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<!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>
|
