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initial work on skipTime

pull/356/head
Vladimir Mandic 2021-10-22 16:09:52 -04:00
parent 2791ee9fa9
commit 87465f99fd
16 changed files with 119 additions and 112 deletions
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3
CHANGELOG.md
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@ -11,6 +11,9 @@
### **HEAD -> main** 2021/10/22 mandic00@live.com
### **origin/main** 2021/10/22 mandic00@live.com
- add optional autodetected custom wasm path
### **2.3.6** 2021/10/21 mandic00@live.com

1
build.json
View File

@ -137,6 +137,7 @@
"format": "esm",
"input": "tfjs/tf-browser.ts",
"output": "dist/tfjs.esm.js",
"minify": true,
"sourcemap": true,
"external": ["fs", "os", "buffer", "util"]
},

6
src/body/blazepose.ts
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@ -3,7 +3,7 @@
*/
import * as tf from '../../dist/tfjs.esm.js';
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import type { BodyKeypoint, BodyResult, Box, Point } from '../result';
import type { GraphModel, Tensor } from '../tfjs/types';
import type { Config } from '../config';
@ -16,6 +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;
export async function loadDetect(config: Config): Promise<GraphModel> {
if (env.initial) models[0] = null;
@ -135,10 +136,11 @@ 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.skipFrame && cache !== null) {
if ((skipped < (config.body.skipFrames || 0)) && ((config.body.skipTime || 0) <= (now() - last)) && config.skipFrame && cache !== null) {
skipped++;
} else {
cache = await detectParts(input, config, outputSize);
last = now();
skipped = 0;
}
if (cache) return [cache];

12
src/body/efficientpose.ts
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@ -4,7 +4,7 @@
* Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import * as coords from './efficientposecoords';
import type { BodyResult, Point } from '../result';
@ -13,7 +13,7 @@ import type { Config } from '../config';
import { env } from '../util/env';
let model: GraphModel | null;
let last = 0;
const cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
// const keypoints: Array<BodyKeypoint> = [];
@ -50,12 +50,7 @@ function max2d(inputs, minScore) {
}
export async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {
/** blazepose caching
* not fully implemented
* 1. if skipFrame returned cached
* 2. run detection based on squared full frame
*/
if ((skipped < (config.body?.skipFrames || 0)) && config.skipFrame && Object.keys(cache.keypoints).length > 0) {
if ((skipped < (config.body?.skipFrames || 0)) && config.skipFrame && Object.keys(cache.keypoints).length > 0 && ((config.body.skipTime || 0) <= (now() - last))) {
skipped++;
return [cache];
}
@ -71,6 +66,7 @@ export async function predict(image: Tensor, config: Config): Promise<BodyResult
let resT;
if (config.body.enabled) resT = await model?.predict(tensor);
last = now();
tf.dispose(tensor);
if (resT) {

14
src/body/movenet.ts
View File

@ -4,7 +4,7 @@
* Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as box from '../util/box';
import * as tf from '../../dist/tfjs.esm.js';
import * as coords from './movenetcoords';
@ -23,9 +23,11 @@ let skipped = Number.MAX_SAFE_INTEGER;
const cache: {
boxes: Array<Box>, // unused
bodies: Array<BodyResult>;
last: number,
} = {
boxes: [],
bodies: [],
last: 0,
};
export async function load(config: Config): Promise<GraphModel> {
@ -129,17 +131,10 @@ async function parseMultiPose(res, config, image, inputBox) {
}
export async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {
/** movenet caching
* 1. if skipFrame returned cached
* 2. if enough cached boxes run using cached boxes
* 3. if not enough detected bodies rerun using full frame
* 4. regenerate cached boxes based on current keypoints
*/
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
skipped++; // increment skip frames
if (config.skipFrame && (skipped <= (config.body.skipFrames || 0))) {
if (config.skipFrame && (skipped <= (config.body.skipFrames || 0) && ((config.body.skipTime || 0) <= (now() - cache.last)))) {
return cache.bodies; // return cached results without running anything
}
return new Promise(async (resolve) => {
@ -181,6 +176,7 @@ export async function predict(input: Tensor, config: Config): Promise<BodyResult
// run detection on squared input and no cached boxes
t.input = fix.padInput(input, inputSize);
t.res = await model?.predict(t.input) as Tensor;
cache.last = now();
const res = await t.res.array();
cache.bodies = (t.res.shape[2] === 17)
? await parseSinglePose(res, config, input, [0, 0, 1, 1])

