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minimal__no_modules__track_one_face_ASMJS.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Beyond Reality Face SDK - BRFv5 - Face Tracking for Browser/JavaScript - Minimal Webcam Example (no modules)</title>
<style>
html, body { width: 100%; height: 100%; background-color: #ffffff; margin: 0; padding: 0; overflow: hidden; }
</style>
</head>
<body>
<!--
A little walk through the basics ...
We need at least a video for the camera stream and a canvas for drawing the camera stream (mirrored),
retrieving the image data and drawing the results.
In the modules version, we have two canvases and only update BRFv5 when a new image was received from
the camera stream. Here we only have one canvas and draw the video with each requestAnimationFrame callback.
This minimal example does not use modules, but plain JavaScript, so the '_no_modules.js' script places the
brfv5Module function right into the window namespace (the script has no exports).
-->
<video id="_webcam" style="display: none;" playsinline></video>
<canvas id="_imageData"></canvas>
<script src="./js/brfv5/brfv5_js_tk261120_v5.2.1_trial_no_modules.asm.js"></script>
<script>
// Set the BRFv5 library name here, also set your own appId for reference.
const _libraryName = 'brfv5_js_tk261120_v5.2.1_trial.asm.brfv5'
const _appId = 'brfv5.browser.minimal.nomodules.asmjs' // (mandatory): 8 to 64 characters, a-z . 0-9 allowed
const brfv5 = {} // The library namespace.
// References to the video and canvas.
const _webcam = document.getElementById('_webcam')
const _imageData = document.getElementById('_imageData')
// Those variables will be retrieved from the stream and the library.
let _brfv5Manager = null
let _brfv5Config = null
let _width = 0
let _height = 0
// loadBRFv5Model and openCamera are being done simultaneously thanks to Promises. Both call
// configureTracking which only gets executed once both Promises were successful. Once configured
// trackFaces will do the tracking work and draw the results.
const loadBRFv5Model = (modelName, numChunksToLoad, pathToModels = '', appId = null, onProgress = null) => {
console.log('loadBRFv5Model')
if(!modelName) { throw 'Please provide a modelName.' }
return new Promise((resolve, reject) => {
if(_brfv5Manager && _brfv5Config) {
resolve({ brfv5Manager: _brfv5Manager, brfv5Config: _brfv5Config })
} else {
try {
brfv5.appId = appId ? appId : _appId
brfv5.binaryLocation = pathToModels + _libraryName
brfv5.modelLocation = pathToModels + modelName + '_c'
brfv5.modelChunks = numChunksToLoad // 4, 6, 8
brfv5.binaryProgress = onProgress
brfv5.binaryError = (e) => { reject(e) }
brfv5.onInit = (brfv5Manager, brfv5Config) => {
_brfv5Manager = brfv5Manager
_brfv5Config = brfv5Config
resolve({ brfv5Manager: _brfv5Manager, brfv5Config: _brfv5Config })
}
brfv5Module(brfv5)
} catch(e) {
reject(e)
}
}
})
}
const openCamera = () => {
console.log('openCamera')
return new Promise((resolve, reject) => {
window.navigator.mediaDevices.getUserMedia({ video: { width: 640, height: 480, frameRate: 30, facingMode: 'user'} })
.then((mediaStream) => {
_webcam.srcObject = mediaStream
_webcam.play().then(() => { resolve({ width: _webcam.videoWidth, height: _webcam.videoHeight }) }).catch((e) => { reject(e) })
}).catch((e) => { reject(e) })
})
}
const configureTracking = () => {
if(_brfv5Config !== null && _width > 0) {
// Camera stream and BRFv5 are ready. Now configure. Internal defaults are set for a 640x480 resolution.
// So the following isn't really necessary.
const brfv5Config = _brfv5Config
const imageWidth = _width
const imageHeight = _height
const inputSize = imageWidth > imageHeight ? imageHeight : imageWidth
// Setup image data dimensions
brfv5Config.imageConfig.inputWidth = imageWidth
brfv5Config.imageConfig.inputHeight = imageHeight
const sizeFactor = inputSize / 480.0
// Set face detection region of interest and parameters scaled to the image base size.
brfv5Config.faceDetectionConfig.regionOfInterest.setTo(0, 0, imageWidth, imageHeight)
brfv5Config.faceDetectionConfig.minFaceSize = 144 * sizeFactor
brfv5Config.faceDetectionConfig.maxFaceSize = 480 * sizeFactor
if(imageWidth < imageHeight) {
// Portrait mode: probably smartphone, faces tend to be closer to the camera, processing time is an issue,
// so save a bit of time and increase minFaceSize.
brfv5Config.faceDetectionConfig.minFaceSize = 240 * sizeFactor
}
// Set face tracking region of interest and parameters scaled to the image base size.
