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human
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added wasm and webgpu backends

pull/50/head
Vladimir Mandic 2020-10-30 10:23:49 -04:00
parent 5ae0cce05a
commit 254deb9c59
27 changed files with 89542 additions and 160 deletions
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9
README.md
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@ -9,7 +9,8 @@
- [**Change Log**](./CHANGELOG.md)
- [**Live Demo**](https://vladmandic.github.io/human/demo/index.html)
Compatible with Browser, WebWorker and NodeJS execution!
Compatible with *Browser*, *WebWorker* and *NodeJS* execution
Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends
(and maybe with React-Native as it doesn't use any DOM objects)
*This is a pre-release project, see [issues](https://github.com/vladmandic/human/issues) for list of known limitations and planned enhancements*
@ -159,10 +160,12 @@ If your application resides in a different folder, modify `modelPath` property i
Demos are included in `/demo`:
Browser:
**Browser**:
- `index.html`, `browser.js`, `worker.js`: Full demo using Browser with ESM module, includes selectable backends and webworkers
NodeJS:
*If you want to test `wasm` or `webgpu` backends, enable loading in `index.html`*
**NodeJS**:
- `node.js`: Demo using NodeJS with CommonJS module
This is a very simple demo as althought `Human` library is compatible with NodeJS execution
and is able to load images and models from local filesystem,

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assets/tf-backend-webgpu.js Normal file
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assets/tf.min.js vendored
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6
demo/browser.js
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@ -29,7 +29,8 @@ const ui = {
// configuration overrides
const config = {
backend: 'webgl', // if you want to use 'wasm' backend, enable script load of tf and tf-backend-wasm in index.html
backend: 'webgl',
wasm: { path: '../assets' },
filter: {
enabled: true,
width: 0,
@ -108,7 +109,7 @@ function drawResults(input, result, canvas) {
// update log
const engine = human.tf.engine();
const memory = `${engine.state.numBytes.toLocaleString()} bytes ${engine.state.numDataBuffers.toLocaleString()} buffers ${engine.state.numTensors.toLocaleString()} tensors`;
const gpu = engine.backendInstance ? `GPU: ${engine.backendInstance.numBytesInGPU.toLocaleString()} bytes` : '';
const gpu = engine.backendInstance ? `GPU: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';
document.getElementById('log').innerText = `
TFJS Version: ${human.tf.version_core} | Backend: ${human.tf.getBackend()} | Memory: ${memory} ${gpu}
Performance: ${str(result.performance)} | Object size: ${(str(result)).length.toLocaleString()} bytes
@ -267,6 +268,7 @@ function setupMenu() {
menu.addButton('Process Images', 'Process Images', () => detectSampleImages());
menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
menu.addList('Backend', ['cpu', 'webgl', 'wasm', 'webgpu'], config.backend, (val) => config.backend = val);
menu.addBool('Use Web Worker', ui, 'useWorker');
menu.addHTML('<hr style="min-width: 200px; border-style: inset; border-color: dimgray">');
menu.addLabel('Enabled Models');

5
demo/index.html
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@ -12,8 +12,9 @@
<meta name="msapplication-tooltip" content="Human: AI-powered 3D Human Detection">
<link rel="manifest" href="../dist/human.esm.json">
<link rel="shortcut icon" href="../favicon.ico" type="image/x-icon">
<!-- <script src="../assets/tf.min.js"></script> -->
<!-- <script src="../assets/tf-backend-wasm.min.js"></script> -->
<!-- <script src="../assets/tf.es2017.js"></script> -->
<!-- <script src="../assets/tf-backend-wasm.es2017.js"></script> -->
<!-- <script src="../assets/tf-backend-webgpu.js"></script> -->
<script src="./browser.js" type="module"></script>
</head>
<body style="margin: 0; background: black; color: white; font-family: 'Segoe UI'; font-size: 16px; font-variant: small-caps; overflow-x: hidden">

