
|
||||
|
||||
// Draw the saved box into the image
|
||||
let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
|
||||
cv.cvtColor(frame, output, cv.COLOR_RGBA2RGB);
|
||||
let boxNum = boxes.length;
|
||||
for (let i = 0; i < boxNum; ++i) {
|
||||
if (boxes[i].toDraw) {
|
||||
drawBox(boxes[i]);
|
||||
}
|
||||
}
|
||||
|
||||
return output;
|
||||
|
||||
|
||||
// Calculate the IOU(Intersection over Union) of two boxes
|
||||
function IOU(box1, box2) {
|
||||
let bounding1 = box1.bounding;
|
||||
let bounding2 = box2.bounding;
|
||||
let s1 = bounding1[2] * bounding1[3];
|
||||
let s2 = bounding2[2] * bounding2[3];
|
||||
|
||||
let left1 = bounding1[0];
|
||||
let right1 = left1 + bounding1[2];
|
||||
let left2 = bounding2[0];
|
||||
let right2 = left2 + bounding2[2];
|
||||
let overlapW = calOverlap([left1, right1], [left2, right2]);
|
||||
|
||||
let top1 = bounding2[1];
|
||||
let bottom1 = top1 + bounding1[3];
|
||||
let top2 = bounding2[1];
|
||||
let bottom2 = top2 + bounding2[3];
|
||||
let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
|
||||
|
||||
let overlapS = overlapW * overlapH;
|
||||
return overlapS / (s1 + s2 + overlapS);
|
||||
}
|
||||
|
||||
// Calculate the overlap range of two vector
|
||||
function calOverlap(range1, range2) {
|
||||
let min1 = range1[0];
|
||||
let max1 = range1[1];
|
||||
let min2 = range2[0];
|
||||
let max2 = range2[1];
|
||||
|
||||
if (min2 > min1 && min2 < max1) {
|
||||
return max1 - min2;
|
||||
} else if (max2 > min1 && max2 < max1) {
|
||||
return max2 - min1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Draw one predict box into the origin image
|
||||
function drawBox(box) {
|
||||
let bounding = box.bounding;
|
||||
let left = bounding[0];
|
||||
let top = bounding[1];
|
||||
let width = bounding[2];
|
||||
let height = bounding[3];
|
||||
|
||||
cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
|
||||
new cv.Scalar(0, 255, 0));
|
||||
cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
|
||||
new cv.Scalar(255, 255, 255), cv.FILLED);
|
||||
let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
|
||||
cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
|
||||
new cv.Scalar(0, 0, 0));
|
||||
}
|
||||
}
|
||||
</script>
|
||||
|
||||
<script type="text/javascript">
|
||||
let jsonUrl = "js_object_detection_model_info.json";
|
||||
drawInfoTable(jsonUrl, 'appendix');
|
||||
|
||||
let utils = new Utils('errorMessage');
|
||||
utils.loadCode('codeSnippet', 'codeEditor');
|
||||
utils.loadCode('codeSnippet1', 'codeEditor1');
|
||||
|
||||
let loadLablesCode = 'loadLables = ' + loadLables.toString();
|
||||
document.getElementById('codeEditor2').value = loadLablesCode;
|
||||
let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
|
||||
document.getElementById('codeEditor3').value = getBlobFromImageCode;
|
||||
let loadModelCode = 'loadModel = ' + loadModel.toString();
|
||||
document.getElementById('codeEditor4').value = loadModelCode;
|
||||
|
||||
utils.loadCode('codeSnippet5', 'codeEditor5');
|
||||
|
||||
let videoInput = document.getElementById('videoInput');
|
||||
let streaming = false;
|
||||
let startAndStop = document.getElementById('startAndStop');
|
||||
startAndStop.addEventListener('click', () => {
|
||||
if (!streaming) {
|
||||
utils.clearError();
|
||||
utils.startCamera('qvga', onVideoStarted, 'videoInput');
|
||||
} else {
|
||||
utils.stopCamera();
|
||||
onVideoStopped();
|
||||
}
|
||||
});
|
||||
|
||||
let configPath = "";
|
||||
let configFile = document.getElementById('configFile');
|
||||
configFile.addEventListener('change', async (e) => {
|
||||
initStatus();
|
||||
configPath = await loadModel(e);
|
||||
document.getElementById('status').innerHTML = `The config file '${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(() => {
|
||||
startAndStop.removeAttribute('disabled');
|
||||
});
|
||||
|
||||
var main = async function(frame) {};
|
||||
var postProcess = function(result, labels, frame) {};
|
||||
|
||||
utils.executeCode('codeEditor1');
|
||||
utils.executeCode('codeEditor2');
|
||||
utils.executeCode('codeEditor3');
|
||||
utils.executeCode('codeEditor4');
|
||||
utils.executeCode('codeEditor5');
|
||||
|
||||
function onVideoStarted() {
|
||||
streaming = true;
|
||||
startAndStop.innerText = 'Stop';
|
||||
