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switched face embedding to mobileface

pull/91/head
Vladimir Mandic 2021-03-12 12:54:08 -05:00
parent ce92c60b54
commit ea5c9b9be6
33 changed files with 1561 additions and 1619 deletions
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10
CHANGELOG.md
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@ -1,6 +1,6 @@
# @vladmandic/human # @vladmandic/human
Version: **1.0.3** Version: **1.1.0**
Description: **Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition** Description: **Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition**
Author: **Vladimir Mandic <mandic00@live.com>** Author: **Vladimir Mandic <mandic00@live.com>**
@ -11,14 +11,16 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
### **HEAD -> main** 2021/03/11 mandic00@live.com ### **HEAD -> main** 2021/03/11 mandic00@live.com
### **1.0.4** 2021/03/11 mandic00@live.com
- add face return tensor
- add test for face descriptors
- wip on embedding - wip on embedding
- simplify face box coordinate calculations - simplify face box coordinate calculations
- annotated models and removed gender-ssrnet - annotated models and removed gender-ssrnet
- autodetect inputsizes - autodetect inputsizes
### **origin/main** 2021/03/10 mandic00@live.com
### **1.0.3** 2021/03/10 mandic00@live.com ### **1.0.3** 2021/03/10 mandic00@live.com
- strong typing for public classes and hide private classes - strong typing for public classes and hide private classes

2
README.md
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@ -69,7 +69,7 @@ Default models in Human library are:
- **Gender Detection**: Oarriaga Gender - **Gender Detection**: Oarriaga Gender
- **Age Detection**: SSR-Net Age IMDB - **Age Detection**: SSR-Net Age IMDB
- **Body Analysis**: PoseNet - **Body Analysis**: PoseNet
- **Face Embedding**: Sirius-AI MobileFaceNet Embedding - **Face Embedding**: BecauseofAI MobileFace Embedding
Note that alternative models are provided and can be enabled via configuration Note that alternative models are provided and can be enabled via configuration
For example, `PoseNet` model can be switched for `BlazePose` model depending on the use case For example, `PoseNet` model can be switched for `BlazePose` model depending on the use case

10
TODO.md
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@ -8,3 +8,13 @@
- Explore EfficientPose - Explore EfficientPose
<https://github.com/daniegr/EfficientPose> <https://github.com/daniegr/EfficientPose>
<https://github.com/PINTO0309/PINTO_model_zoo/tree/main/084_EfficientPose> <https://github.com/PINTO0309/PINTO_model_zoo/tree/main/084_EfficientPose>
## WiP: Embedding
- Implement offsetRaw
full with and without rotation
full with and without embedding
full with any without mesh
embedding with and without mesh
boxRaw and meshRaw with and without mesh

6
config.js
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@ -121,9 +121,9 @@ export default {
}, },
embedding: { embedding: {
enabled: false, // to improve accuracy of face embedding extraction it is recommended enabled: false, // to improve accuracy of face embedding extraction it is
// to enable detector.rotation and mesh.enabled // highly recommended to enable detector.rotation and mesh.enabled
modelPath: '../models/mobilefacenet.json', modelPath: '../models/mobileface.json',
}, },
}, },

13
demo/browser.js
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@ -84,11 +84,22 @@ let original;
async function calcSimmilariry(result) { async function calcSimmilariry(result) {
document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none'; document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none';
if (!human.config.face.embedding.enabled) return; if (!human.config.face.embedding.enabled) return;
if (!(result?.face?.length > 0) || (result?.face[0]?.embedding?.length !== 192)) return; if (!(result?.face?.length > 0) || (result?.face[0]?.embedding?.length !== 256)) return;
if (!original) { if (!original) {
original = result; original = result;
if (result.face[0].tensor) {
const enhanced = human.enhance(result.face[0]);
if (enhanced) {
const c = document.getElementById('orig');
const squeeze = enhanced.squeeze();
human.tf.browser.toPixels(squeeze, c);
enhanced.dispose();
squeeze.dispose();
}
} else {
document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200); document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);
} }
}
const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding); const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);
document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`; document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;
} }

4
demo/embedding.html
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@ -26,9 +26,9 @@
<body> <body>
<br>Sample Images: <br>Sample Images:
<div id="images"></div> <div id="images"></div>
<br>Selected Face<br> <br>Selected Face (Enhanced)<br>
<canvas id="orig" style="width: 200px; height: 200px;"></canvas> <canvas id="orig" style="width: 200px; height: 200px;"></canvas>
<br>Extracted Faces - click on a face to sort by simmilarity:<br> <br><br>Extracted Faces - click on a face to sort by simmilarity:<br>
<div id="faces"></div> <div id="faces"></div>
</body> </body>
</html> </html>

