mirror of https://github.com/vladmandic/human
optimizations
Vladimir Mandic
parent
0cffd084b0
commit
6af1768fa6
85
config.js
85
config.js
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@ -5,20 +5,27 @@ export default {
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backend: 'webgl', // select tfjs backend to use
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console: true, // enable debugging output to console
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async: true, // execute enabled models in parallel
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// this disables per-model performance data but slightly increases performance
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// this disables per-model performance data but
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// slightly increases performance
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// cannot be used if profiling is enabled
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profile: false, // enable tfjs profiling
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// this has significant performance impact, only enable for debugging purposes
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// this has significant performance impact
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// only enable for debugging purposes
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// currently only implemented for age,gender,emotion models
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deallocate: false, // aggresively deallocate gpu memory after each usage
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// only valid for webgl backend and only during first call, cannot be changed unless library is reloaded
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// this has significant performance impact, only enable on low-memory devices
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// only valid for webgl backend and only during first call
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// cannot be changed unless library is reloaded
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// this has significant performance impact
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// only enable on low-memory devices
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scoped: false, // enable scoped runs
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// some models *may* have memory leaks, this wrapps everything in a local scope at a cost of performance
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// some models *may* have memory leaks,
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// this wrapps everything in a local scope at a cost of performance
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// typically not needed
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videoOptimized: true, // perform additional optimizations when input is video, must be disabled for images
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videoOptimized: true, // perform additional optimizations when input is video,
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// must be disabled for images
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// basically this skips object box boundary detection for every n frames
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// while maintaining in-box detection since objects cannot move that fast
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filter: {
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enabled: true, // enable image pre-processing filters
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width: 0, // resize input width
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@ -41,50 +48,67 @@ export default {
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polaroid: false, // image polaroid camera effect
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pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)
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},
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gesture: {
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enabled: true, // enable simple gesture recognition
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},
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face: {
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enabled: true, // controls if specified modul is enabled
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// face.enabled is required for all face models: detector, mesh, iris, age, gender, emotion
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// face.enabled is required for all face models:
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// detector, mesh, iris, age, gender, emotion
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// (note: module is not loaded until it is required)
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detector: {
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modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.
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// 'front' is optimized for large faces such as front-facing camera and 'back' is optimized for distanct faces.
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// 'front' is optimized for large faces
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// such as front-facing camera and
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// 'back' is optimized for distanct faces.
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inputSize: 256, // fixed value: 128 for front and 256 for 'back'
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maxFaces: 10, // maximum number of faces detected in the input, should be set to the minimum number for performance
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skipFrames: 15, // how many frames to go without re-running the face bounding box detector, only used for video inputs
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// if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis
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// as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
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maxFaces: 10, // maximum number of faces detected in the input
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// should be set to the minimum number for performance
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skipFrames: 15, // how many frames to go without re-running the face bounding box detector
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// only used for video inputs
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// e.g., if model is running st 25 FPS, we can re-use existing bounding
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// box for updated face analysis as the head probably hasn't moved much
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// in short time (10 * 1/25 = 0.25 sec)
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minConfidence: 0.1, // threshold for discarding a prediction
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iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in non-maximum suppression (0.1 means drop if overlap 10%)
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scoreThreshold: 0.2, // threshold for deciding when to remove boxes based on score in non-maximum suppression, this is applied on detection objects only and before minConfidence
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iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in
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// non-maximum suppression (0.1 means drop if overlap 10%)
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scoreThreshold: 0.2, // threshold for deciding when to remove boxes based on score
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// in non-maximum suppression,
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// this is applied on detection objects only and before minConfidence
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},
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mesh: {
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enabled: true,
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modelPath: '../models/facemesh.json',
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inputSize: 192, // fixed value
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},
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iris: {
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enabled: true,
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modelPath: '../models/iris.json',
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enlargeFactor: 2.3, // empiric tuning
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inputSize: 64, // fixed value
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},
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age: {