videoInput.width = videoInput.videoWidth;
|
||||
videoInput.height = videoInput.videoHeight;
|
||||
utils.executeCode('codeEditor');
|
||||
utils.executeCode('codeEditor1');
|
||||
}
|
||||
|
||||
function onVideoStopped() {
|
||||
streaming = false;
|
||||
startAndStop.innerText = 'Start';
|
||||
initStatus();
|
||||
}
|
||||
|
||||
function updateResult(output, time) {
|
||||
try{
|
||||
let canvasOutput = document.getElementById('canvasOutput');
|
||||
canvasOutput.style.visibility = "visible";
|
||||
cv.imshow('canvasOutput', output);
|
||||
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>
|
||||
327
doc/js_tutorials/js_assets/js_pose_estimation.html
Normal file
327
doc/js_tutorials/js_assets/js_pose_estimation.html
Normal file
@ -0,0 +1,327 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Pose Estimation Example</title>
|
||||
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<h2>Pose Estimation Example</h2>
|
||||
<p>
|
||||
This tutorial shows you how to write an pose estimation example with OpenCV.js.<br>
|
||||
To try the example you should click the <b>modelFile</b> button(and <b>configInput</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="250"></canvas>
|
||||
</td>
|
||||
<td>
|
||||
<canvas id="canvasOutput" style="visibility: hidden;" width="400" height="250"></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="9" 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 pairs of keypoints of different dataset.</p>
|
||||
<textarea class="code" rows="30" cols="100" id="codeEditor4" spellcheck="false"></textarea>
|
||||
<p>6.The post-processing, including get the predicted points and draw lines into the image.</p>
|
||||
<textarea class="code" rows="30" 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 = [368, 368];
|
||||
mean = [0, 0, 0];
|
||||
std = 0.00392;
|
||||
swapRB = false;
|
||||
threshold = 0.1;
|
||||
|
||||
// the pairs of keypoint, can be "COCO", "MPI" and "BODY_25"
|
||||
dataset = "COCO";
|
||||
</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">
|
||||
BODY_PARTS = {};
|
||||
POSE_PAIRS = [];
|
||||
|
||||
if (dataset === 'COCO') {
|
||||
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
|
||||
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
|
||||
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
|
||||
"LEye": 15, "REar": 16, "LEar": 17, "Background": 18 };
|
||||
|
||||
POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
|
||||
["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
|
||||
["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
|
||||
["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
|
||||
["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
|
||||
} else if (dataset === 'MPI') {
|
||||
BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
|
||||
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
|
||||
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
|
||||
"Background": 15 }
|
||||
|
||||
POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
|
||||
["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
|
||||
["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
|
||||
["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
|
||||
} else if (dataset === 'BODY_25') {
|
||||
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
|
||||
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "MidHip": 8, "RHip": 9,
|
||||
"RKnee": 10, "RAnkle": 11, "LHip": 12, "LKnee": 13, "LAnkle": 14,
|
||||
"REye": 15, "LEye": 16, "REar": 17, "LEar": 18, "LBigToe": 19,
|
||||
"LSmallToe": 20, "LHeel": 21, "RBigToe": 22, "RSmallToe": 23,
|
||||
"RHeel": 24, "Background": 25 }
|
||||
|
||||
POSE_PAIRS = [ ["Neck", "Nose"], ["Neck", "RShoulder"],
|
||||
["Neck", "LShoulder"], ["RShoulder", "RElbow"],
|
||||
["RElbow", "RWrist"], ["LShoulder", "LElbow"],
|
||||
["LElbow", "LWrist"], ["Nose", "REye"],
|
||||
["REye", "REar"], ["Neck", "LEye"],
|
||||
["LEye", "LEar"], ["Neck", "MidHip"],
|
||||
["MidHip", "RHip"], ["RHip", "RKnee"],
|
||||
["RKnee", "RAnkle"], ["RAnkle", "RBigToe"],
|
||||
["RBigToe", "RSmallToe"], ["RAnkle", "RHeel"],
|
||||
["MidHip", "LHip"], ["LHip", "LKnee"],
|
||||
["LKnee", "LAnkle"], ["LAnkle", "LBigToe"],
|
||||
["LBigToe", "LSmallToe"], ["LAnkle", "LHeel"] ]
|
||||
}
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet5" type="text/code-snippet">
