mirror of https://github.com/vladmandic/human
enhanced age, gender, emotion detection
Vladimir Mandic
parent
02696a65b3
commit
74ad5a2837
5
TODO.md
5
TODO.md
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@ -1,7 +1,6 @@
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# To-Do list for Human library
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- Fix BlazeFace NodeJS missing ops
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- Prune pre-packaged models
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- Strong typing
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- Build Face embedding database
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- Dynamic sample processing
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- Optimize for v1 release
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- Explore EfficientPose: <https://github.com/daniegr/EfficientPose> <https://github.com/PINTO0309/PINTO_model_zoo/tree/main/084_EfficientPose>
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@ -80,10 +80,10 @@ export default {
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// in short time (10 * 1/25 = 0.25 sec)
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skipInitial: false, // if previous detection resulted in no faces detected,
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// should skipFrames be reset immediately
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minConfidence: 0.1, // threshold for discarding a prediction
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minConfidence: 0.2, // threshold for discarding a prediction
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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: 0.1, // threshold for deciding when to remove boxes based on score
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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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@ -1,4 +1,5 @@
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import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human
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// import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human
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import Human from '../src/human';
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import Menu from './menu.js';
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import GLBench from './gl-bench.js';
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@ -8,7 +9,7 @@ const userConfig = { backend: 'webgl' }; // add any user configuration overrides
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const userConfig = {
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backend: 'wasm',
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async: false,
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warmup: 'face',
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warmup: 'none',
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videoOptimized: false,
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face: { enabled: true, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: false } },
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hand: { enabled: false },
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@ -360,7 +361,6 @@ async function processImage(input) {
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// just initialize everything and call main function
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async function detectVideo() {
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userConfig.videoOptimized = true;
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document.getElementById('samples-container').style.display = 'none';
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document.getElementById('canvas').style.display = 'block';
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const video = document.getElementById('video');
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@ -389,8 +389,8 @@ async function detectVideo() {
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// just initialize everything and call main function
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async function detectSampleImages() {
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userConfig.videoOptimized = false; // force disable video optimizations
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document.getElementById('play').style.display = 'none';
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userConfig.videoOptimized = false;
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document.getElementById('canvas').style.display = 'none';
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document.getElementById('samples-container').style.display = 'block';
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log('Running detection of sample images');
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@ -9,7 +9,6 @@ let skipped = Number.MAX_SAFE_INTEGER;
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// tuning values
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const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
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const scale = 1; // score multiplication factor
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export async function load(config) {
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if (!model) {
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@ -58,7 +57,7 @@ export async function predict(image, config) {
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if (config.face.emotion.enabled) {
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let data;
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if (!config.profile) {
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const emotionT = await model.predict(normalize);
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const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation
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data = emotionT.dataSync();
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tf.dispose(emotionT);
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} else {
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@ -68,7 +67,7 @@ export async function predict(image, config) {
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profile.run('emotion', profileData);
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}
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for (let i = 0; i < data.length; i++) {
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if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });
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if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
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}
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obj.sort((a, b) => b.score - a.score);
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}
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@ -37,10 +37,11 @@ export async function predict(image, config) {
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const greenNorm = tf.mul(green, rgb[1]);
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const blueNorm = tf.mul(blue, rgb[2]);
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const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
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return grayscale.sub(0.5).mul(2);
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const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1
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return normalize;
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});
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} else {
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enhance = tf.mul(resize, [255.0]);
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enhance = tf.mul(resize, [255.0]); // range RGB:0..255