39
demo/embedding.js
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@ -11,7 +11,7 @@ const userConfig = {
enabled: true, enabled: true,
detector: { rotation: true, return: true }, detector: { rotation: true, return: true },
mesh: { enabled: true }, mesh: { enabled: true },
embedding: { enabled: true, modelPath: '../models/mobilefacenet.json' }, embedding: { enabled: true },
iris: { enabled: false }, iris: { enabled: false },
age: { enabled: false }, age: { enabled: false },
gender: { enabled: false }, gender: { enabled: false },
@ -21,12 +21,15 @@ const userConfig = {
gesture: { enabled: false }, gesture: { enabled: false },
body: { enabled: false }, body: { enabled: false },
}; };
const human = new Human(userConfig);
const human = new Human(userConfig); // new instance of human
const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg']; const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'];
// const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg', // const samples = ['../assets/sample-me.jpg', '../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg',
// '../private/me (1).jpg', '../private/me (2).jpg', '../private/me (3).jpg', '../private/me (4).jpg', '../private/me (5).jpg', '../private/me (6).jpg', '../private/me (7).jpg', '../private/me (8).jpg', // '../private/me (1).jpg', '../private/me (2).jpg', '../private/me (3).jpg', '../private/me (4).jpg', '../private/me (5).jpg', '../private/me (6).jpg', '../private/me (7).jpg', '../private/me (8).jpg',
// '../private/me (9).jpg', '../private/me (10).jpg', '../private/me (11).jpg', '../private/me (12).jpg', '../private/me (13).jpg']; // '../private/me (9).jpg', '../private/me (10).jpg', '../private/me (11).jpg', '../private/me (12).jpg', '../private/me (13).jpg'];
const all = [];
const all = []; // array that will hold all detected faces
function log(...msg) { function log(...msg) {
const dt = new Date(); const dt = new Date();
@ -38,14 +41,24 @@ function log(...msg) {
async function analyze(face) { async function analyze(face) {
log('Face:', face); log('Face:', face);
const box = [[0.05, 0.15, 0.90, 0.85]]; // top, left, bottom, right // if we have face image tensor, enhance it and display it
const crop = human.tf.image.cropAndResize(face.tensor.expandDims(0), box, [0], [200, 200]); // optionally do a tight box crop if (face.tensor) {
const enhanced = human.enhance(face);
if (enhanced) {
const c = document.getElementById('orig'); const c = document.getElementById('orig');
human.tf.browser.toPixels(crop.squeeze(), c); const squeeze = enhanced.squeeze();
human.tf.browser.toPixels(squeeze, c);
enhanced.dispose();
squeeze.dispose();
}
}
// loop through all canvases that contain faces
const canvases = document.getElementsByClassName('face'); const canvases = document.getElementsByClassName('face');
for (const canvas of canvases) { for (const canvas of canvases) {
// calculate simmilarity from selected face to current one in the loop
const res = human.simmilarity(face.embedding, all[canvas.tag.sample][canvas.tag.face].embedding); const res = human.simmilarity(face.embedding, all[canvas.tag.sample][canvas.tag.face].embedding);
// draw the canvas and simmilarity score
canvas.title = res; canvas.title = res;
await human.tf.browser.toPixels(all[canvas.tag.sample][canvas.tag.face].tensor, canvas); await human.tf.browser.toPixels(all[canvas.tag.sample][canvas.tag.face].tensor, canvas);
const ctx = canvas.getContext('2d'); const ctx = canvas.getContext('2d');
@ -55,6 +68,8 @@ async function analyze(face) {
ctx.fillStyle = 'rgba(255, 255, 255, 1)'; ctx.fillStyle = 'rgba(255, 255, 255, 1)';
ctx.fillText(`${(100 * res).toFixed(1)}%`, 4, 20); ctx.fillText(`${(100 * res).toFixed(1)}%`, 4, 20);
} }
// sort all faces by simmilarity
const sorted = document.getElementById('faces'); const sorted = document.getElementById('faces');
[...sorted.children] [...sorted.children]
.sort((a, b) => parseFloat(b.title) - parseFloat(a.title)) .sort((a, b) => parseFloat(b.title) - parseFloat(a.title))
@ -70,22 +85,26 @@ async function faces(index, res) {
canvas.width = 200; canvas.width = 200;
canvas.height = 200; canvas.height = 200;
canvas.className = 'face'; canvas.className = 'face';
// mouse click on any face canvas triggers analysis
canvas.addEventListener('click', (evt) => { canvas.addEventListener('click', (evt) => {
log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face); log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face);
analyze(all[evt.target.tag.sample][evt.target.tag.face]); analyze(all[evt.target.tag.sample][evt.target.tag.face]);
}); });
// if we actually got face image tensor, draw canvas with that face
if (res.face[i].tensor) {
human.tf.browser.toPixels(res.face[i].tensor, canvas); human.tf.browser.toPixels(res.face[i].tensor, canvas);
document.getElementById('faces').appendChild(canvas); document.getElementById('faces').appendChild(canvas);
} }
}
} }
async function add(index) { async function add(index) {
log('Add image:', samples[index]); log('Add image:', samples[index]);
return new Promise((resolve) => { return new Promise((resolve) => {
const img = new Image(100, 100); const img = new Image(100, 100);
img.onload = () => { img.onload = () => { // must wait until image is loaded
human.detect(img).then((res) => faces(index, res)); human.detect(img).then((res) => faces(index, res)); // then wait until image is analyzed
document.getElementById('images').appendChild(img); document.getElementById('images').appendChild(img); // and finally we can add it
resolve(true); resolve(true);
}; };
img.title = samples[index]; img.title = samples[index];
@ -95,7 +114,7 @@ async function add(index) {
async function main() { async function main() {
await human.load(); await human.load();
for (const i in samples) await add(i); for (const i in samples) await add(i); // download and analyze all images
log('Ready'); log('Ready');
} }

