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human 1.9.0 beta with breaking changes regarding caching

pull/280/head
Vladimir Mandic 2021-05-18 11:26:16 -04:00
parent 53c070e3c5
commit 8741695dbd
19 changed files with 100 additions and 98 deletions
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3
demo/facematch.js
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@ -7,7 +7,6 @@ const userConfig = {
async: false,
warmup: 'none',
debug: true,
videoOptimized: false,
face: {
enabled: true,
detector: { rotation: true, return: true },
@ -16,7 +15,7 @@ const userConfig = {
iris: { enabled: false },
age: { enabled: false },
gender: { enabled: false },
emotion: { enabled: false },
emotion: { enabled: true },
description: { enabled: true },
},
hand: { enabled: false },

2
demo/index.js
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@ -13,7 +13,6 @@ const userConfig = {
/*
backend: 'webgl',
async: true,
videoOptimized: false,
filter: {
enabled: false,
flip: false,
@ -487,7 +486,6 @@ async function detectVideo() {
// just initialize everything and call main function
async function detectSampleImages() {
userConfig.videoOptimized = false; // force disable video optimizations
document.getElementById('canvas').style.display = 'none';
document.getElementById('samples-container').style.display = 'block';
log('running detection of sample images');

1
demo/node-multiprocess-worker.js
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@ -14,7 +14,6 @@ const myConfig = {
backend: 'tensorflow',
modelBasePath: 'file://models/',
debug: false,
videoOptimized: false,
async: true,
face: {
enabled: true,

1
demo/node-video.js
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@ -25,7 +25,6 @@ const humanConfig = {
backend: 'tensorflow',
modelBasePath: 'file://node_modules/@vladmandic/human/models/',
debug: false,
videoOptimized: true,
async: true,
filter: { enabled: false },
face: {

1
demo/node.js
View File

@ -16,7 +16,6 @@ const myConfig = {
backend: 'tensorflow',
modelBasePath: 'file://models/',
debug: true,
videoOptimized: false,
async: false,
filter: {
enabled: true,

2
package.json
View File

@ -1,6 +1,6 @@
{
"name": "@vladmandic/human",
"version": "1.8.5",
"version": "1.9.0",
"description": "Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition",
"sideEffects": false,
"main": "dist/human.node.js",

5
src/age/age.ts
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@ -16,12 +16,11 @@ export async function load(config) {
export async function predict(image, config) {
if (!model) return null;
if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
if ((skipped < config.face.age.skipFrames) && config.skipFrame && last.age && (last.age > 0)) {
skipped++;
return last;
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
const enhance = tf.mul(resize, [255.0]);