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enabled: true,
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modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'
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// which determines training set for model
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inputSize: 64, // fixed value
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skipFrames: 15, // how many frames to go without re-running the detector, only used for video inputs
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skipFrames: 15, // how many frames to go without re-running the detector
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// only used for video inputs
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},
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gender: {
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enabled: true,
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minConfidence: 0.1, // threshold for discarding a prediction
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modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'
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inputSize: 64, // fixed value
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skipFrames: 15, // how many frames to go without re-running the detector, only used for video inputs
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skipFrames: 15, // how many frames to go without re-running the detector
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// only used for video inputs
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},
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emotion: {
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enabled: true,
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inputSize: 64, // fixed value
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@ -93,26 +117,33 @@ export default {
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modelPath: '../models/emotion-large.json', // can be 'mini', 'large'
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},
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},
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body: {
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enabled: true,
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modelPath: '../models/posenet.json',
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inputResolution: 257, // fixed value
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outputStride: 16, // fixed value
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maxDetections: 10, // maximum number of people detected in the input, should be set to the minimum number for performance
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scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score in non-maximum suppression
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maxDetections: 10, // maximum number of people detected in the input
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// should be set to the minimum number for performance
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scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score
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// in non-maximum suppression
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nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression
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},
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hand: {
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enabled: true,
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inputSize: 256, // fixed value
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skipFrames: 15, // how many frames to go without re-running the hand bounding box detector, only used for video inputs
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// if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis
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// as the hand probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
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skipFrames: 15, // how many frames to go without re-running the hand bounding box detector
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// only used for video inputs
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// e.g., if model is running st 25 FPS, we can re-use existing bounding
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// box for updated hand skeleton analysis as the hand probably
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// hasn't moved much in short time (10 * 1/25 = 0.25 sec)
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minConfidence: 0.5, // threshold for discarding a prediction
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iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in non-maximum suppression
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scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score in non-maximum suppression
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enlargeFactor: 1.65, // empiric tuning as skeleton prediction prefers hand box with some whitespace
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maxHands: 1, // maximum number of hands detected in the input, should be set to the minimum number for performance
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iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much
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// in non-maximum suppression
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scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on
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// score in non-maximum suppression
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maxHands: 1, // maximum number of hands detected in the input
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// should be set to the minimum number for performance
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landmarks: true, // detect hand landmarks or just hand boundary box
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detector: {
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modelPath: '../models/handdetect.json',
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@ -12,6 +12,7 @@ const ui = {
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baseFontProto: 'small-caps {size} "Segoe UI"',
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baseLineWidth: 12,
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baseLineHeightProto: 2,
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crop: true,
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columns: 2,
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busy: false,
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facing: true,
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@ -21,7 +22,7 @@ const ui = {
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drawBoxes: true,
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drawPoints: false,
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drawPolygons: true,
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fillPolygons: true,
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fillPolygons: false,
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useDepth: true,
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console: true,
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maxFrames: 10,
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@ -132,7 +133,7 @@ async function setupCamera() {
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audio: false,
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video: {
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facingMode: (ui.facing ? 'user' : 'environment'),
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resizeMode: 'none',
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resizeMode: ui.crop ? 'crop-and-scale' : 'none',
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width: { ideal: window.innerWidth },
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height: { ideal: window.innerHeight },
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},
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@ -206,7 +207,8 @@ function runHumanDetect(input, canvas) {
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const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
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if (!live && input.srcObject) {
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// if we want to continue and camera not ready, retry in 0.5sec, else just give up
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if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);
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if (input.paused) log('camera paused');