73
src/config.ts
View File

@ -1,53 +1,42 @@
/* eslint-disable indent */
/* eslint-disable no-multi-spaces */
/** Dectector part of face configuration */
export interface FaceDetectorConfig {
export interface GenericConfig {
enabled: boolean,
modelPath: string,
skipFrames: number,
skipTime: number,
}
/** Dectector part of face configuration */
export interface FaceDetectorConfig extends GenericConfig {
rotation: boolean,
maxDetected: number,
skipFrames: number,
minConfidence: number,
iouThreshold: number,
return: boolean,
}
/** Mesh part of face configuration */
export interface FaceMeshConfig {
enabled: boolean,
modelPath: string,
}
export type FaceMeshConfig = GenericConfig
/** Iris part of face configuration */
export interface FaceIrisConfig {
enabled: boolean,
modelPath: string,
}
export type FaceIrisConfig = GenericConfig
/** Description or face embedding part of face configuration
* - also used by age and gender detection
*/
export interface FaceDescriptionConfig {
enabled: boolean,
modelPath: string,
skipFrames: number,
export interface FaceDescriptionConfig extends GenericConfig {
minConfidence: number,
}
/** Emotion part of face configuration */
export interface FaceEmotionConfig {
enabled: boolean,
export interface FaceEmotionConfig extends GenericConfig {
minConfidence: number,
skipFrames: number,
modelPath: string,
}
/** Emotion part of face configuration */
export interface FaceAntiSpoofConfig {
enabled: boolean,
skipFrames: number,
modelPath: string,
}
export type FaceAntiSpoofConfig = GenericConfig
/** Controlls and configures all face-specific options:
* - face detection, face mesh detection, age, gender, emotion detection and face description
@ -86,18 +75,15 @@ export interface FaceConfig {
* Changing `modelPath` will change module responsible for hand detection and tracking
* Allowed values are `posenet.json`, `blazepose.json`, `efficientpose.json`, `movenet-lightning.json`, `movenet-thunder.json`, `movenet-multipose.json`
*/
export interface BodyConfig {
enabled: boolean,
modelPath: string,
export interface BodyConfig extends GenericConfig {
maxDetected: number,
minConfidence: number,
skipFrames: number,
detector?: {
modelPath: string
},
}
/** Controlls and configures all hand detection specific options
/** Controls and configures all hand detection specific options
*
* Parameters:
* - enabled: true/false
@ -113,10 +99,8 @@ export interface BodyConfig {
* Changing `detector.modelPath` will change module responsible for hand detection and tracking
* Allowed values are `handdetect.json` and `handtrack.json`
*/
export interface HandConfig {
enabled: boolean,
export interface HandConfig extends GenericConfig {
rotation: boolean,
skipFrames: number,
minConfidence: number,
iouThreshold: number,
maxDetected: number,
@ -139,13 +123,10 @@ export interface HandConfig {
* Changing `modelPath` will change module responsible for hand detection and tracking
* Allowed values are `mb3-centernet.json` and `nanodet.json`
*/
export interface ObjectConfig {
enabled: boolean,
modelPath: string,
export interface ObjectConfig extends GenericConfig {
minConfidence: number,
iouThreshold: number,
maxDetected: number,
skipFrames: number,
}
/** Controlls and configures all body segmentation module
@ -368,9 +349,8 @@ const config: Config = {
// 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
// only used when cacheSensitivity is not zero
// e.g., if model is running st 25 FPS, we can re-use existing bounding
// box for updated face analysis as the head does not move fast
// in short time (10 * 1/25 = 0.25 sec)
skipTime: 2000, // 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
return: false, // return extracted face as tensor
@ -393,6 +373,8 @@ const config: Config = {
minConfidence: 0.1, // threshold for discarding a prediction
skipFrames: 12, // 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
// only used when cacheSensitivity is not zero
modelPath: 'emotion.json', // face emotion model, can be absolute path or relative to modelBasePath
},
@ -403,6 +385,8 @@ const config: Config = {
// can be either absolute path or relative to modelBasePath
skipFrames: 13, // 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
// only used when cacheSensitivity is not zero
minConfidence: 0.1, // threshold for discarding a prediction
},
@ -410,6 +394,8 @@ const config: Config = {
enabled: false,
skipFrames: 14, // 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
// only used when cacheSensitivity is not zero
modelPath: 'antispoof.json', // face description model
// can be either absolute path or relative to modelBasePath
},
@ -429,6 +415,8 @@ 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
// only used when cacheSensitivity is not zero
},
hand: {
@ -438,9 +426,8 @@ const config: Config = {
// only valid for `handdetect` variation
skipFrames: 2, // how many max frames to go without re-running the hand bounding box detector
// only used when cacheSensitivity is not zero
// e.g., if model is running st 25 FPS, we can re-use existing bounding
// box for updated hand skeleton analysis as the hand
// hasn't moved much in short time (10 * 1/25 = 0.25 sec)
skipTime: 2000, // how many ms to go without re-running the face bounding box detector
// only used when cacheSensitivity is not zero
minConfidence: 0.50, // threshold for discarding a prediction
iouThreshold: 0.2, // ammount of overlap between two detected objects before one object is removed
maxDetected: -1, // maximum number of hands detected in the input
@ -465,6 +452,8 @@ const config: Config = {
maxDetected: 10, // maximum number of objects detected in the input
skipFrames: 15, // 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
// only used when cacheSensitivity is not zero
},
segmentation: {