|
||||
postProcess = function(result) {
|
||||
const resultData = result.data32F;
|
||||
const matSize = result.matSize;
|
||||
const size1 = matSize[1];
|
||||
const size2 = matSize[2];
|
||||
const size3 = matSize[3];
|
||||
const mapSize = size2 * size3;
|
||||
|
||||
let canvasOutput = document.getElementById('canvasOutput');
|
||||
const outputWidth = canvasOutput.width;
|
||||
const outputHeight = canvasOutput.height;
|
||||
|
||||
let image = cv.imread("canvasInput");
|
||||
let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
|
||||
cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
|
||||
|
||||
// get position of keypoints from output
|
||||
let points = [];
|
||||
for (let i = 0; i < Object.keys(BODY_PARTS).length; ++i) {
|
||||
heatMap = resultData.slice(i*mapSize, (i+1)*mapSize);
|
||||
|
||||
let maxIndex = 0;
|
||||
let maxConf = heatMap[0];
|
||||
for (index in heatMap) {
|
||||
if (heatMap[index] > heatMap[maxIndex]) {
|
||||
maxIndex = index;
|
||||
maxConf = heatMap[index];
|
||||
}
|
||||
}
|
||||
|
||||
if (maxConf > threshold) {
|
||||
indexX = maxIndex % size3;
|
||||
indexY = maxIndex / size3;
|
||||
|
||||
x = outputWidth * indexX / size3;
|
||||
y = outputHeight * indexY / size2;
|
||||
|
||||
points[i] = [Math.round(x), Math.round(y)];
|
||||
}
|
||||
}
|
||||
|
||||
// draw the points and lines into the image
|
||||
for (pair of POSE_PAIRS) {
|
||||
partFrom = pair[0];
|
||||
partTo = pair[1];
|
||||
idFrom = BODY_PARTS[partFrom];
|
||||
idTo = BODY_PARTS[partTo];
|
||||
pointFrom = points[idFrom];
|
||||
pointTo = points[idTo];
|
||||
|
||||
if (points[idFrom] && points[idTo]) {
|
||||
cv.line(output, new cv.Point(pointFrom[0], pointFrom[1]),
|
||||
new cv.Point(pointTo[0], pointTo[1]), new cv.Scalar(0, 255, 0), 3);
|
||||
cv.ellipse(output, new cv.Point(pointFrom[0], pointFrom[1]), new cv.Size(3, 3), 0, 0, 360,
|
||||
new cv.Scalar(0, 0, 255), cv.FILLED);
|
||||
cv.ellipse(output, new cv.Point(pointTo[0], pointTo[1]), new cv.Size(3, 3), 0, 0, 360,
|
||||
new cv.Scalar(0, 0, 255), cv.FILLED);
|
||||
}
|
||||
}
|
||||
|
||||
return output;
|
||||
}
|
||||
</script>
|
||||
|
||||
<script type="text/javascript">
|
||||
let jsonUrl = "js_pose_estimation_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');
|
||||
utils.loadCode('codeSnippet5', 'codeEditor5');
|
||||
|
||||
let canvas = document.getElementById('canvasInput');
|
||||
let ctx = canvas.getContext('2d');
|
||||
let img = new Image();
|
||||
img.crossOrigin = 'anonymous';
|
||||
img.src = 'roi.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 postProcess = function(result) {};
|
||||
|
||||
utils.executeCode('codeEditor');
|
||||
utils.executeCode('codeEditor1');
|
||||
utils.executeCode('codeEditor2');
|
||||
utils.executeCode('codeEditor3');
|
||||
utils.executeCode('codeEditor4');
|
||||
utils.executeCode('codeEditor5');
|
||||
|
||||
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>
|
||||
@ -0,0 +1,34 @@
|
||||
{
|
||||
"caffe": [
|
||||
{
|
||||
"model": "body_25",
|
||||
"inputSize": "368, 368",
|
||||
"mean": "0, 0, 0",
|
||||
"std": "0.00392",
|
||||
"swapRB": "false",
|
||||
"dataset": "BODY_25",
|
||||
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/body_25/pose_deploy.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "coco",
|
||||
"inputSize": "368, 368",
|
||||
"mean": "0, 0, 0",
|
||||
"std": "0.00392",
|
||||
"swapRB": "false",
|
||||
"dataset": "COCO",
|
||||
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/coco/pose_deploy_linevec.prototxt"
|
||||
},
|
||||
{
|
||||
"model": "mpi",
|
||||
"inputSize": "368, 368",
|
||||
"mean": "0, 0, 0",
|
||||
"std": "0.00392",
|
||||
"swapRB": "false",
|
||||
"dataset": "MPI",
|
||||
"modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel",
|
||||
"configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/mpi/pose_deploy_linevec.prototxt"
|
||||
}
|
||||
]
|
||||
}
|
||||
243
doc/js_tutorials/js_assets/js_semantic_segmentation.html
Normal file
243
doc/js_tutorials/js_assets/js_semantic_segmentation.html
Normal file
@ -0,0 +1,243 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<title>Semantic Segmentation Example</title>
|
||||
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<h2>Semantic Segmentation Example</h2>
|
||||
<p>
|
||||
