mirror of https://github.com/vladmandic/human
new frame change detection algorithm
Vladimir Mandic
parent
8de38b07eb
commit
df933ccf8e
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@ -126,7 +126,7 @@
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"format": "iife",
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"input": "src/human.ts",
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"output": "dist/human.js",
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"minify": true,
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"minify": false,
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"globalName": "Human",
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"external": ["fs", "os", "buffer", "util"]
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},
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@ -11,11 +11,10 @@
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### **HEAD -> main** 2021/11/05 mandic00@live.com
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- add histogram equalization
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- implement wasm missing ops
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- performance and memory optimizations
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### **origin/main** 2021/11/04 mandic00@live.com
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- fix react compatibility issues
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- improve box rescaling for all modules
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- improve precision using wasm backend
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- refactor predict with execute
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31
TODO.md
31
TODO.md
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@ -41,17 +41,20 @@ MoveNet MultiPose model does not work with WASM backend due to missing F32 broad
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### Pending release
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- Supports all modules on all backends
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via custom implementation of missing kernel ops
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- Performance and precision improvements
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**face** and **hand** modules
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- Use custom built TFJS for bundled version
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reduced bundle size and built-in support for all backends
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`nobundle` and `node` versions link to standard `@tensorflow` packages
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- Add optional input histogram equalization
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auto-level input for optimal brightness/contrast via `config.filter.equalization`
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- Fix **ReactJS** compatibility
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- Better precision using **WASM**
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Previous issues due to math low-precision in WASM implementation
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- Full **TS** type definitions for all modules and imports
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- Focus on simplified demo
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- Supports all modules on all backends
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via custom implementation of missing kernel ops
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- New frame change detection algorithm used for cache determination
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based on temporal input difference
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- New optional input histogram equalization
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auto-level input for optimal brightness/contrast via `config.filter.equalization`
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- Performance and precision improvements
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**face**, **hand** and **gestures** modules
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- Use custom built TFJS for bundled version
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reduced bundle size and built-in support for all backends
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`nobundle` and `node` versions link to standard `@tensorflow` packages
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- Fix **ReactJS** compatibility
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- Better precision using **WASM**
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Previous issues due to math low-precision in WASM implementation
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- Full **TS** type definitions for all modules and imports
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- Focus on simplified demo
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<https://vladmandic.github.io/human/demo/typescript/>
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@ -9,25 +9,28 @@
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import Human from '../../dist/human.esm.js';
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const userConfig = {
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backend: 'wasm',
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async: false,
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backend: 'humangl',
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async: true,
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warmup: 'none',
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cacheSensitivity: 0,
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debug: true,
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modelBasePath: '../../models/',
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deallocate: true,
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filter: {
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enabled: true,
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equalization: true,
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},
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face: {
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enabled: true,
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detector: { rotation: true, return: true, maxDetected: 50 },
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mesh: { enabled: true },
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embedding: { enabled: false },
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iris: { enabled: true },
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iris: { enabled: false },
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emotion: { enabled: true },
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description: { enabled: true },
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},
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hand: { enabled: false },
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gesture: { enabled: true },
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gesture: { enabled: false },
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body: { enabled: false },
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filter: { enabled: true },
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segmentation: { enabled: false },
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};
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@ -73,9 +76,7 @@ async function SelectFaceCanvas(face) {
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const squeeze = human.tf.squeeze(enhanced);
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const normalize = human.tf.div(squeeze, 255);
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await human.tf.browser.toPixels(normalize, c);
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human.tf.dispose(enhanced);
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human.tf.dispose(squeeze);
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human.tf.dispose(normalize);
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human.tf.dispose([enhanced, squeeze, normalize]);
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const ctx = c.getContext('2d');
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ctx.font = 'small-caps 0.4rem "Lato"';
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ctx.fillStyle = 'rgba(255, 255, 255, 1)';
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@ -134,7 +135,7 @@ async function SelectFaceCanvas(face) {
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title('Selected Face');
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}
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async function AddFaceCanvas(index, res, fileName) {
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function AddFaceCanvas(index, res, fileName) {
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all[index] = res.face;
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let ok = false;
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for (const i in res.face) {
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@ -161,7 +162,7 @@ async function AddFaceCanvas(index, res, fileName) {
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});
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// if we actually got face image tensor, draw canvas with that face
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if (res.face[i].tensor) {
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await human.tf.browser.toPixels(res.face[i].tensor, canvas);
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human.tf.browser.toPixels(res.face[i].tensor, canvas);
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document.getElementById('faces').appendChild(canvas);
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const ctx = canvas.getContext('2d');
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if (!ctx) return false;
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@ -169,7 +170,7 @@ async function AddFaceCanvas(index, res, fileName) {
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ctx.fillStyle = 'rgba(255, 255, 255, 1)';
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ctx.fillText(`${res.face[i].age}y ${(100 * (res.face[i].genderScore || 0)).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
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const arr = db.map((rec) => rec.embedding);
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const result = await human.match(res.face[i].embedding, arr);
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const result = human.match(res.face[i].embedding, arr);
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ctx.font = 'small-caps 1rem "Lato"';
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if (result.similarity && res.similarity > minScore) ctx.fillText(`${(100 * result.similarity).toFixed(1)}% ${db[result.index].name}`, 4, canvas.height - 30);
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}
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@ -184,7 +185,7 @@ async function AddImageElement(index, image, length) {
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const img = new Image(128, 128);
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img.onload = () => { // must wait until image is loaded
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human.detect(img, userConfig).then(async (res) => {
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const ok = await AddFaceCanvas(index, res, image); // then wait until image is analyzed
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const ok = AddFaceCanvas(index, res, image); // then wait until image is analyzed
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// log('Add image:', index + 1, image, 'faces:', res.face.length);
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if (ok) document.getElementById('images').appendChild(img); // and finally we can add it
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resolve(true);
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@ -199,7 +200,7 @@ async function AddImageElement(index, image, length) {
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});
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}
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async function createFaceMatchDB() {
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function createFaceMatchDB() {
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log('Creating Faces DB...');
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for (const image of all) {
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for (const face of image) db.push({ name: 'unknown', source: face.fileName, embedding: face.embedding });
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@ -246,6 +247,9 @@ async function main() {
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// images = ['/samples/in/solvay1927.jpg'];
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// download and analyze all images
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// const promises = [];
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// for (let i = 0; i < images.length; i++) promises.push(AddImageElement(i, images[i], images.length));
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// await Promise.all(promises);
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for (let i = 0; i < images.length; i++) await AddImageElement(i, images[i], images.length);
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// print stats
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@ -254,7 +258,7 @@ async function main() {
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log(human.tf.engine().memory());
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// if we didn't download db, generate it from current faces
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if (!db || db.length === 0) await createFaceMatchDB();
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if (!db || db.length === 0) createFaceMatchDB();
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title('');
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log('Ready');
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@ -712,6 +712,7 @@ function setupMenu() {
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menu.image = new Menu(document.body, '', { top, left: x[1] });
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menu.image.addBool('enabled', userConfig.filter, 'enabled', (val) => userConfig.filter.enabled = val);
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menu.image.addBool('histogram equalization', userConfig.filter, 'equalization', (val) => userConfig.filter.equalization = val);
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ui.menuWidth = menu.image.addRange('image width', userConfig.filter, 'width', 0, 3840, 10, (val) => userConfig.filter.width = parseInt(val));
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ui.menuHeight = menu.image.addRange('image height', userConfig.filter, 'height', 0, 2160, 10, (val) => userConfig.filter.height = parseInt(val));
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menu.image.addHTML('<hr style="border-style: inset; border-color: dimgray">');
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@ -7,7 +7,8 @@
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// demo/typescript/index.ts
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import Human from "../../dist/human.esm.js";
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var humanConfig = {
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modelBasePath: "../../models"
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modelBasePath: "../../models",
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filter: { equalization: false }
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};
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var human = new Human(humanConfig);
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human.env["perfadd"] = false;
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@ -79,8 +80,8 @@ async function drawLoop() {
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setTimeout(drawLoop, 30);
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}
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async function main() {
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log("human version:", human.version, "tfjs version:", human.tf.version_core);
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log("platform:", human.env.platform, "agent:", human.env.agent);
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log("human version:", human.version, "| tfjs version:", human.tf.version_core);
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log("platform:", human.env.platform, "| agent:", human.env.agent);
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status("loading...");
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await human.load();
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log("backend:", human.tf.getBackend(), "| available:", human.env.backends);
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File diff suppressed because one or more lines are too long
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@ -13,7 +13,7 @@ import Human from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
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const humanConfig = { // user configuration for human, used to fine-tune behavior
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modelBasePath: '../../models',
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filter: { equalization: true },
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filter: { equalization: false },
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// backend: 'webgpu',
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// async: true,
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// face: { enabled: false, detector: { rotation: true }, iris: { enabled: false }, description: { enabled: false }, emotion: { enabled: false } },
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@ -99,8 +99,8 @@ async function drawLoop() { // main screen refresh loop
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}
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async function main() { // main entry point
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log('human version:', human.version, 'tfjs version:', human.tf.version_core);
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log('platform:', human.env.platform, 'agent:', human.env.agent);
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log('human version:', human.version, '| tfjs version:', human.tf.version_core);
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log('platform:', human.env.platform, '| agent:', human.env.agent);
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status('loading...');
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await human.load(); // preload all models
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log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
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@ -117,6 +117,7 @@ var config = {
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warmup: "full",
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cacheSensitivity: 0.7,
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skipAllowed: false,
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deallocate: false,
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filter: {
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enabled: true,
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equalization: false,
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@ -233,7 +234,9 @@ __export(tfjs_esm_exports, {
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version: () => version9
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});
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__reExport(tfjs_esm_exports, dist_star);
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__reExport(tfjs_esm_exports, dist_star2);
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import * as dist_star from "@tensorflow/tfjs/dist/index.js";
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import * as dist_star2 from "@tensorflow/tfjs-backend-webgl/dist/index.js";
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import { Tensor } from "@tensorflow/tfjs/dist/index.js";
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import { GraphModel } from "@tensorflow/tfjs-converter/dist/index";
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var version = "3.11.0";
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@ -907,8 +910,8 @@ function GLImageFilter() {
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this.get = function() {
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return filterChain;
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};
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this.apply = function(image25) {
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resize(image25.width, image25.height);
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this.apply = function(image24) {
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resize(image24.width, image24.height);
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drawCount = 0;
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if (!sourceTexture)
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sourceTexture = gl.createTexture();
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@ -917,7 +920,7 @@ function GLImageFilter() {
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gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
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gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
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gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
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gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
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gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
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for (let i = 0; i < filterChain.length; i++) {
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lastInChain = i === filterChain.length - 1;
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const f = filterChain[i];