24
demo/menu.js
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@ -8,9 +8,13 @@ const css = `
.menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }
.menu-item { display: flex; white-space: nowrap; background: darkslategray; padding: 0.2rem; width: max-content; }
.menu-title { text-align: right; cursor: pointer; }
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5); }
.menu-label { padding: 0; }
.menu-list { margin-right: 0.8rem; }
select:focus { outline: none; }
.menu-list-item { background: black; color: white; border: none; padding: 0.2rem; font-family: inherit; font-variant: inherit; border-radius: 1rem; }
.menu-chart-title { align-items: center; }
.menu-chart-canvas { background: transparent; height: 40px; width: 180px; margin: 0.2rem 0.2rem 0.2rem 1rem; }
@ -130,6 +134,24 @@ class Menu {
});
}
async addList(title, items, selected, callback) {
const el = document.createElement('div');
el.className = 'menu-item';
let options = '';
for (const item of items) {
const def = item === selected ? 'selected' : '';
options += `<option value="${item}" ${def}>${item}</option>`;
}
el.innerHTML = `<div class="menu-list"><select name="${this.ID}" class="menu-list-item">${options}</select><label for="${this.ID}"></label></div>${title}`;
el.style.fontFamily = document.body.style.fontFamily;
el.style.fontSize = document.body.style.fontSize;
el.style.fontVariant = document.body.style.fontVariant;
this.container.appendChild(el);
el.addEventListener('change', (evt) => {
if (callback) callback(items[evt.target.selectedIndex]);
});
}
async addRange(title, object, variable, min, max, step, callback) {
const el = document.createElement('div');
el.className = 'menu-item';

51
dist/human.esm-nobundle.js vendored
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@ -5777,7 +5777,7 @@ var require_config = __commonJS((exports) => {
var require_package = __commonJS((exports, module) => {
module.exports = {
name: "@vladmandic/human",
version: "0.4.10",
version: "0.5.1",
description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
sideEffects: false,
main: "dist/human.node.js",
@ -5799,10 +5799,9 @@ var require_package = __commonJS((exports, module) => {
dependencies: {},
peerDependencies: {},
devDependencies: {
seedrandom: "^3.0.5",
"@tensorflow/tfjs": "^2.7.0",
"@tensorflow/tfjs-node": "^2.7.0",
"@vladmandic/pilogger": "^0.2.6",
"@vladmandic/pilogger": "^0.2.7",
dayjs: "^1.9.4",
esbuild: "^0.7.22",
eslint: "^7.12.1",
@ -5812,6 +5811,7 @@ var require_package = __commonJS((exports, module) => {
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
rimraf: "^3.0.2",
seedrandom: "^3.0.5",
"simple-git": "^2.21.0"
},
scripts: {
@ -5958,17 +5958,33 @@ class Human {
this.models.emotion = await emotion.load(this.config);
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
if (backendName in tf.engine().registry) {
this.log("Human library setting backend:", this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log("Human library backend not registred:", backendName);
}
}
}
tfImage(input) {
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
if (this.config.filter.width > 0)
targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0)
targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0)
targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0)
@ -6015,7 +6031,19 @@ class Human {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
if (this.config.backend === "webgl" || canvas instanceof ImageData)
pixels = tf.browser.fromPixels(canvas);
else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -6039,12 +6067,7 @@ class Human {
return new Promise(async (resolve) => {
const timeStart = now();
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("Human library setting backend:", this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
if (first) {
this.log("Human library starting");

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dist/human.esm-nobundle.json vendored
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@ -5,7 +5,7 @@
"imports": []
},
"package.json": {
"bytes": 2871,
"bytes": 2870,
"imports": []
},
"src/emotion/emotion.js": {
@ -116,7 +116,7 @@
"imports": []
},
"src/human.js": {
"bytes": 11908,
"bytes": 13144,
"imports": [
{
"path": "src/facemesh/facemesh.js"
@ -260,7 +260,7 @@
"dist/human.esm-nobundle.js.map": {
"imports": [],
"inputs": {},
"bytes": 248630
"bytes": 250346
},
"dist/human.esm-nobundle.js": {
"imports": [],
@ -353,16 +353,16 @@
"bytesInOutput": 2230
},
"package.json": {
"bytesInOutput": 3013
"bytesInOutput": 3012
},
"src/human.js": {
"bytesInOutput": 10775
"bytesInOutput": 11781
},
"src/human.js": {
"bytesInOutput": 0
}
},
"bytes": 155239
"bytes": 156244
}
}
}

51
dist/human.esm.js vendored
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@ -72826,7 +72826,7 @@ var require_config = __commonJS((exports) => {
var require_package = __commonJS((exports, module) => {
module.exports = {
name: "@vladmandic/human",
version: "0.4.10",
version: "0.5.1",
description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
sideEffects: false,
main: "dist/human.node.js",
@ -72848,10 +72848,9 @@ var require_package = __commonJS((exports, module) => {
dependencies: {},
peerDependencies: {},
devDependencies: {
seedrandom: "^3.0.5",
"@tensorflow/tfjs": "^2.7.0",
"@tensorflow/tfjs-node": "^2.7.0",
"@vladmandic/pilogger": "^0.2.6",
"@vladmandic/pilogger": "^0.2.7",
dayjs: "^1.9.4",
esbuild: "^0.7.22",
eslint: "^7.12.1",
@ -72861,6 +72860,7 @@ var require_package = __commonJS((exports, module) => {
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
rimraf: "^3.0.2",
seedrandom: "^3.0.5",
"simple-git": "^2.21.0"
},
scripts: {
@ -73007,17 +73007,33 @@ class Human {
this.models.emotion = await emotion.load(this.config);
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
if (backendName in tf.engine().registry) {
this.log("Human library setting backend:", this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log("Human library backend not registred:", backendName);
}
}
}
tfImage(input) {
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
if (this.config.filter.width > 0)
targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0)
targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0)
targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0)
@ -73064,7 +73080,19 @@ class Human {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
if (this.config.backend === "webgl" || canvas instanceof ImageData)
pixels = tf.browser.fromPixels(canvas);
else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -73088,12 +73116,7 @@ class Human {
return new Promise(async (resolve) => {
const timeStart = now();
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("Human library setting backend:", this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
if (first) {
this.log("Human library starting");

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dist/human.esm.js.map vendored
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dist/human.esm.json vendored
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@ -149,7 +149,7 @@
]
},
"package.json": {
"bytes": 2871,
"bytes": 2870,
"imports": []
},
"src/emotion/emotion.js": {
@ -291,7 +291,7 @@
"imports": []
},
"src/human.js": {
"bytes": 11908,
"bytes": 13144,
"imports": [
{
"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@ -468,7 +468,7 @@
"dist/human.esm.js.map": {
"imports": [],
"inputs": {},
"bytes": 5121936
"bytes": 5123652
},
"dist/human.esm.js": {
"imports": [],
@ -618,16 +618,16 @@
"bytesInOutput": 2230
},
"package.json": {
"bytesInOutput": 3013
"bytesInOutput": 3012
},
"src/human.js": {
"bytesInOutput": 10765
"bytesInOutput": 11771
},
"src/human.js": {
"bytesInOutput": 0
}
},
"bytes": 2924203
"bytes": 2925208
}
}
}

51
dist/human.js vendored
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@ -72827,7 +72827,7 @@ var Human = (() => {
var require_package = __commonJS((exports, module) => {
module.exports = {
name: "@vladmandic/human",
version: "0.4.10",
version: "0.5.1",
description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
sideEffects: false,
main: "dist/human.node.js",
@ -72849,10 +72849,9 @@ var Human = (() => {
dependencies: {},
peerDependencies: {},
devDependencies: {
seedrandom: "^3.0.5",
"@tensorflow/tfjs": "^2.7.0",
"@tensorflow/tfjs-node": "^2.7.0",
"@vladmandic/pilogger": "^0.2.6",
"@vladmandic/pilogger": "^0.2.7",
dayjs: "^1.9.4",
esbuild: "^0.7.22",
eslint: "^7.12.1",
@ -72862,6 +72861,7 @@ var Human = (() => {
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
rimraf: "^3.0.2",
seedrandom: "^3.0.5",
"simple-git": "^2.21.0"
},
scripts: {
@ -73012,17 +73012,33 @@ var Human = (() => {
this.models.emotion = await emotion.load(this.config);
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
if (backendName in tf.engine().registry) {
this.log("Human library setting backend:", this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log("Human library backend not registred:", backendName);
}
}
}
tfImage(input) {
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
if (this.config.filter.width > 0)
targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0)
targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0)
targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0)
@ -73069,7 +73085,19 @@ var Human = (() => {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
if (this.config.backend === "webgl" || canvas instanceof ImageData)
pixels = tf.browser.fromPixels(canvas);
else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -73093,12 +73121,7 @@ var Human = (() => {
return new Promise(async (resolve) => {
const timeStart = now();
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("Human library setting backend:", this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
if (first) {
this.log("Human library starting");

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@ -149,7 +149,7 @@
]
},
"package.json": {
"bytes": 2871,
"bytes": 2870,
"imports": []
},
"src/emotion/emotion.js": {
@ -291,7 +291,7 @@
"imports": []
},
"src/human.js": {
"bytes": 11908,
"bytes": 13144,
"imports": [
{
"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@ -468,7 +468,7 @@
"dist/human.js.map": {
"imports": [],
"inputs": {},
"bytes": 5125792
"bytes": 5127510
},
"dist/human.js": {
"imports": [],
@ -618,13 +618,13 @@
"bytesInOutput": 2424
},
"package.json": {
"bytesInOutput": 3145
"bytesInOutput": 3144
},
"src/human.js": {
"bytesInOutput": 11994
"bytesInOutput": 13092
}
},
"bytes": 3070080
"bytes": 3071177
}
}
}

51
dist/human.node-nobundle.js vendored
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@ -5777,7 +5777,7 @@ var require_config = __commonJS((exports2) => {
var require_package = __commonJS((exports2, module2) => {
module2.exports = {
name: "@vladmandic/human",
version: "0.4.10",
version: "0.5.1",
description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
sideEffects: false,
main: "dist/human.node.js",
@ -5799,10 +5799,9 @@ var require_package = __commonJS((exports2, module2) => {
dependencies: {},
peerDependencies: {},
devDependencies: {
seedrandom: "^3.0.5",
"@tensorflow/tfjs": "^2.7.0",
"@tensorflow/tfjs-node": "^2.7.0",
"@vladmandic/pilogger": "^0.2.6",
"@vladmandic/pilogger": "^0.2.7",
dayjs: "^1.9.4",
esbuild: "^0.7.22",
eslint: "^7.12.1",
@ -5812,6 +5811,7 @@ var require_package = __commonJS((exports2, module2) => {
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
rimraf: "^3.0.2",
seedrandom: "^3.0.5",
"simple-git": "^2.21.0"
},
scripts: {
@ -5961,17 +5961,33 @@ class Human {
this.models.emotion = await emotion.load(this.config);
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
if (backendName in tf.engine().registry) {
this.log("Human library setting backend:", this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log("Human library backend not registred:", backendName);
}
}
}
tfImage(input) {
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
if (this.config.filter.width > 0)
targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0)
targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0)
targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0)
@ -6018,7 +6034,19 @@ class Human {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
if (this.config.backend === "webgl" || canvas instanceof ImageData)
pixels = tf.browser.fromPixels(canvas);
else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -6042,12 +6070,7 @@ class Human {
return new Promise(async (resolve) => {
const timeStart = now();
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("Human library setting backend:", this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
if (first) {
this.log("Human library starting");

4
dist/human.node-nobundle.js.map vendored
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51
dist/human.node.js vendored
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@ -73868,7 +73868,7 @@ var require_config = __commonJS((exports2) => {
var require_package = __commonJS((exports2, module2) => {
module2.exports = {
name: "@vladmandic/human",
version: "0.4.10",
version: "0.5.1",
description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
sideEffects: false,
main: "dist/human.node.js",
@ -73890,10 +73890,9 @@ var require_package = __commonJS((exports2, module2) => {
dependencies: {},
peerDependencies: {},
devDependencies: {
seedrandom: "^3.0.5",
"@tensorflow/tfjs": "^2.7.0",
"@tensorflow/tfjs-node": "^2.7.0",
"@vladmandic/pilogger": "^0.2.6",
"@vladmandic/pilogger": "^0.2.7",
dayjs: "^1.9.4",
esbuild: "^0.7.22",
eslint: "^7.12.1",
@ -73903,6 +73902,7 @@ var require_package = __commonJS((exports2, module2) => {
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
rimraf: "^3.0.2",
seedrandom: "^3.0.5",
"simple-git": "^2.21.0"
},
scripts: {
@ -74052,17 +74052,33 @@ class Human {
this.models.emotion = await emotion.load(this.config);
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
if (backendName in tf.engine().registry) {
this.log("Human library setting backend:", this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log("Human library backend not registred:", backendName);
}
}
}
tfImage(input) {
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || input.shape && input.shape[1] > 0;
const originalHeight = input.naturalHeight || input.videoHeight || input.height || input.shape && input.shape[2] > 0;
let targetWidth = originalWidth;
if (this.config.filter.width > 0)
targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0)
targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0)
targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0)
@ -74109,7 +74125,19 @@ class Human {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
if (this.config.backend === "webgl" || canvas instanceof ImageData)
pixels = tf.browser.fromPixels(canvas);
else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -74133,12 +74161,7 @@ class Human {
return new Promise(async (resolve) => {
const timeStart = now();
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("Human library setting backend:", this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
if (first) {
this.log("Human library starting");

4
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12
dist/human.node.json vendored
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@ -5,7 +5,7 @@
"imports": []
},
"package.json": {
"bytes": 2871,
"bytes": 2870,
"imports": []
},
"src/emotion/emotion.js": {
@ -116,7 +116,7 @@
"imports": []
},
"src/human.js": {
"bytes": 11908,
"bytes": 13144,
"imports": [
{
"path": "src/facemesh/facemesh.js"
@ -260,7 +260,7 @@
"dist/human.node-nobundle.js.map": {
"imports": [],
"inputs": {},
"bytes": 260882
"bytes": 263858
},
"dist/human.node-nobundle.js": {
"imports": [],
@ -353,16 +353,16 @@
"bytesInOutput": 2232
},
"package.json": {
"bytesInOutput": 3016
"bytesInOutput": 3015
},
"src/human.js": {
"bytesInOutput": 47
},
"src/human.js": {
"bytesInOutput": 10775
"bytesInOutput": 11781
}
},
"bytes": 155393
"bytes": 156398
}
}
}

48
src/human.js
View File

@ -126,17 +126,33 @@ class Human {
}
}
async resetBackend(backendName) {
if (tf.getBackend() !== this.config.backend) {
this.state = 'backend';
if (backendName in tf.engine().registry) {
this.log('Human library setting backend:', this.config.backend);
this.config.backend = backendName;
const backendFactory = tf.findBackendFactory(backendName);
tf.removeBackend(backendName);
tf.registerBackend(backendName, backendFactory);
await tf.setBackend(backendName);
await tf.ready();
} else {
this.log('Human library backend not registred:', backendName);
}
}
}
tfImage(input) {
// let imageData;
let filtered;
const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));
const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (this.fx && this.config.filter.enabled && !(input instanceof tf.Tensor)) {
const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));
const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));
let targetWidth = originalWidth;
if (this.config.filter.width > 0) targetWidth = this.config.filter.width;
else if (this.config.filter.height > 0) targetWidth = originalWidth * (this.config.filter.height / originalHeight);
let targetHeight = originalHeight;
if (this.config.filter.height > 0) targetHeight = this.config.filter.height;
else if (this.config.filter.width > 0) targetHeight = originalHeight * (this.config.filter.width / originalWidth);
const offscreenCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');
@ -166,7 +182,20 @@ class Human {
if (input instanceof tf.Tensor) {
tensor = tf.clone(input);
} else {
const pixels = tf.browser.fromPixels(filtered || input);
const canvas = filtered || input;
let pixels;
// tf kernel-optimized method to get imagedata, also if input is imagedata, just use it
if ((this.config.backend === 'webgl') || (canvas instanceof ImageData)) pixels = tf.browser.fromPixels(canvas);
// cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas
else {
const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext('2d');
tempCtx.drawImage(canvas, 0, 0);
const data = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = tf.browser.fromPixels(data);
}
const casted = pixels.toFloat();
tensor = casted.expandDims(0);
pixels.dispose();
@ -197,12 +226,7 @@ class Human {
// configure backend
timeStamp = now();
if (tf.getBackend() !== this.config.backend) {
this.state = 'backend';
this.log('Human library setting backend:', this.config.backend);
await tf.setBackend(this.config.backend);
await tf.ready();
}
await this.resetBackend(this.config.backend);
perf.backend = Math.trunc(now() - timeStamp);
// check number of loaded models