This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
|
||||
To try the example you should click the <b>modelFile</b> button(and <b>configInput</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="16" 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 gengerate colors for different classes and argmax to get the classes for each pixel.</p>
|
||||
<textarea class="code" rows="34" 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 = [513, 513];
|
||||
mean = [127.5, 127.5, 127.5];
|
||||
std = 0.007843;
|
||||
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 colors = generateColors(result);
|
||||
const output = argmax(result, colors);
|
||||
|
||||
updateResult(output, time);
|
||||
input.delete();
|
||||
net.delete();
|
||||
result.delete();
|
||||
}
|
||||
</script>
|
||||
|
||||
<script id="codeSnippet4" type="text/code-snippet">
|
||||
generateColors = function(result) {
|
||||
const numClasses = result.matSize[1];
|
||||
let colors = [0,0,0];
|
||||
while(colors.length < numClasses*3){
|
||||
colors.push(Math.round((Math.random()*255 + colors[colors.length-3]) / 2));
|
||||
}
|
||||
return colors;
|
||||
}
|
||||
|
||||
argmax = function(result, colors) {
|
||||
const C = result.matSize[1];
|
||||
const H = result.matSize[2];
|
||||
const W = result.matSize[3];
|
||||
const resultData = result.data32F;
|
||||
const imgSize = H*W;
|
||||
|
||||
let classId = [];
|
||||
for (i = 0; i<imgSize; ++i) {
|
||||
let id = 0;
|
||||
for (j = 0; j < C; ++j) {
|
||||
if (resultData[j*imgSize+i] > resultData[id*imgSize+i]) {
|
||||
id = j;
|
||||
}
|
||||
}
|
||||
classId.push(colors[id*3]);
|
||||
classId.push(colors[id*3+1]);
|
||||
classId.push(colors[id*3+2]);
|
||||
classId.push(255);
|
||||
}
|
||||
|
||||
output = cv.matFromArray(H,W,cv.CV_8UC4,classId);
|
||||
return output;
|
||||
}
|
||||
</script>
|
||||
|
||||
<script type="text/javascript">
|
||||
let jsonUrl = "js_semantic_segmentation_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 = 'roi.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 generateColors = function(result) {};
|
||||
var argmax = function(result, colors) {};
|
||||
|
||||
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>
|
||||
@ -0,0 +1,12 @@
|
||||
{
|
||||
"tensorflow": [
|
||||
{
|
||||
"model": "deeplabv3",
|
||||
"inputSize": "513, 513",
|
||||
"mean": "127.5, 127.5, 127.5",
|
||||
"std": "0.007843",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://drive.google.com/uc?id=1v-hfGenaE9tiGOzo5qdgMNG_gqQ5-Xn4&export=download"
|
||||
}
|
||||
]
|
||||
}
|
||||
228
doc/js_tutorials/js_assets/js_style_transfer.html
Normal file
228
doc/js_tutorials/js_assets/js_style_transfer.html
Normal file
@ -0,0 +1,228 @@
|
||||
<!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>
|
||||
76
doc/js_tutorials/js_assets/js_style_transfer_model_info.json
Normal file
76
doc/js_tutorials/js_assets/js_style_transfer_model_info.json
Normal file
@ -0,0 +1,76 @@
|
||||
{
|
||||
"torch": [
|
||||
{
|
||||
"model": "candy.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/candy.t7"
|
||||
},
|
||||
{
|
||||
"model": "composition_vii.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/composition_vii.t7"
|
||||
},
|
||||
{
|
||||
"model": "feathers.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/feathers.t7"
|
||||
},
|
||||
{
|
||||
"model": "la_muse.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/la_muse.t7"
|
||||
},
|
||||
{
|
||||
"model": "mosaic.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/mosaic.t7"
|
||||
},
|
||||
{
|
||||
"model": "starry_night.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/starry_night.t7"
|
||||
},
|
||||
{
|
||||
"model": "the_scream.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/the_scream.t7"
|
||||
},
|
||||
{
|
||||
"model": "the_wave.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/the_wave.t7"
|
||||
},
|
||||
{
|
||||
"model": "udnie.t7",
|
||||
"inputSize": "224, 224",
|
||||
"mean": "104, 117, 123",
|
||||
"std": "1",
|
||||
"swapRB": "false",
|
||||
"modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/udnie.t7"
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -7,7 +7,7 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
|
||||
let script = document.createElement('script');
|
||||
script.setAttribute('async', '');
|
||||