6
src/face/antispoof.ts
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@ -2,7 +2,7 @@
* Anti-spoofing model implementation
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import type { Config } from '../config';
import type { GraphModel, Tensor } from '../tfjs/types';
import * as tf from '../../dist/tfjs.esm.js';
@ -12,6 +12,7 @@ let model: GraphModel | null;
const cached: Array<number> = [];
let skipped = Number.MAX_SAFE_INTEGER;
let lastCount = 0;
let last = 0;
export async function load(config: Config): Promise<GraphModel> {
if (env.initial) model = null;
@ -25,7 +26,7 @@ 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.skipFrame && (lastCount === count) && cached[idx]) {
if ((skipped < (config.face.antispoof?.skipFrames || 0)) && ((config.face.antispoof?.skipTime || 0) <= (now() - last)) && config.skipFrame && (lastCount === count) && cached[idx]) {
skipped++;
return cached[idx];
}
@ -36,6 +37,7 @@ 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();
tf.dispose([resize, res]);
resolve(cached[idx]);
});

7
src/face/facemesh.ts
View File

@ -7,7 +7,7 @@
* - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import * as blazeface from './blazeface';
import * as util from './facemeshutil';
@ -23,11 +23,14 @@ let boxCache: Array<BoxCache> = [];
let model: GraphModel | null = null;
let inputSize = 0;
let skipped = Number.MAX_SAFE_INTEGER;
let lastTime = 0;
let detectedFaces = 0;
export async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {
if (!config.skipFrame || (((detectedFaces !== config.face.detector?.maxDetected) || !config.face.mesh?.enabled)) && (skipped > (config.face.detector?.skipFrames || 0))) { // reset cached boxes
// 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 newBoxes = await blazeface.getBoxes(input, config); // get results from blazeface detector
lastTime = now();
boxCache = []; // empty cache
for (const possible of newBoxes.boxes) { // extract data from detector
const startPoint = await possible.box.startPoint.data() as unknown as Point;

29
src/face/faceres.ts
View File

@ -7,7 +7,7 @@
* Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import type { Tensor, GraphModel } from '../tfjs/types';
import type { Config } from '../config';
@ -21,6 +21,7 @@ const last: Array<{
descriptor: number[],
}> = [];
let lastTime = 0;
let lastCount = 0;
let skipped = Number.MAX_SAFE_INTEGER;
@ -90,15 +91,12 @@ 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.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {
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)) {
skipped++;
return last[idx];
}
skipped = 0;
return new Promise(async (resolve) => {
const enhanced = enhance(image);
let resT;
const obj = {
age: <number>0,
gender: <string>'unknown',
@ -106,11 +104,13 @@ export async function predict(image: Tensor, config: Config, idx, count) {
descriptor: <number[]>[],
};
if (config.face.description?.enabled) resT = await model?.predict(enhanced);
tf.dispose(enhanced);
if (resT) {
const gender = await resT.find((t) => t.shape[1] === 1).data();
if (config.face.description?.enabled) {
const enhanced = enhance(image);
const resT = await model?.predict(enhanced) as Tensor[];
lastTime = now();
tf.dispose(enhanced);
const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;
const gender = await genderT.data();
const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;
if (confidence > (config.face.description?.minConfidence || 0)) {
obj.gender = gender[0] <= 0.5 ? 'female' : 'male';
@ -119,15 +119,16 @@ export async function predict(image: Tensor, config: Config, idx, count) {
const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);
const age = (await argmax.data())[0];
tf.dispose(argmax);
const all = await resT.find((t) => t.shape[1] === 100).data();
const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;
const all = await ageT.data();
obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;
const desc = resT.find((t) => t.shape[1] === 1024);
// const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8
// const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor
const descriptor = await desc.data();
obj.descriptor = [...descriptor];
const descriptor = desc ? await desc.data() : <number[]>[];
// obj.descriptor = [...descriptor];
obj.descriptor = Array.from(descriptor);
resT.forEach((t) => tf.dispose(t));
}
last[idx] = obj;