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@ -925,24 +928,27 @@ function GLImageFilter() {
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}
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return fxcanvas;
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};
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this.draw = function(image25) {
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this.draw = function(image24) {
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this.add("brightness", 0);
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return this.apply(image25);
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return this.apply(image24);
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};
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}
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// src/image/enhance.ts
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function histogramEqualization(input) {
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const channels = tfjs_esm_exports.split(input, 3, 2);
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async function histogramEqualization(inputImage) {
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const squeeze9 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage;
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const channels = tfjs_esm_exports.split(squeeze9, 3, 2);
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const min2 = [tfjs_esm_exports.min(channels[0]), tfjs_esm_exports.min(channels[1]), tfjs_esm_exports.min(channels[2])];
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const max4 = [tfjs_esm_exports.max(channels[0]), tfjs_esm_exports.max(channels[1]), tfjs_esm_exports.max(channels[2])];
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const sub6 = [tfjs_esm_exports.sub(channels[0], min2[0]), tfjs_esm_exports.sub(channels[1], min2[1]), tfjs_esm_exports.sub(channels[2], min2[2])];
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const absMax = await Promise.all(max4.map((channel) => channel.data()));
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const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
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const sub7 = [tfjs_esm_exports.sub(channels[0], min2[0]), tfjs_esm_exports.sub(channels[1], min2[1]), tfjs_esm_exports.sub(channels[2], min2[2])];
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const range = [tfjs_esm_exports.sub(max4[0], min2[0]), tfjs_esm_exports.sub(max4[1], min2[1]), tfjs_esm_exports.sub(max4[2], min2[2])];
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const fact = [tfjs_esm_exports.div(255, range[0]), tfjs_esm_exports.div(255, range[1]), tfjs_esm_exports.div(255, range[2])];
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const enh = [tfjs_esm_exports.mul(sub6[0], fact[0]), tfjs_esm_exports.mul(sub6[1], fact[1]), tfjs_esm_exports.mul(sub6[2], fact[2])];
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const fact = [tfjs_esm_exports.div(maxValue, range[0]), tfjs_esm_exports.div(maxValue, range[1]), tfjs_esm_exports.div(maxValue, range[2])];
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const enh = [tfjs_esm_exports.mul(sub7[0], fact[0]), tfjs_esm_exports.mul(sub7[1], fact[1]), tfjs_esm_exports.mul(sub7[2], fact[2])];
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const rgb2 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2);
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const reshape8 = tfjs_esm_exports.reshape(rgb2, [1, input.shape[0], input.shape[1], 3]);
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tfjs_esm_exports.dispose([...channels, ...min2, ...max4, ...sub6, ...range, ...fact, ...enh, rgb2]);
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const reshape8 = tfjs_esm_exports.reshape(rgb2, [1, squeeze9.shape[0], squeeze9.shape[1], 3]);
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tfjs_esm_exports.dispose([...channels, ...min2, ...max4, ...sub7, ...range, ...fact, ...enh, rgb2, squeeze9]);
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return reshape8;
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}
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@ -952,6 +958,12 @@ var inCanvas = null;
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var outCanvas = null;
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var tmpCanvas = null;
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var fx;
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var last = {
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inputSum: 0,
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cacheDiff: 1,
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sumMethod: 0,
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inputTensor: void 0
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};
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function canvas(width, height) {
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let c;
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if (env.browser) {
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@ -978,7 +990,7 @@ function copy(input, output) {
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ctx.drawImage(input, 0, 0);
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return outputCanvas;
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}
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function process2(input, config3, getTensor = true) {
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async function process2(input, config3, getTensor = true) {
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if (!input) {
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if (config3.debug)
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log("input is missing");
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@ -1028,7 +1040,7 @@ function process2(input, config3, getTensor = true) {
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targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
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if (!targetWidth || !targetHeight)
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throw new Error("input cannot determine dimension");
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if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
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if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
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inCanvas = canvas(targetWidth, targetHeight);
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const inCtx = inCanvas.getContext("2d");
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if (typeof ImageData !== "undefined" && input instanceof ImageData) {
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@ -1037,13 +1049,13 @@ function process2(input, config3, getTensor = true) {
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if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
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inCtx.translate(originalWidth, 0);
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inCtx.scale(-1, 1);
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inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
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inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
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inCtx.setTransform(1, 0, 0, 1, 0, 0);
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} else {
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inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
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inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
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}
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}
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if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
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if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
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outCanvas = canvas(inCanvas.width, inCanvas.height);
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if (config3.filter.enabled && env.webgl.supported) {
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if (!fx)
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@ -1131,51 +1143,31 @@ function process2(input, config3, getTensor = true) {
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if (!pixels)
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throw new Error("cannot create tensor from input");
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const casted = tfjs_esm_exports.cast(pixels, "float32");
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const tensor3 = config3.filter.equalization ? histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0);
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const tensor3 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0);
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tfjs_esm_exports.dispose([pixels, casted]);
|
||||
return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null };
|
||||
}
|
||||
}
|
||||
var lastInputSum = 0;
|
||||
var lastCacheDiff = 1;
|
||||
var benchmarked = 0;
|
||||
var checksum = async (input) => {
|
||||
const resizeFact = 48;
|
||||
const reduced = tfjs_esm_exports.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);
|
||||
const tfSum = async () => {
|
||||
const sumT = tfjs_esm_exports.sum(reduced);
|
||||
const sum0 = await sumT.data();
|
||||
tfjs_esm_exports.dispose(sumT);
|
||||
return sum0[0];
|
||||
};
|
||||
const jsSum = async () => {
|
||||
const reducedData = await reduced.data();
|
||||
let sum0 = 0;
|
||||
for (let i = 0; i < reducedData.length / 3; i++)
|
||||
sum0 += reducedData[3 * i + 2];
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
tfjs_esm_exports.dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
async function skip(config3, input) {
|
||||
let skipFrame = false;
|
||||
if (config3.cacheSensitivity === 0)
|
||||
return false;
|
||||
const sum2 = await checksum(input);
|
||||
const diff = 100 * (Math.max(sum2, lastInputSum) / Math.min(sum2, lastInputSum) - 1);
|
||||
lastInputSum = sum2;
|
||||
let skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
|
||||
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && lastCacheDiff > 0;
|
||||
return skipFrame;
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = tfjs_esm_exports.clone(input);
|
||||
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
|
||||
tfjs_esm_exports.dispose(last.inputTensor);
|
||||
last.inputTensor = tfjs_esm_exports.clone(input);
|
||||
} else {
|
||||
const t = {};
|
||||
t.diff = tfjs_esm_exports.sub(input, last.inputTensor);
|
||||
t.squared = tfjs_esm_exports.mul(t.diff, t.diff);
|
||||
t.sum = tfjs_esm_exports.sum(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
|
||||
tfjs_esm_exports.dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = tfjs_esm_exports.clone(input);
|
||||
skipFrame = diffRelative <= config3.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
||||
|
|
@ -1329,7 +1321,7 @@ async function load2(config3) {
|
|||
log("cached model:", model2["modelUrl"]);
|
||||
return model2;
|
||||
}
|
||||
async function predict(image25, config3, idx, count2) {
|
||||
async function predict(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model2)
|
||||
return null;
|
||||
|
|
@ -1341,7 +1333,7 @@ async function predict(image25, config3, idx, count2) {
|
|||
}
|
||||
skipped2 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const res = model2 == null ? void 0 : model2.execute(resize);
|
||||
const num = (await res.data())[0];
|
||||
cached[idx] = Math.round(100 * num) / 100;
|
||||
|
|
@ -4655,10 +4647,10 @@ var scaleBoxCoordinates = (box4, factor) => {
|
|||
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
|
||||
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
|
||||
};
|
||||
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
const crop2 = tfjs_esm_exports.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const crop2 = tfjs_esm_exports.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
const norm = tfjs_esm_exports.div(crop2, 255);
|
||||
tfjs_esm_exports.dispose(crop2);
|
||||
return norm;
|
||||
|
|
@ -5157,7 +5149,7 @@ var labels = [
|
|||
// src/object/centernet.ts
|
||||
var model4;
|
||||
var inputSize3 = 0;
|
||||
var last = [];
|
||||
var last2 = [];
|
||||
var lastTime3 = 0;
|
||||
var skipped4 = Number.MAX_SAFE_INTEGER;
|
||||
async function load4(config3) {
|
||||
|
|
@ -5224,9 +5216,9 @@ async function process3(res, outputShape, config3) {
|
|||
async function predict3(input, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
|
||||
const skipFrame = skipped4 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
|
||||
skipped4++;
|
||||
return last;
|
||||
return last2;
|
||||
}
|
||||
skipped4 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5236,7 +5228,7 @@ async function predict3(input, config3) {
|
|||
lastTime3 = now();
|
||||
tfjs_esm_exports.dispose(resize);
|
||||
const obj = await process3(objectT, outputSize2, config3);
|
||||
last = obj;
|
||||
last2 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -5306,7 +5298,7 @@ function max2d(inputs, minScore) {
|
|||
return [0, 0, newScore];
|
||||
});
|
||||
}
|
||||
async function predict4(image25, config3) {
|
||||
async function predict4(image24, config3) {
|
||||
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
|
||||
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
|
||||
|
|
@ -5319,7 +5311,7 @@ async function predict4(image25, config3) {
|
|||
const tensor3 = tfjs_esm_exports.tidy(() => {
|
||||
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
|
||||
return null;
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const enhance3 = tfjs_esm_exports.mul(resize, 2);
|
||||
const norm = enhance3.sub(1);
|
||||
return norm;
|
||||
|
|
@ -5331,10 +5323,10 @@ async function predict4(image25, config3) {
|
|||
tfjs_esm_exports.dispose(tensor3);
|
||||
if (resT) {
|
||||
cache2.keypoints.length = 0;
|
||||
const squeeze8 = resT.squeeze();
|
||||
const squeeze9 = resT.squeeze();
|
||||
tfjs_esm_exports.dispose(resT);
|
||||
const stack4 = squeeze8.unstack(2);
|
||||
tfjs_esm_exports.dispose(squeeze8);
|
||||
const stack4 = squeeze9.unstack(2);
|
||||
tfjs_esm_exports.dispose(squeeze9);
|
||||
for (let id = 0; id < stack4.length; id++) {
|
||||
const [x2, y2, partScore] = max2d(stack4[id], config3.body.minConfidence);
|
||||
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
|
||||
|
|
@ -5346,8 +5338,8 @@ async function predict4(image25, config3) {
|
|||
y2 / model5.inputs[0].shape[1]
|
||||
],
|
||||
position: [
|
||||
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
|
|
@ -5388,7 +5380,7 @@ async function predict4(image25, config3) {
|
|||
// src/gear/emotion.ts
|
||||
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
|
||||
var model6;
|
||||
var last2 = [];
|
||||
var last3 = [];
|
||||
var lastCount2 = 0;
|
||||
var lastTime5 = 0;
|
||||
var skipped6 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5407,15 +5399,15 @@ async function load6(config3) {
|
|||
log("cached model:", model6["modelUrl"]);
|
||||
return model6;
|
||||
}
|
||||
async function predict5(image25, config3, idx, count2) {
|
||||
async function predict5(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model6)
|
||||
return null;
|
||||
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
|
||||
skipped6++;
|
||||
return last2[idx];
|
||||
return last3[idx];
|
||||
}
|
||||
skipped6 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5424,7 +5416,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
|
||||
const t = {};
|
||||
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
|
||||
t.resize = tfjs_esm_exports.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
|
||||
t.resize = tfjs_esm_exports.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
|
||||
[t.red, t.green, t.blue] = tfjs_esm_exports.split(t.resize, 3, 3);
|
||||
t.redNorm = tfjs_esm_exports.mul(t.red, rgb[0]);
|
||||
t.greenNorm = tfjs_esm_exports.mul(t.green, rgb[1]);
|
||||
|
|
@ -5442,7 +5434,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
obj.sort((a, b) => b.score - a.score);
|
||||
Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3]));
|
||||
}
|
||||
last2[idx] = obj;
|
||||
last3[idx] = obj;
|
||||
lastCount2 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5588,7 +5580,7 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
|
|||
var lastTime6 = 0;
|
||||
var enlargeFact = 1.6;
|
||||
async function predict6(input, config3) {
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h;
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
|
||||
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
|
||||
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
|
||||
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
|
||||
|
|
@ -5632,8 +5624,13 @@ async function predict6(input, config3) {
|
|||
rotationMatrix = fixedRotationMatrix;
|
||||
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
|
||||
}
|
||||
if ((_f = config3 == null ? void 0 : config3.filter) == null ? void 0 : _f.equalization) {
|
||||
const equilized = await histogramEqualization(face5.tensor);
|
||||
tfjs_esm_exports.dispose(face5.tensor);
|
||||
face5.tensor = equilized;
|
||||
}
|
||||
face5.boxScore = Math.round(100 * box4.confidence) / 100;
|
||||
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
|
||||
if (!((_g = config3.face.mesh) == null ? void 0 : _g.enabled)) {
|
||||
face5.box = getClampedBox(box4, input);
|
||||
face5.boxRaw = getRawBox(box4, input);
|
||||
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
|
||||
|
|
@ -5655,10 +5652,10 @@ async function predict6(input, config3) {
|
|||
const coordsReshaped = tfjs_esm_exports.reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = await coordsReshaped.array();
|
||||
tfjs_esm_exports.dispose([contourCoords, coordsReshaped, confidence, contours]);
|
||||
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
|
||||
if (face5.faceScore < (((_h = config3.face.detector) == null ? void 0 : _h.minConfidence) || 1)) {
|
||||
box4.confidence = face5.faceScore;
|
||||
} else {
|
||||
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
|
||||
if ((_i = config3.face.iris) == null ? void 0 : _i.enabled)
|
||||
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
|
||||
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
|
||||
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
|
||||
|
|
@ -5698,7 +5695,7 @@ var uvmap = UV468;
|
|||
|
||||
// src/face/faceres.ts
|
||||
var model9;
|
||||
var last3 = [];
|
||||
var last4 = [];
|
||||
var lastTime7 = 0;
|
||||
var lastCount3 = 0;
|
||||
var skipped8 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5726,15 +5723,15 @@ function enhance2(input) {
|
|||
tfjs_esm_exports.dispose(crop2);
|
||||
return norm;
|
||||
}
|
||||
async function predict7(image25, config3, idx, count2) {
|
||||
async function predict7(image24, config3, idx, count2) {
|
||||
var _a, _b, _c, _d;
|
||||
if (!model9)
|
||||
return null;
|
||||
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.age) && ((_d = last4[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
skipped8++;
|
||||
return last3[idx];
|
||||
return last4[idx];
|
||||
}
|
||||
skipped8 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5746,7 +5743,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
descriptor: []
|
||||
};
|
||||
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
|
||||
const enhanced = enhance2(image25);
|
||||
const enhanced = enhance2(image24);
|
||||
const resT = model9 == null ? void 0 : model9.execute(enhanced);
|
||||
lastTime7 = now();
|
||||
tfjs_esm_exports.dispose(enhanced);
|
||||
|
|
@ -5768,7 +5765,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
obj.descriptor = Array.from(descriptor);
|
||||
resT.forEach((t) => tfjs_esm_exports.dispose(t));
|
||||
}
|
||||
last3[idx] = obj;
|
||||
last4[idx] = obj;
|
||||
lastCount3 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5787,16 +5784,16 @@ function getBoxCenter2(box4) {
|
|||
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
|
||||
];
|
||||
}
|
||||
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const boxes = [[
|
||||
box4.startPoint[1] / h,
|
||||
box4.startPoint[0] / w,
|
||||
box4.endPoint[1] / h,
|
||||
box4.endPoint[0] / w
|
||||
]];
|
||||
return tfjs_esm_exports.image.cropAndResize(image25, boxes, [0], cropSize);
|
||||
return tfjs_esm_exports.image.cropAndResize(image24, boxes, [0], cropSize);
|
||||
}
|
||||
function scaleBoxCoordinates2(box4, factor) {
|
||||
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
|
||||
|
|
@ -8978,13 +8975,13 @@ var HandPipeline = class {
|
|||
Math.trunc(coord[2])
|
||||
]);
|
||||
}
|
||||
async estimateHands(image25, config3) {
|
||||
async estimateHands(image24, config3) {
|
||||
let useFreshBox = false;
|
||||
let boxes;
|
||||
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
|
||||
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame) {
|
||||
boxes = await this.handDetector.predict(image25, config3);
|
||||
boxes = await this.handDetector.predict(image24, config3);
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config3.skipAllowed)
|
||||
|
|
@ -9003,8 +9000,8 @@ var HandPipeline = class {
|
|||
if (config3.hand.landmarks) {
|
||||
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
|
||||
const palmCenter = getBoxCenter2(currentBox);
|
||||
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
|
||||
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
|
||||
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
|
||||
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
|
||||
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
|
||||
|
|
@ -9942,7 +9939,7 @@ async function load11(config3) {
|
|||
inputSize7 = 256;
|
||||
return model10;
|
||||
}
|
||||
async function parseSinglePose(res, config3, image25, inputBox) {
|
||||
async function parseSinglePose(res, config3, image24, inputBox) {
|
||||
const kpt4 = res[0][0];
|
||||
const keypoints = [];
|
||||
let score = 0;
|
||||
|
|
@ -9958,15 +9955,15 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
part: kpt3[id],
|
||||
positionRaw,
|
||||
position: [
|
||||
Math.round((image25.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image25.shape[1] || 0) * positionRaw[1])
|
||||
Math.round((image24.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image24.shape[1] || 0) * positionRaw[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
}
|
||||
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
|
||||
const bodies = [];