brfv5Config.faceTrackingConfig.regionOfInterest.setTo(0, 0, imageWidth, imageHeight)
brfv5Config.faceTrackingConfig.minFaceScaleStart = 50.0 * sizeFactor
brfv5Config.faceTrackingConfig.maxFaceScaleStart = 320.0 * sizeFactor
brfv5Config.faceTrackingConfig.minFaceScaleReset = 35.0 * sizeFactor
brfv5Config.faceTrackingConfig.maxFaceScaleReset = 420.0 * sizeFactor
brfv5Config.faceTrackingConfig.confidenceThresholdReset = 0.001
brfv5Config.faceTrackingConfig.enableStabilizer = true
brfv5Config.faceTrackingConfig.maxRotationXReset = 35.0
brfv5Config.faceTrackingConfig.maxRotationYReset = 45.0
brfv5Config.faceTrackingConfig.maxRotationZReset = 34.0
brfv5Config.faceTrackingConfig.numTrackingPasses = 3
brfv5Config.faceTrackingConfig.enableFreeRotation = true
brfv5Config.faceTrackingConfig.maxRotationZReset = 999.0
brfv5Config.faceTrackingConfig.numFacesToTrack = 1
brfv5Config.enableFaceTracking = true
console.log('configureTracking:', _brfv5Config)
_brfv5Manager.configure(_brfv5Config)
trackFaces()
}
}
const trackFaces = () => {
if(!_brfv5Manager || !_brfv5Config || !_imageData) { return }
const ctx = _imageData.getContext('2d')
ctx.setTransform(-1.0, 0, 0, 1, _width, 0) // A virtual mirror should be... mirrored
ctx.drawImage(_webcam, 0, 0, _width, _height)
ctx.setTransform(1.0, 0, 0, 1, 0, 0) // unmirror to draw the results
_brfv5Manager.update(ctx.getImageData(0, 0, _width, _height))
let doDrawFaceDetection = !_brfv5Config.enableFaceTracking
if(_brfv5Config.enableFaceTracking) {
const sizeFactor = Math.min(_width, _height) / 480.0
const faces = _brfv5Manager.getFaces()
for(let i = 0; i < faces.length; i++) {
const face = faces[i]
if(face.state === brfv5.BRFv5State.FACE_TRACKING) {
drawRect(ctx, _brfv5Config.faceTrackingConfig.regionOfInterest, '#00a0ff', 2.0)
drawCircles(ctx, face.landmarks, '#00a0ff', 2.0 * sizeFactor)
drawRect(ctx, face.bounds, '#ffffff', 1.0)
} else {
doDrawFaceDetection = true
}
}
}
if(doDrawFaceDetection) {
// Only draw face detection results, if face detection was performed.
drawRect( ctx, _brfv5Config.faceDetectionConfig.regionOfInterest, '#ffffff', 2.0)
drawRects(ctx, _brfv5Manager.getDetectedRects(), '#00a0ff', 1.0)
drawRects(ctx, _brfv5Manager.getMergedRects(), '#ffffff', 3.0)
}
requestAnimationFrame(trackFaces)
}
openCamera().then(({ width, height }) => {
console.log('openCamera: done: ' + width + 'x' + height)
_width = width
_height = height
_imageData.width = _width
_imageData.height = _height
configureTracking()
}).catch((e) => { if(e) { console.error('Camera failed: ', e) } })
loadBRFv5Model('68l', 8, './js/brfv5/models/', _appId,
(progress) => { console.log(progress) }).then(({ brfv5Manager, brfv5Config }) => {
console.log('loadBRFv5Model: done')
_brfv5Manager = brfv5Manager
_brfv5Config = brfv5Config
configureTracking()
}).catch((e) => { console.error('BRFv5 failed: ', e) })
const drawCircles = (ctx, array, color, radius) => {
ctx.strokeStyle = null
ctx.fillStyle = getColor(color, 1.0)
let _radius = radius || 2.0
for(let i = 0; i < array.length; ++i) {
ctx.beginPath()
ctx.arc(array[i].x, array[i].y, _radius, 0, 2 * Math.PI)
ctx.fill()
}
}
const drawRect = (ctx, rect, color, lineWidth) => {
ctx.strokeStyle = getColor(color, 1.0)
ctx.fillStyle = null
ctx.lineWidth = lineWidth || 1.0
ctx.beginPath()
ctx.rect(rect.x, rect.y, rect.width, rect.height)
ctx.stroke()
}
const drawRects = (ctx, rects, color, lineWidth) => {
ctx.strokeStyle = getColor(color, 1.0)
ctx.fillStyle = null
ctx.lineWidth = lineWidth || 1.0
for(let i = 0; i < rects.length; ++i) {
let rect = rects[i]
ctx.beginPath()
ctx.rect(rect.x, rect.y, rect.width, rect.height)
ctx.stroke()
}
}
const getColor = (color, alpha) => {
const colorStr = color + ''
if(colorStr.startsWith('rgb')) {
return color
}
if(colorStr.startsWith('#')) {
color = parseInt('0x' + colorStr.substr(1))
}
return 'rgb(' +
(((color >> 16) & 0xff).toString(10)) + ', ' +
(((color >> 8) & 0xff).toString(10)) + ', ' +
(((color) & 0xff).toString(10)) + ', ' + alpha +')'
}
</script>
</body>
</html>