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}
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tf.dispose(resize);
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@ -61,10 +62,9 @@ export async function predict(image, config) {
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const data = genderT.dataSync();
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if (alternative) {
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// returns two values 0..1, bigger one is prediction
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const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;
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if (confidence > config.face.gender.minConfidence) {
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if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {
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obj.gender = data[0] > data[1] ? 'female' : 'male';
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obj.confidence = confidence;
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obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
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}
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} else {
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// returns one value 0..1, .5 is prediction threshold
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220
src/human.ts
220
src/human.ts
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@ -40,45 +40,57 @@ function mergeDeep(...objects) {
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}
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class Human {
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tf: any;
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draw: any;
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package: any;
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version: string;
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config: any;
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fx: any;
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config: typeof config.default;
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state: string;
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numTensors: number;
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analyzeMemoryLeaks: boolean;
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checkSanity: boolean;
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firstRun: boolean;
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perf: any;
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image: any;
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models: any;
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image: { tensor, canvas };
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// classes
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tf: typeof tf;
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draw: typeof draw;
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// models
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facemesh: any;
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age: any;
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gender: any;
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emotion: any;
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body: any;
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hand: any;
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sysinfo: any;
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models: {
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face,
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posenet,
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blazepose,
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handpose,
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iris,
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age,
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gender,
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emotion,
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embedding,
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};
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classes: {
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facemesh: typeof facemesh;
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age: typeof age;
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gender: typeof gender;
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emotion: typeof emotion;
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body: typeof posenet | typeof blazepose;
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hand: typeof handpose;
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};
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sysinfo: { platform, agent };
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#package: any;
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#perf: any;
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#numTensors: number;
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#analyzeMemoryLeaks: boolean;
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#checkSanity: boolean;
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#firstRun: boolean;
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// definition end
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constructor(userConfig = {}) {
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this.tf = tf;
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this.draw = draw;
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this.package = app;
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this.#package = app;
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this.version = app.version;
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this.config = mergeDeep(config.default, userConfig);
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this.fx = null;
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this.state = 'idle';
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this.numTensors = 0;
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this.analyzeMemoryLeaks = false;
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this.checkSanity = false;
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this.firstRun = true;
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this.perf = {};
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this.#numTensors = 0;
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this.#analyzeMemoryLeaks = false;
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this.#checkSanity = false;
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this.#firstRun = true;
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this.#perf = {};
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// object that contains all initialized models
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this.models = {
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facemesh: null,
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face: null,
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posenet: null,
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blazepose: null,
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handpose: null,
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@ -86,38 +98,42 @@ class Human {
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age: null,
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gender: null,
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emotion: null,
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embedding: null,
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};
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// export access to image processing
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this.image = (input) => image.process(input, this.config);