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else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);
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else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
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return;
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}
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@ -223,7 +225,6 @@ function runHumanDetect(input, canvas) {
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human.detect(input).then((result) => {
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if (result.error) log(result.error);
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else drawResults(input, result, canvas);
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if (human.config.profile) log('profile data:', human.profile());
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});
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}
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}
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@ -300,48 +301,48 @@ function setupMenu() {
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document.getElementById('play').addEventListener('click', () => btn.click());
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menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menu.addList('Backend', ['cpu', 'webgl', 'wasm', 'webgpu'], human.config.backend, (val) => human.config.backend = val);
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menu.addBool('Async Operations', human.config, 'async', (val) => human.config.async = val);
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menu.addBool('Enable Profiler', human.config, 'profile', (val) => human.config.profile = val);
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menu.addBool('Memory Shield', human.config, 'deallocate', (val) => human.config.deallocate = val);
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menu.addBool('Use Web Worker', ui, 'useWorker');
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menu.addList('backend', ['cpu', 'webgl', 'wasm', 'webgpu'], human.config.backend, (val) => human.config.backend = val);
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menu.addBool('async operations', human.config, 'async', (val) => human.config.async = val);
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menu.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);
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menu.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);
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menu.addBool('use web worker', ui, 'useWorker');
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menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menu.addLabel('Enabled Models');
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menu.addBool('Face Detect', human.config.face, 'enabled');
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menu.addBool('Face Mesh', human.config.face.mesh, 'enabled');
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menu.addBool('Face Iris', human.config.face.iris, 'enabled');
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menu.addBool('Face Age', human.config.face.age, 'enabled');
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menu.addBool('Face Gender', human.config.face.gender, 'enabled');
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menu.addBool('Face Emotion', human.config.face.emotion, 'enabled');
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menu.addBool('Body Pose', human.config.body, 'enabled');
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menu.addBool('Hand Pose', human.config.hand, 'enabled');
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menu.addBool('Gesture Analysis', human.config.gesture, 'enabled');
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menu.addLabel('enabled models');
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menu.addBool('face detect', human.config.face, 'enabled');
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menu.addBool('face mesh', human.config.face.mesh, 'enabled');
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menu.addBool('face iris', human.config.face.iris, 'enabled');
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menu.addBool('face age', human.config.face.age, 'enabled');
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menu.addBool('face gender', human.config.face.gender, 'enabled');
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menu.addBool('face emotion', human.config.face.emotion, 'enabled');
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menu.addBool('body pose', human.config.body, 'enabled');
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menu.addBool('hand pose', human.config.hand, 'enabled');
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menu.addBool('gesture analysis', human.config.gesture, 'enabled');
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menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menu.addLabel('Model Parameters');
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menu.addRange('Max Objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {
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menu.addLabel('model parameters');
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menu.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {
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human.config.face.detector.maxFaces = parseInt(val);
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human.config.body.maxDetections = parseInt(val);
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human.config.hand.maxHands = parseInt(val);
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});
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menu.addRange('Skip Frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {
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menu.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {
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human.config.face.detector.skipFrames = parseInt(val);
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human.config.face.emotion.skipFrames = parseInt(val);
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human.config.face.age.skipFrames = parseInt(val);
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human.config.hand.skipFrames = parseInt(val);
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});
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menu.addRange('Min Confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {
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menu.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {
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human.config.face.detector.minConfidence = parseFloat(val);
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human.config.face.gender.minConfidence = parseFloat(val);
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human.config.face.emotion.minConfidence = parseFloat(val);
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human.config.hand.minConfidence = parseFloat(val);
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});
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menu.addRange('Score Threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {
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menu.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {
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human.config.face.detector.scoreThreshold = parseFloat(val);
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human.config.hand.scoreThreshold = parseFloat(val);
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human.config.body.scoreThreshold = parseFloat(val);
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});
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menu.addRange('IOU Threshold', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {
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menu.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {
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human.config.face.detector.iouThreshold = parseFloat(val);
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human.config.hand.iouThreshold = parseFloat(val);
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});
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@ -350,31 +351,32 @@ function setupMenu() {
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menu.addChart('FPS', 'FPS');
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menuFX = new Menu(document.body, '...', { top: '1rem', right: '18rem' });
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menuFX.addLabel('UI Options');
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menuFX.addBool('Camera Front/Back', ui, 'facing', () => setupCamera());
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menuFX.addBool('Use 3D Depth', ui, 'useDepth');