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demo/node.js
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@ -1,8 +1,10 @@
const log = require('@vladmandic/pilogger'); const log = require('@vladmandic/pilogger');
const fs = require('fs'); const fs = require('fs');
const process = require('process'); const process = require('process');
// for Node, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
// for NodeJS, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu'); const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
// load specific version of Human library that matches TensorFlow mode // load specific version of Human library that matches TensorFlow mode
const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default; const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
@ -15,15 +17,16 @@ const myConfig = {
async: false, async: false,
face: { face: {
enabled: true, enabled: true,
detector: { modelPath: 'file://models/blazeface-back.json', enabled: true }, detector: { modelPath: 'file://models/blazeface-back.json', enabled: true, rotation: false },
mesh: { modelPath: 'file://models/facemesh.json', enabled: true }, mesh: { modelPath: 'file://models/facemesh.json', enabled: true },
iris: { modelPath: 'file://models/iris.json', enabled: true }, iris: { modelPath: 'file://models/iris.json', enabled: true },
age: { modelPath: 'file://models/age.json', enabled: true }, age: { modelPath: 'file://models/age.json', enabled: true },
gender: { modelPath: 'file://models/gender.json', enabled: true }, gender: { modelPath: 'file://models/gender.json', enabled: true },
emotion: { modelPath: 'file://models/emotion.json', enabled: true }, emotion: { modelPath: 'file://models/emotion.json', enabled: true },
embedding: { modelPath: 'file://models/mobileface.json', enabled: true },
}, },
// body: { modelPath: 'file://models/blazepose.json', modelType: 'blazepose', enabled: true }, // body: { modelPath: 'file://models/blazepose.json', enabled: true },
body: { modelPath: 'file://models/posenet.json', modelType: 'posenet', enabled: true }, body: { modelPath: 'file://models/posenet.json', enabled: true },
hand: { hand: {
enabled: true, enabled: true,
detector: { modelPath: 'file://models/handdetect.json' }, detector: { modelPath: 'file://models/handdetect.json' },

