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.
orb-features.html
ORB features demo
-->
<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>ORB features</title>
<script src="../dist/speedy-vision.js"></script>
<link href="style.css" rel="stylesheet">
</head>
<body>
<h1>ORB features</h1>
<form autocomplete="off">
<div>
<label for="detection-method">Detector</label>
<select id="detection-method">
<option value="fast" selected>FAST</option>
<option value="harris">Harris</option>
</select>
</div>
<div>
<label for="max-features">Clipping</label>
<select id="max-features">
<option value="100">100</option>
<option value="200">200</option>
<option value="300">300</option>
<option value="500">500</option>
<option value="800" selected>800</option>
<option value="1200">1200</option>
<option value="2000">2000</option>
</select>
</div>
<div class="separator"></div>
<div>
<label for="sensitivity">Sensitivity</label>
<input type="range" min="0.0" max="0.90" value="0.50" step="0.01" id="sensitivity">
</div>
<div>
<label for="speed-slider">Video speed</label>
<input type="range" id="speed-slider" min="0.10" max="2" value="1" step="0.01">
</div>
</form>
<div>
<span id="status"></span>
<canvas id="canvas-demo"></canvas>
</div>
<div>
<button id="play">Play / pause</button>
</div>
<video
src="../assets/corridor.webm"
poster="../assets/loading.jpg"
width="640" height="360"
preload="auto"
loop muted hidden
title="Free video by Ricardo Esquivel (pexels.com)">
</video>
<script>
window.onload = async function()
{
/*
This is our pipeline:
Image ---> Convert to ---> Image ------> Keypoint -----> Keypoint ---> ORB ----------> Keypoint
Source greyscale Pyramid detector Clipper descriptors Sink
| ^
| |
+-------------------------> Gaussian ------------------------+
Blur
The Keypoint detector is either FAST or Harris
*/
// form elements
const detectionMethod = document.getElementById('detection-method');
const sensitivity = document.getElementById('sensitivity');
const maxFeatures = document.getElementById('max-features');
const playButton = document.getElementById('play');
const speedSlider = document.getElementById('speed-slider');
// load the video
const video = document.querySelector('video');
const media = await Speedy.load(video);
video.play();
// create the pipelines
function createPipelineWith(detector)
{
const pipeline = Speedy.Pipeline();
const source = Speedy.Image.Source();
const greyscale = Speedy.Filter.Greyscale();
const pyramid = Speedy.Image.Pyramid();
const blur = Speedy.Filter.GaussianBlur(); // reduce noise before computing the descriptors
const clipper = Speedy.Keypoint.Clipper('clipper');
const descriptor = Speedy.Keypoint.Descriptor.ORB();
const sink = Speedy.Keypoint.Sink();
source.media = media;
blur.kernelSize = Speedy.Size(9, 9);
blur.sigma = Speedy.Vector2(2, 2);
detector.levels = 8; // pyramid levels
detector.scaleFactor = 1.19; // approx. 2^0.25
detector.capacity = 8192;
clipper.size = 800; // up to how many features?
sink.turbo = true;
source.output().connectTo(greyscale.input());
greyscale.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(descriptor.input('image'));
descriptor.output().connectTo(sink.input());
pipeline.init(source, greyscale, pyramid, blur, detector, clipper, descriptor, sink);
return pipeline;
}
const fast = Speedy.Keypoint.Detector.FAST();
const harris = Speedy.Keypoint.Detector.Harris();
const pipelines = {
fast: createPipelineWith(fast),
harris: createPipelineWith(harris)
};
// Main loop
(function() {
const canvas = createCanvas(media.width, media.height, video.title);
let keypoints = [], frameReady = false;
async function update()
{
// pick a pipeline
const pipeline = pipelines[detectionMethod.value];
const clipper = pipeline.node('clipper');
// adjust the sensitivity and the clipper
fast.threshold = 255 * (1.0 - Number(sensitivity.value));
harris.quality = 1.0 - Number(sensitivity.value);
clipper.size = Number(maxFeatures.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, '#0fa', 2);
}
frameReady = false;
requestAnimationFrame(render);
}
render();
setInterval(() => renderStatus(keypoints), 200);
})();
// misc
playButton.onclick = () => video.paused ? video.play() : video.pause();
speedSlider.oninput = () => video.playbackRate = speedSlider.value;
}
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', thickness = 1)
{
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.lineWidth = thickness;
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>