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -9983,7 +9980,7 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
bodies.push(body4);
|
||||
return bodies;
|
||||
}
|
||||
async function parseMultiPose(res, config3, image25, inputBox) {
|
||||
async function parseMultiPose(res, config3, image24, inputBox) {
|
||||
const bodies = [];
|
||||
for (let id = 0; id < res[0].length; id++) {
|
||||
const kpt4 = res[0][id];
|
||||
|
|
@ -10001,11 +9998,11 @@ async function parseMultiPose(res, config3, image25, inputBox) {
|
|||
part: kpt3[i],
|
||||
score: Math.round(100 * score) / 100,
|
||||
positionRaw,
|
||||
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
|
||||
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
|
||||
});
|
||||
}
|
||||
}
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10057,7 +10054,7 @@ async function predict10(input, config3) {
|
|||
|
||||
// src/object/nanodet.ts
|
||||
var model11;
|
||||
var last4 = [];
|
||||
var last5 = [];
|
||||
var lastTime10 = 0;
|
||||
var skipped11 = Number.MAX_SAFE_INTEGER;
|
||||
var scaleBox = 2.5;
|
||||
|
|
@ -10137,19 +10134,19 @@ async function process4(res, inputSize8, outputShape, config3) {
|
|||
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
|
||||
return results;
|
||||
}
|
||||
async function predict11(image25, config3) {
|
||||
async function predict11(image24, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
|
||||
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last4.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
|
||||
skipped11++;
|
||||
return last4;
|
||||
return last5;
|
||||
}
|
||||
skipped11 = 0;
|
||||
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
|
||||
return last4;
|
||||
return last5;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputSize2 = [image25.shape[2], image25.shape[1]];
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
|
||||
const outputSize2 = [image24.shape[2], image24.shape[1]];
|
||||
const resize = tfjs_esm_exports.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
|
||||
const norm = tfjs_esm_exports.div(resize, 255);
|
||||
const transpose = norm.transpose([0, 3, 1, 2]);
|
||||
tfjs_esm_exports.dispose(norm);
|
||||
|
|
@ -10160,7 +10157,7 @@ async function predict11(image25, config3) {
|
|||
lastTime10 = now();
|
||||
tfjs_esm_exports.dispose(transpose);
|
||||
const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
|
||||
last4 = obj;
|
||||
last5 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -10526,7 +10523,7 @@ async function process5(input, background, config3) {
|
|||
busy = true;
|
||||
if (!model13)
|
||||
await load14(config3);
|
||||
const inputImage = process2(input, config3);
|
||||
const inputImage = await process2(input, config3);
|
||||
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
|
||||
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
|
||||
if (!inputImage.tensor)
|
||||
|
|
@ -10577,7 +10574,7 @@ async function process5(input, background, config3) {
|
|||
let mergedCanvas = null;
|
||||
if (background && compositeCanvas) {
|
||||
mergedCanvas = canvas(width, height);
|
||||
const bgImage = process2(background, config3);
|
||||
const bgImage = await process2(background, config3);
|
||||
tfjs_esm_exports.dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext("2d");
|
||||
ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
@ -10819,6 +10816,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b)))
|
||||
};
|
||||
tfjs_esm_exports.registerKernel(kernelMod);
|
||||
env.kernels.push("mod");
|
||||
}
|
||||
if (!env.kernels.includes("floormod")) {
|
||||
const kernelMod = {
|
||||
|
|
@ -10827,8 +10825,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tfjs_esm_exports.mod(op.inputs.a, op.inputs.b))
|
||||
};
|
||||
tfjs_esm_exports.registerKernel(kernelMod);
|
||||
env.kernels.push("floormod");
|
||||
}
|
||||
env.updateBackend();
|
||||
}
|
||||
async function check(instance, force = false) {
|
||||
instance.state = "backend";
|
||||
|
|
@ -10915,7 +10913,7 @@ async function check(instance, force = false) {
|
|||
await tfjs_esm_exports.ready();
|
||||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = tfjs_esm_exports.getBackend();
|
||||
env.updateBackend();
|
||||
await env.updateBackend();
|
||||
registerCustomOps();
|
||||
}
|
||||
return true;
|
||||
|
|
@ -11611,8 +11609,9 @@ var body2 = (res) => {
|
|||
gestures.push({ body: i, gesture: "raise right hand" });
|
||||
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
|
||||
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
|
||||
if (leftShoulder && rightShoulder)
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
@ -12949,7 +12948,7 @@ var Human = class {
|
|||
await this.load();
|
||||
timeStamp = now();
|
||||
this.state = "image";
|
||||
const img = process2(input, this.config);
|
||||
const img = await process2(input, this.config);
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
|
|
@ -12969,7 +12968,7 @@ var Human = class {
|
|||
this.performance.totalFrames++;
|
||||
if (this.config.skipAllowed)
|
||||
this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Check Changed:");
|
||||
let faceRes = [];
|
||||
let bodyRes = [];
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -106,6 +106,7 @@ var config = {
|
|||
warmup: "full",
|
||||
cacheSensitivity: 0.7,
|
||||
skipAllowed: false,
|
||||
deallocate: false,
|
||||
filter: {
|
||||
enabled: true,
|
||||
equalization: false,
|
||||
|
|
@ -70569,7 +70570,7 @@ registerBackend("wasm", async () => {
|
|||
const { wasm } = await init();
|
||||
return new BackendWasm(wasm);
|
||||
}, WASM_PRIORITY);
|
||||
var externalVersion = "3.11.0-20211105";
|
||||
var externalVersion = "3.11.0-20211106";
|
||||
var version8 = {
|
||||
tfjs: externalVersion,
|
||||
"tfjs-core": externalVersion,
|
||||
|
|
@ -71259,17 +71260,20 @@ function GLImageFilter() {
|
|||
}
|
||||
|
||||
// src/image/enhance.ts
|
||||
function histogramEqualization(input2) {
|
||||
const channels = split(input2, 3, 2);
|
||||
async function histogramEqualization(inputImage) {
|
||||
const squeeze2 = inputImage.shape.length === 4 ? squeeze(inputImage) : inputImage;
|
||||
const channels = split(squeeze2, 3, 2);
|
||||
const min7 = [min(channels[0]), min(channels[1]), min(channels[2])];
|
||||
const max7 = [max(channels[0]), max(channels[1]), max(channels[2])];
|
||||
const absMax = await Promise.all(max7.map((channel) => channel.data()));
|
||||
const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
|
||||
const sub5 = [sub(channels[0], min7[0]), sub(channels[1], min7[1]), sub(channels[2], min7[2])];
|
||||
const range7 = [sub(max7[0], min7[0]), sub(max7[1], min7[1]), sub(max7[2], min7[2])];
|
||||
const fact = [div(255, range7[0]), div(255, range7[1]), div(255, range7[2])];
|
||||
const fact = [div(maxValue, range7[0]), div(maxValue, range7[1]), div(maxValue, range7[2])];
|
||||
const enh = [mul(sub5[0], fact[0]), mul(sub5[1], fact[1]), mul(sub5[2], fact[2])];
|
||||
const rgb2 = stack([enh[0], enh[1], enh[2]], 2);
|
||||
const reshape7 = reshape(rgb2, [1, input2.shape[0], input2.shape[1], 3]);
|
||||
dispose([...channels, ...min7, ...max7, ...sub5, ...range7, ...fact, ...enh, rgb2]);
|
||||
const reshape7 = reshape(rgb2, [1, squeeze2.shape[0], squeeze2.shape[1], 3]);
|
||||
dispose([...channels, ...min7, ...max7, ...sub5, ...range7, ...fact, ...enh, rgb2, squeeze2]);
|
||||
return reshape7;
|
||||
}
|
||||
|
||||
|
|
@ -71279,6 +71283,12 @@ var inCanvas = null;
|
|||
var outCanvas = null;
|
||||
var tmpCanvas = null;
|
||||
var fx;
|
||||
var last = {
|
||||
inputSum: 0,
|
||||
cacheDiff: 1,
|
||||
sumMethod: 0,
|
||||
inputTensor: void 0
|
||||
};
|
||||
function canvas(width, height) {
|
||||
let c;
|
||||
if (env2.browser) {
|
||||
|
|
@ -71305,7 +71315,7 @@ function copy(input2, output) {
|
|||
ctx.drawImage(input2, 0, 0);
|
||||
return outputCanvas;
|
||||
}
|
||||
function process2(input2, config3, getTensor2 = true) {
|
||||
async function process2(input2, config3, getTensor2 = true) {
|
||||
if (!input2) {
|
||||
if (config3.debug)
|
||||
log("input is missing");
|
||||
|
|
@ -71355,7 +71365,7 @@ function process2(input2, config3, getTensor2 = true) {
|
|||
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
|
||||
if (!targetWidth || !targetHeight)
|
||||
throw new Error("input cannot determine dimension");
|
||||
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
|
||||
if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
|
||||
inCanvas = canvas(targetWidth, targetHeight);
|
||||
const inCtx = inCanvas.getContext("2d");
|
||||
if (typeof ImageData !== "undefined" && input2 instanceof ImageData) {
|
||||
|
|
@ -71364,13 +71374,13 @@ function process2(input2, config3, getTensor2 = true) {
|
|||
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
|
||||
inCtx.translate(originalWidth, 0);
|
||||
inCtx.scale(-1, 1);
|
||||
inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
inCtx.setTransform(1, 0, 0, 1, 0, 0);
|
||||
} else {
|
||||
inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
}
|
||||
}
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
|
||||
outCanvas = canvas(inCanvas.width, inCanvas.height);
|
||||
if (config3.filter.enabled && env2.webgl.supported) {
|
||||
if (!fx)
|
||||
|
|
@ -71458,51 +71468,31 @@ function process2(input2, config3, getTensor2 = true) {
|
|||
if (!pixels)
|
||||
throw new Error("cannot create tensor from input");
|
||||
const casted = cast(pixels, "float32");
|
||||
const tensor2 = config3.filter.equalization ? histogramEqualization(casted) : expandDims(casted, 0);
|
||||
const tensor2 = config3.filter.equalization ? await histogramEqualization(casted) : expandDims(casted, 0);
|
||||
dispose([pixels, casted]);
|
||||
return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null };
|
||||
}
|
||||
}
|
||||
var lastInputSum = 0;
|
||||
var lastCacheDiff = 1;
|
||||
var benchmarked = 0;
|
||||
var checksum = async (input2) => {
|
||||
const resizeFact = 48;
|
||||
const reduced = image.resizeBilinear(input2, [Math.trunc((input2.shape[1] || 1) / resizeFact), Math.trunc((input2.shape[2] || 1) / resizeFact)]);
|
||||
const tfSum = async () => {
|
||||
const sumT = sum2(reduced);
|
||||
const sum0 = await sumT.data();
|
||||
dispose(sumT);
|
||||
return sum0[0];
|
||||
};
|
||||
const jsSum = async () => {
|
||||
const reducedData = await reduced.data();
|
||||
let sum0 = 0;
|
||||
for (let i = 0; i < reducedData.length / 3; i++)
|
||||
sum0 += reducedData[3 * i + 2];
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
async function skip(config3, input2) {
|
||||
let skipFrame = false;
|
||||
if (config3.cacheSensitivity === 0)
|
||||
return false;
|
||||
const sum7 = await checksum(input2);
|
||||
const diff = 100 * (Math.max(sum7, lastInputSum) / Math.min(sum7, lastInputSum) - 1);
|
||||
lastInputSum = sum7;
|
||||
let skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
|
||||
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && lastCacheDiff > 0;
|
||||
return skipFrame;
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = clone(input2);
|
||||
} else if (last.inputTensor.shape[1] !== input2.shape[1] || last.inputTensor.shape[2] !== input2.shape[2]) {
|
||||
dispose(last.inputTensor);
|
||||
last.inputTensor = clone(input2);
|
||||
} else {
|
||||
const t = {};
|
||||
t.diff = sub(input2, last.inputTensor);
|
||||
t.squared = mul(t.diff, t.diff);
|
||||
t.sum = sum2(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input2.shape[1] || 1) / (input2.shape[2] || 1) / 255 / 3;
|
||||
dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = clone(input2);
|
||||
skipFrame = diffRelative <= config3.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
||||
|
|
@ -75484,7 +75474,7 @@ var labels = [
|
|||
// src/object/centernet.ts
|
||||
var model5;
|
||||
var inputSize3 = 0;
|
||||
var last = [];
|
||||
var last2 = [];
|
||||
var lastTime3 = 0;
|
||||
var skipped4 = Number.MAX_SAFE_INTEGER;
|
||||
async function load4(config3) {
|
||||
|
|
@ -75551,9 +75541,9 @@ async function process3(res, outputShape, config3) {
|
|||
async function predict3(input2, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
|
||||
const skipFrame = skipped4 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
|
||||
skipped4++;
|
||||
return last;
|
||||
return last2;
|
||||
}
|
||||
skipped4 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -75563,7 +75553,7 @@ async function predict3(input2, config3) {
|
|||
lastTime3 = now();
|
||||
dispose(resize);
|
||||
const obj = await process3(objectT, outputSize2, config3);
|
||||
last = obj;
|
||||
last2 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -75715,7 +75705,7 @@ async function predict4(image7, config3) {
|
|||
// src/gear/emotion.ts
|
||||
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
|
||||
var model7;
|
||||
var last2 = [];
|
||||
var last3 = [];
|
||||
var lastCount2 = 0;
|
||||
var lastTime5 = 0;
|
||||
var skipped6 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -75740,9 +75730,9 @@ async function predict5(image7, config3, idx, count3) {
|
|||
return null;
|
||||
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count3 && last2[idx] && last2[idx].length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count3 && last3[idx] && last3[idx].length > 0) {
|
||||
skipped6++;
|
||||
return last2[idx];
|
||||
return last3[idx];
|
||||
}
|
||||
skipped6 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -75769,7 +75759,7 @@ async function predict5(image7, config3, idx, count3) {
|
|||
obj.sort((a, b) => b.score - a.score);
|
||||
Object.keys(t).forEach((tensor2) => dispose(t[tensor2]));
|
||||
}
|
||||
last2[idx] = obj;
|
||||
last3[idx] = obj;
|
||||
lastCount2 = count3;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -75915,7 +75905,7 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
|
|||
var lastTime6 = 0;
|
||||
var enlargeFact = 1.6;
|
||||
async function predict6(input2, config3) {
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h;
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
|
||||
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
|
||||
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
|
||||
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
|
||||
|
|
@ -75959,8 +75949,13 @@ async function predict6(input2, config3) {
|
|||
rotationMatrix = fixedRotationMatrix;
|
||||
face5.tensor = cutBoxFromImageAndResize(box4, input2, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
|
||||
}
|
||||
if ((_f = config3 == null ? void 0 : config3.filter) == null ? void 0 : _f.equalization) {
|
||||
const equilized = await histogramEqualization(face5.tensor);
|
||||
dispose(face5.tensor);
|
||||
face5.tensor = equilized;
|
||||
}
|
||||
face5.boxScore = Math.round(100 * box4.confidence) / 100;
|
||||
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
|
||||
if (!((_g = config3.face.mesh) == null ? void 0 : _g.enabled)) {
|
||||
face5.box = getClampedBox(box4, input2);
|
||||
face5.boxRaw = getRawBox(box4, input2);
|
||||
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
|
||||
|
|
@ -75982,10 +75977,10 @@ async function predict6(input2, config3) {
|
|||
const coordsReshaped = reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = await coordsReshaped.array();
|
||||
dispose([contourCoords, coordsReshaped, confidence, contours]);
|
||||
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
|
||||
if (face5.faceScore < (((_h = config3.face.detector) == null ? void 0 : _h.minConfidence) || 1)) {
|
||||
box4.confidence = face5.faceScore;
|
||||
} else {
|
||||
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
|
||||
if ((_i = config3.face.iris) == null ? void 0 : _i.enabled)
|
||||
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
|
||||
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
|
||||
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input2.shape[2] || 0), pt[1] / (input2.shape[1] || 0), (pt[2] || 0) / inputSize5]);
|
||||
|
|
@ -76025,7 +76020,7 @@ var uvmap = UV468;
|
|||
|
||||
// src/face/faceres.ts
|
||||
var model10;
|
||||
var last3 = [];
|
||||
var last4 = [];
|
||||
var lastTime7 = 0;
|
||||
var lastCount3 = 0;
|
||||
var skipped8 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -76059,9 +76054,9 @@ async function predict7(image7, config3, idx, count3) {
|
|||
return null;
|
||||
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count3 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count3 && ((_c = last4[idx]) == null ? void 0 : _c.age) && ((_d = last4[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
skipped8++;
|
||||
return last3[idx];
|
||||
return last4[idx];
|
||||
}
|
||||
skipped8 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -76095,7 +76090,7 @@ async function predict7(image7, config3, idx, count3) {
|
|||
obj.descriptor = Array.from(descriptor);
|
||||
resT.forEach((t) => dispose(t));
|
||||
}
|
||||
last3[idx] = obj;
|
||||
last4[idx] = obj;
|
||||
lastCount3 = count3;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -80384,7 +80379,7 @@ async function predict10(input2, config3) {
|
|||
|
||||
// src/object/nanodet.ts
|
||||
var model12;
|
||||
var last4 = [];
|
||||
var last5 = [];
|
||||
var lastTime10 = 0;
|
||||
var skipped11 = Number.MAX_SAFE_INTEGER;
|
||||
var scaleBox = 2.5;
|
||||
|
|
@ -80467,13 +80462,13 @@ async function process4(res, inputSize8, outputShape, config3) {
|
|||
async function predict11(image7, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
|
||||
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last4.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
|
||||
skipped11++;
|
||||
return last4;
|
||||
return last5;
|
||||
}
|
||||
skipped11 = 0;
|
||||
if (!env2.kernels.includes("mod") || !env2.kernels.includes("sparsetodense"))
|
||||
return last4;
|
||||
return last5;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputSize2 = [image7.shape[2], image7.shape[1]];
|
||||
const resize = image.resizeBilinear(image7, [model12.inputSize, model12.inputSize], false);
|
||||
|
|
@ -80487,7 +80482,7 @@ async function predict11(image7, config3) {
|
|||
lastTime10 = now();
|
||||
dispose(transpose6);
|
||||
const obj = await process4(objectT, model12.inputSize, outputSize2, config3);
|
||||
last4 = obj;
|
||||
last5 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -80853,7 +80848,7 @@ async function process5(input2, background, config3) {
|
|||
busy = true;
|
||||
if (!model14)
|
||||
await load14(config3);
|
||||
const inputImage = process2(input2, config3);
|
||||
const inputImage = await process2(input2, config3);
|
||||
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
|
||||
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
|
||||
if (!inputImage.tensor)
|
||||
|
|
@ -80904,7 +80899,7 @@ async function process5(input2, background, config3) {
|
|||
let mergedCanvas = null;
|
||||
if (background && compositeCanvas) {
|
||||
mergedCanvas = canvas(width, height);
|
||||
const bgImage = process2(background, config3);
|
||||
const bgImage = await process2(background, config3);
|
||||
dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext("2d");
|
||||
ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
@ -81146,6 +81141,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op2) => tidy(() => sub(op2.inputs.a, mul(div(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
|
||||
};
|
||||
registerKernel(kernelMod);
|
||||
env2.kernels.push("mod");
|
||||
}
|
||||
if (!env2.kernels.includes("floormod")) {
|
||||
const kernelMod = {
|
||||
|
|
@ -81154,8 +81150,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op2) => tidy(() => floorDiv(op2.inputs.a / op2.inputs.b) * op2.inputs.b + mod(op2.inputs.a, op2.inputs.b))
|
||||
};
|
||||
registerKernel(kernelMod);
|
||||
env2.kernels.push("floormod");
|
||||
}
|
||||
env2.updateBackend();
|
||||
}
|
||||
async function check(instance, force = false) {
|
||||
instance.state = "backend";
|
||||
|
|
@ -81242,7 +81238,7 @@ async function check(instance, force = false) {
|
|||
await ready();
|
||||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = getBackend();
|
||||
env2.updateBackend();
|
||||
await env2.updateBackend();
|
||||
registerCustomOps();
|
||||
}
|
||||
return true;
|
||||
|
|
@ -81938,8 +81934,9 @@ var body2 = (res) => {
|
|||
gestures.push({ body: i, gesture: "raise right hand" });
|
||||
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
|
||||
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
|
||||
if (leftShoulder && rightShoulder)
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
@ -83276,7 +83273,7 @@ var Human = class {
|
|||
await this.load();
|
||||
timeStamp = now();
|
||||
this.state = "image";
|
||||
const img = process2(input2, this.config);
|
||||
const img = await process2(input2, this.config);
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
|
|
@ -83296,7 +83293,7 @@ var Human = class {
|
|||
this.performance.totalFrames++;
|
||||
if (this.config.skipAllowed)
|
||||
this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Check Changed:");
|
||||
let faceRes = [];
|
||||
let bodyRes = [];
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -152,6 +152,7 @@ var config = {
|
|||
warmup: "full",
|
||||
cacheSensitivity: 0.7,
|
||||
skipAllowed: false,
|
||||
deallocate: false,
|
||||
filter: {
|
||||
enabled: true,
|
||||
equalization: false,
|
||||
|
|
@ -918,8 +919,8 @@ function GLImageFilter() {
|
|||
this.get = function() {
|
||||
return filterChain;
|
||||
};
|
||||
this.apply = function(image25) {
|
||||
resize(image25.width, image25.height);
|
||||
this.apply = function(image24) {
|
||||
resize(image24.width, image24.height);
|
||||
drawCount = 0;
|
||||
if (!sourceTexture)
|
||||
sourceTexture = gl.createTexture();
|
||||
|
|
@ -928,7 +929,7 @@ function GLImageFilter() {
|
|||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
|
||||
for (let i = 0; i < filterChain.length; i++) {
|
||||
lastInChain = i === filterChain.length - 1;
|
||||
const f = filterChain[i];
|
||||
|
|
@ -936,25 +937,28 @@ function GLImageFilter() {
|
|||
}
|
||||
return fxcanvas;
|
||||
};
|
||||
this.draw = function(image25) {
|
||||
this.draw = function(image24) {
|
||||
this.add("brightness", 0);
|
||||
return this.apply(image25);
|
||||
return this.apply(image24);
|
||||
};
|
||||
}
|
||||
|
||||
// src/image/enhance.ts
|
||||
var tf = __toModule(require_tfjs_esm());
|
||||
function histogramEqualization(input) {
|
||||
const channels = tf.split(input, 3, 2);
|
||||
async function histogramEqualization(inputImage) {
|
||||
const squeeze9 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
|
||||
const channels = tf.split(squeeze9, 3, 2);
|
||||
const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
|
||||
const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
|
||||
const sub6 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const absMax = await Promise.all(max4.map((channel) => channel.data()));
|
||||
const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
|
||||
const sub7 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
|
||||
const fact = [tf.div(255, range[0]), tf.div(255, range[1]), tf.div(255, range[2])];
|
||||
const enh = [tf.mul(sub6[0], fact[0]), tf.mul(sub6[1], fact[1]), tf.mul(sub6[2], fact[2])];
|
||||
const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
|
||||
const enh = [tf.mul(sub7[0], fact[0]), tf.mul(sub7[1], fact[1]), tf.mul(sub7[2], fact[2])];
|
||||
const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
|
||||
const reshape8 = tf.reshape(rgb2, [1, input.shape[0], input.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub6, ...range, ...fact, ...enh, rgb2]);
|
||||
const reshape8 = tf.reshape(rgb2, [1, squeeze9.shape[0], squeeze9.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub7, ...range, ...fact, ...enh, rgb2, squeeze9]);
|
||||
return reshape8;
|
||||
}
|
||||
|
||||
|
|
@ -964,6 +968,12 @@ var inCanvas = null;
|
|||
var outCanvas = null;
|
||||
var tmpCanvas = null;
|
||||
var fx;
|
||||
var last = {
|
||||
inputSum: 0,
|
||||
cacheDiff: 1,
|
||||
sumMethod: 0,
|
||||
inputTensor: void 0
|
||||
};
|
||||
function canvas(width, height) {
|
||||
let c;
|
||||
if (env.browser) {
|
||||
|
|
@ -990,7 +1000,7 @@ function copy(input, output) {
|
|||
ctx.drawImage(input, 0, 0);
|
||||
return outputCanvas;
|
||||
}
|
||||
function process2(input, config3, getTensor = true) {
|
||||
async function process2(input, config3, getTensor = true) {
|
||||
if (!input) {
|
||||
if (config3.debug)
|
||||
log("input is missing");
|
||||
|
|
@ -1040,7 +1050,7 @@ function process2(input, config3, getTensor = true) {
|
|||
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
|
||||
if (!targetWidth || !targetHeight)
|
||||
throw new Error("input cannot determine dimension");
|
||||
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
|
||||
if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
|
||||
inCanvas = canvas(targetWidth, targetHeight);
|
||||
const inCtx = inCanvas.getContext("2d");
|
||||
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
|
||||
|
|
@ -1049,13 +1059,13 @@ function process2(input, config3, getTensor = true) {
|
|||
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
|
||||
inCtx.translate(originalWidth, 0);
|
||||
inCtx.scale(-1, 1);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
inCtx.setTransform(1, 0, 0, 1, 0, 0);
|
||||
} else {
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
}
|
||||
}
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
|
||||
outCanvas = canvas(inCanvas.width, inCanvas.height);
|
||||
if (config3.filter.enabled && env.webgl.supported) {
|
||||
if (!fx)
|
||||
|
|
@ -1143,51 +1153,31 @@ function process2(input, config3, getTensor = true) {
|
|||
if (!pixels)
|
||||
throw new Error("cannot create tensor from input");
|
||||
const casted = tf2.cast(pixels, "float32");
|
||||
const tensor3 = config3.filter.equalization ? histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
const tensor3 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
tf2.dispose([pixels, casted]);
|
||||
return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null };
|
||||
}
|
||||
}
|
||||
var lastInputSum = 0;
|
||||
var lastCacheDiff = 1;
|
||||
var benchmarked = 0;
|
||||
var checksum = async (input) => {
|
||||
const resizeFact = 48;
|
||||
const reduced = tf2.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);
|
||||
const tfSum = async () => {
|
||||
const sumT = tf2.sum(reduced);
|
||||
const sum0 = await sumT.data();
|
||||
tf2.dispose(sumT);
|
||||
return sum0[0];
|
||||
};
|
||||
const jsSum = async () => {
|
||||
const reducedData = await reduced.data();
|
||||
let sum0 = 0;
|
||||
for (let i = 0; i < reducedData.length / 3; i++)
|
||||
sum0 += reducedData[3 * i + 2];
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
tf2.dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
async function skip(config3, input) {
|
||||
let skipFrame = false;
|
||||
if (config3.cacheSensitivity === 0)
|
||||
return false;
|
||||
const sum2 = await checksum(input);
|
||||
const diff = 100 * (Math.max(sum2, lastInputSum) / Math.min(sum2, lastInputSum) - 1);
|
||||
lastInputSum = sum2;
|
||||
let skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
|
||||
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && lastCacheDiff > 0;
|
||||
return skipFrame;
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
|
||||
tf2.dispose(last.inputTensor);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else {
|
||||
const t = {};
|
||||
t.diff = tf2.sub(input, last.inputTensor);
|
||||
t.squared = tf2.mul(t.diff, t.diff);
|
||||
t.sum = tf2.sum(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
|
||||
tf2.dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
skipFrame = diffRelative <= config3.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
||||
|
|
@ -1349,7 +1339,7 @@ async function load2(config3) {
|
|||
log("cached model:", model2["modelUrl"]);
|
||||
return model2;
|
||||
}
|
||||
async function predict(image25, config3, idx, count2) {
|
||||
async function predict(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model2)
|
||||
return null;
|
||||
|
|
@ -1361,7 +1351,7 @@ async function predict(image25, config3, idx, count2) {
|
|||
}
|
||||
skipped2 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const res = model2 == null ? void 0 : model2.execute(resize);
|
||||
const num = (await res.data())[0];
|
||||
cached[idx] = Math.round(100 * num) / 100;
|
||||
|
|
@ -4681,10 +4671,10 @@ var scaleBoxCoordinates = (box4, factor) => {
|
|||
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
|
||||
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
|
||||
};
|
||||
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
const norm = tf6.div(crop2, 255);
|
||||
tf6.dispose(crop2);
|
||||
return norm;
|
||||
|
|
@ -5189,7 +5179,7 @@ var labels = [
|
|||
// src/object/centernet.ts
|
||||
var model4;
|
||||
var inputSize3 = 0;
|
||||
var last = [];
|
||||
var last2 = [];
|
||||
var lastTime3 = 0;
|
||||
var skipped4 = Number.MAX_SAFE_INTEGER;
|
||||
async function load4(config3) {
|
||||
|
|
@ -5256,9 +5246,9 @@ async function process3(res, outputShape, config3) {
|
|||
async function predict3(input, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
|
||||
const skipFrame = skipped4 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
|
||||
skipped4++;
|
||||
return last;
|
||||
return last2;
|
||||
}
|
||||
skipped4 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5268,7 +5258,7 @@ async function predict3(input, config3) {
|
|||
lastTime3 = now();
|
||||
tf9.dispose(resize);
|
||||
const obj = await process3(objectT, outputSize2, config3);
|
||||
last = obj;
|
||||
last2 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -5341,7 +5331,7 @@ function max2d(inputs, minScore) {
|
|||
return [0, 0, newScore];
|
||||
});
|
||||
}
|
||||
async function predict4(image25, config3) {
|
||||
async function predict4(image24, config3) {
|
||||
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
|
||||
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
|
||||
|
|
@ -5354,7 +5344,7 @@ async function predict4(image25, config3) {
|
|||
const tensor3 = tf10.tidy(() => {
|
||||
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
|
||||
return null;
|
||||
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const enhance3 = tf10.mul(resize, 2);
|
||||
const norm = enhance3.sub(1);
|
||||
return norm;
|
||||
|
|
@ -5366,10 +5356,10 @@ async function predict4(image25, config3) {
|
|||
tf10.dispose(tensor3);
|
||||
if (resT) {
|
||||
cache2.keypoints.length = 0;
|
||||
const squeeze8 = resT.squeeze();
|
||||
const squeeze9 = resT.squeeze();
|
||||
tf10.dispose(resT);
|
||||
const stack4 = squeeze8.unstack(2);
|
||||
tf10.dispose(squeeze8);
|
||||
const stack4 = squeeze9.unstack(2);
|
||||
tf10.dispose(squeeze9);
|
||||
for (let id = 0; id < stack4.length; id++) {
|
||||
const [x2, y2, partScore] = max2d(stack4[id], config3.body.minConfidence);
|
||||
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
|
||||
|
|
@ -5381,8 +5371,8 @@ async function predict4(image25, config3) {
|
|||
y2 / model5.inputs[0].shape[1]
|
||||
],
|
||||
position: [
|
||||
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
|
|
@ -5424,7 +5414,7 @@ async function predict4(image25, config3) {
|
|||
var tf11 = __toModule(require_tfjs_esm());
|
||||
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
|
||||
var model6;
|
||||
var last2 = [];
|
||||
var last3 = [];
|
||||
var lastCount2 = 0;
|
||||
var lastTime5 = 0;
|
||||
var skipped6 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5443,15 +5433,15 @@ async function load6(config3) {
|
|||
log("cached model:", model6["modelUrl"]);
|
||||
return model6;
|
||||
}
|
||||
async function predict5(image25, config3, idx, count2) {
|
||||
async function predict5(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model6)
|
||||
return null;
|
||||
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
|
||||
skipped6++;
|
||||
return last2[idx];
|
||||
return last3[idx];
|
||||
}
|
||||
skipped6 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5460,7 +5450,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
|
||||
const t = {};
|
||||
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
|
||||
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
|
||||
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
|
||||
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
|
||||
t.redNorm = tf11.mul(t.red, rgb[0]);
|
||||
t.greenNorm = tf11.mul(t.green, rgb[1]);
|
||||
|
|
@ -5478,7 +5468,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
obj.sort((a, b) => b.score - a.score);
|
||||
Object.keys(t).forEach((tensor3) => tf11.dispose(t[tensor3]));
|
||||
}
|
||||
last2[idx] = obj;
|
||||
last3[idx] = obj;
|
||||
lastCount2 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5628,7 +5618,7 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
|
|||
var lastTime6 = 0;
|
||||
var enlargeFact = 1.6;
|
||||
async function predict6(input, config3) {
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h;
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
|
||||
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
|
||||
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
|
||||
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
|
||||
|
|
@ -5672,8 +5662,13 @@ async function predict6(input, config3) {
|
|||
rotationMatrix = fixedRotationMatrix;
|
||||
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
|
||||
}
|
||||
if ((_f = config3 == null ? void 0 : config3.filter) == null ? void 0 : _f.equalization) {
|
||||
const equilized = await histogramEqualization(face5.tensor);
|
||||
tf13.dispose(face5.tensor);
|
||||
face5.tensor = equilized;
|
||||
}
|
||||
face5.boxScore = Math.round(100 * box4.confidence) / 100;
|
||||
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
|
||||
if (!((_g = config3.face.mesh) == null ? void 0 : _g.enabled)) {
|
||||
face5.box = getClampedBox(box4, input);
|
||||
face5.boxRaw = getRawBox(box4, input);
|
||||
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
|
||||
|
|
@ -5695,10 +5690,10 @@ async function predict6(input, config3) {
|
|||
const coordsReshaped = tf13.reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = await coordsReshaped.array();
|
||||
tf13.dispose([contourCoords, coordsReshaped, confidence, contours]);
|
||||
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
|
||||
if (face5.faceScore < (((_h = config3.face.detector) == null ? void 0 : _h.minConfidence) || 1)) {
|
||||
box4.confidence = face5.faceScore;
|
||||
} else {
|
||||
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
|
||||
if ((_i = config3.face.iris) == null ? void 0 : _i.enabled)
|
||||
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
|
||||
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
|
||||
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
|
||||
|
|
@ -5739,7 +5734,7 @@ var uvmap = UV468;
|
|||
// src/face/faceres.ts
|
||||
var tf14 = __toModule(require_tfjs_esm());
|
||||
var model9;
|
||||
var last3 = [];
|
||||
var last4 = [];
|
||||
var lastTime7 = 0;
|
||||
var lastCount3 = 0;
|
||||
var skipped8 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5767,15 +5762,15 @@ function enhance2(input) {
|
|||
tf14.dispose(crop2);
|
||||
return norm;
|
||||
}
|
||||
async function predict7(image25, config3, idx, count2) {
|
||||
async function predict7(image24, config3, idx, count2) {
|
||||
var _a, _b, _c, _d;
|
||||
if (!model9)
|
||||
return null;
|
||||
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.age) && ((_d = last4[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
skipped8++;
|
||||
return last3[idx];
|
||||
return last4[idx];
|
||||
}
|
||||
skipped8 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5787,7 +5782,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
descriptor: []
|
||||
};
|
||||
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
|
||||
const enhanced = enhance2(image25);
|
||||
const enhanced = enhance2(image24);
|
||||
const resT = model9 == null ? void 0 : model9.execute(enhanced);
|
||||
lastTime7 = now();
|
||||
tf14.dispose(enhanced);
|
||||
|
|
@ -5809,7 +5804,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
obj.descriptor = Array.from(descriptor);
|
||||
resT.forEach((t) => tf14.dispose(t));
|
||||
}
|
||||
last3[idx] = obj;
|
||||
last4[idx] = obj;
|
||||
lastCount3 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5835,16 +5830,16 @@ function getBoxCenter2(box4) {
|
|||
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
|
||||
];
|
||||
}
|
||||
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const boxes = [[
|
||||
box4.startPoint[1] / h,
|
||||
box4.startPoint[0] / w,
|
||||
box4.endPoint[1] / h,
|
||||
box4.endPoint[0] / w
|
||||
]];
|
||||
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
|
||||
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
|
||||
}
|
||||
function scaleBoxCoordinates2(box4, factor) {
|
||||
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
|
||||
|
|
@ -9027,13 +9022,13 @@ var HandPipeline = class {
|
|||
Math.trunc(coord[2])
|
||||
]);
|
||||
}
|
||||
async estimateHands(image25, config3) {
|
||||
async estimateHands(image24, config3) {
|
||||
let useFreshBox = false;
|
||||
let boxes;
|
||||
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
|
||||
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame) {
|
||||
boxes = await this.handDetector.predict(image25, config3);
|
||||
boxes = await this.handDetector.predict(image24, config3);
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config3.skipAllowed)
|
||||
|
|
@ -9052,8 +9047,8 @@ var HandPipeline = class {
|
|||
if (config3.hand.landmarks) {
|
||||
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
|
||||
const palmCenter = getBoxCenter2(currentBox);
|
||||
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
|
||||
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
|
||||
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
|
||||
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
|
||||
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
|
||||
|
|
@ -9996,7 +9991,7 @@ async function load11(config3) {
|
|||
inputSize7 = 256;
|
||||
return model10;
|
||||
}
|
||||
async function parseSinglePose(res, config3, image25, inputBox) {
|
||||
async function parseSinglePose(res, config3, image24, inputBox) {
|
||||
const kpt4 = res[0][0];
|
||||
const keypoints = [];
|
||||
let score = 0;
|
||||
|
|
@ -10012,15 +10007,15 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
part: kpt3[id],
|
||||
positionRaw,
|
||||
position: [
|
||||
Math.round((image25.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image25.shape[1] || 0) * positionRaw[1])
|
||||
Math.round((image24.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image24.shape[1] || 0) * positionRaw[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
}
|
||||
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
|
||||
const bodies = [];
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10037,7 +10032,7 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
bodies.push(body4);
|
||||
return bodies;
|
||||
}
|
||||
async function parseMultiPose(res, config3, image25, inputBox) {
|
||||
async function parseMultiPose(res, config3, image24, inputBox) {
|
||||
const bodies = [];
|
||||
for (let id = 0; id < res[0].length; id++) {
|
||||
const kpt4 = res[0][id];
|
||||
|
|
@ -10055,11 +10050,11 @@ async function parseMultiPose(res, config3, image25, inputBox) {
|
|||
part: kpt3[i],
|
||||
score: Math.round(100 * score) / 100,
|
||||
positionRaw,
|
||||
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
|
||||
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
|
||||
});
|
||||
}
|
||||
}
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10112,7 +10107,7 @@ async function predict10(input, config3) {
|
|||
// src/object/nanodet.ts
|
||||
var tf22 = __toModule(require_tfjs_esm());
|
||||
var model11;
|
||||
var last4 = [];
|
||||
var last5 = [];
|
||||
var lastTime10 = 0;
|
||||
var skipped11 = Number.MAX_SAFE_INTEGER;
|
||||
var scaleBox = 2.5;
|
||||
|
|
@ -10192,19 +10187,19 @@ async function process4(res, inputSize8, outputShape, config3) {
|
|||
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
|
||||
return results;
|
||||
}
|
||||
async function predict11(image25, config3) {
|
||||
async function predict11(image24, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
|
||||
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last4.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
|
||||
skipped11++;
|
||||
return last4;
|
||||
return last5;
|
||||
}
|
||||
skipped11 = 0;
|
||||
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
|
||||
return last4;
|
||||
return last5;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputSize2 = [image25.shape[2], image25.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
|
||||
const outputSize2 = [image24.shape[2], image24.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
|
||||
const norm = tf22.div(resize, 255);
|
||||
const transpose = norm.transpose([0, 3, 1, 2]);
|
||||
tf22.dispose(norm);
|
||||
|
|
@ -10215,7 +10210,7 @@ async function predict11(image25, config3) {
|
|||
lastTime10 = now();
|
||||
tf22.dispose(transpose);
|
||||
const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
|
||||
last4 = obj;
|
||||
last5 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -10585,7 +10580,7 @@ async function process5(input, background, config3) {
|
|||
busy = true;
|
||||
if (!model13)
|
||||
await load14(config3);
|
||||
const inputImage = process2(input, config3);
|
||||
const inputImage = await process2(input, config3);
|
||||
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
|
||||
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
|
||||
if (!inputImage.tensor)
|
||||
|
|
@ -10636,7 +10631,7 @@ async function process5(input, background, config3) {
|
|||
let mergedCanvas = null;
|
||||
if (background && compositeCanvas) {
|
||||
mergedCanvas = canvas(width, height);
|
||||
const bgImage = process2(background, config3);
|
||||
const bgImage = await process2(background, config3);
|
||||
tf24.dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext("2d");
|
||||
ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
@ -10879,6 +10874,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.sub(op.inputs.a, tf26.mul(tf26.div(op.inputs.a, op.inputs.b), op.inputs.b)))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("mod");
|
||||
}
|
||||
if (!env.kernels.includes("floormod")) {
|
||||
const kernelMod = {
|
||||
|
|
@ -10887,8 +10883,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf26.mod(op.inputs.a, op.inputs.b))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("floormod");
|
||||
}
|
||||
env.updateBackend();
|
||||
}
|
||||
async function check(instance, force = false) {
|
||||
instance.state = "backend";
|
||||
|
|
@ -10975,7 +10971,7 @@ async function check(instance, force = false) {
|
|||
await tf26.ready();
|
||||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = tf26.getBackend();
|
||||
env.updateBackend();
|
||||
await env.updateBackend();
|
||||
registerCustomOps();
|
||||
}
|
||||
return true;
|
||||
|
|
@ -11674,8 +11670,9 @@ var body2 = (res) => {
|
|||
gestures.push({ body: i, gesture: "raise right hand" });
|
||||
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
|
||||
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
|
||||
if (leftShoulder && rightShoulder)
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
@ -13013,7 +13010,7 @@ var Human = class {
|
|||
await this.load();
|
||||
timeStamp = now();
|
||||
this.state = "image";
|
||||
const img = process2(input, this.config);
|
||||
const img = await process2(input, this.config);
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
|
|
@ -13033,7 +13030,7 @@ var Human = class {
|
|||
this.performance.totalFrames++;
|
||||
if (this.config.skipAllowed)
|
||||
this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Check Changed:");
|
||||
let faceRes = [];
|
||||
let bodyRes = [];
|
||||
|
|
|
|||
|
|
@ -153,6 +153,7 @@ var config = {
|
|||
warmup: "full",
|
||||
cacheSensitivity: 0.7,
|
||||
skipAllowed: false,
|
||||
deallocate: false,
|
||||
filter: {
|
||||
enabled: true,
|
||||
equalization: false,
|
||||
|
|
@ -919,8 +920,8 @@ function GLImageFilter() {
|
|||
this.get = function() {
|
||||
return filterChain;
|
||||
};
|
||||
this.apply = function(image25) {
|
||||
resize(image25.width, image25.height);
|
||||
this.apply = function(image24) {
|
||||
resize(image24.width, image24.height);
|
||||
drawCount = 0;
|
||||
if (!sourceTexture)
|
||||
sourceTexture = gl.createTexture();
|
||||
|
|
@ -929,7 +930,7 @@ function GLImageFilter() {
|
|||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
|
||||
for (let i = 0; i < filterChain.length; i++) {
|
||||
lastInChain = i === filterChain.length - 1;
|
||||
const f = filterChain[i];
|
||||
|
|
@ -937,25 +938,28 @@ function GLImageFilter() {
|
|||
}
|
||||
return fxcanvas;
|
||||
};
|
||||
this.draw = function(image25) {
|
||||
this.draw = function(image24) {
|
||||
this.add("brightness", 0);
|
||||
return this.apply(image25);
|
||||
return this.apply(image24);
|
||||
};
|
||||
}
|
||||
|
||||
// src/image/enhance.ts
|
||||
var tf = __toModule(require_tfjs_esm());
|
||||
function histogramEqualization(input) {
|
||||
const channels = tf.split(input, 3, 2);
|
||||
async function histogramEqualization(inputImage) {
|
||||
const squeeze9 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
|
||||
const channels = tf.split(squeeze9, 3, 2);
|
||||
const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
|
||||
const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
|
||||
const sub6 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const absMax = await Promise.all(max4.map((channel) => channel.data()));
|
||||
const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
|
||||
const sub7 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
|
||||
const fact = [tf.div(255, range[0]), tf.div(255, range[1]), tf.div(255, range[2])];
|
||||
const enh = [tf.mul(sub6[0], fact[0]), tf.mul(sub6[1], fact[1]), tf.mul(sub6[2], fact[2])];
|
||||
const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
|
||||
const enh = [tf.mul(sub7[0], fact[0]), tf.mul(sub7[1], fact[1]), tf.mul(sub7[2], fact[2])];
|
||||
const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
|
||||
const reshape8 = tf.reshape(rgb2, [1, input.shape[0], input.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub6, ...range, ...fact, ...enh, rgb2]);
|
||||
const reshape8 = tf.reshape(rgb2, [1, squeeze9.shape[0], squeeze9.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub7, ...range, ...fact, ...enh, rgb2, squeeze9]);
|
||||
return reshape8;
|
||||
}
|
||||
|
||||
|
|
@ -965,6 +969,12 @@ var inCanvas = null;
|
|||
var outCanvas = null;
|
||||
var tmpCanvas = null;
|
||||
var fx;
|
||||
var last = {
|
||||
inputSum: 0,
|
||||
cacheDiff: 1,
|
||||
sumMethod: 0,
|
||||
inputTensor: void 0
|
||||
};
|
||||
function canvas(width, height) {
|
||||
let c;
|
||||
if (env.browser) {
|
||||
|
|
@ -991,7 +1001,7 @@ function copy(input, output) {
|
|||
ctx.drawImage(input, 0, 0);
|
||||
return outputCanvas;
|
||||
}
|
||||
function process2(input, config3, getTensor = true) {
|
||||
async function process2(input, config3, getTensor = true) {
|
||||
if (!input) {
|
||||
if (config3.debug)
|
||||
log("input is missing");
|
||||
|
|
@ -1041,7 +1051,7 @@ function process2(input, config3, getTensor = true) {
|
|||
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
|
||||
if (!targetWidth || !targetHeight)
|
||||
throw new Error("input cannot determine dimension");
|
||||
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
|
||||
if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
|
||||
inCanvas = canvas(targetWidth, targetHeight);
|
||||
const inCtx = inCanvas.getContext("2d");
|
||||
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
|
||||
|
|
@ -1050,13 +1060,13 @@ function process2(input, config3, getTensor = true) {
|
|||
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
|
||||
inCtx.translate(originalWidth, 0);
|
||||
inCtx.scale(-1, 1);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
inCtx.setTransform(1, 0, 0, 1, 0, 0);
|
||||
} else {
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
}
|
||||
}
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
|
||||
outCanvas = canvas(inCanvas.width, inCanvas.height);
|
||||
if (config3.filter.enabled && env.webgl.supported) {
|
||||
if (!fx)
|
||||
|
|
@ -1144,51 +1154,31 @@ function process2(input, config3, getTensor = true) {
|
|||
if (!pixels)
|
||||
throw new Error("cannot create tensor from input");
|
||||
const casted = tf2.cast(pixels, "float32");
|
||||
const tensor3 = config3.filter.equalization ? histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
const tensor3 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
tf2.dispose([pixels, casted]);
|
||||
return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null };
|
||||
}
|
||||
}
|
||||
var lastInputSum = 0;
|
||||
var lastCacheDiff = 1;
|
||||
var benchmarked = 0;
|
||||
var checksum = async (input) => {
|
||||
const resizeFact = 48;
|
||||
const reduced = tf2.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);
|
||||
const tfSum = async () => {
|
||||
const sumT = tf2.sum(reduced);
|
||||
const sum0 = await sumT.data();
|
||||
tf2.dispose(sumT);
|
||||
return sum0[0];
|
||||
};
|
||||
const jsSum = async () => {
|
||||
const reducedData = await reduced.data();
|
||||
let sum0 = 0;
|
||||
for (let i = 0; i < reducedData.length / 3; i++)
|
||||
sum0 += reducedData[3 * i + 2];
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
tf2.dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
async function skip(config3, input) {
|
||||
let skipFrame = false;
|
||||
if (config3.cacheSensitivity === 0)
|
||||
return false;
|
||||
const sum2 = await checksum(input);
|
||||
const diff = 100 * (Math.max(sum2, lastInputSum) / Math.min(sum2, lastInputSum) - 1);
|
||||
lastInputSum = sum2;
|
||||
let skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
|
||||
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && lastCacheDiff > 0;
|
||||
return skipFrame;
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
|
||||
tf2.dispose(last.inputTensor);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else {
|
||||
const t = {};
|
||||
t.diff = tf2.sub(input, last.inputTensor);
|
||||
t.squared = tf2.mul(t.diff, t.diff);
|
||||
t.sum = tf2.sum(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
|
||||
tf2.dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
skipFrame = diffRelative <= config3.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
||||
|
|
@ -1350,7 +1340,7 @@ async function load2(config3) {
|
|||
log("cached model:", model2["modelUrl"]);
|
||||
return model2;
|
||||
}
|
||||
async function predict(image25, config3, idx, count2) {
|
||||
async function predict(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model2)
|
||||
return null;
|
||||
|
|
@ -1362,7 +1352,7 @@ async function predict(image25, config3, idx, count2) {
|
|||
}
|
||||
skipped2 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const res = model2 == null ? void 0 : model2.execute(resize);
|
||||
const num = (await res.data())[0];
|
||||
cached[idx] = Math.round(100 * num) / 100;
|
||||
|
|
@ -4682,10 +4672,10 @@ var scaleBoxCoordinates = (box4, factor) => {
|
|||
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
|
||||
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
|
||||
};
|
||||
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
const norm = tf6.div(crop2, 255);
|
||||
tf6.dispose(crop2);
|
||||
return norm;
|
||||
|
|
@ -5190,7 +5180,7 @@ var labels = [
|
|||
// src/object/centernet.ts
|
||||
var model4;
|
||||
var inputSize3 = 0;
|
||||
var last = [];
|
||||
var last2 = [];
|
||||
var lastTime3 = 0;
|
||||
var skipped4 = Number.MAX_SAFE_INTEGER;
|
||||
async function load4(config3) {
|
||||
|
|
@ -5257,9 +5247,9 @@ async function process3(res, outputShape, config3) {
|
|||
async function predict3(input, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
|
||||
const skipFrame = skipped4 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
|
||||
skipped4++;
|
||||
return last;
|
||||
return last2;
|
||||
}
|
||||
skipped4 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5269,7 +5259,7 @@ async function predict3(input, config3) {
|
|||
lastTime3 = now();
|
||||
tf9.dispose(resize);
|
||||
const obj = await process3(objectT, outputSize2, config3);
|
||||
last = obj;
|
||||
last2 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -5342,7 +5332,7 @@ function max2d(inputs, minScore) {
|
|||
return [0, 0, newScore];
|
||||
});
|
||||
}
|
||||
async function predict4(image25, config3) {
|
||||
async function predict4(image24, config3) {
|
||||
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
|
||||
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
|
||||
|
|
@ -5355,7 +5345,7 @@ async function predict4(image25, config3) {
|
|||
const tensor3 = tf10.tidy(() => {
|
||||
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
|
||||
return null;
|
||||
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const enhance3 = tf10.mul(resize, 2);
|
||||
const norm = enhance3.sub(1);
|
||||
return norm;
|
||||
|
|
@ -5367,10 +5357,10 @@ async function predict4(image25, config3) {
|
|||
tf10.dispose(tensor3);
|
||||
if (resT) {
|
||||
cache2.keypoints.length = 0;
|
||||
const squeeze8 = resT.squeeze();
|
||||
const squeeze9 = resT.squeeze();
|
||||
tf10.dispose(resT);
|
||||
const stack4 = squeeze8.unstack(2);
|
||||
tf10.dispose(squeeze8);
|
||||
const stack4 = squeeze9.unstack(2);
|
||||
tf10.dispose(squeeze9);
|
||||
for (let id = 0; id < stack4.length; id++) {
|
||||
const [x2, y2, partScore] = max2d(stack4[id], config3.body.minConfidence);
|
||||
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
|
||||
|
|
@ -5382,8 +5372,8 @@ async function predict4(image25, config3) {
|
|||
y2 / model5.inputs[0].shape[1]
|
||||
],
|
||||
position: [
|
||||
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
|
|
@ -5425,7 +5415,7 @@ async function predict4(image25, config3) {
|
|||
var tf11 = __toModule(require_tfjs_esm());
|
||||
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
|
||||
var model6;
|
||||
var last2 = [];
|
||||
var last3 = [];
|
||||
var lastCount2 = 0;
|
||||
var lastTime5 = 0;
|
||||
var skipped6 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5444,15 +5434,15 @@ async function load6(config3) {
|
|||
log("cached model:", model6["modelUrl"]);
|
||||
return model6;
|
||||
}
|
||||
async function predict5(image25, config3, idx, count2) {
|
||||
async function predict5(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model6)
|
||||
return null;
|
||||
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
|
||||
skipped6++;
|
||||
return last2[idx];
|
||||
return last3[idx];
|
||||
}
|
||||
skipped6 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5461,7 +5451,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
|
||||
const t = {};
|
||||
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
|
||||
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
|
||||
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
|
||||
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
|
||||
t.redNorm = tf11.mul(t.red, rgb[0]);
|
||||
t.greenNorm = tf11.mul(t.green, rgb[1]);
|
||||
|
|
@ -5479,7 +5469,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
obj.sort((a, b) => b.score - a.score);
|
||||
Object.keys(t).forEach((tensor3) => tf11.dispose(t[tensor3]));
|
||||
}
|
||||
last2[idx] = obj;
|
||||
last3[idx] = obj;
|
||||
lastCount2 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5629,7 +5619,7 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
|
|||
var lastTime6 = 0;
|
||||
var enlargeFact = 1.6;
|
||||
async function predict6(input, config3) {
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h;
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
|
||||
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
|
||||
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
|
||||
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
|
||||
|
|
@ -5673,8 +5663,13 @@ async function predict6(input, config3) {
|
|||
rotationMatrix = fixedRotationMatrix;
|
||||
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
|
||||
}
|
||||
if ((_f = config3 == null ? void 0 : config3.filter) == null ? void 0 : _f.equalization) {
|
||||
const equilized = await histogramEqualization(face5.tensor);
|
||||
tf13.dispose(face5.tensor);
|
||||
face5.tensor = equilized;
|
||||
}
|
||||
face5.boxScore = Math.round(100 * box4.confidence) / 100;
|
||||
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
|
||||
if (!((_g = config3.face.mesh) == null ? void 0 : _g.enabled)) {
|
||||
face5.box = getClampedBox(box4, input);
|
||||
face5.boxRaw = getRawBox(box4, input);
|
||||
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
|
||||
|
|
@ -5696,10 +5691,10 @@ async function predict6(input, config3) {
|
|||
const coordsReshaped = tf13.reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = await coordsReshaped.array();
|
||||
tf13.dispose([contourCoords, coordsReshaped, confidence, contours]);
|
||||
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
|
||||
if (face5.faceScore < (((_h = config3.face.detector) == null ? void 0 : _h.minConfidence) || 1)) {
|
||||
box4.confidence = face5.faceScore;
|
||||
} else {
|
||||
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
|
||||
if ((_i = config3.face.iris) == null ? void 0 : _i.enabled)
|
||||
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
|
||||
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
|
||||
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
|
||||
|
|
@ -5740,7 +5735,7 @@ var uvmap = UV468;
|
|||
// src/face/faceres.ts
|
||||
var tf14 = __toModule(require_tfjs_esm());
|
||||
var model9;
|
||||
var last3 = [];
|
||||
var last4 = [];
|
||||
var lastTime7 = 0;
|
||||
var lastCount3 = 0;
|
||||
var skipped8 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5768,15 +5763,15 @@ function enhance2(input) {
|
|||
tf14.dispose(crop2);
|
||||
return norm;
|
||||
}
|
||||
async function predict7(image25, config3, idx, count2) {
|
||||
async function predict7(image24, config3, idx, count2) {
|
||||
var _a, _b, _c, _d;
|
||||
if (!model9)
|
||||
return null;
|
||||
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.age) && ((_d = last4[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
skipped8++;
|
||||
return last3[idx];
|
||||
return last4[idx];
|
||||
}
|
||||
skipped8 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5788,7 +5783,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
descriptor: []
|
||||
};
|
||||
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
|
||||
const enhanced = enhance2(image25);
|
||||
const enhanced = enhance2(image24);
|
||||
const resT = model9 == null ? void 0 : model9.execute(enhanced);
|
||||
lastTime7 = now();
|
||||
tf14.dispose(enhanced);
|
||||
|
|
@ -5810,7 +5805,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
obj.descriptor = Array.from(descriptor);
|
||||
resT.forEach((t) => tf14.dispose(t));
|
||||
}
|
||||
last3[idx] = obj;
|
||||
last4[idx] = obj;
|
||||
lastCount3 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5836,16 +5831,16 @@ function getBoxCenter2(box4) {
|
|||
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
|
||||
];
|
||||
}
|
||||
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const boxes = [[
|
||||
box4.startPoint[1] / h,
|
||||
box4.startPoint[0] / w,
|
||||
box4.endPoint[1] / h,
|
||||
box4.endPoint[0] / w
|
||||
]];
|
||||
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
|
||||
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
|
||||
}
|
||||
function scaleBoxCoordinates2(box4, factor) {
|
||||
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
|
||||
|
|
@ -9028,13 +9023,13 @@ var HandPipeline = class {
|
|||
Math.trunc(coord[2])
|
||||
]);
|
||||
}
|
||||
async estimateHands(image25, config3) {
|
||||
async estimateHands(image24, config3) {
|
||||
let useFreshBox = false;
|
||||
let boxes;
|
||||
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
|
||||
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame) {
|
||||
boxes = await this.handDetector.predict(image25, config3);
|
||||
boxes = await this.handDetector.predict(image24, config3);
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config3.skipAllowed)
|
||||
|
|
@ -9053,8 +9048,8 @@ var HandPipeline = class {
|
|||
if (config3.hand.landmarks) {
|
||||
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
|
||||
const palmCenter = getBoxCenter2(currentBox);
|
||||
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
|
||||
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
|
||||
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
|
||||
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
|
||||
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
|
||||
|
|
@ -9997,7 +9992,7 @@ async function load11(config3) {
|
|||
inputSize7 = 256;
|
||||
return model10;
|
||||
}
|
||||
async function parseSinglePose(res, config3, image25, inputBox) {
|
||||
async function parseSinglePose(res, config3, image24, inputBox) {
|
||||
const kpt4 = res[0][0];
|
||||
const keypoints = [];
|
||||
let score = 0;
|
||||
|
|
@ -10013,15 +10008,15 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
part: kpt3[id],
|
||||
positionRaw,
|
||||
position: [
|
||||
Math.round((image25.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image25.shape[1] || 0) * positionRaw[1])
|
||||
Math.round((image24.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image24.shape[1] || 0) * positionRaw[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
}
|
||||
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
|
||||
const bodies = [];
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10038,7 +10033,7 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
bodies.push(body4);
|
||||
return bodies;
|
||||
}
|
||||
async function parseMultiPose(res, config3, image25, inputBox) {
|
||||
async function parseMultiPose(res, config3, image24, inputBox) {
|
||||
const bodies = [];
|
||||
for (let id = 0; id < res[0].length; id++) {
|
||||
const kpt4 = res[0][id];
|
||||
|
|
@ -10056,11 +10051,11 @@ async function parseMultiPose(res, config3, image25, inputBox) {
|
|||
part: kpt3[i],
|
||||
score: Math.round(100 * score) / 100,
|
||||
positionRaw,
|
||||
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
|
||||
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
|
||||
});
|
||||
}
|
||||
}
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10113,7 +10108,7 @@ async function predict10(input, config3) {
|
|||
// src/object/nanodet.ts
|
||||
var tf22 = __toModule(require_tfjs_esm());
|
||||
var model11;
|
||||
var last4 = [];
|
||||
var last5 = [];
|
||||
var lastTime10 = 0;
|
||||
var skipped11 = Number.MAX_SAFE_INTEGER;
|
||||
var scaleBox = 2.5;
|
||||
|
|
@ -10193,19 +10188,19 @@ async function process4(res, inputSize8, outputShape, config3) {
|
|||
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
|
||||
return results;
|
||||
}
|
||||
async function predict11(image25, config3) {
|
||||
async function predict11(image24, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
|
||||
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last4.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
|
||||
skipped11++;
|
||||
return last4;
|
||||
return last5;
|
||||
}
|
||||
skipped11 = 0;
|
||||
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
|
||||
return last4;
|
||||
return last5;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputSize2 = [image25.shape[2], image25.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
|
||||
const outputSize2 = [image24.shape[2], image24.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
|
||||
const norm = tf22.div(resize, 255);
|
||||
const transpose = norm.transpose([0, 3, 1, 2]);
|
||||
tf22.dispose(norm);
|
||||
|
|
@ -10216,7 +10211,7 @@ async function predict11(image25, config3) {
|
|||
lastTime10 = now();
|
||||
tf22.dispose(transpose);
|
||||
const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
|
||||
last4 = obj;
|
||||
last5 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -10586,7 +10581,7 @@ async function process5(input, background, config3) {
|
|||
busy = true;
|
||||
if (!model13)
|
||||
await load14(config3);
|
||||
const inputImage = process2(input, config3);
|
||||
const inputImage = await process2(input, config3);
|
||||
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
|
||||
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
|
||||
if (!inputImage.tensor)
|
||||
|
|
@ -10637,7 +10632,7 @@ async function process5(input, background, config3) {
|
|||
let mergedCanvas = null;
|
||||
if (background && compositeCanvas) {
|
||||
mergedCanvas = canvas(width, height);
|
||||
const bgImage = process2(background, config3);
|
||||
const bgImage = await process2(background, config3);
|
||||
tf24.dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext("2d");
|
||||
ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
@ -10880,6 +10875,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.sub(op.inputs.a, tf26.mul(tf26.div(op.inputs.a, op.inputs.b), op.inputs.b)))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("mod");
|
||||
}
|
||||
if (!env.kernels.includes("floormod")) {
|
||||
const kernelMod = {
|
||||
|
|
@ -10888,8 +10884,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf26.mod(op.inputs.a, op.inputs.b))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("floormod");
|
||||
}
|
||||
env.updateBackend();
|
||||
}
|
||||
async function check(instance, force = false) {
|
||||
instance.state = "backend";
|
||||
|
|
@ -10976,7 +10972,7 @@ async function check(instance, force = false) {
|
|||
await tf26.ready();
|
||||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = tf26.getBackend();
|
||||
env.updateBackend();
|
||||
await env.updateBackend();
|
||||
registerCustomOps();
|
||||
}
|
||||
return true;
|
||||
|
|
@ -11675,8 +11671,9 @@ var body2 = (res) => {
|
|||
gestures.push({ body: i, gesture: "raise right hand" });
|
||||
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
|
||||
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
|
||||
if (leftShoulder && rightShoulder)
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
@ -13014,7 +13011,7 @@ var Human = class {
|
|||
await this.load();
|
||||
timeStamp = now();
|
||||
this.state = "image";
|
||||
const img = process2(input, this.config);
|
||||
const img = await process2(input, this.config);
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
|
|
@ -13034,7 +13031,7 @@ var Human = class {
|
|||
this.performance.totalFrames++;
|
||||
if (this.config.skipAllowed)
|
||||
this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Check Changed:");
|
||||
let faceRes = [];
|
||||
let bodyRes = [];
|
||||
|
|
|
|||
|
|
@ -152,6 +152,7 @@ var config = {
|
|||
warmup: "full",
|
||||
cacheSensitivity: 0.7,
|
||||
skipAllowed: false,
|
||||
deallocate: false,
|
||||
filter: {
|
||||
enabled: true,
|
||||
equalization: false,
|
||||
|
|
@ -918,8 +919,8 @@ function GLImageFilter() {
|
|||
this.get = function() {
|
||||
return filterChain;
|
||||
};
|
||||
this.apply = function(image25) {
|
||||
resize(image25.width, image25.height);
|
||||
this.apply = function(image24) {
|
||||
resize(image24.width, image24.height);
|
||||
drawCount = 0;
|
||||
if (!sourceTexture)
|
||||
sourceTexture = gl.createTexture();
|
||||
|
|
@ -928,7 +929,7 @@ function GLImageFilter() {
|
|||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
|
||||
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image25);
|
||||
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24);
|
||||
for (let i = 0; i < filterChain.length; i++) {
|
||||
lastInChain = i === filterChain.length - 1;
|
||||
const f = filterChain[i];
|
||||
|
|
@ -936,25 +937,28 @@ function GLImageFilter() {
|
|||
}
|
||||
return fxcanvas;
|
||||
};
|
||||
this.draw = function(image25) {
|
||||
this.draw = function(image24) {
|
||||
this.add("brightness", 0);
|
||||
return this.apply(image25);
|
||||
return this.apply(image24);
|
||||
};
|
||||
}
|
||||
|
||||
// src/image/enhance.ts
|
||||
var tf = __toModule(require_tfjs_esm());
|
||||
function histogramEqualization(input) {
|
||||
const channels = tf.split(input, 3, 2);
|
||||
async function histogramEqualization(inputImage) {
|
||||
const squeeze9 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
|
||||
const channels = tf.split(squeeze9, 3, 2);
|
||||
const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
|
||||
const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
|
||||
const sub6 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const absMax = await Promise.all(max4.map((channel) => channel.data()));
|
||||
const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
|
||||
const sub7 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
|
||||
const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
|
||||
const fact = [tf.div(255, range[0]), tf.div(255, range[1]), tf.div(255, range[2])];
|
||||
const enh = [tf.mul(sub6[0], fact[0]), tf.mul(sub6[1], fact[1]), tf.mul(sub6[2], fact[2])];
|
||||
const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
|
||||
const enh = [tf.mul(sub7[0], fact[0]), tf.mul(sub7[1], fact[1]), tf.mul(sub7[2], fact[2])];
|
||||
const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
|
||||
const reshape8 = tf.reshape(rgb2, [1, input.shape[0], input.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub6, ...range, ...fact, ...enh, rgb2]);
|
||||
const reshape8 = tf.reshape(rgb2, [1, squeeze9.shape[0], squeeze9.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min2, ...max4, ...sub7, ...range, ...fact, ...enh, rgb2, squeeze9]);
|
||||
return reshape8;
|
||||
}
|
||||
|
||||
|
|
@ -964,6 +968,12 @@ var inCanvas = null;
|
|||
var outCanvas = null;
|
||||
var tmpCanvas = null;
|
||||
var fx;
|
||||
var last = {
|
||||
inputSum: 0,
|
||||
cacheDiff: 1,
|
||||
sumMethod: 0,
|
||||
inputTensor: void 0
|
||||
};
|
||||
function canvas(width, height) {
|
||||
let c;
|
||||
if (env.browser) {
|
||||
|
|
@ -990,7 +1000,7 @@ function copy(input, output) {
|
|||
ctx.drawImage(input, 0, 0);
|
||||
return outputCanvas;
|
||||
}
|
||||
function process2(input, config3, getTensor = true) {
|
||||
async function process2(input, config3, getTensor = true) {
|
||||
if (!input) {
|
||||
if (config3.debug)
|
||||
log("input is missing");
|
||||
|
|
@ -1040,7 +1050,7 @@ function process2(input, config3, getTensor = true) {
|
|||
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
|
||||
if (!targetWidth || !targetHeight)
|
||||
throw new Error("input cannot determine dimension");
|
||||
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
|
||||
if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
|
||||
inCanvas = canvas(targetWidth, targetHeight);
|
||||
const inCtx = inCanvas.getContext("2d");
|
||||
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
|
||||
|
|
@ -1049,13 +1059,13 @@ function process2(input, config3, getTensor = true) {
|
|||
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
|
||||
inCtx.translate(originalWidth, 0);
|
||||
inCtx.scale(-1, 1);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
inCtx.setTransform(1, 0, 0, 1, 0, 0);
|
||||
} else {
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
|
||||
}
|
||||
}
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
|
||||
if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
|
||||
outCanvas = canvas(inCanvas.width, inCanvas.height);
|
||||
if (config3.filter.enabled && env.webgl.supported) {
|
||||
if (!fx)
|
||||
|
|
@ -1143,51 +1153,31 @@ function process2(input, config3, getTensor = true) {
|
|||
if (!pixels)
|
||||
throw new Error("cannot create tensor from input");
|
||||
const casted = tf2.cast(pixels, "float32");
|
||||
const tensor3 = config3.filter.equalization ? histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
const tensor3 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
|
||||
tf2.dispose([pixels, casted]);
|
||||
return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null };
|
||||
}
|
||||
}
|
||||
var lastInputSum = 0;
|
||||
var lastCacheDiff = 1;
|
||||
var benchmarked = 0;
|
||||
var checksum = async (input) => {
|
||||
const resizeFact = 48;
|
||||
const reduced = tf2.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);
|
||||
const tfSum = async () => {
|
||||
const sumT = tf2.sum(reduced);
|
||||
const sum0 = await sumT.data();
|
||||
tf2.dispose(sumT);
|
||||
return sum0[0];
|
||||
};
|
||||
const jsSum = async () => {
|
||||
const reducedData = await reduced.data();
|
||||
let sum0 = 0;
|
||||
for (let i = 0; i < reducedData.length / 3; i++)
|
||||
sum0 += reducedData[3 * i + 2];
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
tf2.dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
async function skip(config3, input) {
|
||||
let skipFrame = false;
|
||||
if (config3.cacheSensitivity === 0)
|
||||
return false;
|
||||
const sum2 = await checksum(input);
|
||||
const diff = 100 * (Math.max(sum2, lastInputSum) / Math.min(sum2, lastInputSum) - 1);
|
||||
lastInputSum = sum2;
|
||||
let skipFrame = diff < Math.max(config3.cacheSensitivity, lastCacheDiff);
|
||||
lastCacheDiff = diff > 10 * config3.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && lastCacheDiff > 0;
|
||||
return skipFrame;
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
|
||||
tf2.dispose(last.inputTensor);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
} else {
|
||||
const t = {};
|
||||
t.diff = tf2.sub(input, last.inputTensor);
|
||||
t.squared = tf2.mul(t.diff, t.diff);
|
||||
t.sum = tf2.sum(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
|
||||
tf2.dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = tf2.clone(input);
|
||||
skipFrame = diffRelative <= config3.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
||||
|
|
@ -1349,7 +1339,7 @@ async function load2(config3) {
|
|||
log("cached model:", model2["modelUrl"]);
|
||||
return model2;
|
||||
}
|
||||
async function predict(image25, config3, idx, count2) {
|
||||
async function predict(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model2)
|
||||
return null;
|
||||
|
|
@ -1361,7 +1351,7 @@ async function predict(image25, config3, idx, count2) {
|
|||
}
|
||||
skipped2 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
const resize = tf5.image.resizeBilinear(image25, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const resize = tf5.image.resizeBilinear(image24, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
|
||||
const res = model2 == null ? void 0 : model2.execute(resize);
|
||||
const num = (await res.data())[0];
|
||||
cached[idx] = Math.round(100 * num) / 100;
|
||||
|
|
@ -4681,10 +4671,10 @@ var scaleBoxCoordinates = (box4, factor) => {
|
|||
const endPoint = [box4.endPoint[0] * factor[0], box4.endPoint[1] * factor[1]];
|
||||
return { startPoint, endPoint, landmarks: box4.landmarks, confidence: box4.confidence };
|
||||
};
|
||||
var cutBoxFromImageAndResize = (box4, image25, cropSize) => {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image25, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
var cutBoxFromImageAndResize = (box4, image24, cropSize) => {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const crop2 = tf6.image.cropAndResize(image24, [[box4.startPoint[1] / h, box4.startPoint[0] / w, box4.endPoint[1] / h, box4.endPoint[0] / w]], [0], cropSize);
|
||||
const norm = tf6.div(crop2, 255);
|
||||
tf6.dispose(crop2);
|
||||
return norm;
|
||||
|
|
@ -5189,7 +5179,7 @@ var labels = [
|
|||
// src/object/centernet.ts
|
||||
var model4;
|
||||
var inputSize3 = 0;
|
||||
var last = [];
|
||||
var last2 = [];
|
||||
var lastTime3 = 0;
|
||||
var skipped4 = Number.MAX_SAFE_INTEGER;
|
||||
async function load4(config3) {
|
||||
|
|
@ -5256,9 +5246,9 @@ async function process3(res, outputShape, config3) {
|
|||
async function predict3(input, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
|
||||
const skipFrame = skipped4 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
|
||||
skipped4++;
|
||||
return last;
|
||||
return last2;
|
||||
}
|
||||
skipped4 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5268,7 +5258,7 @@ async function predict3(input, config3) {
|
|||
lastTime3 = now();
|
||||
tf9.dispose(resize);
|
||||
const obj = await process3(objectT, outputSize2, config3);
|
||||
last = obj;
|
||||
last2 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -5341,7 +5331,7 @@ function max2d(inputs, minScore) {
|
|||
return [0, 0, newScore];
|
||||
});
|
||||
}
|
||||
async function predict4(image25, config3) {
|
||||
async function predict4(image24, config3) {
|
||||
const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
|
||||
const skipFrame = skipped5 < (config3.body.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
|
||||
|
|
@ -5354,7 +5344,7 @@ async function predict4(image25, config3) {
|
|||
const tensor3 = tf10.tidy(() => {
|
||||
if (!(model5 == null ? void 0 : model5.inputs[0].shape))
|
||||
return null;
|
||||
const resize = tf10.image.resizeBilinear(image25, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const resize = tf10.image.resizeBilinear(image24, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
|
||||
const enhance3 = tf10.mul(resize, 2);
|
||||
const norm = enhance3.sub(1);
|
||||
return norm;
|
||||
|
|
@ -5366,10 +5356,10 @@ async function predict4(image25, config3) {
|
|||
tf10.dispose(tensor3);
|
||||
if (resT) {
|
||||
cache2.keypoints.length = 0;
|
||||
const squeeze8 = resT.squeeze();
|
||||
const squeeze9 = resT.squeeze();
|
||||
tf10.dispose(resT);
|
||||
const stack4 = squeeze8.unstack(2);
|
||||
tf10.dispose(squeeze8);
|
||||
const stack4 = squeeze9.unstack(2);
|
||||
tf10.dispose(squeeze9);
|
||||
for (let id = 0; id < stack4.length; id++) {
|
||||
const [x2, y2, partScore] = max2d(stack4[id], config3.body.minConfidence);
|
||||
if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
|
||||
|
|
@ -5381,8 +5371,8 @@ async function predict4(image25, config3) {
|
|||
y2 / model5.inputs[0].shape[1]
|
||||
],
|
||||
position: [
|
||||
Math.round(image25.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image25.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
Math.round(image24.shape[2] * x2 / model5.inputs[0].shape[2]),
|
||||
Math.round(image24.shape[1] * y2 / model5.inputs[0].shape[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
|
|
@ -5424,7 +5414,7 @@ async function predict4(image25, config3) {
|
|||
var tf11 = __toModule(require_tfjs_esm());
|
||||
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
|
||||
var model6;
|
||||
var last2 = [];
|
||||
var last3 = [];
|
||||
var lastCount2 = 0;
|
||||
var lastTime5 = 0;
|
||||
var skipped6 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5443,15 +5433,15 @@ async function load6(config3) {
|
|||
log("cached model:", model6["modelUrl"]);
|
||||
return model6;
|
||||
}
|
||||
async function predict5(image25, config3, idx, count2) {
|
||||
async function predict5(image24, config3, idx, count2) {
|
||||
var _a, _b;
|
||||
if (!model6)
|
||||
return null;
|
||||
const skipFrame = skipped6 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last2[idx] && last2[idx].length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
|
||||
skipped6++;
|
||||
return last2[idx];
|
||||
return last3[idx];
|
||||
}
|
||||
skipped6 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5460,7 +5450,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
|
||||
const t = {};
|
||||
const inputSize8 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
|
||||
t.resize = tf11.image.resizeBilinear(image25, [inputSize8, inputSize8], false);
|
||||
t.resize = tf11.image.resizeBilinear(image24, [inputSize8, inputSize8], false);
|
||||
[t.red, t.green, t.blue] = tf11.split(t.resize, 3, 3);
|
||||
t.redNorm = tf11.mul(t.red, rgb[0]);
|
||||
t.greenNorm = tf11.mul(t.green, rgb[1]);
|
||||
|
|
@ -5478,7 +5468,7 @@ async function predict5(image25, config3, idx, count2) {
|
|||
obj.sort((a, b) => b.score - a.score);
|
||||
Object.keys(t).forEach((tensor3) => tf11.dispose(t[tensor3]));
|
||||
}
|
||||
last2[idx] = obj;
|
||||
last3[idx] = obj;
|
||||
lastCount2 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5628,7 +5618,7 @@ var skipped7 = Number.MAX_SAFE_INTEGER;
|
|||
var lastTime6 = 0;
|
||||
var enlargeFact = 1.6;
|
||||
async function predict6(input, config3) {
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h;
|
||||
var _a, _b, _c, _d, _e, _f, _g, _h, _i;
|
||||
const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6;
|
||||
const skipFrame = skipped7 < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
|
||||
if (!config3.skipAllowed || !skipTime || !skipFrame || boxCache.length === 0) {
|
||||
|
|
@ -5672,8 +5662,13 @@ async function predict6(input, config3) {
|
|||
rotationMatrix = fixedRotationMatrix;
|
||||
face5.tensor = cutBoxFromImageAndResize(box4, input, ((_e = config3.face.mesh) == null ? void 0 : _e.enabled) ? [inputSize5, inputSize5] : [size(), size()]);
|
||||
}
|
||||
if ((_f = config3 == null ? void 0 : config3.filter) == null ? void 0 : _f.equalization) {
|
||||
const equilized = await histogramEqualization(face5.tensor);
|
||||
tf13.dispose(face5.tensor);
|
||||
face5.tensor = equilized;
|
||||
}
|
||||
face5.boxScore = Math.round(100 * box4.confidence) / 100;
|
||||
if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
|
||||
if (!((_g = config3.face.mesh) == null ? void 0 : _g.enabled)) {
|
||||
face5.box = getClampedBox(box4, input);
|
||||
face5.boxRaw = getRawBox(box4, input);
|
||||
face5.boxScore = Math.round(100 * box4.confidence || 0) / 100;
|
||||
|
|
@ -5695,10 +5690,10 @@ async function predict6(input, config3) {
|
|||
const coordsReshaped = tf13.reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = await coordsReshaped.array();
|
||||
tf13.dispose([contourCoords, coordsReshaped, confidence, contours]);
|
||||
if (face5.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
|
||||
if (face5.faceScore < (((_h = config3.face.detector) == null ? void 0 : _h.minConfidence) || 1)) {
|
||||
box4.confidence = face5.faceScore;
|
||||
} else {
|
||||
if ((_h = config3.face.iris) == null ? void 0 : _h.enabled)
|
||||
if ((_i = config3.face.iris) == null ? void 0 : _i.enabled)
|
||||
rawCoords = await augmentIris(rawCoords, face5.tensor, config3, inputSize5);
|
||||
face5.mesh = transformRawCoords(rawCoords, box4, angle, rotationMatrix, inputSize5);
|
||||
face5.meshRaw = face5.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize5]);
|
||||
|
|
@ -5739,7 +5734,7 @@ var uvmap = UV468;
|
|||
// src/face/faceres.ts
|
||||
var tf14 = __toModule(require_tfjs_esm());
|
||||
var model9;
|
||||
var last3 = [];
|
||||
var last4 = [];
|
||||
var lastTime7 = 0;
|
||||
var lastCount3 = 0;
|
||||
var skipped8 = Number.MAX_SAFE_INTEGER;
|
||||
|
|
@ -5767,15 +5762,15 @@ function enhance2(input) {
|
|||
tf14.dispose(crop2);
|
||||
return norm;
|
||||
}
|
||||
async function predict7(image25, config3, idx, count2) {
|
||||
async function predict7(image24, config3, idx, count2) {
|
||||
var _a, _b, _c, _d;
|
||||
if (!model9)
|
||||
return null;
|
||||
const skipFrame = skipped8 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
|
||||
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.age) && ((_d = last4[idx]) == null ? void 0 : _d.age) > 0) {
|
||||
skipped8++;
|
||||
return last3[idx];
|
||||
return last4[idx];
|
||||
}
|
||||
skipped8 = 0;
|
||||
return new Promise(async (resolve) => {
|
||||
|
|
@ -5787,7 +5782,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
descriptor: []
|
||||
};
|
||||
if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
|
||||
const enhanced = enhance2(image25);
|
||||
const enhanced = enhance2(image24);
|
||||
const resT = model9 == null ? void 0 : model9.execute(enhanced);
|
||||
lastTime7 = now();
|
||||
tf14.dispose(enhanced);
|
||||
|
|
@ -5809,7 +5804,7 @@ async function predict7(image25, config3, idx, count2) {
|
|||
obj.descriptor = Array.from(descriptor);
|
||||
resT.forEach((t) => tf14.dispose(t));
|
||||
}
|
||||
last3[idx] = obj;
|
||||
last4[idx] = obj;
|
||||
lastCount3 = count2;
|
||||
resolve(obj);
|
||||
});
|
||||
|
|
@ -5835,16 +5830,16 @@ function getBoxCenter2(box4) {
|
|||
box4.startPoint[1] + (box4.endPoint[1] - box4.startPoint[1]) / 2
|
||||
];
|
||||
}
|
||||
function cutBoxFromImageAndResize2(box4, image25, cropSize) {
|
||||
const h = image25.shape[1];
|
||||
const w = image25.shape[2];
|
||||
function cutBoxFromImageAndResize2(box4, image24, cropSize) {
|
||||
const h = image24.shape[1];
|
||||
const w = image24.shape[2];
|
||||
const boxes = [[
|
||||
box4.startPoint[1] / h,
|
||||
box4.startPoint[0] / w,
|
||||
box4.endPoint[1] / h,
|
||||
box4.endPoint[0] / w
|
||||
]];
|
||||
return tf15.image.cropAndResize(image25, boxes, [0], cropSize);
|
||||
return tf15.image.cropAndResize(image24, boxes, [0], cropSize);
|
||||
}
|
||||
function scaleBoxCoordinates2(box4, factor) {
|
||||
const startPoint = [box4.startPoint[0] * factor[0], box4.startPoint[1] * factor[1]];
|
||||
|
|
@ -9027,13 +9022,13 @@ var HandPipeline = class {
|
|||
Math.trunc(coord[2])
|
||||
]);
|
||||
}
|
||||
async estimateHands(image25, config3) {
|
||||
async estimateHands(image24, config3) {
|
||||
let useFreshBox = false;
|
||||
let boxes;
|
||||
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
|
||||
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame) {
|
||||
boxes = await this.handDetector.predict(image25, config3);
|
||||
boxes = await this.handDetector.predict(image24, config3);
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config3.skipAllowed)
|
||||
|
|
@ -9052,8 +9047,8 @@ var HandPipeline = class {
|
|||
if (config3.hand.landmarks) {
|
||||
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
|
||||
const palmCenter = getBoxCenter2(currentBox);
|
||||
const palmCenterNormalized = [palmCenter[0] / image25.shape[2], palmCenter[1] / image25.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image25, angle, 0, palmCenterNormalized) : image25.clone();
|
||||
const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]];
|
||||
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf17.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone();
|
||||
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
|
||||
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
|
||||
const croppedInput = cutBoxFromImageAndResize2(newBox, rotatedImage, [this.inputSize, this.inputSize]);
|
||||
|
|
@ -9996,7 +9991,7 @@ async function load11(config3) {
|
|||
inputSize7 = 256;
|
||||
return model10;
|
||||
}
|
||||
async function parseSinglePose(res, config3, image25, inputBox) {
|
||||
async function parseSinglePose(res, config3, image24, inputBox) {
|
||||
const kpt4 = res[0][0];
|
||||
const keypoints = [];
|
||||
let score = 0;
|
||||
|
|
@ -10012,15 +10007,15 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
part: kpt3[id],
|
||||
positionRaw,
|
||||
position: [
|
||||
Math.round((image25.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image25.shape[1] || 0) * positionRaw[1])
|
||||
Math.round((image24.shape[2] || 0) * positionRaw[0]),
|
||||
Math.round((image24.shape[1] || 0) * positionRaw[1])
|
||||
]
|
||||
});
|
||||
}
|
||||
}
|
||||
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
|
||||
const bodies = [];
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10037,7 +10032,7 @@ async function parseSinglePose(res, config3, image25, inputBox) {
|
|||
bodies.push(body4);
|
||||
return bodies;
|
||||
}
|
||||
async function parseMultiPose(res, config3, image25, inputBox) {
|
||||
async function parseMultiPose(res, config3, image24, inputBox) {
|
||||
const bodies = [];
|
||||
for (let id = 0; id < res[0].length; id++) {
|
||||
const kpt4 = res[0][id];
|
||||
|
|
@ -10055,11 +10050,11 @@ async function parseMultiPose(res, config3, image25, inputBox) {
|
|||
part: kpt3[i],
|
||||
score: Math.round(100 * score) / 100,
|
||||
positionRaw,
|
||||
position: [Math.round((image25.shape[2] || 0) * positionRaw[0]), Math.round((image25.shape[1] || 0) * positionRaw[1])]
|
||||
position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])]
|
||||
});
|
||||
}
|
||||
}
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image25.shape[2], image25.shape[1]]);
|
||||
const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]);
|
||||
const annotations2 = {};
|
||||
for (const [name, indexes] of Object.entries(connected3)) {
|
||||
const pt = [];
|
||||
|
|
@ -10112,7 +10107,7 @@ async function predict10(input, config3) {
|
|||
// src/object/nanodet.ts
|
||||
var tf22 = __toModule(require_tfjs_esm());
|
||||
var model11;
|
||||
var last4 = [];
|
||||
var last5 = [];
|
||||
var lastTime10 = 0;
|
||||
var skipped11 = Number.MAX_SAFE_INTEGER;
|
||||
var scaleBox = 2.5;
|
||||
|
|
@ -10192,19 +10187,19 @@ async function process4(res, inputSize8, outputShape, config3) {
|
|||
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
|
||||
return results;
|
||||
}
|
||||
async function predict11(image25, config3) {
|
||||
async function predict11(image24, config3) {
|
||||
const skipTime = (config3.object.skipTime || 0) > now() - lastTime10;
|
||||
const skipFrame = skipped11 < (config3.object.skipFrames || 0);
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last4.length > 0) {
|
||||
if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
|
||||
skipped11++;
|
||||
return last4;
|
||||
return last5;
|
||||
}
|
||||
skipped11 = 0;
|
||||
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
|
||||
return last4;
|
||||
return last5;
|
||||
return new Promise(async (resolve) => {
|
||||
const outputSize2 = [image25.shape[2], image25.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image25, [model11.inputSize, model11.inputSize], false);
|
||||
const outputSize2 = [image24.shape[2], image24.shape[1]];
|
||||
const resize = tf22.image.resizeBilinear(image24, [model11.inputSize, model11.inputSize], false);
|
||||
const norm = tf22.div(resize, 255);
|
||||
const transpose = norm.transpose([0, 3, 1, 2]);
|
||||
tf22.dispose(norm);
|
||||
|
|
@ -10215,7 +10210,7 @@ async function predict11(image25, config3) {
|
|||
lastTime10 = now();
|
||||
tf22.dispose(transpose);
|
||||
const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
|
||||
last4 = obj;
|
||||
last5 = obj;
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
@ -10585,7 +10580,7 @@ async function process5(input, background, config3) {
|
|||
busy = true;
|
||||
if (!model13)
|
||||
await load14(config3);
|
||||
const inputImage = process2(input, config3);
|
||||
const inputImage = await process2(input, config3);
|
||||
const width = ((_a = inputImage.canvas) == null ? void 0 : _a.width) || 0;
|
||||
const height = ((_b = inputImage.canvas) == null ? void 0 : _b.height) || 0;
|
||||
if (!inputImage.tensor)
|
||||
|
|
@ -10636,7 +10631,7 @@ async function process5(input, background, config3) {
|
|||
let mergedCanvas = null;
|
||||
if (background && compositeCanvas) {