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// @ts-ignore
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this.image = (input: any) => image.process(input, this.config);
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// export raw access to underlying models
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this.facemesh = facemesh;
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this.age = age;
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this.gender = gender;
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this.emotion = emotion;
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this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;
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this.hand = handpose;
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this.classes = {
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facemesh,
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age,
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gender,
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emotion,
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body: this.config.body.modelType.startsWith('posenet') ? posenet : blazepose,
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hand: handpose,
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};
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// include platform info
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this.sysinfo = sysinfo.info();
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}
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profile() {
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profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {
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if (this.config.profile) return profile.data;
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return {};
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}
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// helper function: measure tensor leak
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analyze(...msg) {
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if (!this.analyzeMemoryLeaks) return;
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#analyze = (...msg) => {
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if (!this.#analyzeMemoryLeaks) return;
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const current = this.tf.engine().state.numTensors;
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const previous = this.numTensors;
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this.numTensors = current;
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const previous = this.#numTensors;
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this.#numTensors = current;
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const leaked = current - previous;
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if (leaked !== 0) log(...msg, leaked);
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}
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// quick sanity check on inputs
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sanity(input) {
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if (!this.checkSanity) return null;
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#sanity = (input) => {
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if (!this.#checkSanity) return null;
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if (!input) return 'input is not defined';
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if (this.tf.ENV.flags.IS_NODE && !(input instanceof this.tf.Tensor)) {
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return 'input must be a tensor';
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return null;
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}
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simmilarity(embedding1, embedding2) {
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simmilarity(embedding1, embedding2): number {
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if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);
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return 0;
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}
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@ -141,13 +157,13 @@ class Human {
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const timeStamp = now();
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if (userConfig) this.config = mergeDeep(this.config, userConfig);
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if (this.firstRun) {
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if (this.#firstRun) {
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if (this.config.debug) log(`version: ${this.version}`);
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if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);
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if (this.config.debug) log('platform:', this.sysinfo.platform);
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if (this.config.debug) log('agent:', this.sysinfo.agent);
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await this.checkBackend(true);
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await this.#checkBackend(true);
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if (this.tf.ENV.flags.IS_BROWSER) {
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if (this.config.debug) log('configuration:', this.config);
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if (this.config.debug) log('tf flags:', this.tf.ENV.flags);
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if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);
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}
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if (this.firstRun) {
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if (this.#firstRun) {
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if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');
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this.firstRun = false;
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this.#firstRun = false;
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}
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const current = Math.trunc(now() - timeStamp);
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if (current > (this.perf.load || 0)) this.perf.load = current;
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if (current > (this.#perf.load || 0)) this.#perf.load = current;
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}
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// check if backend needs initialization if it changed
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async checkBackend(force = false) {
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#checkBackend = async (force = false) => {
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if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {
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const timeStamp = now();
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this.state = 'backend';
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if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);
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}
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await this.tf.ready();
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this.perf.backend = Math.trunc(now() - timeStamp);
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this.#perf.backend = Math.trunc(now() - timeStamp);
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}
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}
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calculateFaceAngle = (mesh) => {
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#calculateFaceAngle = (mesh) => {