49
src/config.ts
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@ -19,13 +19,6 @@ export interface Config {
/** Perform model loading and inference concurrently or sequentially */
async: boolean,
/** Perform additional optimizations when input is video,
* - must be disabled for images
* - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs
* - skips boundary detection for every `skipFrames` frames specified for each model
* - while maintaining in-box detection since objects don't change definition as fast */
videoOptimized: boolean,
/** What to use for `human.warmup()`
* - warmup pre-initializes all models for faster inference but can take significant time on startup
* - only used for `webgl` and `humangl` backends
@ -37,6 +30,12 @@ export interface Config {
*/
modelBasePath: string,
/** Cache sensitivity
* - values 0..1 where 0.01 means reset cache if input changed more than 1%
* - set to 0 to disable caching
*/
cacheSensitivity: number;
/** Run input through image filters before inference
* - image filters run with near-zero latency as they are executed on the GPU
*/
@ -101,8 +100,6 @@ export interface Config {
* - iouThreshold: ammount of overlap between two detected objects before one object is removed
* - maxDetected: maximum number of faces detected in the input, should be set to the minimum number for performance
* - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles
* - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true
* - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle
* - return: return extracted face as tensor for futher user processing
*/
face: {
@ -112,7 +109,6 @@ export interface Config {
rotation: boolean,
maxDetected: number,
skipFrames: number,
skipInitial: boolean,
minConfidence: number,
iouThreshold: number,
return: boolean,
@ -160,14 +156,11 @@ export interface Config {
* - iouThreshold: ammount of overlap between two detected objects before one object is removed
* - maxDetected: maximum number of hands detected in the input, should be set to the minimum number for performance
* - rotation: 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
* - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true
* - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle
*/
hand: {
enabled: boolean,
rotation: boolean,
skipFrames: number,
skipInitial: boolean,
minConfidence: number,
iouThreshold: number,
maxDetected: number,
@ -186,7 +179,6 @@ export interface Config {
* - minConfidence: minimum score that detection must have to return as valid object
* - iouThreshold: ammount of overlap between two detected objects before one object is removed
* - maxDetected: maximum number of detections to return
* - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true
*/
object: {
enabled: boolean,
@ -205,14 +197,13 @@ const config: Config = {
wasmPath: '../node_modules/@tensorflow/tfjs-backend-wasm/dist//', // path for wasm binaries, only used for backend: wasm
debug: true, // print additional status messages to console
async: true, // execute enabled models in parallel
videoOptimized: true, // perform additional optimizations when input is video,
// automatically disabled for Image, ImageData, ImageBitmap
// skips boundary detection for every n frames
// while maintaining in-box detection since objects cannot move that fast
warmup: 'full', // what to use for human.warmup(), can be 'none', 'face', 'full'
// warmup pre-initializes all models for faster inference but can take
// significant time on startup
// only used for `webgl` and `humangl` backends
cacheSensitivity: 0.005, // cache sensitivity
// values 0..1 where 0.01 means reset cache if input changed more than 1%
// set to 0 to disable caching
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
@ -254,13 +245,11 @@ const config: Config = {
// this parameter is not valid in nodejs
maxDetected: 10, // maximum number of faces detected in the input
// should be set to the minimum number for performance
skipFrames: 21, // how many frames to go without re-running the face bounding box detector
// only used for video inputs
skipFrames: 21, // 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 probably hasn't moved much
// in short time (10 * 1/25 = 0.25 sec)
skipInitial: false, // if previous detection resulted in no faces detected,
// should skipFrames be reset immediately to force new detection cycle
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
@ -282,15 +271,16 @@ 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: 31, // how many frames to go without re-running the detector
// only used for video inputs
skipFrames: 31, // how many max frames to go without re-running the detector
// only used when cacheSensitivity is not zero
minConfidence: 0.1, // threshold for discarding a prediction
},
emotion: {
enabled: true,
minConfidence: 0.1, // threshold for discarding a prediction
skipFrames: 32, // how many frames to go without re-running the detector
skipFrames: 32, // how max many frames to go without re-running the detector
// only used when cacheSensitivity is not zero
modelPath: 'emotion.json', // face emotion model, can be absolute path or relative to modelBasePath
},
},
@ -309,13 +299,11 @@ const config: Config = {
enabled: true,
rotation: false, // 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
skipFrames: 12, // how many frames to go without re-running the hand bounding box detector
// only used for video inputs
skipFrames: 12, // 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 probably
// hasn't moved much in short time (10 * 1/25 = 0.25 sec)
skipInitial: false, // if previous detection resulted in no hands detected,
// should skipFrames be reset immediately to force new detection cycle
minConfidence: 0.1, // threshold for discarding a prediction
iouThreshold: 0.1, // ammount of overlap between two detected objects before one object is removed
maxDetected: 2, // maximum number of hands detected in the input
@ -335,7 +323,8 @@ 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: 41, // how many frames to go without re-running the detector
skipFrames: 41, // how many max frames to go without re-running the detector
// only used when cacheSensitivity is not zero
},
};
export { config as defaults };

5
src/efficientpose/efficientpose.ts
View File

@ -39,12 +39,11 @@ function max2d(inputs, minScore) {
export async function predict(image, config) {
if (!model) return null;
if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {
if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {
skipped++;
return keypoints;
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const tensor = tf.tidy(() => {
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);

16
src/emotion/emotion.ts
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@ -3,7 +3,9 @@ import * as tf from '../../dist/tfjs.esm.js';
const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];
let model;
let last: Array<{ score: number, emotion: string }> = [];
// let last: Array<{ score: number, emotion: string }> = [];
const last: Array<Array<{ score: number, emotion: string }>> = [];
let lastCount = 0;
let skipped = Number.MAX_SAFE_INTEGER;
// tuning values
@ -18,14 +20,13 @@ export async function load(config) {
return model;
}
export async function predict(image, config) {
export async function predict(image, config, idx, count) {
if (!model) return null;
if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {
if ((skipped < config.face.emotion.skipFrames) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
skipped++;
return last;
return last[idx];
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
const [red, green, blue] = tf.split(resize, 3, 3);
@ -54,7 +55,8 @@ export async function predict(image, config) {
obj.sort((a, b) => b.score - a.score);
}
normalize.dispose();
last = obj;
last[idx] = obj;
lastCount = count;
resolve(obj);
});
}

36
src/faceall.ts → src/face.ts
View File

@ -57,8 +57,7 @@ const calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: nu
const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);
// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
const angle = {
// values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees
// value of 0 means center
// values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center
// pitch is face move up/down
pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face
// yaw is face turn left/right
@ -134,25 +133,26 @@ export const detectFace = async (parent, input): Promise<any> => {
const faces = await facemesh.predict(input, parent.config);
parent.perf.face = Math.trunc(now() - timeStamp);
if (!faces) return [];
for (const face of faces) {
// for (const face of faces) {
for (let i = 0; i < faces.length; i++) {
parent.analyze('Get Face');
// is something went wrong, skip the face
if (!face.image || face.image.isDisposedInternal) {
log('Face object is disposed:', face.image);
if (!faces[i].image || faces[i].image.isDisposedInternal) {
log('Face object is disposed:', faces[i].image);
continue;
}
const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);
const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
// run emotion, inherits face from blazeface
parent.analyze('Start Emotion:');
if (parent.config.async) {
emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};
emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].image, parent.config, i, faces.length) : {};
} else {
parent.state = 'run:emotion';
timeStamp = now();
emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};
emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].image, parent.config, i, faces.length) : {};
parent.perf.emotion = Math.trunc(now() - timeStamp);
}
parent.analyze('End Emotion:');
@ -160,11 +160,11 @@ export const detectFace = async (parent, input): Promise<any> => {
// run emotion, inherits face from blazeface
parent.analyze('Start Description:');
if (parent.config.async) {
descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];
descRes = parent.config.face.description.enabled ? faceres.predict(faces[i], parent.config, i, faces.length) : [];
} else {
parent.state = 'run:description';
timeStamp = now();
descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];
descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].image, parent.config, i, faces.length) : [];
parent.perf.embedding = Math.trunc(now() - timeStamp);
}
parent.analyze('End Description:');
@ -178,18 +178,18 @@ export const detectFace = async (parent, input): Promise<any> => {
// calculate iris distance
// iris: array[ center, left, top, right, bottom]
if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {
delete face.annotations.leftEyeIris;
delete face.annotations.rightEyeIris;
if (!parent.config.face.iris.enabled && faces[i]?.annotations?.leftEyeIris && faces[i]?.annotations?.rightEyeIris) {
delete faces[i].annotations.leftEyeIris;
delete faces[i].annotations.rightEyeIris;
}
const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)
const irisSize = (faces[i].annotations?.leftEyeIris && faces[i].annotations?.rightEyeIris)
/* average human iris size is 11.7mm */
? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))
? 11.7 * Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1]))
: 0;
// combine results
faceRes.push({
...face,
...faces[i],
age: descRes.age,
gender: descRes.gender,
genderConfidence: descRes.genderConfidence,
@ -197,10 +197,10 @@ export const detectFace = async (parent, input): Promise<any> => {
emotion: emotionRes,
iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,
rotation,
tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,
tensor: parent.config.face.detector.return ? faces[i].image?.squeeze() : null,
});
// dispose original face tensor
face.image?.dispose();
faces[i].image?.dispose();
parent.analyze('End Face');
}

13
src/faceres/faceres.ts
View File

@ -2,7 +2,8 @@ import { log, join } from '../helpers';
import * as tf from '../../dist/tfjs.esm.js';
let model;
let last = { age: 0 };
const last: Array<{ age: number}> = [];
let lastCount = 0;
let skipped = Number.MAX_SAFE_INTEGER;
type Tensor = typeof tf.Tensor;
@ -94,14 +95,13 @@ export function enhance(input): Tensor {
return image;
}
export async function predict(image, config) {
export async function predict(image, config, idx, count) {
if (!model) return null;
if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
if ((skipped < config.face.description.skipFrames) && config.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {
skipped++;
return last;
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const enhanced = enhance(image);
@ -136,7 +136,8 @@ export async function predict(image, config) {
resT.forEach((t) => tf.dispose(t));
}
last = obj;
last[idx] = obj;
lastCount = count;
resolve(obj);
});
}

5
src/gender/gender.ts
View File

@ -21,12 +21,11 @@ export async function load(config) {
export async function predict(image, config) {
if (!model) return null;
if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
if ((skipped < config.face.gender.skipFrames) && config.skipFrame && last.gender !== '') {
skipped++;
return last;
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
let enhance;

7
src/handpose/handpipeline.ts
View File

@ -81,11 +81,12 @@ export class HandPipeline {
// run new detector every skipFrames unless we only want box to start with
let boxes;
if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {
if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.skipFrame) {
boxes = await this.handDetector.estimateHandBounds(image, config);
this.skipped = 0;
}
if (config.videoOptimized) this.skipped++;
if (config.skipFrame) 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)) {
@ -96,8 +97,6 @@ export class HandPipeline {
}
const hands: Array<{}> = [];
if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;
// go through working set of boxes
for (let i = 0; i < this.storedBoxes.length; i++) {
const currentBox = this.storedBoxes[i];