|
||||
mergedCanvas = canvas(width, height);
|
||||
const bgImage = process2(background, config3);
|
||||
const bgImage = await process2(background, config3);
|
||||
tf24.dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext("2d");
|
||||
ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
@ -10879,6 +10874,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.sub(op.inputs.a, tf26.mul(tf26.div(op.inputs.a, op.inputs.b), op.inputs.b)))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("mod");
|
||||
}
|
||||
if (!env.kernels.includes("floormod")) {
|
||||
const kernelMod = {
|
||||
|
|
@ -10887,8 +10883,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf26.tidy(() => tf26.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf26.mod(op.inputs.a, op.inputs.b))
|
||||
};
|
||||
tf26.registerKernel(kernelMod);
|
||||
env.kernels.push("floormod");
|
||||
}
|
||||
env.updateBackend();
|
||||
}
|
||||
async function check(instance, force = false) {
|
||||
instance.state = "backend";
|
||||
|
|
@ -10975,7 +10971,7 @@ async function check(instance, force = false) {
|
|||
await tf26.ready();
|
||||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = tf26.getBackend();
|
||||
env.updateBackend();
|
||||
await env.updateBackend();
|
||||
registerCustomOps();
|
||||
}
|
||||
return true;
|
||||
|
|
@ -11674,8 +11670,9 @@ var body2 = (res) => {
|
|||
gestures.push({ body: i, gesture: "raise right hand" });
|
||||
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
|
||||
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
|
||||
if (leftShoulder && rightShoulder)
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
@ -13013,7 +13010,7 @@ var Human = class {
|
|||
await this.load();
|
||||
timeStamp = now();
|
||||
this.state = "image";
|
||||
const img = process2(input, this.config);
|
||||
const img = await process2(input, this.config);
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
|
|
@ -13033,7 +13030,7 @@ var Human = class {
|
|||
this.performance.totalFrames++;
|
||||
if (this.config.skipAllowed)
|
||||
this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze("Check Changed:");
|
||||
let faceRes = [];
|
||||
let bodyRes = [];
|
||||
|
|
|
|||
|
|
@ -69864,7 +69864,7 @@ registerBackend("wasm", async () => {
|
|||
const { wasm } = await init();
|
||||
return new BackendWasm(wasm);
|
||||
}, WASM_PRIORITY);
|
||||
var externalVersion = "3.11.0-20211105";
|
||||
var externalVersion = "3.11.0-20211106";
|
||||
var version8 = {
|
||||
tfjs: externalVersion,
|
||||
"tfjs-core": externalVersion,
|
||||
|
|
|
|||
|
|
@ -74,7 +74,7 @@
|
|||
"canvas": "^2.8.0",
|
||||
"dayjs": "^1.10.7",
|
||||
"esbuild": "^0.13.12",
|
||||
"eslint": "8.1.0",
|
||||
"eslint": "8.2.0",
|
||||
"eslint-config-airbnb-base": "^14.2.1",
|
||||
"eslint-plugin-html": "^6.2.0",
|
||||
"eslint-plugin-import": "^2.25.2",
|
||||
|
|
|
|||
|
|
@ -122,7 +122,9 @@ export interface SegmentationConfig extends GenericConfig {
|
|||
export interface FilterConfig {
|
||||
/** @property are image filters enabled? */
|
||||
enabled: boolean,
|
||||
/** @property perform image histogram equalization */
|
||||
/** @property perform image histogram equalization
|
||||
* - equalization is performed on input as a whole and detected face before its passed for further analysis
|
||||
*/
|
||||
equalization: boolean,
|
||||
/** resize input width
|
||||
* - if both width and height are set to 0, there is no resizing
|
||||
|
|
@ -229,6 +231,9 @@ export interface Config {
|
|||
*/
|
||||
cacheSensitivity: number;
|
||||
|
||||
/** Perform immediate garbage collection on deallocated tensors instead of caching them */
|
||||
deallocate: boolean;
|
||||
|
||||
/** Internal Variable */
|
||||
skipAllowed: boolean;
|
||||
|
||||
|
|
@ -264,6 +269,7 @@ const config: Config = {
|
|||
warmup: 'full',
|
||||
cacheSensitivity: 0.70,
|
||||
skipAllowed: false,
|
||||
deallocate: false,
|
||||
filter: {
|
||||
enabled: true,
|
||||
equalization: false,
|
||||
|
|
|
|||
|
|
@ -13,10 +13,11 @@ import * as blazeface from './blazeface';
|
|||
import * as util from './facemeshutil';
|
||||
import * as coords from './facemeshcoords';
|
||||
import * as iris from './iris';
|
||||
import { histogramEqualization } from '../image/enhance';
|
||||
import { env } from '../util/env';
|
||||
import type { GraphModel, Tensor } from '../tfjs/types';
|
||||
import type { FaceResult, Point } from '../result';
|
||||
import type { Config } from '../config';
|
||||
import { env } from '../util/env';
|
||||
|
||||
type BoxCache = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };
|
||||
let boxCache: Array<BoxCache> = [];
|
||||
|
|
@ -73,6 +74,11 @@ export async function predict(input: Tensor, config: Config): Promise<FaceResult
|
|||
rotationMatrix = util.fixedRotationMatrix;
|
||||
face.tensor = util.cutBoxFromImageAndResize(box, input, config.face.mesh?.enabled ? [inputSize, inputSize] : [blazeface.size(), blazeface.size()]);
|
||||
}
|
||||
if (config?.filter?.equalization) {
|
||||
const equilized = await histogramEqualization(face.tensor as Tensor);
|
||||
tf.dispose(face.tensor);
|
||||
face.tensor = equilized;
|
||||
}
|
||||
face.boxScore = Math.round(100 * box.confidence) / 100;
|
||||
if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only
|
||||
face.box = util.getClampedBox(box, input);
|
||||
|
|
|
|||
|
|
@ -41,6 +41,9 @@ export function enhance(input): Tensor {
|
|||
if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing
|
||||
// do a tight crop of image and resize it to fit the model
|
||||
const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
|
||||
const norm = tf.mul(crop, 255);
|
||||
tf.dispose(crop);
|
||||
return norm;
|
||||
/*
|
||||
const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right
|
||||
const crop = (tensor.shape.length === 3)
|
||||
|
|
@ -78,9 +81,6 @@ export function enhance(input): Tensor {
|
|||
const darken = crop.sub(crop.min());
|
||||
const lighten = darken.div(darken.max());
|
||||
*/
|
||||
const norm = tf.mul(crop, 255);
|
||||
tf.dispose(crop);
|
||||
return norm;
|
||||
}
|
||||
|
||||
export async function predict(image: Tensor, config: Config, idx, count) {
|
||||
|
|
|
|||
|
|
@ -54,7 +54,9 @@ export const body = (res): GestureResult[] => {
|
|||
// leaning
|
||||
const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));
|
||||
const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));
|
||||
if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });
|
||||
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
|
||||
gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });
|
||||
}
|
||||
}
|
||||
return gestures;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -405,7 +405,7 @@ export class Human {
|
|||
|
||||
timeStamp = now();
|
||||
this.state = 'image';
|
||||
const img = image.process(input, this.config) as { canvas: HTMLCanvasElement | OffscreenCanvas, tensor: Tensor };
|
||||
const img = await image.process(input, this.config) as { canvas: HTMLCanvasElement | OffscreenCanvas, tensor: Tensor };
|
||||
this.process = img;
|
||||
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze('Get Image:');
|
||||
|
|
@ -423,7 +423,7 @@ export class Human {
|
|||
if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;
|
||||
(this.performance.totalFrames as number)++;
|
||||
if (this.config.skipAllowed) this.performance.cachedFrames++;
|
||||
this.performance.inputCheck = this.env.perfadd ? (this.performance.inputCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
|
||||
this.analyze('Check Changed:');
|
||||
|
||||
// prepare where to store model results
|
||||
|
|
|
|||
|
|
@ -5,16 +5,20 @@
|
|||
import * as tf from '../../dist/tfjs.esm.js';
|
||||
import type { Tensor } from '../exports';
|
||||
|
||||
export function histogramEqualization(input: Tensor): Tensor {
|
||||
const channels = tf.split(input, 3, 2);
|
||||
export async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {
|
||||
// const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors
|
||||
const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
|
||||
const channels = tf.split(squeeze, 3, 2);
|
||||
const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
|
||||
const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
|
||||
const absMax = await Promise.all(max.map((channel) => channel.data()));
|
||||
const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
|
||||
const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];
|
||||
const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];
|
||||
const fact = [tf.div(255, range[0]), tf.div(255, range[1]), tf.div(255, range[2])];
|
||||
const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
|
||||
const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];
|
||||
const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);
|
||||
const reshape = tf.reshape(rgb, [1, input.shape[0], input.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb]);
|
||||
return reshape;
|
||||
const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);
|
||||
tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);
|
||||
return reshape; // output shape is [1, height, width, 3]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import * as tf from '../../dist/tfjs.esm.js';
|
|||
import * as fxImage from './imagefx';
|
||||
import type { Input, AnyCanvas, Tensor, Config } from '../exports';
|
||||
import { env } from '../util/env';
|
||||
import { log, now } from '../util/util';
|
||||
import { log } from '../util/util';
|
||||
import * as enhance from './enhance';
|
||||
|
||||
const maxSize = 2048;
|
||||
|
|
@ -17,6 +17,13 @@ let tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreati
|
|||
// @ts-ignore // imagefx is js module that should be converted to a class
|
||||
let fx: fxImage.GLImageFilter | null; // instance of imagefx
|
||||
|
||||
const last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {
|
||||
inputSum: 0,
|
||||
cacheDiff: 1,
|
||||
sumMethod: 0,
|
||||
inputTensor: undefined,
|
||||
};
|
||||
|
||||
export function canvas(width, height): AnyCanvas {
|
||||
let c;
|
||||
if (env.browser) { // browser defines canvas object
|
||||
|
|
@ -48,7 +55,7 @@ export function copy(input: AnyCanvas, output?: AnyCanvas) {
|
|||
// 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: Input, config: Config, getTensor: boolean = true): { tensor: Tensor | null, canvas: AnyCanvas | null } {
|
||||
export async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {
|
||||
if (!input) {
|
||||
// throw new Error('input is missing');
|
||||
if (config.debug) log('input is missing');
|
||||
|
|
@ -108,7 +115,7 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
|
|||
if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;
|
||||
else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);
|
||||
if (!targetWidth || !targetHeight) throw new Error('input cannot determine dimension');
|
||||
if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);
|
||||
if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);
|
||||
|
||||
// draw input to our canvas
|
||||
const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;
|
||||
|
|
@ -118,14 +125,14 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
|
|||
if (config.filter.flip && typeof inCtx.translate !== 'undefined') {
|
||||
inCtx.translate(originalWidth, 0);
|
||||
inCtx.scale(-1, 1);
|
||||
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
|
||||
inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults
|
||||
} else {
|
||||
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
|
||||
inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);
|
||||
}
|
||||
}
|
||||
|
||||
if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas
|
||||
if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas
|
||||
|
||||
// imagefx transforms using gl from input canvas to output canvas
|
||||
if (config.filter.enabled && env.webgl.supported) {
|
||||
|
|
@ -192,26 +199,16 @@ export function process(input: Input, config: Config, getTensor: boolean = true)
|
|||
const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel
|
||||
tf.dispose(pixels);
|
||||
pixels = rgb;
|
||||
/*
|
||||
const channels = tf.split(pixels, 4, 2); // split rgba to channels
|
||||
tf.dispose(pixels);
|
||||
const rgb = tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
|
||||
pixels = tf.reshape(rgb, [rgb.shape[0], rgb.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
|
||||
tf.dispose([rgb, ...channels]);
|
||||
*/
|
||||
}
|
||||
if (!pixels) throw new Error('cannot create tensor from input');
|
||||
const casted = tf.cast(pixels, 'float32');
|
||||
const tensor = config.filter.equalization ? enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);
|
||||
const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);
|
||||
tf.dispose([pixels, casted]);
|
||||
return { tensor, canvas: (config.filter.return ? outCanvas : null) };
|
||||
}
|
||||
}
|
||||
|
||||
let lastInputSum = 0;
|
||||
let lastCacheDiff = 1;
|
||||
let benchmarked = 0;
|
||||
|
||||
/*
|
||||
const checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster
|
||||
const resizeFact = 48;
|
||||
const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);
|
||||
|
|
@ -227,29 +224,51 @@ const checksum = async (input: Tensor): Promise<number> => { // use tf sum or js
|
|||
for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel
|
||||
return sum0;
|
||||
};
|
||||
if (benchmarked === 0) {
|
||||
if (last.sumMethod === 0) {
|
||||
const t0 = now();
|
||||
await jsSum();
|
||||
const t1 = now();
|
||||
await tfSum();
|
||||
const t2 = now();
|
||||
benchmarked = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;
|
||||
}
|
||||
const res = benchmarked === 1 ? await jsSum() : await tfSum();
|
||||
const res = last.sumMethod === 1 ? await jsSum() : await tfSum();
|
||||
tf.dispose(reduced);
|
||||
return res;
|
||||
};
|
||||
*/
|
||||
|
||||
export async function skip(config, input: Tensor) {
|
||||
if (config.cacheSensitivity === 0) return false;
|
||||
const sum = await checksum(input);
|
||||
const diff = 100 * (Math.max(sum, lastInputSum) / Math.min(sum, lastInputSum) - 1);
|
||||
lastInputSum = sum;
|
||||
let skipFrame = false;
|
||||
if (config.cacheSensitivity === 0) return skipFrame;
|
||||
|
||||
/*
|
||||
const checkSum = await checksum(input);
|
||||
const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);
|
||||
last.inputSum = checkSum;
|
||||
// if previous frame was skipped, skip this frame if changed more than cacheSensitivity
|
||||
// if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily
|
||||
let skipFrame = diff < Math.max(config.cacheSensitivity, lastCacheDiff);
|
||||
let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);
|
||||
// if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images
|
||||
lastCacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && (lastCacheDiff > 0); // if no cached diff value then force no skip
|
||||
last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;
|
||||
skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip
|
||||
*/
|
||||
|
||||
if (!last.inputTensor) {
|
||||
last.inputTensor = tf.clone(input);
|
||||
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed
|
||||
tf.dispose(last.inputTensor);
|
||||
last.inputTensor = tf.clone(input);
|
||||
} else {
|
||||
const t: Record<string, Tensor> = {};
|
||||
t.diff = tf.sub(input, last.inputTensor);
|
||||
t.squared = tf.mul(t.diff, t.diff);
|
||||
t.sum = tf.sum(t.squared);
|
||||
const diffSum = await t.sum.data();
|
||||
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel
|
||||
tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);
|
||||
last.inputTensor = tf.clone(input);
|
||||
skipFrame = diffRelative <= config.cacheSensitivity;
|
||||
}
|
||||
return skipFrame;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ export async function process(input: Input, background: Input | undefined, confi
|
|||
if (busy) return { data: [], canvas: null, alpha: null };
|
||||
busy = true;
|
||||
if (!model) await load(config);
|
||||
const inputImage = image.process(input, config);
|
||||
const inputImage = await image.process(input, config);
|
||||
const width = inputImage.canvas?.width || 0;
|
||||
const height = inputImage.canvas?.height || 0;
|
||||
if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };
|
||||
|
|
@ -85,7 +85,7 @@ export async function process(input: Input, background: Input | undefined, confi
|
|||
let mergedCanvas: HTMLCanvasElement | OffscreenCanvas | null = null;
|
||||
if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present
|
||||
mergedCanvas = image.canvas(width, height);
|
||||
const bgImage = image.process(background, config);
|
||||
const bgImage = await image.process(background, config);
|
||||
tf.dispose(bgImage.tensor);
|
||||
const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;
|
||||
ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),
|
||||
};
|
||||
tf.registerKernel(kernelMod);
|
||||
env.kernels.push('mod');
|
||||
}
|
||||
if (!env.kernels.includes('floormod')) {
|
||||
const kernelMod = {
|
||||
|
|
@ -21,8 +22,8 @@ function registerCustomOps() {
|
|||
kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),
|
||||
};
|
||||
tf.registerKernel(kernelMod);
|
||||
env.kernels.push('floormod');
|
||||
}
|
||||
env.updateBackend();
|
||||
}
|
||||
|
||||
export async function check(instance, force = false) {
|
||||
|
|
@ -123,8 +124,9 @@ export async function check(instance, force = false) {
|
|||
instance.performance.initBackend = Math.trunc(now() - timeStamp);
|
||||
instance.config.backend = tf.getBackend();
|
||||
|
||||
env.updateBackend(); // update env on backend init
|
||||
await env.updateBackend(); // update env on backend init
|
||||
registerCustomOps();
|
||||
// await env.updateBackend(); // update env on backend init
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -68,13 +68,11 @@ export async function register(instance): Promise<void> {
|
|||
log('possible browser memory leak using webgl or conflict with multiple backend registrations');
|
||||
instance.emit('error');
|
||||
throw new Error('browser webgl error');
|
||||
/*
|
||||
log('resetting humangl backend');
|
||||
env.initial = true;
|
||||
models.reset(instance);
|
||||
await tf.removeBackend(config.name);
|
||||
await register(instance); // re-register
|
||||
*/
|
||||
// log('resetting humangl backend');
|
||||
// env.initial = true;
|
||||
// models.reset(instance);
|
||||
// await tf.removeBackend(config.name);
|
||||
// await register(instance); // re-register
|
||||
});
|
||||
config.canvas.addEventListener('webglcontextrestored', (e) => {
|
||||
log('error: humangl context restored:', e);
|
||||
|
|
|
|||
|
|
@ -1,25 +1,25 @@
|
|||
2021-11-05 15:16:48 [36mINFO: [39m @vladmandic/human version 2.5.0
|
||||
2021-11-05 15:16:48 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v17.0.1
|
||||
2021-11-05 15:16:48 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.5.0"}
|
||||
2021-11-05 15:16:48 [36mINFO: [39m Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
|
||||
2021-11-05 15:16:48 [36mINFO: [39m Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.1.0"}
|
||||
2021-11-05 15:16:48 [36mINFO: [39m Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Clean: {"locations":["dist/*","types/*","typedoc/*"]}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":56,"inputBytes":519015,"outputBytes":439589}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":56,"inputBytes":519023,"outputBytes":439593}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":56,"inputBytes":519090,"outputBytes":439665}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2329,"outputBytes":850}
|
||||
2021-11-05 15:16:48 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":56,"inputBytes":518590,"outputBytes":441280}
|
||||
2021-11-05 15:16:49 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2562175,"outputBytes":2497378}
|
||||
2021-11-05 15:16:49 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":56,"inputBytes":3015118,"outputBytes":1611255}
|
||||
2021-11-05 15:16:49 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":56,"inputBytes":3015118,"outputBytes":2944528}
|
||||
2021-11-05 15:17:05 [35mSTATE:[39m Typings: {"input":"src/human.ts","output":"types","files":49}
|
||||
2021-11-05 15:17:11 [35mSTATE:[39m TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
|
||||
2021-11-05 15:17:11 [35mSTATE:[39m Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5812,"outputBytes":3779}
|
||||
2021-11-05 15:17:41 [35mSTATE:[39m Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":90,"errors":0,"warnings":0}
|
||||
2021-11-05 15:17:42 [35mSTATE:[39m ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
|
||||
2021-11-05 15:17:42 [36mINFO: [39m Done...
|
||||
2021-11-06 10:12:23 [36mINFO: [39m @vladmandic/human version 2.5.0
|
||||
2021-11-06 10:12:23 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v17.0.1
|
||||
2021-11-06 10:12:23 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.5.0"}
|
||||
2021-11-06 10:12:23 [36mINFO: [39m Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
|
||||
2021-11-06 10:12:23 [36mINFO: [39m Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.2.0"}
|
||||
2021-11-06 10:12:23 [36mINFO: [39m Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Clean: {"locations":["dist/*","types/*","typedoc/*"]}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":56,"inputBytes":520786,"outputBytes":440096}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":56,"inputBytes":520794,"outputBytes":440100}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":56,"inputBytes":520861,"outputBytes":440172}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2326,"outputBytes":912}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":56,"inputBytes":520423,"outputBytes":441983}
|
||||
2021-11-06 10:12:23 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2562175,"outputBytes":2497378}
|
||||
2021-11-06 10:12:24 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":56,"inputBytes":3016889,"outputBytes":3096312}
|
||||
2021-11-06 10:12:24 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":56,"inputBytes":3016889,"outputBytes":2945011}
|
||||
2021-11-06 10:12:43 [35mSTATE:[39m Typings: {"input":"src/human.ts","output":"types","files":49}
|
||||
2021-11-06 10:12:49 [35mSTATE:[39m TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true}
|
||||
2021-11-06 10:12:49 [35mSTATE:[39m Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5851,"outputBytes":3818}
|
||||
2021-11-06 10:13:25 [35mSTATE:[39m Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":90,"errors":0,"warnings":0}
|
||||
2021-11-06 10:13:26 [35mSTATE:[39m ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
|
||||
2021-11-06 10:13:26 [36mINFO: [39m Done...
|
||||
|
|
|
|||
|
|
@ -192,7 +192,7 @@ async function test(Human, inputConfig) {
|
|||
else log('state', 'passed: warmup face result match');
|
||||
config.warmup = 'body';
|
||||
res = await testWarmup(human, 'default');
|
||||
if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 1 || res?.gesture?.length !== 6) log('error', 'failed: warmup body result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
|
||||
if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 1 || res?.gesture?.length !== 5) log('error', 'failed: warmup body result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
|
||||
else log('state', 'passed: warmup body result match');
|
||||
log('state', 'details:', {
|
||||
face: { boxScore: res.face[0].boxScore, faceScore: res.face[0].faceScore, age: res.face[0].age, gender: res.face[0].gender, genderScore: res.face[0].genderScore },
|
||||
|
|
@ -278,7 +278,7 @@ async function test(Human, inputConfig) {
|
|||
config.body = { minConfidence: 0.0001 };
|
||||
config.hand = { minConfidence: 0.0001 };
|
||||
res = await testDetect(human, 'samples/in/ai-body.jpg', 'default');
|
||||
if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 2 || res?.gesture?.length !== 8) log('error', 'failed: sensitive result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
|
||||
if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 2 || res?.gesture?.length !== 7) log('error', 'failed: sensitive result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
|
||||
else log('state', 'passed: sensitive result match');
|
||||
|
||||
// test sensitive details face
|
||||
|
|
|
|||
1024
test/test.log
1024
test/test.log
File diff suppressed because it is too large
Load Diff
|
|
@ -5,7 +5,7 @@
|
|||
|
||||
// export all from build bundle
|
||||
export * from '@tensorflow/tfjs/dist/index.js';
|
||||
// export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
|
||||
export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
|
||||
// export * from '@tensorflow/tfjs-backend-wasm/dist/index.js';
|
||||
|
||||
// add webgpu to bundle, experimental
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -99,9 +99,9 @@ Possible events:</p>
|
|||
</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="enhance" class="tsd-anchor"></a><h3>enhance</h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">enhance<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L266">src/human.ts:266</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Enhance method performs additional enhacements to face image previously detected for futher processing</p>
|
||||
</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span></h4><div><p>Tensor</p>
|
||||
</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="image" class="tsd-anchor"></a><h3>image</h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, getTensor<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> }</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L239">src/human.ts:239</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="image" class="tsd-anchor"></a><h3>image</h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, getTensor<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">></span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L239">src/human.ts:239</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Process input as return canvas and tensor</p>
|
||||
</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5></li><li><h5>getTensor: <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> }</span></h4><div></div><ul class="tsd-parameters"><li class="tsd-parameter"><h5>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li><li class="tsd-parameter"><h5>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span></h5></li></ul></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="init" class="tsd-anchor"></a><h3>init</h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">init<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L277">src/human.ts:277</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>input: <a href="../index.html#Input" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5></li><li><h5>getTensor: <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../index.html#AnyCanvas" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">></span></h4><div></div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="init" class="tsd-anchor"></a><h3>init</h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">init<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L277">src/human.ts:277</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Explicit backend initialization</p>
|
||||
<ul>
|
||||
<li>Normally done implicitly during initial load phase</li>
|
||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@
|
|||
<li>Can be TFJS that is bundled with <code>Human</code> or a manually imported TFJS library</li>
|
||||
</ul>
|
||||
</div><dl class="tsd-comment-tags"><dt>external</dt><dd><p><a href="https://js.tensorflow.org/api/latest/">API</a></p>
|
||||
</dd></dl></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3>defaults</h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L258">src/config.ts:258</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</dd></dl></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3>defaults</h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L263">src/config.ts:263</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<ul>
|
||||
<li><a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L253">See all default Config values...</a></li>
|
||||
</ul>
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
<!DOCTYPE html><html class="default no-js"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Config | @vladmandic/human - v2.5.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.5.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.5.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.5.0</a></li><li><a href="Config.html">Config</a></li></ul><h1>Interface Config</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Configuration interface definition for <strong>Human</strong> library</p>
|
||||
</div><div><p>Contains all configurable parameters</p>
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Config</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="async" class="tsd-anchor"></a><h3>async</h3><div class="tsd-signature tsd-kind-icon">async<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L209">src/config.ts:209</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Config</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#deallocate" class="tsd-kind-icon">deallocate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="async" class="tsd-anchor"></a><h3>async</h3><div class="tsd-signature tsd-kind-icon">async<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L211">src/config.ts:211</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Perform model loading and inference concurrently or sequentially
|
||||
default: <code>true</code></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="backend" class="tsd-anchor"></a><h3>backend</h3><div class="tsd-signature tsd-kind-icon">backend<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">""</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"cpu"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"wasm"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"webgl"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"humangl"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"tensorflow"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"webgpu"</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L194">src/config.ts:194</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="backend" class="tsd-anchor"></a><h3>backend</h3><div class="tsd-signature tsd-kind-icon">backend<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">""</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"cpu"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"wasm"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"webgl"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"humangl"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"tensorflow"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"webgpu"</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L196">src/config.ts:196</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Backend used for TFJS operations
|
||||
valid build-in backends are:</p>
|
||||
<ul>
|
||||
|
|
@ -12,46 +12,48 @@ valid build-in backends are:</p>
|
|||
<li>NodeJS: <code>cpu</code>, <code>wasm</code>, <code>tensorflow</code>
|
||||
default: <code>humangl</code> for browser and <code>tensorflow</code> for nodejs</li>
|
||||
</ul>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a><h3>body</h3><div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L245">src/config.ts:245</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a><h3>body</h3><div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L250">src/config.ts:250</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="BodyConfig.html">BodyConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheSensitivity" class="tsd-anchor"></a><h3>cache<wbr/>Sensitivity</h3><div class="tsd-signature tsd-kind-icon">cache<wbr/>Sensitivity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L230">src/config.ts:230</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheSensitivity" class="tsd-anchor"></a><h3>cache<wbr/>Sensitivity</h3><div class="tsd-signature tsd-kind-icon">cache<wbr/>Sensitivity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L232">src/config.ts:232</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Cache sensitivity</p>
|
||||
<ul>
|
||||
<li>values 0..1 where 0.01 means reset cache if input changed more than 1%</li>
|
||||
<li>set to 0 to disable caching
|
||||
default: 0.7</li>
|
||||
</ul>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="debug" class="tsd-anchor"></a><h3>debug</h3><div class="tsd-signature tsd-kind-icon">debug<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L204">src/config.ts:204</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="deallocate" class="tsd-anchor"></a><h3>deallocate</h3><div class="tsd-signature tsd-kind-icon">deallocate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L235">src/config.ts:235</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Perform immediate garbage collection on deallocated tensors instead of caching them</p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="debug" class="tsd-anchor"></a><h3>debug</h3><div class="tsd-signature tsd-kind-icon">debug<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L206">src/config.ts:206</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Print debug statements to console
|
||||
default: <code>true</code></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a><h3>face</h3><div class="tsd-signature tsd-kind-icon">face<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L242">src/config.ts:242</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a><h3>face</h3><div class="tsd-signature tsd-kind-icon">face<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L247">src/config.ts:247</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="FaceConfig.html">FaceConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="filter" class="tsd-anchor"></a><h3>filter</h3><div class="tsd-signature tsd-kind-icon">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="FilterConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FilterConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L236">src/config.ts:236</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="filter" class="tsd-anchor"></a><h3>filter</h3><div class="tsd-signature tsd-kind-icon">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="FilterConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FilterConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L241">src/config.ts:241</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="FilterConfig.html">FilterConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gesture" class="tsd-anchor"></a><h3>gesture</h3><div class="tsd-signature tsd-kind-icon">gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="GestureConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GestureConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L239">src/config.ts:239</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gesture" class="tsd-anchor"></a><h3>gesture</h3><div class="tsd-signature tsd-kind-icon">gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="GestureConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GestureConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L244">src/config.ts:244</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="GestureConfig.html">GestureConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hand" class="tsd-anchor"></a><h3>hand</h3><div class="tsd-signature tsd-kind-icon">hand<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L248">src/config.ts:248</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hand" class="tsd-anchor"></a><h3>hand</h3><div class="tsd-signature tsd-kind-icon">hand<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L253">src/config.ts:253</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="HandConfig.html">HandConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="modelBasePath" class="tsd-anchor"></a><h3>model<wbr/>Base<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Base<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L223">src/config.ts:223</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="modelBasePath" class="tsd-anchor"></a><h3>model<wbr/>Base<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Base<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L225">src/config.ts:225</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Base model path (typically starting with file://, http:// or https://) for all models</p>
|
||||
<ul>
|
||||
<li>individual modelPath values are relative to this path
|
||||
default: <code>../models/</code> for browsers and <code>file://models/</code> for nodejs</li>
|
||||
</ul>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="object" class="tsd-anchor"></a><h3>object</h3><div class="tsd-signature tsd-kind-icon">object<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L251">src/config.ts:251</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="object" class="tsd-anchor"></a><h3>object</h3><div class="tsd-signature tsd-kind-icon">object<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L256">src/config.ts:256</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="ObjectConfig.html">ObjectConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="segmentation" class="tsd-anchor"></a><h3>segmentation</h3><div class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L254">src/config.ts:254</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="segmentation" class="tsd-anchor"></a><h3>segmentation</h3><div class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol"><</span><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L259">src/config.ts:259</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p><a href="SegmentationConfig.html">SegmentationConfig</a></p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="skipAllowed" class="tsd-anchor"></a><h3>skip<wbr/>Allowed</h3><div class="tsd-signature tsd-kind-icon">skip<wbr/>Allowed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L233">src/config.ts:233</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="skipAllowed" class="tsd-anchor"></a><h3>skip<wbr/>Allowed</h3><div class="tsd-signature tsd-kind-icon">skip<wbr/>Allowed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L238">src/config.ts:238</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Internal Variable</p>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="warmup" class="tsd-anchor"></a><h3>warmup</h3><div class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">"face"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"body"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"none"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"full"</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L216">src/config.ts:216</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="warmup" class="tsd-anchor"></a><h3>warmup</h3><div class="tsd-signature tsd-kind-icon">warmup<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">"face"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"body"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"none"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"full"</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L218">src/config.ts:218</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>What to use for <code>human.warmup()</code></p>
|
||||
<ul>
|
||||
<li>warmup pre-initializes all models for faster inference but can take significant time on startup</li>
|
||||
<li>used by <code>webgl</code>, <code>humangl</code> and <code>webgpu</code> backends
|
||||
default: <code>full</code></li>
|
||||
</ul>
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="wasmPath" class="tsd-anchor"></a><h3>wasm<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">wasm<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L199">src/config.ts:199</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="wasmPath" class="tsd-anchor"></a><h3>wasm<wbr/>Path</h3><div class="tsd-signature tsd-kind-icon">wasm<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L201">src/config.ts:201</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Path to *.wasm files if backend is set to <code>wasm</code>
|
||||
default: auto-detects to link to CDN <code>jsdelivr</code> when running in browser</p>
|
||||
</div></div></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-interface"><a href="Config.html" class="tsd-kind-icon">Config</a><ul><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
|
||||
</div></div></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-interface"><a href="Config.html" class="tsd-kind-icon">Config</a><ul><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#async" class="tsd-kind-icon">async</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#backend" class="tsd-kind-icon">backend</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#cacheSensitivity" class="tsd-kind-icon">cache<wbr/>Sensitivity</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#deallocate" class="tsd-kind-icon">deallocate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#debug" class="tsd-kind-icon">debug</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#modelBasePath" class="tsd-kind-icon">model<wbr/>Base<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#segmentation" class="tsd-kind-icon">segmentation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#skipAllowed" class="tsd-kind-icon">skip<wbr/>Allowed</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#warmup" class="tsd-kind-icon">warmup</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Config.html#wasmPath" class="tsd-kind-icon">wasm<wbr/>Path</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
|
||||
|
|
@ -4,31 +4,34 @@
|
|||
<li>available only in Browser environments</li>
|
||||
<li>image filters run with near-zero latency as they are executed on the GPU using WebGL</li>
|
||||
</ul>
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">FilterConfig</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#blur" class="tsd-kind-icon">blur</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#brightness" class="tsd-kind-icon">brightness</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#contrast" class="tsd-kind-icon">contrast</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#equalization" class="tsd-kind-icon">equalization</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#flip" class="tsd-kind-icon">flip</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#height" class="tsd-kind-icon">height</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#hue" class="tsd-kind-icon">hue</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#kodachrome" class="tsd-kind-icon">kodachrome</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#negative" class="tsd-kind-icon">negative</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#pixelate" class="tsd-kind-icon">pixelate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#polaroid" class="tsd-kind-icon">polaroid</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#return" class="tsd-kind-icon">return</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#saturation" class="tsd-kind-icon">saturation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#sepia" class="tsd-kind-icon">sepia</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#sharpness" class="tsd-kind-icon">sharpness</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#technicolor" class="tsd-kind-icon">technicolor</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#vintage" class="tsd-kind-icon">vintage</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#width" class="tsd-kind-icon">width</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="blur" class="tsd-anchor"></a><h3>blur</h3><div class="tsd-signature tsd-kind-icon">blur<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L152">src/config.ts:152</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no blur) to N (blur radius in pixels)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="brightness" class="tsd-anchor"></a><h3>brightness</h3><div class="tsd-signature tsd-kind-icon">brightness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L146">src/config.ts:146</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (darken) to 1 (lighten)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="contrast" class="tsd-anchor"></a><h3>contrast</h3><div class="tsd-signature tsd-kind-icon">contrast<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L148">src/config.ts:148</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (reduce contrast) to 1 (increase contrast)</p>
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">FilterConfig</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#blur" class="tsd-kind-icon">blur</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#brightness" class="tsd-kind-icon">brightness</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#contrast" class="tsd-kind-icon">contrast</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#equalization" class="tsd-kind-icon">equalization</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#flip" class="tsd-kind-icon">flip</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#height" class="tsd-kind-icon">height</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#hue" class="tsd-kind-icon">hue</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#kodachrome" class="tsd-kind-icon">kodachrome</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#negative" class="tsd-kind-icon">negative</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#pixelate" class="tsd-kind-icon">pixelate</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#polaroid" class="tsd-kind-icon">polaroid</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#return" class="tsd-kind-icon">return</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#saturation" class="tsd-kind-icon">saturation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#sepia" class="tsd-kind-icon">sepia</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#sharpness" class="tsd-kind-icon">sharpness</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#technicolor" class="tsd-kind-icon">technicolor</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#vintage" class="tsd-kind-icon">vintage</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#width" class="tsd-kind-icon">width</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="blur" class="tsd-anchor"></a><h3>blur</h3><div class="tsd-signature tsd-kind-icon">blur<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L154">src/config.ts:154</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no blur) to N (blur radius in pixels)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="brightness" class="tsd-anchor"></a><h3>brightness</h3><div class="tsd-signature tsd-kind-icon">brightness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L148">src/config.ts:148</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (darken) to 1 (lighten)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="contrast" class="tsd-anchor"></a><h3>contrast</h3><div class="tsd-signature tsd-kind-icon">contrast<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L150">src/config.ts:150</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (reduce contrast) to 1 (increase contrast)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a><h3>enabled</h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L124">src/config.ts:124</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>are image filters enabled?</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="equalization" class="tsd-anchor"></a><h3>equalization</h3><div class="tsd-signature tsd-kind-icon">equalization<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L126">src/config.ts:126</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>perform image histogram equalization</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="flip" class="tsd-anchor"></a><h3>flip</h3><div class="tsd-signature tsd-kind-icon">flip<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L144">src/config.ts:144</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>flip input as mirror image</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="height" class="tsd-anchor"></a><h3>height</h3><div class="tsd-signature tsd-kind-icon">height<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L140">src/config.ts:140</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="equalization" class="tsd-anchor"></a><h3>equalization</h3><div class="tsd-signature tsd-kind-icon">equalization<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L128">src/config.ts:128</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>perform image histogram equalization</p>
|
||||
<ul>
|
||||
<li>equalization is performed on input as a whole and detected face before its passed for further analysis</li>
|
||||
</ul>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="flip" class="tsd-anchor"></a><h3>flip</h3><div class="tsd-signature tsd-kind-icon">flip<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L146">src/config.ts:146</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>flip input as mirror image</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="height" class="tsd-anchor"></a><h3>height</h3><div class="tsd-signature tsd-kind-icon">height<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L142">src/config.ts:142</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>resize input height</p>
|
||||
<ul>
|
||||
<li>if both width and height are set to 0, there is no resizing</li>
|
||||
<li>if just one is set, second one is scaled automatically</li>
|
||||
<li>if both are set, values are used as-is</li>
|
||||
</ul>
|
||||
</div><dl class="tsd-comment-tags"><dt>property</dt><dd></dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hue" class="tsd-anchor"></a><h3>hue</h3><div class="tsd-signature tsd-kind-icon">hue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L156">src/config.ts:156</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no change) to 360 (hue rotation in degrees)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="kodachrome" class="tsd-anchor"></a><h3>kodachrome</h3><div class="tsd-signature tsd-kind-icon">kodachrome<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L164">src/config.ts:164</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image kodachrome colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="negative" class="tsd-anchor"></a><h3>negative</h3><div class="tsd-signature tsd-kind-icon">negative<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L158">src/config.ts:158</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image negative</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="pixelate" class="tsd-anchor"></a><h3>pixelate</h3><div class="tsd-signature tsd-kind-icon">pixelate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L170">src/config.ts:170</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no pixelate) to N (number of pixels to pixelate)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="polaroid" class="tsd-anchor"></a><h3>polaroid</h3><div class="tsd-signature tsd-kind-icon">polaroid<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L168">src/config.ts:168</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image polaroid camera effect</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="return" class="tsd-anchor"></a><h3>return</h3><div class="tsd-signature tsd-kind-icon">return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L142">src/config.ts:142</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>return processed canvas imagedata in result</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="saturation" class="tsd-anchor"></a><h3>saturation</h3><div class="tsd-signature tsd-kind-icon">saturation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L154">src/config.ts:154</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (reduce saturation) to 1 (increase saturation)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sepia" class="tsd-anchor"></a><h3>sepia</h3><div class="tsd-signature tsd-kind-icon">sepia<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L160">src/config.ts:160</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image sepia colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sharpness" class="tsd-anchor"></a><h3>sharpness</h3><div class="tsd-signature tsd-kind-icon">sharpness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L150">src/config.ts:150</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no sharpening) to 1 (maximum sharpening)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="technicolor" class="tsd-anchor"></a><h3>technicolor</h3><div class="tsd-signature tsd-kind-icon">technicolor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L166">src/config.ts:166</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image technicolor colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="vintage" class="tsd-anchor"></a><h3>vintage</h3><div class="tsd-signature tsd-kind-icon">vintage<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L162">src/config.ts:162</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image vintage colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="width" class="tsd-anchor"></a><h3>width</h3><div class="tsd-signature tsd-kind-icon">width<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L133">src/config.ts:133</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
</div><dl class="tsd-comment-tags"><dt>property</dt><dd></dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hue" class="tsd-anchor"></a><h3>hue</h3><div class="tsd-signature tsd-kind-icon">hue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L158">src/config.ts:158</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no change) to 360 (hue rotation in degrees)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="kodachrome" class="tsd-anchor"></a><h3>kodachrome</h3><div class="tsd-signature tsd-kind-icon">kodachrome<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L166">src/config.ts:166</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image kodachrome colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="negative" class="tsd-anchor"></a><h3>negative</h3><div class="tsd-signature tsd-kind-icon">negative<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L160">src/config.ts:160</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image negative</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="pixelate" class="tsd-anchor"></a><h3>pixelate</h3><div class="tsd-signature tsd-kind-icon">pixelate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L172">src/config.ts:172</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no pixelate) to N (number of pixels to pixelate)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="polaroid" class="tsd-anchor"></a><h3>polaroid</h3><div class="tsd-signature tsd-kind-icon">polaroid<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L170">src/config.ts:170</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image polaroid camera effect</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="return" class="tsd-anchor"></a><h3>return</h3><div class="tsd-signature tsd-kind-icon">return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L144">src/config.ts:144</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>return processed canvas imagedata in result</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="saturation" class="tsd-anchor"></a><h3>saturation</h3><div class="tsd-signature tsd-kind-icon">saturation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L156">src/config.ts:156</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: -1 (reduce saturation) to 1 (increase saturation)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sepia" class="tsd-anchor"></a><h3>sepia</h3><div class="tsd-signature tsd-kind-icon">sepia<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L162">src/config.ts:162</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image sepia colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sharpness" class="tsd-anchor"></a><h3>sharpness</h3><div class="tsd-signature tsd-kind-icon">sharpness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L152">src/config.ts:152</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>range: 0 (no sharpening) to 1 (maximum sharpening)</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="technicolor" class="tsd-anchor"></a><h3>technicolor</h3><div class="tsd-signature tsd-kind-icon">technicolor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L168">src/config.ts:168</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image technicolor colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="vintage" class="tsd-anchor"></a><h3>vintage</h3><div class="tsd-signature tsd-kind-icon">vintage<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L164">src/config.ts:164</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>image vintage colors</p>
|
||||
</dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="width" class="tsd-anchor"></a><h3>width</h3><div class="tsd-signature tsd-kind-icon">width<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L135">src/config.ts:135</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>resize input width</p>
|
||||
<ul>
|
||||
<li>if both width and height are set to 0, there is no resizing</li>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
<!DOCTYPE html><html class="default no-js"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GestureConfig | @vladmandic/human - v2.5.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.5.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.5.0</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.5.0</a></li><li><a href="GestureConfig.html">GestureConfig</a></li></ul><h1>Interface GestureConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
|
||||
<p>Controlls gesture detection</p>
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GestureConfig</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GestureConfig.html#enabled" class="tsd-kind-icon">enabled</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a><h3>enabled</h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L176">src/config.ts:176</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>is gesture detection enabled?</p>
|
||||
</div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GestureConfig</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GestureConfig.html#enabled" class="tsd-kind-icon">enabled</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a><h3>enabled</h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L178">src/config.ts:178</a></li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>property</dt><dd><p>is gesture detection enabled?</p>
|
||||
</dd></dl></div></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-interface"><a href="GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a><ul><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GestureConfig.html#enabled" class="tsd-kind-icon">enabled</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
|
||||
|
|
@ -110,7 +110,9 @@ export interface SegmentationConfig extends GenericConfig {
|
|||
export interface FilterConfig {
|
||||
/** @property are image filters enabled? */
|
||||
enabled: boolean;
|
||||
/** @property perform image histogram equalization */
|
||||
/** @property perform image histogram equalization
|
||||
* - equalization is performed on input as a whole and detected face before its passed for further analysis
|
||||
*/
|
||||
equalization: boolean;
|
||||
/** resize input width
|
||||
* - if both width and height are set to 0, there is no resizing
|
||||
|
|
@ -207,6 +209,8 @@ export interface Config {
|
|||
* default: 0.7
|
||||
*/
|
||||
cacheSensitivity: number;
|
||||
/** Perform immediate garbage collection on deallocated tensors instead of caching them */
|
||||
deallocate: boolean;
|
||||
/** Internal Variable */
|
||||
skipAllowed: boolean;
|
||||
/** {@link FilterConfig} */
|
||||
|
|
|
|||
|
|
@ -132,10 +132,10 @@ export declare class Human {
|
|||
* @param {boolean} input.getTensor should image processing also return tensor or just canvas
|
||||
* @returns { tensor, canvas }
|
||||
*/
|
||||
image(input: Input, getTensor?: boolean): {
|
||||
image(input: Input, getTensor?: boolean): Promise<{
|
||||
tensor: Tensor<import("@tensorflow/tfjs-core").Rank> | null;
|
||||
canvas: import("./exports").AnyCanvas | null;
|
||||
};
|
||||
}>;
|
||||
/** Segmentation method takes any input and returns processed canvas with body segmentation
|
||||
* - Segmentation is not triggered as part of detect process
|
||||
*
|
||||
|
|
|
|||
|
|
@ -2,4 +2,4 @@
|
|||
* Image enhancements
|
||||
*/
|
||||
import type { Tensor } from '../exports';
|
||||
export declare function histogramEqualization(input: Tensor): Tensor;
|
||||
export declare function histogramEqualization(inputImage: Tensor): Promise<Tensor>;
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@
|
|||
import type { Input, AnyCanvas, Tensor, Config } from '../exports';
|
||||
export declare function canvas(width: any, height: any): AnyCanvas;
|
||||
export declare function copy(input: AnyCanvas, output?: AnyCanvas): AnyCanvas;
|
||||
export declare function process(input: Input, config: Config, getTensor?: boolean): {
|
||||
export declare function process(input: Input, config: Config, getTensor?: boolean): Promise<{
|
||||
tensor: Tensor | null;
|
||||
canvas: AnyCanvas | null;
|
||||
};
|
||||
}>;
|
||||
export declare function skip(config: any, input: Tensor): Promise<boolean>;
|
||||
|
|
|
|||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit 0deb501cf47e1783e8ca4426b7bf4697196f09e2
|
||||
Subproject commit e5a6342e4e2dd5d79b73cafada222ef4b1d1621a
|
||||
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Reference in New Issue