40
src/gear/emotion.ts
View File

@ -4,7 +4,7 @@
* [**Oarriaga**](https://github.com/oarriaga/face_classification)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import type { Config } from '../config';
import type { GraphModel, Tensor } from '../tfjs/types';
import * as tf from '../../dist/tfjs.esm.js';
@ -15,6 +15,7 @@ let model: GraphModel | null;
// let last: Array<{ score: number, emotion: string }> = [];
const last: Array<Array<{ score: number, emotion: string }>> = [];
let lastCount = 0;
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
// tuning values
@ -32,39 +33,40 @@ 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.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
if ((skipped < (config.face.emotion?.skipFrames || 0)) && ((config.face.emotion?.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
skipped++;
return last[idx];
}
skipped = 0;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);
const [red, green, blue] = tf.split(resize, 3, 3);
tf.dispose(resize);
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
const redNorm = tf.mul(red, rgb[0]);
const greenNorm = tf.mul(green, rgb[1]);
const blueNorm = tf.mul(blue, rgb[2]);
tf.dispose(red);
tf.dispose(green);
tf.dispose(blue);
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
tf.dispose(redNorm);
tf.dispose(greenNorm);
tf.dispose(blueNorm);
const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));
tf.dispose(grayscale);
const obj: Array<{ score: number, emotion: string }> = [];
if (config.face.emotion?.enabled) {
const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);
const [red, green, blue] = tf.split(resize, 3, 3);
tf.dispose(resize);
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
const redNorm = tf.mul(red, rgb[0]);
const greenNorm = tf.mul(green, rgb[1]);
const blueNorm = tf.mul(blue, rgb[2]);
tf.dispose(red);
tf.dispose(green);
tf.dispose(blue);
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
tf.dispose(redNorm);
tf.dispose(greenNorm);
tf.dispose(blueNorm);
const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));
tf.dispose(grayscale);
const emotionT = await model?.predict(normalize) as Tensor; // result is already in range 0..1, no need for additional activation
lastTime = now();
const data = await emotionT.data();
tf.dispose(emotionT);
for (let i = 0; i < data.length; i++) {
if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
tf.dispose(normalize);
}
tf.dispose(normalize);
last[idx] = obj;
lastCount = count;
resolve(obj);

7
src/gear/ssrnet-age.ts
View File

@ -6,15 +6,15 @@
* Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import type { Config } from '../config';
import type { GraphModel, Tensor } from '../tfjs/types';
import { env } from '../util/env';
let model: GraphModel | null;
let last = { age: 0 };
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
@ -33,7 +33,7 @@ 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.skipFrame && last.age && (last.age > 0)) {
if ((skipped < config.face.age.skipFrames) && ((config.face.age.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && last.age && (last.age > 0)) {
skipped++;
return last;
}
@ -48,6 +48,7 @@ export async function predict(image: Tensor, config: Config | any) {
const obj = { age: 0 };
if (config.face.age.enabled) ageT = await model.predict(enhance);
lastTime = now();
tf.dispose(enhance);
if (ageT) {

6
src/gear/ssrnet-gender.ts
View File

@ -6,7 +6,7 @@
* Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import type { Config } from '../config';
import type { GraphModel, Tensor } from '../tfjs/types';
@ -14,6 +14,7 @@ import { env } from '../util/env';
let model: GraphModel | null;
let last = { gender: '' };
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
let alternative = false;
@ -35,7 +36,7 @@ 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.skipFrame && last.gender !== '') {
if ((skipped < config.face.gender.skipFrames) && ((config.face.gender.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && last.gender !== '') {
skipped++;
return last;
}
@ -63,6 +64,7 @@ export async function predict(image: Tensor, config: Config | any) {
const obj = { gender: '', confidence: 0 };
if (config.face.gender.enabled) genderT = await model.predict(enhance);
lastTime = now();
tf.dispose(enhance);
if (genderT) {