script.setAttribute('type', 'text/javascript');
|
||||
script.addEventListener('load', () => {
|
||||
script.addEventListener('load', async () => {
|
||||
if (cv.getBuildInformation)
|
||||
{
|
||||
console.log(cv.getBuildInformation());
|
||||
@ -16,9 +16,15 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
|
||||
else
|
||||
{
|
||||
// WASM
|
||||
cv['onRuntimeInitialized']=()=>{
|
||||
if (cv instanceof Promise) {
|
||||
cv = await cv;
|
||||
console.log(cv.getBuildInformation());
|
||||
onloadCallback();
|
||||
} else {
|
||||
cv['onRuntimeInitialized']=()=>{
|
||||
console.log(cv.getBuildInformation());
|
||||
onloadCallback();
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
@ -0,0 +1,13 @@
|
||||
Image Classification Example {#tutorial_js_image_classification}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for image classification.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_image_classification.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,15 @@
|
||||
Image Classification Example with Camera {#tutorial_js_image_classification_with_camera}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for image classification example with camera.
|
||||
|
||||
@note If you don't know how to capture video from camera, please review @ref tutorial_js_video_display.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_image_classification_with_camera.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,13 @@
|
||||
Object Detection Example {#tutorial_js_object_detection}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for object detection.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_object_detection.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,13 @@
|
||||
Object Detection Example with Camera{#tutorial_js_object_detection_with_camera}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for object detection with camera.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_object_detection_with_camera.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,13 @@
|
||||
Pose Estimation Example {#tutorial_js_pose_estimation}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for pose estimation.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_pose_estimation.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,13 @@
|
||||
Semantic Segmentation Example {#tutorial_js_semantic_segmentation}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for semantic segmentation.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_semantic_segmentation.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
@ -0,0 +1,13 @@
|
||||
Style Transfer Example {#tutorial_js_style_transfer}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
- In this tutorial you will learn how to use OpenCV.js dnn module for style transfer.
|
||||
|
||||
\htmlonly
|
||||
<iframe src="../../js_style_transfer.html" width="100%"
|
||||
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
|
||||
</iframe>
|
||||
\endhtmlonly
|
||||
30
doc/js_tutorials/js_dnn/js_table_of_contents_dnn.markdown
Normal file
30
doc/js_tutorials/js_dnn/js_table_of_contents_dnn.markdown
Normal file
@ -0,0 +1,30 @@
|
||||
Deep Neural Networks (dnn module) {#tutorial_js_table_of_contents_dnn}
|
||||
============
|
||||
|
||||
- @subpage tutorial_js_image_classification
|
||||
|
||||
Image classification example
|
||||
|
||||
- @subpage tutorial_js_image_classification_with_camera
|
||||
|
||||
Image classification example with camera
|
||||
|
||||
- @subpage tutorial_js_object_detection
|
||||
|
||||
Object detection example
|
||||
|
||||
- @subpage tutorial_js_object_detection_with_camera
|
||||
|
||||
Object detection example with camera
|
||||
|
||||
- @subpage tutorial_js_semantic_segmentation
|
||||
|
||||
Semantic segmentation example
|
||||
|
||||
- @subpage tutorial_js_style_transfer
|
||||
|
||||
Style transfer example
|
||||
|
||||
- @subpage tutorial_js_pose_estimation
|
||||
|
||||
Pose estimation example
|
||||
@ -26,3 +26,7 @@ OpenCV.js Tutorials {#tutorial_js_root}
|
||||
|
||||
In this section you
|
||||
will object detection techniques like face detection etc.
|
||||
|
||||
- @subpage tutorial_js_table_of_contents_dnn
|
||||
|
||||
These tutorials show how to use dnn module in JavaScript
|
||||
|
||||
Loading…
Reference in New Issue
Block a user