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models/mobileface.json Normal file
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@ -0,0 +1,202 @@
{
"format": "graph-model",
"generatedBy": "2.4.1",
"convertedBy": "https://github.com/vladmandic",
"signature":
{
"inputs":
{
"data:0": {"name":"data:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"-1"},{"size":"112"},{"size":"112"},{"size":"3"}]}}
},
"outputs":
{
"batchnorm0/add_1:0": {"name":"batchnorm0/add_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"-1"},{"size":"256"}]}}
}
},
"modelTopology":
{
"node":
[
{"name":"Maximum_12/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"stage4_unit1_prelu2_gamma","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}}}},
{"name":"Minimum_12/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Maximum_11/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"stage4_unit1_prelu1_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_11/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Pad_11/paddings","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
{"name":"stage4_unit1_conv1_df","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"128"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Pad_12/paddings","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
{"name":"stage4_unit1_conv2_df","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"128"},{"size":"1"}]}}}}},
{"name":"Maximum_10/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"stage4_unit1_prelu0_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_10/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"Maximum_9/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage3_unit1_prelu2_gamma","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"64"}]}}}}},
{"name":"Minimum_9/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"Maximum_8/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage3_unit1_prelu1_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"64"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_8/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Pad_8/paddings","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
{"name":"stage3_unit1_conv1_df","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"64"},{"size":"1"}]}}}}},
{"name":"Pad_9/paddings","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}}}},
{"name":"stage3_unit1_conv2_df","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"64"},{"size":"1"}]}}}}},
{"name":"Maximum_7/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage3_unit1_prelu0_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"64"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_7/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"Maximum_6/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage2_unit1_prelu2_gamma","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}}}},
{"name":"Minimum_6/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Maximum_5/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage2_unit1_prelu1_gamma","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}}}},
{"name":"Minimum_5/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"Pad_5/paddings","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}}}},
{"name":"stage2_unit1_conv1_df","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"32"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Pad_6/paddings","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
{"name":"stage2_unit1_conv2_df","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"32"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Maximum_4/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"stage2_unit1_prelu0_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_4/x","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
{"name":"Maximum_3/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"stage1_unit1_prelu2_gamma","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Minimum_3/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Maximum_2/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"stage1_unit1_prelu1_gamma","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}}}},
{"name":"Minimum_2/x","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
{"name":"Pad_2/paddings","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}}}},
{"name":"stage1_unit1_conv1_df","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"32"},{"size":"1"}]}}}}},
{"name":"Pad_3/paddings","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"},{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
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"weights": [{"name":"Maximum_12/x","shape":[],"dtype":"float32"},{"name":"stage4_unit1_prelu2_gamma","shape":[128],"dtype":"float32"},{"name":"Minimum_12/x","shape":[],"dtype":"float32"},{"name":"Maximum_11/x","shape":[],"dtype":"float32"},{"name":"stage4_unit1_prelu1_gamma","shape":[128],"dtype":"float32"},{"name":"Minimum_11/x","shape":[],"dtype":"float32"},{"name":"Pad_11/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage4_unit1_conv1_df","shape":[3,3,128,1],"dtype":"float32"},{"name":"Pad_12/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage4_unit1_conv2_df","shape":[3,3,128,1],"dtype":"float32"},{"name":"Maximum_10/x","shape":[],"dtype":"float32"},{"name":"stage4_unit1_prelu0_gamma","shape":[128],"dtype":"float32"},{"name":"Minimum_10/x","shape":[],"dtype":"float32"},{"name":"Maximum_9/x","shape":[],"dtype":"float32"},{"name":"stage3_unit1_prelu2_gamma","shape":[64],"dtype":"float32"},{"name":"Minimum_9/x","shape":[],"dtype":"float32"},{"name":"Maximum_8/x","shape":[],"dtype":"float32"},{"name":"stage3_unit1_prelu1_gamma","shape":[64],"dtype":"float32"},{"name":"Minimum_8/x","shape":[],"dtype":"float32"},{"name":"Pad_8/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage3_unit1_conv1_df","shape":[3,3,64,1],"dtype":"float32"},{"name":"Pad_9/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage3_unit1_conv2_df","shape":[3,3,64,1],"dtype":"float32"},{"name":"Maximum_7/x","shape":[],"dtype":"float32"},{"name":"stage3_unit1_prelu0_gamma","shape":[64],"dtype":"float32"},{"name":"Minimum_7/x","shape":[],"dtype":"float32"},{"name":"Maximum_6/x","shape":[],"dtype":"float32"},{"name":"stage2_unit1_prelu2_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_6/x","shape":[],"dtype":"float32"},{"name":"Maximum_5/x","shape":[],"dtype":"float32"},{"name":"stage2_unit1_prelu1_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_5/x","shape":[],"dtype":"float32"},{"name":"Pad_5/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage2_unit1_conv1_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"Pad_6/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage2_unit1_conv2_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"Maximum_4/x","shape":[],"dtype":"float32"},{"name":"stage2_unit1_prelu0_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_4/x","shape":[],"dtype":"float32"},{"name":"Maximum_3/x","shape":[],"dtype":"float32"},{"name":"stage1_unit1_prelu2_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_3/x","shape":[],"dtype":"float32"},{"name":"Maximum_2/x","shape":[],"dtype":"float32"},{"name":"stage1_unit1_prelu1_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_2/x","shape":[],"dtype":"float32"},{"name":"Pad_2/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage1_unit1_conv1_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"Pad_3/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage1_unit1_conv2_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"Maximum_1/x","shape":[],"dtype":"float32"},{"name":"stage1_unit1_prelu0_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum_1/x","shape":[],"dtype":"float32"},{"name":"Maximum/x","shape":[],"dtype":"float32"},{"name":"prelu1_gamma","shape":[32],"dtype":"float32"},{"name":"Minimum/x","shape":[],"dtype":"float32"},{"name":"minusscalar0_second","shape":[1],"dtype":"float32"},{"name":"mulscalar0_second","shape":[1],"dtype":"float32"},{"name":"Pad/paddings","shape":[4,2],"dtype":"int32"},{"name":"conv1_weight","shape":[3,3,3,32],"dtype":"float32"},{"name":"stage1_unit1_conv0_0_weight","shape":[1,1,32,32],"dtype":"float32"},{"name":"Pad_1/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage1_unit1_conv0_1_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"stage2_unit1_conv0_0_weight","shape":[1,1,32,32],"dtype":"float32"},{"name":"Pad_4/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage2_unit1_conv0_1_df","shape":[3,3,32,1],"dtype":"float32"},{"name":"stage3_unit1_conv0_0_weight","shape":[1,1,32,64],"dtype":"float32"},{"name":"Pad_7/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage3_unit1_conv0_1_df","shape":[3,3,64,1],"dtype":"float32"},{"name":"stage4_unit1_conv0_0_weight","shape":[1,1,64,128],"dtype":"float32"},{"name":"Pad_10/paddings","shape":[4,2],"dtype":"int32"},{"name":"stage4_unit1_conv0_1_df","shape":[3,3,128,1],"dtype":"float32"},{"name":"flatten/Const","shape":[2],"dtype":"int32"},{"name":"dense/kernel","shape":[2048,256],"dtype":"float32"},{"name":"batchnorm0/mul","shape":[256],"dtype":"float32"},{"name":"batchnorm0/sub","shape":[256],"dtype":"float32"}]
}
]
}

