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.
normalize-demo.html
Image normalization 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>Normalize camera stream</title>
<script src="../dist/speedy-vision.js"></script>
<link href="style.css" rel="stylesheet">
</head>
<body>
<h1>Normalize camera stream</h1>
<form autocomplete="off">
<div>
Map pixels to range:
</div>
<div>
<label for="min-value">min</label>
<input type="range" id="min-value" min="0" max="255" value="0" step="1">
</div>
<div>
<label for="max-value">max</label>
<input type="range" id="max-value" min="0" max="255" value="255" step="1">
</div>
</form>
<div>
<span id="status"></span>
<canvas id="canvas-demo"></canvas>
</div>
<script>
window.onload = async function()
{
/*
Our pipeline:
Image ---> Convert to ---> Normalize ---> Image
Source greyscale image Sink
|
|
+-------------> Image
Sink
*/
// get camera stream
const camera = await Speedy.camera();
// setup the pipeline
const pipeline = Speedy.Pipeline();
const source = Speedy.Image.Source();
const sink1 = Speedy.Image.Sink('grey');
const sink2 = Speedy.Image.Sink('norm');
const greyscale = Speedy.Filter.Greyscale();
const normalize = Speedy.Filter.Normalize();
source.output().connectTo(greyscale.input());
greyscale.output().connectTo(normalize.input());
greyscale.output().connectTo(sink1.input());
normalize.output().connectTo(sink2.input());
pipeline.init(source, sink1, sink2, greyscale, normalize);
source.media = camera;
normalize.minValue = 0;
normalize.maxValue = 255;
// Main loop
(function() {
let grey = null, norm = null, frameReady = false;
const canvas = createCanvas(camera.width, camera.height * 2);
async function update()
{
const result = await pipeline.run();
grey = result.grey;
norm = result.norm;
frameReady = true;
setTimeout(update, 1000 / 60);
}
update();
function render()
{
if(frameReady) {
draw(norm, canvas);
draw(grey, canvas, 0, norm.height);
}
frameReady = false;
requestAnimationFrame(render);
}
render();
setInterval(renderStatus, 200);
})();
// setup sliders
const minSlider = document.getElementById('min-value');
const maxSlider = document.getElementById('max-value');
minSlider.oninput = () => normalize.minValue = minSlider.value;
maxSlider.oninput = () => normalize.maxValue = maxSlider.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 renderStatus()
{
const status = document.getElementById('status');
status.innerText = `FPS: ${Speedy.fps}`;
}
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>