43
src/human.ts
View File

@ -4,7 +4,7 @@ import { Result } from './result';
import * as sysinfo from './sysinfo';
import * as tf from '../dist/tfjs.esm.js';
import * as backend from './tfjs/backend';
import * as faceall from './faceall';
import * as face from './face';
import * as facemesh from './blazeface/facemesh';
import * as faceres from './faceres/faceres';
import * as emotion from './emotion/emotion';
@ -116,6 +116,7 @@ export class Human {
#analyzeMemoryLeaks: boolean;
#checkSanity: boolean;
#firstRun: boolean;
#lastInputSum: number
// definition end
@ -165,6 +166,7 @@ export class Human {
this.faceUVMap = facemesh.uvmap;
// include platform info
this.sysinfo = sysinfo.info();
this.#lastInputSum = 1;
}
// helper function: measure tensor leak
@ -338,6 +340,21 @@ export class Human {
}
}
// check if input changed sufficiently to trigger new detections
/** @hidden */
#skipFrame = async (input) => {
if (this.config.cacheSensitivity === 0) return true;
const resizeFact = 32;
const reduced = input.resizeBilinear([Math.trunc(input.shape[1] / resizeFact), Math.trunc(input.shape[2] / resizeFact)]);
const sumT = this.tf.sum(reduced);
reduced.dispose();
const sum = sumT.dataSync()[0] as number;
sumT.dispose();
const diff = Math.max(sum, this.#lastInputSum) / Math.min(sum, this.#lastInputSum) - 1;
this.#lastInputSum = sum;
return diff < this.config.cacheSensitivity;
}
/** Main detection method
* - Analyze configuration: {@link Config}
* - Pre-process input: {@link Input}
@ -369,6 +386,8 @@ export class Human {
// load models if enabled
await this.load();
/*
// function disabled in favor of inputChanged
// disable video optimization for inputs of type image, but skip if inside worker thread
let previousVideoOptimized;
// @ts-ignore ignore missing type for WorkerGlobalScope as that is the point
@ -382,6 +401,7 @@ export class Human {
previousVideoOptimized = this.config.videoOptimized;
this.config.videoOptimized = false;
}
*/
timeStamp = now();
const process = image.process(input, this.config);
@ -393,6 +413,17 @@ export class Human {
this.perf.image = Math.trunc(now() - timeStamp);
this.analyze('Get Image:');
timeStamp = now();
// @ts-ignore hidden dynamic property that is not part of definitions
this.config.skipFrame = await this.#skipFrame(process.tensor);
if (!this.perf.frames) this.perf.frames = 0;
if (!this.perf.cached) this.perf.cached = 0;
this.perf.frames++;
// @ts-ignore hidden dynamic property that is not part of definitions
if (this.config.skipFrame) this.perf.cached++;
this.perf.changed = Math.trunc(now() - timeStamp);
this.analyze('Check Changed:');
// prepare where to store model results
let bodyRes;
let handRes;
@ -402,12 +433,12 @@ export class Human {
// run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion
if (this.config.async) {
faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];
faceRes = this.config.face.enabled ? face.detectFace(this, process.tensor) : [];
if (this.perf.face) delete this.perf.face;
} else {
this.state = 'run:face';
timeStamp = now();
faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];
faceRes = this.config.face.enabled ? await face.detectFace(this, process.tensor) : [];
current = Math.trunc(now() - timeStamp);
if (current > 0) this.perf.face = current;
}
@ -471,9 +502,6 @@ export class Human {
else if (this.perf.gesture) delete this.perf.gesture;
}
// restore video optimizations if previously disabled
if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;
this.perf.total = Math.trunc(now() - timeStart);
this.state = 'idle';
const result = {
@ -577,13 +605,10 @@ export class Human {
const t0 = now();
if (userConfig) this.config = mergeDeep(this.config, userConfig);
if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };
const save = this.config.videoOptimized;
this.config.videoOptimized = false;
let res;
if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();
else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();
else res = await this.#warmupNode();
this.config.videoOptimized = save;
const t1 = now();
if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);
return res;

6
src/nanodet/nanodet.ts
View File

@ -97,13 +97,11 @@ async function process(res, inputSize, outputShape, config) {
export async function predict(image, config) {
if (!model) return null;
// console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));
if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {
if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {
skipped++;
return last;
}
if (config.videoOptimized) skipped = 0;
else skipped = Number.MAX_SAFE_INTEGER;
skipped = 0;
return new Promise(async (resolve) => {
const outputSize = [image.shape[2], image.shape[1]];
const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);

1
test/test-node-gpu.js
View File

@ -5,7 +5,6 @@ const config = {
modelBasePath: 'file://models/',
backend: 'tensorflow',
debug: false,
videoOptimized: false,
async: false,
filter: {
enabled: true,

1
test/test-node-wasm.js
View File

@ -6,7 +6,6 @@ const config = {
backend: 'wasm',
wasmPath: 'node_modules/@tensorflow/tfjs-backend-wasm/dist/',
debug: false,
videoOptimized: false,
async: false,
filter: {
enabled: true,

1
test/test-node.js
View File

@ -5,7 +5,6 @@ const config = {
modelBasePath: 'file://models/',
backend: 'tensorflow',
debug: false,
videoOptimized: false,
async: false,
filter: {
enabled: true,