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if (!mesh || mesh.length < 300) return {};
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const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);
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// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
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return angle;
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}
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async detectFace(input) {
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#detectFace = async (input) => {
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// run facemesh, includes blazeface and iris
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// eslint-disable-next-line no-async-promise-executor
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let timeStamp;
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@ -293,9 +309,9 @@ class Human {
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this.state = 'run:face';
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timeStamp = now();
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const faces = await this.models.face?.estimateFaces(input, this.config);
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this.perf.face = Math.trunc(now() - timeStamp);
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this.#perf.face = Math.trunc(now() - timeStamp);
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for (const face of faces) {
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this.analyze('Get Face');
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this.#analyze('Get Face');
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// is something went wrong, skip the face
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if (!face.image || face.image.isDisposedInternal) {
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continue;
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}
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const angle = this.calculateFaceAngle(face.mesh);
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const angle = this.#calculateFaceAngle(face.mesh);
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// run age, inherits face from blazeface
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this.analyze('Start Age:');
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this.#analyze('Start Age:');
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if (this.config.async) {
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ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};
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} else {
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this.state = 'run:age';
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timeStamp = now();
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ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};
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this.perf.age = Math.trunc(now() - timeStamp);
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this.#perf.age = Math.trunc(now() - timeStamp);
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}
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// run gender, inherits face from blazeface
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this.analyze('Start Gender:');
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this.#analyze('Start Gender:');
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if (this.config.async) {
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genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};
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} else {
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this.state = 'run:gender';
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timeStamp = now();
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genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};
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this.perf.gender = Math.trunc(now() - timeStamp);
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this.#perf.gender = Math.trunc(now() - timeStamp);
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}
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// run emotion, inherits face from blazeface
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this.analyze('Start Emotion:');
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this.#analyze('Start Emotion:');
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if (this.config.async) {
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emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};
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} else {
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this.state = 'run:emotion';
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timeStamp = now();
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emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};
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this.perf.emotion = Math.trunc(now() - timeStamp);
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this.#perf.emotion = Math.trunc(now() - timeStamp);
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}
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this.analyze('End Emotion:');
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this.#analyze('End Emotion:');
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// run emotion, inherits face from blazeface
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this.analyze('Start Embedding:');
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this.#analyze('Start Embedding:');
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if (this.config.async) {
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embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : [];
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} else {
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this.state = 'run:embedding';
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timeStamp = now();
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embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : [];
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this.perf.embedding = Math.trunc(now() - timeStamp);
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this.#perf.embedding = Math.trunc(now() - timeStamp);
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}
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this.analyze('End Emotion:');
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this.#analyze('End Emotion:');
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// if async wait for results
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if (this.config.async) {
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[ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);
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}
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this.analyze('Finish Face:');
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this.#analyze('Finish Face:');
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// calculate iris distance
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// iris: array[ center, left, top, right, bottom]
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@ -391,20 +407,20 @@ class Human {
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// dont need face anymore
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face.image?.dispose();
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this.analyze('End Face');
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this.#analyze('End Face');
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}
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this.analyze('End FaceMesh:');
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this.#analyze('End FaceMesh:');
