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time based caching

pull/293/head
Vladimir Mandic 2021-10-23 09:38:52 -04:00
parent 267c87536a
commit c38773bf26
22 changed files with 98 additions and 79 deletions
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1
README.md
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@ -60,6 +60,7 @@ Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) ap
- [**Usage & Functions**](https://github.com/vladmandic/human/wiki/Usage)
- [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration)
- [**Output Details**](https://github.com/vladmandic/human/wiki/Outputs)
- [**Caching & Smoothing**](https://github.com/vladmandic/human/wiki/Caching)
- [**Face Recognition & Face Description**](https://github.com/vladmandic/human/wiki/Embedding)
- [**Gesture Recognition**](https://github.com/vladmandic/human/wiki/Gesture)
- [**Common Issues**](https://github.com/vladmandic/human/wiki/Issues)

9
TODO.md
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@ -2,16 +2,14 @@
## Work in Progress
- `skipTime`: testing, documentation
<br>
### Exploring
- Optical Flow: <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
- TFLite Models: <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
- Histogram Equalization: Regular, Adaptive, Contrast Limited
- Switch to custom `tfjs` for main `human` ESM bundle
- Optical Flow: <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
- Histogram Equalization: Regular, Adaptive, Contrast Limited
- TFLite Models: <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
- Body segmentation: `robust-video-matting`
#### WebGPU
@ -55,6 +53,7 @@ Object detection using CenterNet or NanoDet models is not working when using WAS
## Pending Release
- Update to TFJS 3.10.0
- Time based caching
- Multiple bug fixes
- Utility class `human.env`
- Add `skipTime` in addition to `skipFrames`

1
demo/index.js
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@ -1016,6 +1016,7 @@ async function main() {
// create instance of human
human = new Human(userConfig);
human.env.perfadd = true;
log('human version:', human.version);
// we've merged human defaults with user config and now lets store it back so it can be accessed by methods such as menu

8
src/body/blazepose.ts
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@ -16,7 +16,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
let outputNodes: string[]; // different for lite/full/heavy
let cache: BodyResult | null = null;
let padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];
let last = 0;
let lastTime = 0;
export async function loadDetect(config: Config): Promise<GraphModel> {
if (env.initial) models[0] = null;
@ -136,11 +136,13 @@ async function detectParts(input: Tensor, config: Config, outputSize: [number, n
export async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {
const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];
if ((skipped < (config.body.skipFrames || 0)) && ((config.body.skipTime || 0) <= (now() - last)) && config.skipFrame && cache !== null) {
const skipTime = (config.body.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.body.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && cache !== null) {
skipped++;
} else {
cache = await detectParts(input, config, outputSize);
last = now();
lastTime = now();
skipped = 0;
}
if (cache) return [cache];

8
src/body/efficientpose.ts
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@ -13,7 +13,7 @@ import type { Config } from '../config';
import { env } from '../util/env';
let model: GraphModel | null;
let last = 0;
let lastTime = 0;
const cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
// const keypoints: Array<BodyKeypoint> = [];
@ -50,7 +50,9 @@ function max2d(inputs, minScore) {
}
export async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {
if ((skipped < (config.body?.skipFrames || 0)) && config.skipFrame && Object.keys(cache.keypoints).length > 0 && ((config.body.skipTime || 0) <= (now() - last))) {
const skipTime = (config.body.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.body.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {
skipped++;
return [cache];
}
@ -66,7 +68,7 @@ export async function predict(image: Tensor, config: Config): Promise<BodyResult
let resT;
if (config.body.enabled) resT = await model?.predict(tensor);
last = now();
lastTime = now();
tf.dispose(tensor);
if (resT) {

6
src/body/movenet.ts
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@ -132,9 +132,11 @@ async function parseMultiPose(res, config, image, inputBox) {
export async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {
if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model
if (!config.skipFrame) cache.boxes.length = 0; // allowed to use cache or not
if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not
skipped++; // increment skip frames
if (config.skipFrame && (skipped <= (config.body.skipFrames || 0) && ((config.body.skipTime || 0) <= (now() - cache.last)))) {
const skipTime = (config.body.skipTime || 0) > (now() - cache.last);
const skipFrame = skipped < (config.body.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame) {
return cache.bodies; // return cached results without running anything
}
return new Promise(async (resolve) => {

30
src/config.ts
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@ -255,7 +255,7 @@ export interface Config {
cacheSensitivity: number;
/** Internal Variable */
skipFrame: boolean;
skipAllowed: boolean;
/** Run input through image filters before inference
* - image filters run with near-zero latency as they are executed on the GPU
@ -302,10 +302,10 @@ const config: Config = {
// warmup pre-initializes all models for faster inference but can take
// significant time on startup
// only used for `webgl` and `humangl` backends
cacheSensitivity: 0.75, // cache sensitivity
cacheSensitivity: 0.70, // cache sensitivity
// values 0..1 where 0.01 means reset cache if input changed more than 1%
// set to 0 to disable caching
skipFrame: false, // internal & dynamic
skipAllowed: false, // internal & dynamic
filter: { // run input through image filters before inference
// image filters run with near-zero latency as they are executed on the GPU
enabled: true, // enable image pre-processing filters
@ -347,9 +347,9 @@ const config: Config = {
// this parameter is not valid in nodejs
maxDetected: 1, // maximum number of faces detected in the input
// should be set to the minimum number for performance
skipFrames: 11, // how many max frames to go without re-running the face bounding box detector
skipFrames: 99, // how many max frames to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 2500, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
minConfidence: 0.2, // threshold for discarding a prediction
iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed
@ -371,9 +371,9 @@ const config: Config = {
emotion: {
enabled: true,
minConfidence: 0.1, // threshold for discarding a prediction
skipFrames: 12, // how max many frames to go without re-running the detector
skipFrames: 99, // how max many frames to go without re-running the detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 1500, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
modelPath: 'emotion.json', // face emotion model, can be absolute path or relative to modelBasePath
},
@ -383,18 +383,18 @@ const config: Config = {
// recommended to enable detector.rotation and mesh.enabled
modelPath: 'faceres.json', // face description model
// can be either absolute path or relative to modelBasePath
skipFrames: 13, // how many max frames to go without re-running the detector
skipFrames: 99, // how many max frames to go without re-running the detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 3000, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
minConfidence: 0.1, // threshold for discarding a prediction
},
antispoof: {
enabled: false,
skipFrames: 14, // how max many frames to go without re-running the detector
skipFrames: 99, // how max many frames to go without re-running the detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 4000, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
modelPath: 'antispoof.json', // face description model
// can be either absolute path or relative to modelBasePath
@ -415,7 +415,7 @@ const config: Config = {
minConfidence: 0.3, // threshold for discarding a prediction
skipFrames: 1, // how many max frames to go without re-running the detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 200, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
},
@ -424,7 +424,7 @@ const config: Config = {
rotation: true, // use best-guess rotated hand image or just box with rotation as-is
// false means higher performance, but incorrect finger mapping if hand is inverted
// only valid for `handdetect` variation
skipFrames: 2, // how many max frames to go without re-running the hand bounding box detector
skipFrames: 99, // how many max frames to go without re-running the hand bounding box detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
@ -450,9 +450,9 @@ const config: Config = {
minConfidence: 0.2, // threshold for discarding a prediction
iouThreshold: 0.4, // ammount of overlap between two detected objects before one object is removed
maxDetected: 10, // maximum number of objects detected in the input
skipFrames: 15, // how many max frames to go without re-running the detector
skipFrames: 99, // how many max frames to go without re-running the detector
// only used when cacheSensitivity is not zero
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
skipTime: 1000, // how many ms to go without re-running object detector
// only used when cacheSensitivity is not zero
},

8
src/face/antispoof.ts
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@ -12,7 +12,7 @@ let model: GraphModel | null;
const cached: Array<number> = [];
let skipped = Number.MAX_SAFE_INTEGER;
let lastCount = 0;
let last = 0;
let lastTime = 0;
export async function load(config: Config): Promise<GraphModel> {
if (env.initial) model = null;
@ -26,7 +26,9 @@ export async function load(config: Config): Promise<GraphModel> {
export async function predict(image: Tensor, config: Config, idx, count) {
if (!model) return null;
if ((skipped < (config.face.antispoof?.skipFrames || 0)) && ((config.face.antispoof?.skipTime || 0) <= (now() - last)) && config.skipFrame && (lastCount === count) && cached[idx]) {
const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {
skipped++;
return cached[idx];
}
@ -37,7 +39,7 @@ export async function predict(image: Tensor, config: Config, idx, count) {
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount = count;
last = now();
lastTime = now();
tf.dispose([resize, res]);
resolve(cached[idx]);
});

9
src/face/face.ts
View File

@ -4,6 +4,7 @@
*/
import { log, now } from '../util/util';
import { env } from '../util/env';
import * as tf from '../../dist/tfjs.esm.js';
import * as facemesh from './facemesh';
import * as emotion from '../gear/emotion';
@ -29,7 +30,7 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
timeStamp = now();
const faces = await facemesh.predict(input, parent.config);
parent.performance.face = Math.trunc(now() - timeStamp);
parent.performance.face = env.perfadd ? (parent.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
if (!input.shape || input.shape.length !== 4) return [];
if (!faces) return [];
// for (const face of faces) {
@ -53,7 +54,7 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
parent.state = 'run:emotion';
timeStamp = now();
emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : null;
parent.performance.emotion = Math.trunc(now() - timeStamp);
parent.performance.emotion = env.perfadd ? (parent.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
parent.analyze('End Emotion:');
@ -65,7 +66,7 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
parent.state = 'run:antispoof';
timeStamp = now();
antispoofRes = parent.config.face.antispoof.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : null;
parent.performance.antispoof = Math.trunc(now() - timeStamp);
parent.performance.antispoof = env.perfadd ? (parent.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
parent.analyze('End AntiSpoof:');
@ -91,7 +92,7 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
parent.state = 'run:description';
timeStamp = now();
descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : null;
parent.performance.embedding = Math.trunc(now() - timeStamp);
parent.performance.embedding = env.perfadd ? (parent.performance.embedding || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
parent.analyze('End Description:');

5
src/face/facemesh.ts
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@ -28,7 +28,10 @@ let detectedFaces = 0;
export async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {
// reset cached boxes
if (!config.skipFrame || (((detectedFaces !== config.face.detector?.maxDetected) || !config.face.mesh?.enabled)) && (skipped > (config.face.detector?.skipFrames || 0) && ((config.face.description?.skipTime || 0) <= (now() - lastTime)))) {
const skipTime = (config.face.detector?.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.face.detector?.skipFrames || 0);
if (!config.skipAllowed || !skipTime || !skipFrame || detectedFaces === 0) {
const newBoxes = await blazeface.getBoxes(input, config); // get results from blazeface detector
lastTime = now();
boxCache = []; // empty cache

4
src/face/faceres.ts
View File

@ -91,7 +91,9 @@ export function enhance(input): Tensor {
export async function predict(image: Tensor, config: Config, idx, count) {
if (!model) return null;
if ((skipped < (config.face.description?.skipFrames || 0)) && ((config.face.description?.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {
const skipFrame = skipped < (config.face.description?.skipFrames || 0);
const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);
if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {
skipped++;
return last[idx];
}

4
src/gear/emotion.ts
View File

@ -33,7 +33,9 @@ export async function load(config: Config): Promise<GraphModel> {
export async function predict(image: Tensor, config: Config, idx, count) {
if (!model) return null;
if ((skipped < (config.face.emotion?.skipFrames || 0)) && ((config.face.emotion?.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);
const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);
if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
skipped++;
return last[idx];
}

4
src/gear/ssrnet-age.ts
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@ -33,7 +33,9 @@ export async function load(config: Config | any) {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export async function predict(image: Tensor, config: Config | any) {
if (!model) return null;
if ((skipped < config.face.age.skipFrames) && ((config.face.age.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && last.age && (last.age > 0)) {
const skipTime = (config.face.age?.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.face.age?.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && last.age && (last.age > 0)) {
skipped++;
return last;
}

4
src/gear/ssrnet-gender.ts
View File

@ -36,7 +36,9 @@ export async function load(config: Config | any) {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export async function predict(image: Tensor, config: Config | any) {
if (!model) return null;
if ((skipped < config.face.gender.skipFrames) && ((config.face.gender.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && last.gender !== '') {
const skipTime = (config.face.gender?.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.face.gender?.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && last.gender !== '') {
skipped++;
return last;
}

12
src/hand/handposepipeline.ts
View File

@ -30,7 +30,7 @@ export class HandPipeline {
this.handPoseModel = handPoseModel;
this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;
this.storedBoxes = [];
this.skipped = 0;
this.skipped = Number.MAX_SAFE_INTEGER;
this.detectedHands = 0;
}
@ -88,15 +88,15 @@ export class HandPipeline {
async estimateHands(image, config) {
let useFreshBox = false;
// run new detector every skipFrames unless we only want box to start with
// run new detector every skipFrames
let boxes;
// console.log('handpipeline:estimateHands:skip criteria', this.skipped, config.hand.skipFrames, !config.hand.landmarks, !config.skipFrame); // should skip hand detector?
if ((this.skipped === 0) || ((this.skipped > config.hand.skipFrames) && ((config.hand.skipTime || 0) <= (now() - lastTime))) || !config.hand.landmarks || !config.skipFrame) {
const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);
const skipFrame = this.skipped < (config.hand.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.estimateHandBounds(image, config);
this.skipped = 0;
}
if (config.skipFrame) this.skipped++;
if (config.skipAllowed) this.skipped++;
// if detector result count doesn't match current working set, use it to reset current working set
if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {

19
src/hand/handtrack.ts
View File

@ -28,7 +28,7 @@ const boxExpandFact = 1.6;
const maxDetectorResolution = 512;
const detectorExpandFact = 1.4;
let skipped = 0;
let skipped = Number.MAX_SAFE_INTEGER;
let lastTime = 0;
let outputSize: [number, number] = [0, 0];
@ -183,23 +183,20 @@ async function detectFingers(input: Tensor, h: HandDetectResult, config: Config)
}
export async function predict(input: Tensor, config: Config): Promise<HandResult[]> {
/** handtrack caching
* 1. if skipFrame returned cached
* 2. if any cached results but although not sure if its enough we continute anyhow for 3x skipframes
* 3. if not skipframe or eventually rerun detector to generated new cached boxes and reset skipped
* 4. generate cached boxes based on detected keypoints
*/
if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped++; // increment skip frames
if (config.skipFrame && (skipped <= (config.hand.skipFrames || 0)) && ((config.hand.skipTime || 0) <= (now() - lastTime))) {
const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.hand.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame) {
return cache.hands; // return cached results without running anything
}
return new Promise(async (resolve) => {
if (config.skipFrame && cache.hands.length === config.hand.maxDetected) { // we have all detected hands
const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);
const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);
if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping
cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));
} else if (config.skipFrame && skipped < 3 * (config.hand.skipFrames || 0) && ((config.hand.skipTime || 0) <= 3 * (now() - lastTime)) && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer
} else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer
cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));
} else { // finally rerun detector
cache.boxes = await detectHands(input, config);