5
src/hand/handposepipeline.ts
View File

@ -8,12 +8,14 @@ import * as util from './handposeutil';
import type * as detector from './handposedetector';
import type { Tensor, GraphModel } from '../tfjs/types';
import { env } from '../util/env';
import { now } from '../util/util';
const palmBoxEnlargeFactor = 5; // default 3
const handBoxEnlargeFactor = 1.65; // default 1.65
const palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
const palmLandmarksPalmBase = 0;
const palmLandmarksMiddleFingerBase = 2;
let lastTime = 0;
export class HandPipeline {
handDetector: detector.HandDetector;
@ -90,7 +92,7 @@ export class HandPipeline {
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.landmarks || !config.skipFrame) {
if ((this.skipped === 0) || ((this.skipped > config.hand.skipFrames) && ((config.hand.skipTime || 0) <= (now() - lastTime))) || !config.hand.landmarks || !config.skipFrame) {
boxes = await this.handDetector.estimateHandBounds(image, config);
this.skipped = 0;
}
@ -121,6 +123,7 @@ export class HandPipeline {
tf.dispose(croppedInput);
tf.dispose(rotatedImage);
const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage) as Array<Tensor>;
lastTime = now();
tf.dispose(handImage);
const confidence = (await confidenceT.data())[0];
tf.dispose(confidenceT);

10
src/hand/handtrack.ts
View File

@ -6,7 +6,7 @@
* - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as box from '../util/box';
import * as tf from '../../dist/tfjs.esm.js';
import type { HandResult, Box, Point } from '../result';
@ -29,6 +29,7 @@ const maxDetectorResolution = 512;
const detectorExpandFact = 1.4;
let skipped = 0;
let lastTime = 0;
let outputSize: [number, number] = [0, 0];
type HandDetectResult = {
@ -184,7 +185,7 @@ 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 5x skipframes
* 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
*/
@ -192,16 +193,17 @@ export async function predict(input: Tensor, config: Config): Promise<HandResult
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped++; // increment skip frames
if (config.skipFrame && (skipped <= (config.hand.skipFrames || 0))) {
if (config.skipFrame && (skipped <= (config.hand.skipFrames || 0)) && ((config.hand.skipTime || 0) <= (now() - lastTime))) {
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
cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));
} else if (config.skipFrame && skipped < 3 * (config.hand.skipFrames || 0) && cache.hands.length > 0) { // we have some cached results but although not sure if its enough we continute anyhow for bit longer
} 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
cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));
} else { // finally rerun detector
cache.boxes = await detectHands(input, config);
lastTime = now();
cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));
skipped = 0;
}

6
src/object/centernet.ts
View File

@ -4,7 +4,7 @@
* Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import { labels } from './labels';
import type { ObjectResult, Box } from '../result';
@ -16,6 +16,7 @@ import { fakeOps } from '../tfjs/backend';
let model: GraphModel | null;
let inputSize = 0;
let last: ObjectResult[] = [];
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
export async function load(config: Config): Promise<GraphModel> {
@ -78,7 +79,7 @@ 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.skipFrame && (last.length > 0)) {
if ((skipped < (config.object.skipFrames || 0)) && ((config.object.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (last.length > 0)) {
skipped++;
return last;
}
@ -88,6 +89,7 @@ export async function predict(input: Tensor, config: Config): Promise<ObjectResu
const outputSize = [input.shape[2], input.shape[1]];
const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);
const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;
lastTime = now();
tf.dispose(resize);
const obj = await process(objectT, outputSize, config);

6
src/object/nanodet.ts
View File

@ -4,7 +4,7 @@
* Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)
*/
import { log, join } from '../util/util';
import { log, join, now } from '../util/util';
import * as tf from '../../dist/tfjs.esm.js';
import { labels } from './labels';
import type { ObjectResult, Box } from '../result';
@ -14,6 +14,7 @@ import { env } from '../util/env';
let model;
let last: Array<ObjectResult> = [];
let lastTime = 0;
let skipped = Number.MAX_SAFE_INTEGER;
const scaleBox = 2.5; // increase box size
@ -106,7 +107,7 @@ async function process(res, inputSize, outputShape, config) {
}
export async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {
if ((skipped < (config.object.skipFrames || 0)) && config.skipFrame && (last.length > 0)) {
if ((skipped < (config.object.skipFrames || 0)) && ((config.object.skipTime || 0) <= (now() - lastTime)) && config.skipFrame && (last.length > 0)) {
skipped++;
return last;
}
@ -122,6 +123,7 @@ export async function predict(image: Tensor, config: Config): Promise<ObjectResu
let objectT;
if (config.object.enabled) objectT = await model.predict(transpose);
lastTime = now();
tf.dispose(transpose);
const obj = await process(objectT, model.inputSize, outputSize, config);