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<!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/4.x/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>
|