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menuFX.addBool('Draw Boxes', ui, 'drawBoxes');
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menuFX.addBool('Draw Points', ui, 'drawPoints');
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menuFX.addBool('Draw Polygons', ui, 'drawPolygons');
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menuFX.addLabel('ui options');
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menuFX.addBool('crop & scale', ui, 'crop', () => setupCamera());
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menuFX.addBool('camera front/back', ui, 'facing', () => setupCamera());
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menuFX.addBool('use 3D depth', ui, 'useDepth');
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menuFX.addBool('draw boxes', ui, 'drawBoxes');
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menuFX.addBool('draw polygons', ui, 'drawPolygons');
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menuFX.addBool('Fill Polygons', ui, 'fillPolygons');
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menuFX.addBool('draw points', ui, 'drawPoints');
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menuFX.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
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menuFX.addLabel('Image Processing');
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menuFX.addBool('Enabled', human.config.filter, 'enabled');
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ui.menuWidth = menuFX.addRange('Image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));
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ui.menuHeight = menuFX.addRange('Image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));
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menuFX.addRange('Brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));
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menuFX.addRange('Contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));
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menuFX.addRange('Sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));
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menuFX.addRange('Blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));
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menuFX.addRange('Saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));
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menuFX.addRange('Hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));
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menuFX.addRange('Pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));
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menuFX.addBool('Negative', human.config.filter, 'negative');
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menuFX.addBool('Sepia', human.config.filter, 'sepia');
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menuFX.addBool('Vintage', human.config.filter, 'vintage');
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menuFX.addBool('Kodachrome', human.config.filter, 'kodachrome');
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menuFX.addBool('Technicolor', human.config.filter, 'technicolor');
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menuFX.addBool('Polaroid', human.config.filter, 'polaroid');
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menuFX.addLabel('image processing');
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menuFX.addBool('enabled', human.config.filter, 'enabled');
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ui.menuWidth = menuFX.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));
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ui.menuHeight = menuFX.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));
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menuFX.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));
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menuFX.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));
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menuFX.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));
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menuFX.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));
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menuFX.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));
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menuFX.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));
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menuFX.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));
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menuFX.addBool('negative', human.config.filter, 'negative');
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menuFX.addBool('sepia', human.config.filter, 'sepia');
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menuFX.addBool('vintage', human.config.filter, 'vintage');
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menuFX.addBool('kodachrome', human.config.filter, 'kodachrome');
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menuFX.addBool('technicolor', human.config.filter, 'technicolor');
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||||
menuFX.addBool('polaroid', human.config.filter, 'polaroid');
|
||||
}
|
||||
|
||||
async function main() {
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ function createCSS() {
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|||
.menu-item { display: flex; white-space: nowrap; padding: 0.2rem; width: max-content; cursor: default; }
|
||||
.menu-title { text-align: right; cursor: pointer; }
|
||||
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
|
||||
.menu-label { padding: 0; }
|
||||
.menu-label { padding: 0; font-weight: 800; }
|
||||
|
||||
.menu-list { margin-right: 0.8rem; }
|
||||
select:focus { outline: none; }
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ async function predict(image, config) {
|
|||
data = emotionT.dataSync();
|
||||
tf.dispose(emotionT);
|
||||
} else {
|
||||
const profileData = await tf.profile(() => models.emotion.predict(grayscale));
|
||||
const profileData = await tf.profile(() => models.emotion.predict(normalize));
|
||||
data = profileData.result.dataSync();
|
||||
profileData.result.dispose();
|
||||
profile.run('emotion', profileData);
|
||||
|
|
|
|||
|
|
@ -57,6 +57,7 @@ class HandDetector {
|
|||
rawBoxes.dispose();
|
||||
const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.maxHands, config.iouThreshold, config.scoreThreshold);
|
||||
const filtered = filteredT.arraySync();
|
||||
|
||||
scores.dispose();
|
||||
filteredT.dispose();
|
||||
const hands = [];
|
||||
|
|
|
|||
|
|
@ -134,12 +134,12 @@ class Human {
|
|||
this.models.gender || gender.load(this.config),
|
||||
this.models.emotion || emotion.load(this.config),
|
||||
this.models.facemesh || facemesh.load(this.config.face),
|
||||
this.models.posenet || posenet.load(this.config.body),
|
||||
this.models.posenet || posenet.load(this.config),
|
||||
this.models.handpose || handpose.load(this.config.hand),
|
||||
]);
|
||||
} else {
|
||||
if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config.face);
|
||||
if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config.body);
|
||||
if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);
|
||||
if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config.hand);
|
||||
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);
|
||||
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);
|
||||
|
|
@ -327,12 +327,12 @@ class Human {
|
|||
// run posenet
|
||||
this.analyze('Start Body:');
|
||||
if (this.config.async) {
|
||||
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(process.tensor, this.config.body) : [];
|
||||
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(process.tensor, this.config) : [];
|
||||
if (this.perf.body) delete this.perf.body;
|
||||
} else {
|
||||
this.state = 'run:body';
|
||||
timeStamp = now();
|
||||
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(process.tensor, this.config.body) : [];
|
||||
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(process.tensor, this.config) : [];
|
||||
this.perf.body = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
this.analyze('End Body:');
|
||||
|
|
|
|||
|
|
@ -18,7 +18,8 @@ function profile(name, data) {
|
|||
if (largest.length > maxResults) largest.length = maxResults;
|
||||
const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };
|
||||
profileData[name] = res;
|
||||
// eslint-disable-next-line no-console
|
||||
console.log('Human profiler', name, res);
|
||||
}
|
||||
|
||||
exports.run = profile;
|
||||
exports.data = profileData;
|
||||
|
|
|
|||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit cb7e64e4ff87f5288d9a9e2b4250c33e74911c68
|
||||
Subproject commit e73a55ab96efd7d1672d4c71dcef27dd1bee9f1d
|
||||
Loading…
Reference in New Issue