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if (this.config.async) {
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if (this.perf.face) delete this.perf.face;
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||||
if (this.perf.age) delete this.perf.age;
|
||||
if (this.perf.gender) delete this.perf.gender;
|
||||
if (this.perf.emotion) delete this.perf.emotion;
|
||||
if (this.#perf.face) delete this.#perf.face;
|
||||
if (this.#perf.age) delete this.#perf.age;
|
||||
if (this.#perf.gender) delete this.#perf.gender;
|
||||
if (this.#perf.emotion) delete this.#perf.emotion;
|
||||
}
|
||||
return faceRes;
|
||||
}
|
||||
|
||||
// main detect function
|
||||
async detect(input, userConfig = {}) {
|
||||
async detect(input, userConfig = {}): Promise<{ face, body, hand, gesture, performance, canvas } | { error }> {
|
||||
// detection happens inside a promise
|
||||
return new Promise(async (resolve) => {
|
||||
this.state = 'config';
|
||||
|
|
@ -415,7 +431,7 @@ class Human {
|
|||
|
||||
// sanity checks
|
||||
this.state = 'check';
|
||||
const error = this.sanity(input);
|
||||
const error = this.#sanity(input);
|
||||
if (error) {
|
||||
log(error, input);
|
||||
resolve({ error });
|
||||
|
|
@ -424,13 +440,13 @@ class Human {
|
|||
const timeStart = now();
|
||||
|
||||
// configure backend
|
||||
await this.checkBackend();
|
||||
await this.#checkBackend();
|
||||
|
||||
// load models if enabled
|
||||
await this.load();
|
||||
|
||||
if (this.config.scoped) this.tf.engine().startScope();
|
||||
this.analyze('Start Scope:');
|
||||
this.#analyze('Start Scope:');
|
||||
|
||||
timeStamp = now();
|
||||
const process = image.process(input, this.config);
|
||||
|
|
@ -439,8 +455,8 @@ class Human {
|
|||
resolve({ error: 'could not convert input to tensor' });
|
||||
return;
|
||||
}
|
||||
this.perf.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze('Get Image:');
|
||||
this.#perf.image = Math.trunc(now() - timeStamp);
|
||||
this.#analyze('Get Image:');
|
||||
|
||||
// prepare where to store model results
|
||||
let bodyRes;
|
||||
|
|
@ -449,42 +465,42 @@ 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 ? this.detectFace(process.tensor) : [];
|
||||
if (this.perf.face) delete this.perf.face;
|
||||
faceRes = this.config.face.enabled ? this.#detectFace(process.tensor) : [];
|
||||
if (this.#perf.face) delete this.#perf.face;
|
||||
} else {
|
||||
this.state = 'run:face';
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];
|
||||
this.perf.face = Math.trunc(now() - timeStamp);
|
||||
faceRes = this.config.face.enabled ? await this.#detectFace(process.tensor) : [];
|
||||
this.#perf.face = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
|
||||
// run body: can be posenet or blazepose
|
||||
this.analyze('Start Body:');
|
||||
this.#analyze('Start Body:');
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
|
||||
else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];
|
||||
if (this.perf.body) delete this.perf.body;
|
||||
if (this.#perf.body) delete this.#perf.body;
|
||||
} else {
|
||||
this.state = 'run:body';
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
|
||||
else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];
|
||||
this.perf.body = Math.trunc(now() - timeStamp);
|
||||
this.#perf.body = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
this.analyze('End Body:');
|
||||
this.#analyze('End Body:');
|
||||
|
||||
// run handpose
|
||||
this.analyze('Start Hand:');
|
||||
this.#analyze('Start Hand:');
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];
|
||||
if (this.perf.hand) delete this.perf.hand;
|
||||
if (this.#perf.hand) delete this.#perf.hand;
|
||||
} else {
|
||||
this.state = 'run:hand';
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];
|
||||
this.perf.hand = Math.trunc(now() - timeStamp);
|
||||
this.#perf.hand = Math.trunc(now() - timeStamp);
|
||||
}
|
||||
this.analyze('End Hand:');
|
||||
this.#analyze('End Hand:');
|
||||
|
||||
// if async wait for results
|
||||
if (this.config.async) {
|
||||
|
|
@ -493,24 +509,24 @@ class Human {
|
|||
process.tensor.dispose();
|
||||
|
||||
if (this.config.scoped) this.tf.engine().endScope();
|
||||
this.analyze('End Scope:');
|
||||
this.#analyze('End Scope:');
|
||||
|
||||
let gestureRes = [];
|
||||
if (this.config.gesture.enabled) {
|
||||
timeStamp = now();
|
||||
// @ts-ignore
|
||||
gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
|
||||
if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);
|
||||
else if (this.perf.gesture) delete this.perf.gesture;
|
||||
if (!this.config.async) this.#perf.gesture = Math.trunc(now() - timeStamp);
|
||||
else if (this.#perf.gesture) delete this.#perf.gesture;
|
||||
}
|
||||
|
||||
this.perf.total = Math.trunc(now() - timeStart);
|
||||
this.#perf.total = Math.trunc(now() - timeStart);
|
||||
this.state = 'idle';
|
||||
resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });
|
||||
resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.#perf, canvas: process.canvas });
|
||||
});
|
||||
}
|
||||
|
||||
async warmupBitmap() {
|
||||
#warmupBitmap = async () => {
|
||||
const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());
|
||||
let blob;
|
||||
let res;
|
||||
|
|
@ -527,8 +543,7 @@ class Human {
|
|||
return res;
|
||||
}
|
||||
|
||||
async warmupCanvas() {
|
||||
return new Promise((resolve) => {
|
||||
#warmupCanvas = async () => new Promise((resolve) => {
|
||||
let src;
|
||||
let size = 0;
|
||||
switch (this.config.warmup) {
|
||||
|
|
@ -559,9 +574,8 @@ class Human {
|
|||
if (src) img.src = src;
|
||||
else resolve(null);
|
||||
});
|
||||
}
|
||||
|
||||
async warmupNode() {
|
||||
#warmupNode = async () => {
|
||||
const atob = (str) => Buffer.from(str, 'base64');
|
||||
const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);
|
||||
// @ts-ignore
|
||||
|
|
@ -574,15 +588,15 @@ class Human {
|
|||
return res;
|
||||
}
|
||||
|
||||
async warmup(userConfig) {
|
||||
async warmup(userConfig): Promise<{ face, body, hand, gesture, performance, canvas } | { error }> {
|
||||
const t0 = now();
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
const video = 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();
|
||||
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 = video;
|
||||
const t1 = now();
|
||||
if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ let fx = null;
|
|||
// process input image and return tensor
|
||||
// input can be tensor, imagedata, htmlimageelement, htmlvideoelement
|
||||
// input is resized and run through imagefx filter
|
||||
export function process(input, config) {
|
||||
export function process(input, config): { tensor, canvas } {
|
||||
let tensor;
|
||||
if (input instanceof tf.Tensor) {
|
||||
tensor = tf.clone(input);
|
||||
|
|
@ -28,7 +28,7 @@ export function process(input, config) {
|
|||
else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);
|
||||
if (!targetWidth || !targetHeight) {
|
||||
log('Human: invalid input', input);
|
||||
return null;
|
||||
return { tensor: null, canvas: null };
|
||||
}
|
||||
if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {
|
||||
inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');
|
||||
|
|
@ -46,7 +46,7 @@ export function process(input, config) {
|
|||
// log('created FX filter');
|
||||
fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')
|
||||
}
|
||||
if (!fx) return inCanvas;
|
||||
if (!fx) return { tensor: null, canvas: inCanvas };
|
||||
fx.reset();
|
||||
fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled
|
||||
if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);
|
||||
|
|
@ -110,5 +110,6 @@ export function process(input, config) {
|
|||
pixels.dispose();
|
||||
casted.dispose();
|
||||
}
|
||||
return { tensor, canvas: config.filter.return ? outCanvas : null };
|
||||
const canvas = config.filter.return ? outCanvas : null;
|
||||
return { tensor, canvas };
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue