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enable body segmentation and background replacement in demo

pull/134/head
Vladimir Mandic 2021-06-05 16:13:41 -04:00
parent 5bfe5655a7
commit f345538794
21 changed files with 4857 additions and 206711 deletions
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8
CHANGELOG.md
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@ -11,10 +11,12 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
### **HEAD -> main** 2021/06/05 mandic00@live.com
### **origin/main** 2021/06/05 mandic00@live.com
- unified build
- enable body segmentation and background replacement
### **origin/main** 2021/06/04 mandic00@live.com
- work on body segmentation
- added experimental body segmentation module
- add meet and selfie models
- add live hints to demo

8
TODO.md
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@ -4,14 +4,9 @@
N/A
## Explore Models
- InsightFace: RetinaFace detector and ArcFace recognition: <https://github.com/deepinsight/insightface>
## In Progress
## Work in Progress
- Switch to TypeScript 4.3
- Add backgrounds to segmentation
## Known Issues
@ -19,4 +14,3 @@ N/A
- NanoDet with WASM: <https://github.com/tensorflow/tfjs/issues/4824>
- BlazeFace and HandPose rotation in NodeJS: <https://github.com/tensorflow/tfjs/issues/4066>
- TypeDoc with TypeScript 4.3: <https://github.com/TypeStrong/typedoc/issues/1589>
- HandPose lower precision with WASM

135
demo/index.js
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@ -27,7 +27,7 @@ import webRTC from './helpers/webrtc.js';
let human;
const userConfig = {
let userConfig = {
warmup: 'none',
backend: 'humangl',
wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.6.0/dist/',
@ -96,6 +96,7 @@ const ui = {
bench: true, // show gl fps benchmark window
lastFrame: 0, // time of last frame processing
viewportSet: false, // internal, has custom viewport been set
background: null, // holds instance of segmentation background image
// webrtc
useWebRTC: false, // use webrtc as camera source instead of local webcam
@ -210,8 +211,8 @@ async function drawResults(input) {
await menu.process.updateChart('FPS', ui.detectFPS);
// get updated canvas if missing or if we want buffering, but skip if segmentation is enabled
if (human.config.segmentation.enabled) {
result.canvas = await human.segmentation(input);
if (userConfig.segmentation.enabled) {
result.canvas = await human.segmentation(input, ui.background, userConfig);
} else if (!result.canvas || ui.buffered) {
const image = await human.image(input);
result.canvas = image.canvas;
@ -496,8 +497,8 @@ async function processImage(input, title) {
const canvas = document.getElementById('canvas');
image.width = image.naturalWidth;
image.height = image.naturalHeight;
canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;
canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;
canvas.width = userConfig.filter.width && userConfig.filter.width > 0 ? userConfig.filter.width : image.naturalWidth;
canvas.height = userConfig.filter.height && userConfig.filter.height > 0 ? userConfig.filter.height : image.naturalHeight;
const origCacheSensitiry = userConfig.cacheSensitivity;
userConfig.cacheSensitivity = 0;
const result = await human.detect(image, userConfig);
@ -614,55 +615,55 @@ function setupMenu() {
menu.display.addBool('fill polygons', human.draw.options, 'fillPolygons');
menu.image = new Menu(document.body, '', { top, left: x[1] });
menu.image.addBool('enabled', human.config.filter, 'enabled', (val) => human.config.filter.enabled = val);
ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));
ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));
menu.image.addBool('enabled', userConfig.filter, 'enabled', (val) => userConfig.filter.enabled = val);
ui.menuWidth = menu.image.addRange('image width', userConfig.filter, 'width', 0, 3840, 10, (val) => userConfig.filter.width = parseInt(val));
ui.menuHeight = menu.image.addRange('image height', userConfig.filter, 'height', 0, 2160, 10, (val) => userConfig.filter.height = parseInt(val));
menu.image.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));
menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));
menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));
menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));
menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));
menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));
menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));
menu.image.addRange('brightness', userConfig.filter, 'brightness', -1.0, 1.0, 0.05, (val) => userConfig.filter.brightness = parseFloat(val));
menu.image.addRange('contrast', userConfig.filter, 'contrast', -1.0, 1.0, 0.05, (val) => userConfig.filter.contrast = parseFloat(val));
menu.image.addRange('sharpness', userConfig.filter, 'sharpness', 0, 1.0, 0.05, (val) => userConfig.filter.sharpness = parseFloat(val));
menu.image.addRange('blur', userConfig.filter, 'blur', 0, 20, 1, (val) => userConfig.filter.blur = parseInt(val));
menu.image.addRange('saturation', userConfig.filter, 'saturation', -1.0, 1.0, 0.05, (val) => userConfig.filter.saturation = parseFloat(val));
menu.image.addRange('hue', userConfig.filter, 'hue', 0, 360, 5, (val) => userConfig.filter.hue = parseInt(val));
menu.image.addRange('pixelate', userConfig.filter, 'pixelate', 0, 32, 1, (val) => userConfig.filter.pixelate = parseInt(val));
menu.image.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.image.addBool('negative', human.config.filter, 'negative', (val) => human.config.filter.negative = val);
menu.image.addBool('sepia', human.config.filter, 'sepia', (val) => human.config.filter.sepia = val);
menu.image.addBool('vintage', human.config.filter, 'vintage', (val) => human.config.filter.vintage = val);
menu.image.addBool('kodachrome', human.config.filter, 'kodachrome', (val) => human.config.filter.kodachrome = val);
menu.image.addBool('technicolor', human.config.filter, 'technicolor', (val) => human.config.filter.technicolor = val);
menu.image.addBool('polaroid', human.config.filter, 'polaroid', (val) => human.config.filter.polaroid = val);
menu.image.addBool('negative', userConfig.filter, 'negative', (val) => userConfig.filter.negative = val);
menu.image.addBool('sepia', userConfig.filter, 'sepia', (val) => userConfig.filter.sepia = val);
menu.image.addBool('vintage', userConfig.filter, 'vintage', (val) => userConfig.filter.vintage = val);
menu.image.addBool('kodachrome', userConfig.filter, 'kodachrome', (val) => userConfig.filter.kodachrome = val);
menu.image.addBool('technicolor', userConfig.filter, 'technicolor', (val) => userConfig.filter.technicolor = val);
menu.image.addBool('polaroid', userConfig.filter, 'polaroid', (val) => userConfig.filter.polaroid = val);
menu.image.addHTML('<input type="file" id="file-input" class="input-file"></input> &nbsp input');
menu.image.addHTML('<input type="file" id="file-background" class="input-file"></input> &nbsp background');
menu.process = new Menu(document.body, '', { top, left: x[2] });
menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);
menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);
menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], userConfig.backend, (val) => userConfig.backend = val);
menu.process.addBool('async operations', userConfig, 'async', (val) => userConfig.async = val);
menu.process.addBool('use web worker', ui, 'useWorker');
menu.process.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.process.addLabel('model parameters');
menu.process.addRange('max objects', human.config.face.detector, 'maxDetected', 1, 50, 1, (val) => {
human.config.face.detector.maxDetected = parseInt(val);
human.config.body.maxDetected = parseInt(val);
human.config.hand.maxDetected = parseInt(val);
menu.process.addRange('max objects', userConfig.face.detector, 'maxDetected', 1, 50, 1, (val) => {
userConfig.face.detector.maxDetected = parseInt(val);
userConfig.body.maxDetected = parseInt(val);
userConfig.hand.maxDetected = parseInt(val);
});
menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {
human.config.face.detector.skipFrames = parseInt(val);
human.config.face.emotion.skipFrames = parseInt(val);
human.config.hand.skipFrames = parseInt(val);
menu.process.addRange('skip frames', userConfig.face.detector, 'skipFrames', 0, 50, 1, (val) => {
userConfig.face.detector.skipFrames = parseInt(val);
userConfig.face.emotion.skipFrames = parseInt(val);
userConfig.hand.skipFrames = parseInt(val);
});
menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {
human.config.face.detector.minConfidence = parseFloat(val);
human.config.face.emotion.minConfidence = parseFloat(val);
human.config.hand.minConfidence = parseFloat(val);
menu.process.addRange('min confidence', userConfig.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {
userConfig.face.detector.minConfidence = parseFloat(val);
userConfig.face.emotion.minConfidence = parseFloat(val);
userConfig.hand.minConfidence = parseFloat(val);
});
menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {
human.config.face.detector.iouThreshold = parseFloat(val);
human.config.hand.iouThreshold = parseFloat(val);
menu.process.addRange('overlap', userConfig.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {
userConfig.face.detector.iouThreshold = parseFloat(val);
userConfig.hand.iouThreshold = parseFloat(val);
});
menu.process.addBool('rotation detection', human.config.face.detector, 'rotation', (val) => {
human.config.face.detector.rotation = val;
human.config.hand.rotation = val;
menu.process.addBool('rotation detection', userConfig.face.detector, 'rotation', (val) => {
userConfig.face.detector.rotation = val;
userConfig.hand.rotation = val;
});
menu.process.addHTML('<hr style="border-style: inset; border-color: dimgray">');
// menu.process.addButton('process sample images', 'process images', () => detectSampleImages());
@ -670,20 +671,20 @@ function setupMenu() {
menu.process.addChart('FPS', 'FPS');
menu.models = new Menu(document.body, '', { top, left: x[3] });
menu.models.addBool('face detect', human.config.face, 'enabled', (val) => human.config.face.enabled = val);
menu.models.addBool('face mesh', human.config.face.mesh, 'enabled', (val) => human.config.face.mesh.enabled = val);
menu.models.addBool('face iris', human.config.face.iris, 'enabled', (val) => human.config.face.iris.enabled = val);
menu.models.addBool('face description', human.config.face.description, 'enabled', (val) => human.config.face.description.enabled = val);
menu.models.addBool('face emotion', human.config.face.emotion, 'enabled', (val) => human.config.face.emotion.enabled = val);
menu.models.addBool('face detect', userConfig.face, 'enabled', (val) => userConfig.face.enabled = val);
menu.models.addBool('face mesh', userConfig.face.mesh, 'enabled', (val) => userConfig.face.mesh.enabled = val);
menu.models.addBool('face iris', userConfig.face.iris, 'enabled', (val) => userConfig.face.iris.enabled = val);
menu.models.addBool('face description', userConfig.face.description, 'enabled', (val) => userConfig.face.description.enabled = val);
menu.models.addBool('face emotion', userConfig.face.emotion, 'enabled', (val) => userConfig.face.emotion.enabled = val);
menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.models.addBool('body pose', human.config.body, 'enabled', (val) => human.config.body.enabled = val);
menu.models.addBool('hand pose', human.config.hand, 'enabled', (val) => human.config.hand.enabled = val);
menu.models.addBool('body pose', userConfig.body, 'enabled', (val) => userConfig.body.enabled = val);
menu.models.addBool('hand pose', userConfig.hand, 'enabled', (val) => userConfig.hand.enabled = val);
menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.models.addBool('gestures', human.config.gesture, 'enabled', (val) => human.config.gesture.enabled = val);
menu.models.addBool('gestures', userConfig.gesture, 'enabled', (val) => userConfig.gesture.enabled = val);
menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.models.addBool('body segmentation', human.config.segmentation, 'enabled', (val) => human.config.segmentation.enabled = val);
menu.models.addBool('body segmentation', userConfig.segmentation, 'enabled', (val) => userConfig.segmentation.enabled = val);
menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.models.addBool('object detection', human.config.object, 'enabled', (val) => human.config.object.enabled = val);
menu.models.addBool('object detection', userConfig.object, 'enabled', (val) => userConfig.object.enabled = val);
menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
menu.models.addBool('face compare', compare, 'enabled', (val) => {
compare.enabled = val;
@ -748,12 +749,34 @@ async function processDataURL(f, action) {
await processImage(dataURL, f.name);
document.getElementById('canvas').style.display = 'none';
}
if (action === 'background') {
const image = new Image();
image.onerror = async () => status('image loading error');
image.onload = async () => {
ui.background = image;
document.getElementById('canvas').style.display = 'block';
const canvas = document.getElementById('canvas');
const ctx = canvas.getContext('2d');
const overlaid = await human.segmentation(canvas, ui.background, userConfig);
if (overlaid) ctx.drawImage(overlaid, 0, 0);
};
image.src = dataURL;
}
resolve(true);
};
reader.readAsDataURL(f);
});
}
async function runSegmentation() {
document.getElementById('file-background').onchange = async (evt) => {
userConfig.segmentation.enabled = true;
evt.preventDefault();
if (evt.target.files.length < 2) ui.columns = 1;
for (const f of evt.target.files) await processDataURL(f, 'background');
};
}
async function dragAndDrop() {
document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
@ -764,6 +787,11 @@ async function dragAndDrop() {
if (evt.dataTransfer.files.length < 2) ui.columns = 1;
for (const f of evt.dataTransfer.files) await processDataURL(f, 'process');
});
document.getElementById('file-input').onchange = async (evt) => {
evt.preventDefault();
if (evt.target.files.length < 2) ui.columns = 1;
for (const f of evt.target.files) await processDataURL(f, 'process');
};
}
async function drawHints() {
@ -861,6 +889,7 @@ async function main() {
// create instance of human
human = new Human(userConfig);
userConfig = { ...human.config, ...userConfig };
if (typeof tf !== 'undefined') {
// eslint-disable-next-line no-undef
log('TensorFlow external version:', tf.version);
@ -895,9 +924,11 @@ async function main() {
for (const m of Object.values(menu)) m.hide();
// init drag & drop
await dragAndDrop();
// init segmentation
await runSegmentation();
if (params.has('image')) {
try {
const image = JSON.parse(params.get('image'));

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dist/human.node-gpu.js vendored
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@ -1,10 +1,10 @@
/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
@ -4318,6 +4318,8 @@ async function predict3(image18, config3, idx, count2) {
// src/face.ts
var calculateGaze = (face5) => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
if (!face5.annotations["rightEyeIris"] || !face5.annotations["leftEyeIris"])
return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1];
const eyeRatio = 1;
const left = face5.mesh[33][2] > face5.mesh[263][2];
@ -9970,7 +9972,7 @@ async function face2(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing)) {
if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.beginPath();
const leftGaze = [
@ -10419,6 +10421,7 @@ function calc(newResult) {
// src/segmentation/segmentation.ts
var tf20 = __toModule(require_tfjs_esm());
var model9;
var busy = false;
async function load12(config3) {
if (!model9) {
model9 = await tf20.loadGraphModel(join(config3.modelBasePath, config3.segmentation.modelPath));
@ -10489,6 +10492,9 @@ async function predict11(input, config3) {
}
async function process5(input, background, config3) {
var _a;
if (busy)
return null;
busy = true;
if (!config3.segmentation.enabled)
config3.segmentation.enabled = true;
if (!model9)
@ -10516,8 +10522,9 @@ async function process5(input, background, config3) {
cData.data[4 * i + 3] = (255 - alpha[4 * i + 3]) / 255 * cData.data[4 * i + 3] + alpha[4 * i + 3] / 255 * fgData[4 * i + 3];
}
ctx.putImageData(cData, 0, 0);
return c;
img.canvas = c;
}
busy = false;
return img.canvas;
}
@ -11322,8 +11329,6 @@ var Human = class {
this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false);
this.tf.ENV.set("WEBGL_CPU_FORWARD", true);
this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV", true);
if (!this.config.object.enabled)
this.tf.ENV.set("WEBGL_FORCE_F16_TEXTURES", true);
if (typeof this.config["deallocate"] !== "undefined" && this.config["deallocate"]) {
log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);

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@ -1,10 +1,10 @@
/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
@ -4318,6 +4318,8 @@ async function predict3(image18, config3, idx, count2) {
// src/face.ts
var calculateGaze = (face5) => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
if (!face5.annotations["rightEyeIris"] || !face5.annotations["leftEyeIris"])
return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1];
const eyeRatio = 1;
const left = face5.mesh[33][2] > face5.mesh[263][2];
@ -9970,7 +9972,7 @@ async function face2(inCanvas2, result, drawOptions) {
ctx.fill();
}
}
if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing)) {
if (localOptions.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
ctx.strokeStyle = "pink";
ctx.beginPath();
const leftGaze = [
@ -10419,6 +10421,7 @@ function calc(newResult) {
// src/segmentation/segmentation.ts
var tf20 = __toModule(require_tfjs_esm());
var model9;
var busy = false;
async function load12(config3) {
if (!model9) {
model9 = await tf20.loadGraphModel(join(config3.modelBasePath, config3.segmentation.modelPath));
@ -10489,6 +10492,9 @@ async function predict11(input, config3) {
}
async function process5(input, background, config3) {
var _a;
if (busy)
return null;
busy = true;
if (!config3.segmentation.enabled)
config3.segmentation.enabled = true;
if (!model9)
@ -10516,8 +10522,9 @@ async function process5(input, background, config3) {
cData.data[4 * i + 3] = (255 - alpha[4 * i + 3]) / 255 * cData.data[4 * i + 3] + alpha[4 * i + 3] / 255 * fgData[4 * i + 3];
}
ctx.putImageData(cData, 0, 0);
return c;
img.canvas = c;
}
busy = false;
return img.canvas;
}
@ -11322,8 +11329,6 @@ var Human = class {
this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false);
this.tf.ENV.set("WEBGL_CPU_FORWARD", true);
this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV", true);
if (!this.config.object.enabled)
this.tf.ENV.set("WEBGL_FORCE_F16_TEXTURES", true);
if (typeof this.config["deallocate"] !== "undefined" && this.config["deallocate"]) {
log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);

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server/build.log
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@ -1,21 +1,21 @@
2021-06-05 12:59:25 INFO:  @vladmandic/human version 2.0.0
2021-06-05 12:59:25 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
2021-06-05 12:59:25 INFO:  Toolchain: tfjs: 3.7.0 esbuild 0.12.6; typescript 4.2.4; typedoc: 0.20.36 eslint: 7.27.0
2021-06-05 12:59:25 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true}
2021-06-05 12:59:25 STATE: target: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1298,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 12:59:25 STATE: target: node type: node: {"imports":41,"importBytes":429859,"outputBytes":375893,"outputFiles":"dist/human.node.js"}
2021-06-05 12:59:25 STATE: target: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1306,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 12:59:25 STATE: target: nodeGPU type: node: {"imports":41,"importBytes":429867,"outputBytes":375897,"outputFiles":"dist/human.node-gpu.js"}
2021-06-05 12:59:25 STATE: target: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1373,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 12:59:26 STATE: target: nodeWASM type: node: {"imports":41,"importBytes":429934,"outputBytes":375969,"outputFiles":"dist/human.node-wasm.js"}
2021-06-05 12:59:26 STATE: target: browserNoBundle type: tfjs: {"imports":1,"importBytes":2478,"outputBytes":1400,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 12:59:26 STATE: target: browserNoBundle type: esm: {"imports":41,"importBytes":429961,"outputBytes":247750,"outputFiles":"dist/human.esm-nobundle.js"}
2021-06-05 12:59:26 STATE: target: browserBundle type: tfjs: {"modules":1299,"moduleBytes":4230827,"imports":7,"importBytes":2478,"outputBytes":1140326,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 12:59:27 STATE: target: browserBundle type: iife: {"imports":41,"importBytes":1568887,"outputBytes":1383936,"outputFiles":"dist/human.js"}
2021-06-05 12:59:27 STATE: target: browserBundle type: esm: {"imports":41,"importBytes":1568887,"outputBytes":1383928,"outputFiles":"dist/human.esm.js"}
2021-06-05 12:59:27 INFO:  Running Linter: ["server/","demo/","src/","test/"]
2021-06-05 12:59:55 INFO:  Linter complete: files: 69 errors: 0 warnings: 0
2021-06-05 12:59:55 INFO:  Generate Typings: ["src/human.ts"] outDir: ["types"]
2021-06-05 13:00:13 INFO:  Generate ChangeLog: ["/home/vlado/dev/human/CHANGELOG.md"]
2021-06-05 13:00:13 INFO:  Generate TypeDocs: ["src/human.ts"] outDir: ["typedoc"]
2021-06-05 13:00:29 INFO:  Documentation generated at /home/vlado/dev/human/typedoc 1
2021-06-05 16:11:51 INFO:  @vladmandic/human version 2.0.0
2021-06-05 16:11:51 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
2021-06-05 16:11:51 INFO:  Toolchain: tfjs: 3.7.0 esbuild 0.12.6; typescript 4.2.4; typedoc: 0.20.36 eslint: 7.27.0
2021-06-05 16:11:51 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true}
2021-06-05 16:11:51 STATE: target: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1298,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 16:11:51 STATE: target: node type: node: {"imports":41,"importBytes":430197,"outputBytes":376126,"outputFiles":"dist/human.node.js"}
2021-06-05 16:11:51 STATE: target: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1306,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 16:11:51 STATE: target: nodeGPU type: node: {"imports":41,"importBytes":430205,"outputBytes":376130,"outputFiles":"dist/human.node-gpu.js"}
2021-06-05 16:11:51 STATE: target: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1373,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 16:11:51 STATE: target: nodeWASM type: node: {"imports":41,"importBytes":430272,"outputBytes":376202,"outputFiles":"dist/human.node-wasm.js"}
2021-06-05 16:11:51 STATE: target: browserNoBundle type: tfjs: {"imports":1,"importBytes":2478,"outputBytes":1400,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 16:11:51 STATE: target: browserNoBundle type: esm: {"imports":41,"importBytes":430299,"outputBytes":247921,"outputFiles":"dist/human.esm-nobundle.js"}
2021-06-05 16:11:52 STATE: target: browserBundle type: tfjs: {"modules":1299,"moduleBytes":4230827,"imports":7,"importBytes":2478,"outputBytes":1140326,"outputFiles":"dist/tfjs.esm.js"}
2021-06-05 16:11:52 STATE: target: browserBundle type: iife: {"imports":41,"importBytes":1569225,"outputBytes":1384109,"outputFiles":"dist/human.js"}
2021-06-05 16:11:52 STATE: target: browserBundle type: esm: {"imports":41,"importBytes":1569225,"outputBytes":1384101,"outputFiles":"dist/human.esm.js"}
2021-06-05 16:11:52 INFO:  Running Linter: ["server/","demo/","src/","test/"]
2021-06-05 16:12:25 INFO:  Linter complete: files: 69 errors: 0 warnings: 0
2021-06-05 16:12:25 INFO:  Generate Typings: ["src/human.ts"] outDir: ["types"]
2021-06-05 16:12:44 INFO:  Generate ChangeLog: ["/home/vlado/dev/human/CHANGELOG.md"]
2021-06-05 16:12:44 INFO:  Generate TypeDocs: ["src/human.ts"] outDir: ["typedoc"]
2021-06-05 16:13:00 INFO:  Documentation generated at /home/vlado/dev/human/typedoc 1

2
src/draw/draw.ts
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@ -240,7 +240,7 @@ export async function face(inCanvas: HTMLCanvasElement, result: Array<Face>, dra
ctx.fill();
}
}
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing) {
if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {
ctx.strokeStyle = 'pink';
ctx.beginPath();

1
src/face.ts
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@ -16,6 +16,7 @@ const rad2deg = (theta) => Math.round((theta * 180) / Math.PI);
const calculateGaze = (face): { bearing: number, strength: number } => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points
if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes
const eyeRatio = 1; // factor to normalize changes x vs y

2
src/human.ts
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@ -370,7 +370,7 @@ export class Human {
this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);
this.tf.ENV.set('WEBGL_CPU_FORWARD', true);
this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);
if (!this.config.object.enabled) this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision
// if (!this.config.object.enabled) this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision
if (typeof this.config['deallocate'] !== 'undefined' && this.config['deallocate']) { // hidden param
log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);
this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);

9
src/segmentation/segmentation.ts
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@ -12,6 +12,7 @@ import { Config } from '../config';
type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;
let model: GraphModel;
let busy = false;
// let blurKernel;
export async function load(config: Config): Promise<GraphModel> {
@ -95,7 +96,9 @@ export async function predict(input: { tensor: Tensor | null, canvas: OffscreenC
return alpha;
}
export async function process(input: Input, background: Input | undefined, config: Config): Promise<HTMLCanvasElement | OffscreenCanvas> {
export async function process(input: Input, background: Input | undefined, config: Config): Promise<HTMLCanvasElement | OffscreenCanvas | null> {
if (busy) return null;
busy = true;
if (!config.segmentation.enabled) config.segmentation.enabled = true; // override config
if (!model) await load(config);
const img = image.process(input, config);
@ -124,8 +127,8 @@ export async function process(input: Input, background: Input | undefined, confi
cData.data[4 * i + 3] = ((255 - alpha[4 * i + 3]) / 255.0 * cData.data[4 * i + 3]) + (alpha[4 * i + 3] / 255.0 * fgData[4 * i + 3]);
}
ctx.putImageData(cData, 0, 0);
return c;
img.canvas = c;
}
busy = false;
return img.canvas;
}

4
typedoc/classes/human.html
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@ -738,7 +738,7 @@
<a name="segmentation-1" class="tsd-anchor"></a>
<h3>segmentation</h3>
<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
<li class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">any</span>, background<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">any</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">&lt;</span><span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span><span class="tsd-signature-symbol">&gt;</span></li>
<li class="tsd-signature tsd-kind-icon">segmentation<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">any</span>, background<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">any</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">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span><span class="tsd-signature-symbol">&gt;</span></li>
</ul>
<ul class="tsd-descriptions">
<li class="tsd-description">
@ -760,7 +760,7 @@
<h5><span class="tsd-flag ts-flagOptional">Optional</span> background: <span class="tsd-signature-type">any</span></h5>
</li>
</ul>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span><span class="tsd-signature-symbol">&gt;</span></h4>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span><span class="tsd-signature-symbol">&gt;</span></h4>
<p>Canvas</p>
</li>
</ul>

2
types/src/human.d.ts vendored
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@ -130,7 +130,7 @@ export declare class Human {
* @param background?: {@link Input}
* @returns Canvas
*/
segmentation(input: Input, background?: Input): Promise<OffscreenCanvas | HTMLCanvasElement>;
segmentation(input: Input, background?: Input): Promise<OffscreenCanvas | HTMLCanvasElement | null>;
/** Enhance method performs additional enhacements to face image previously detected for futher processing
* @param input: Tensor as provided in human.result.face[n].tensor
* @returns Tensor

2
types/src/segmentation/segmentation.d.ts vendored
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@ -9,5 +9,5 @@ export declare function predict(input: {
tensor: Tensor | null;
canvas: OffscreenCanvas | HTMLCanvasElement;
}, config: Config): Promise<Uint8ClampedArray | null>;
export declare function process(input: Input, background: Input | undefined, config: Config): Promise<HTMLCanvasElement | OffscreenCanvas>;
export declare function process(input: Input, background: Input | undefined, config: Config): Promise<HTMLCanvasElement | OffscreenCanvas | null>;
export {};