speedy-vision
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GPU-accelerated Computer Vision for JavaScript
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HTML
<!--
speedy-vision.js
GPU-accelerated Computer Vision for JavaScript
Copyright 2020-2022 Alexandre Martins <alemartf(at)gmail.com>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
webcam-demo.html
Feature detection using a webcam
-->
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="speedy-vision.js: GPU-accelerated Computer Vision for JavaScript">
<meta name="author" content="Alexandre Martins">
<title>Webcam feature detection</title>
<script src="../dist/speedy-vision.js"></script>
<link href="style.css" rel="stylesheet">
</head>
<body>
<h1>Webcam demo</h1>
<form autocomplete="off">
<div>
Sensitivity
<input type="range" min="0.0" max="0.99" value="0.80" step="0.01" id="sensitivity">
</div>
<div>
<label for="enhance-illumination">Fix uneven illumination</label>
<input type="checkbox" id="enhance-illumination">
</div>
<div>
Method:
<select id="method">
<option value="orb" selected>ORB</option>
<option value="orbharris">ORB-Harris</option>
<option value="brisk" disabled>BRISK (soon)</option>
</select>
</div>
</form>
<div>
<span id="status"></span>
<canvas id="canvas-demo"></canvas>
</div>
<script>
window.onload = async function()
{
try {
// webcam access
const media = await Speedy.camera();
// form controls
const method = document.getElementById('method');
const sensitivity = document.getElementById('sensitivity');
const enhanceIllumination = document.getElementById('enhance-illumination');
// create the pipelines
function orbWith(detector)
{
const pipeline = Speedy.Pipeline();
const source = Speedy.Image.Source();
const greyscale = Speedy.Filter.Greyscale();
const nightvision = Speedy.Filter.Nightvision();
const mux = Speedy.Image.Multiplexer('mux');
const pyramid = Speedy.Image.Pyramid();
const blur = Speedy.Filter.GaussianBlur(); // reduce noise before computing the descriptors
const blurredPyramid = Speedy.Image.Pyramid();
const clipper = Speedy.Keypoint.Clipper();
const descriptor = Speedy.Keypoint.Descriptor.ORB();
const sink = Speedy.Keypoint.Sink();
source.media = media;
nightvision.gain = 0.9; // increase contrast
nightvision.offset = 0.5;
nightvision.decay = 0.0;
nightvision.quality = 'low';
blur.kernelSize = Speedy.Size(9, 9);
blur.sigma = Speedy.Vector2(2, 2);
detector.levels = 12; // pyramid levels
detector.scaleFactor = 1.19; // approx. 2^0.25
detector.capacity = 8192;
clipper.size = 800; // up to how many features?
source.output().connectTo(greyscale.input());
greyscale.output().connectTo(mux.input('in0'));
greyscale.output().connectTo(nightvision.input());
nightvision.output().connectTo(mux.input('in1'));
mux.output().connectTo(pyramid.input());
pyramid.output().connectTo(detector.input());
detector.output().connectTo(clipper.input());
clipper.output().connectTo(descriptor.input('keypoints'));
greyscale.output().connectTo(blur.input());
blur.output().connectTo(blurredPyramid.input());
blurredPyramid.output().connectTo(descriptor.input('image'));
descriptor.output().connectTo(sink.input());
pipeline.init(source, greyscale, nightvision, mux, pyramid, blur, blurredPyramid, detector, clipper, descriptor, sink);
return pipeline;
}
const fast = Speedy.Keypoint.Detector.FAST();
const harris = Speedy.Keypoint.Detector.Harris();
const pipelines = {
orb: orbWith(fast),
orbharris: orbWith(harris),
brisk: null,
};
// Main loop
(function() {
let keypoints = [], frameReady = false;
const canvas = createCanvas(media.width, media.height);
canvas.style.width = (2 * media.width) + 'px';
async function update()
{
// pick a pipeline
const pipeline = pipelines[method.value];
// enhance illumination?
const mux = pipeline.node('mux');
mux.port = enhanceIllumination.checked ? 1 : 0;
// adjust the sensitivity
fast.threshold = 255 * (1.0 - Number(sensitivity.value));
harris.quality = 1.0 - Number(sensitivity.value);
// find the features
const result = await pipeline.run();
keypoints = result.keypoints;
// repeat
frameReady = true;
setTimeout(update, 1000 / 60);
}
update();
function render()
{
if(frameReady) {
draw(media, canvas);
renderFeatures(canvas, keypoints, 2, '#ff0', 2);
}
frameReady = false;
requestAnimationFrame(render);
}
render();
setInterval(() => renderStatus(keypoints), 200);
})();
}
catch(err) {
alert(err.message);
}
}
function createCanvas(width, height, title)
{
const canvas = document.getElementById('canvas-demo') || document.createElement('canvas');
canvas.width = width;
canvas.height = height;
canvas.title = title;
if(!document.body.contains(canvas))
document.body.appendChild(canvas);
return canvas;
}
function renderFeatures(canvas, features, size = 2, color = 'yellow')
{
const context = canvas.getContext('2d');
context.beginPath();
for(let feature of features) {
let radius = size * feature.scale;
// draw scaled circle
context.moveTo(feature.x + radius, feature.y);
context.arc(feature.x, feature.y, radius, 0, Math.PI * 2.0);
// draw rotation line
const sin = Math.sin(feature.rotation);
const cos = Math.cos(feature.rotation);
context.moveTo(feature.x, feature.y);
context.lineTo(feature.x + radius * cos, feature.y + radius * sin);
}
context.strokeStyle = color;
context.stroke();
}
function renderStatus(features)
{
const status = document.getElementById('status');
status.innerText = `FPS: ${Speedy.fps} | Keypoints: ${features.length}`;
}
function draw(media, canvas, x = 0, y = 0, width = media.width, height = media.height)
{
const ctx = canvas.getContext('2d');
ctx.drawImage(media.source, x, y, width, height);
}
</script>
<mark>Powered by <a href="https://github.com/alemart/speedy-vision">speedy-vision.js</a></mark>
</body>
</html>