31
src/human.ts
View File

@ -326,7 +326,7 @@ export class Human {
const count = Object.values(this.models).filter((model) => model).length;
if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
if (env.initial) { // print version info on first run and check for correct backend setup
if (this.env.initial) { // print version info on first run and check for correct backend setup
if (this.config.debug) log(`version: ${this.version}`);
if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);
if (!await backend.check(this)) log('error: backend check failed');
@ -338,8 +338,8 @@ export class Human {
}
await models.load(this); // actually loads models
if (env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run
env.initial = false;
if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run
this.env.initial = false;
const loaded = Object.values(this.models).filter((model) => model).length;
if (loaded !== count) { // number of loaded models changed
@ -348,7 +348,7 @@ export class Human {
}
const current = Math.trunc(now() - timeStamp);
if (current > (this.performance.load as number || 0)) this.performance.load = current;
if (current > (this.performance.load as number || 0)) this.performance.load = this.env.perfadd ? (this.performance.load || 0) + current : current;
}
// emit event
@ -393,7 +393,6 @@ export class Human {
return new Promise(async (resolve) => {
this.state = 'config';
let timeStamp;
let elapsedTime;
// update configuration
this.config = mergeDeep(this.config, userConfig) as Config;
@ -418,7 +417,7 @@ export class Human {
this.state = 'image';
const img = image.process(input, this.config) as { canvas: HTMLCanvasElement | OffscreenCanvas, tensor: Tensor };
this.process = img;
this.performance.image = Math.trunc(now() - timeStamp);
this.performance.image = this.env.perfadd ? (this.performance.image || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze('Get Image:');
if (!img.tensor) {
@ -429,12 +428,12 @@ export class Human {
this.emit('image');
timeStamp = now();
this.config.skipFrame = await image.skip(this.config, img.tensor);
this.config.skipAllowed = await image.skip(this.config, img.tensor);
if (!this.performance.frames) this.performance.frames = 0;
if (!this.performance.cached) this.performance.cached = 0;
(this.performance.frames as number)++;
if (this.config.skipFrame) this.performance.cached++;
this.performance.changed = Math.trunc(now() - timeStamp);
if (this.config.skipAllowed) this.performance.cached++;
this.performance.changed = this.env.perfadd ? (this.performance.changed || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze('Check Changed:');
// prepare where to store model results
@ -452,8 +451,7 @@ export class Human {
} else {
timeStamp = now();
faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];
elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0) this.performance.face = elapsedTime;
this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies
@ -474,8 +472,7 @@ export class Human {
else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];
else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];
elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0) this.performance.body = elapsedTime;
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze('End Body:');
@ -491,8 +488,7 @@ export class Human {
timeStamp = now();
if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];
else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];
elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0) this.performance.hand = elapsedTime;
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze('End Hand:');
@ -507,8 +503,7 @@ export class Human {
timeStamp = now();
if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];
else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];
elapsedTime = Math.trunc(now() - timeStamp);
if (elapsedTime > 0) this.performance.object = elapsedTime;
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze('End Object:');
@ -522,7 +517,7 @@ export class Human {
if (this.config.gesture.enabled) {
timeStamp = now();
gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
if (!this.config.async) this.performance.gesture = Math.trunc(now() - timeStamp);
if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
else if (this.performance.gesture) delete this.performance.gesture;
}

4
src/object/centernet.ts
View File

@ -79,7 +79,9 @@ async function process(res: Tensor | null, outputShape, config: Config) {
}
export async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {
if ((skipped < (config.object.skipFrames || 0)) && ((config.object.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (last.length > 0)) {
const skipTime = (config.object.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.object.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {
skipped++;
return last;
}

4
src/object/nanodet.ts
View File

@ -107,7 +107,9 @@ async function process(res, inputSize, outputShape, config) {
}
export async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {
if ((skipped < (config.object.skipFrames || 0)) && ((config.object.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (last.length > 0)) {
const skipTime = (config.object.skipTime || 0) > (now() - lastTime);
const skipFrame = skipped < (config.object.skipFrames || 0);
if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {
skipped++;
return last;
}

2
src/result.ts
View File

@ -194,7 +194,7 @@ export interface Result {
/** {@link ObjectResult}: detection & analysis results */
object: Array<ObjectResult>
/** global performance object with timing values for each operation */
performance: Record<string, unknown>,
performance: Record<string, number>,
/** optional processed canvas that can be used to draw input on screen */
canvas?: OffscreenCanvas | HTMLCanvasElement | null | undefined,
/** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */

2
src/util/env.ts
View File

@ -25,6 +25,8 @@ export class Env {
};
/** Is offscreenCanvas supported? */
offscreen: undefined | boolean;
/** Are performance counter instant values or additive */
perfadd: boolean = false;
/** WASM detected capabilities */
wasm: {
supported: undefined | boolean,

2
wiki

@ -1 +1 @@
Subproject commit d6c2c8c474f1d55f36e4ab4ffc9a1852f2f2b4fb
Subproject commit 97e86c65f64df007c25250bcb513d48e5c602242