BIN
models/mobilefacenet.bin
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386
models/mobilefacenet.json
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32
package-lock.json generated
View File

@ -1,12 +1,12 @@
{ {
"name": "@vladmandic/human", "name": "@vladmandic/human",
"version": "1.0.4", "version": "1.1.0",
"lockfileVersion": 2, "lockfileVersion": 2,
"requires": true, "requires": true,
"packages": { "packages": {
"": { "": {
"name": "@vladmandic/human", "name": "@vladmandic/human",
"version": "1.0.4", "version": "1.1.0",
"license": "MIT", "license": "MIT",
"devDependencies": { "devDependencies": {
"@tensorflow/tfjs": "^3.3.0", "@tensorflow/tfjs": "^3.3.0",
@ -19,13 +19,13 @@
"@tensorflow/tfjs-layers": "^3.3.0", "@tensorflow/tfjs-layers": "^3.3.0",
"@tensorflow/tfjs-node": "^3.3.0", "@tensorflow/tfjs-node": "^3.3.0",
"@tensorflow/tfjs-node-gpu": "^3.3.0", "@tensorflow/tfjs-node-gpu": "^3.3.0",
"@types/node": "^14.14.33", "@types/node": "^14.14.34",
"@typescript-eslint/eslint-plugin": "^4.17.0", "@typescript-eslint/eslint-plugin": "^4.17.0",
"@typescript-eslint/parser": "^4.17.0", "@typescript-eslint/parser": "^4.17.0",
"@vladmandic/pilogger": "^0.2.14", "@vladmandic/pilogger": "^0.2.14",
"chokidar": "^3.5.1", "chokidar": "^3.5.1",
"dayjs": "^1.10.4", "dayjs": "^1.10.4",
"esbuild": "^0.9.0", "esbuild": "=0.9.0",
"eslint": "^7.21.0", "eslint": "^7.21.0",
"eslint-config-airbnb-base": "^14.2.1", "eslint-config-airbnb-base": "^14.2.1",
"eslint-plugin-import": "^2.22.1", "eslint-plugin-import": "^2.22.1",
@ -419,9 +419,9 @@
"dev": true "dev": true
}, },
"node_modules/@types/node": { "node_modules/@types/node": {
"version": "14.14.33", "version": "14.14.34",
"resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.33.tgz", "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.34.tgz",
"integrity": "sha512-oJqcTrgPUF29oUP8AsUqbXGJNuPutsetaa9kTQAQce5Lx5dTYWV02ScBiT/k1BX/Z7pKeqedmvp39Wu4zR7N7g==", "integrity": "sha512-dBPaxocOK6UVyvhbnpFIj2W+S+1cBTkHQbFQfeeJhoKFbzYcVUGHvddeWPSucKATb3F0+pgDq0i6ghEaZjsugA==",
"dev": true "dev": true
}, },
"node_modules/@types/node-fetch": { "node_modules/@types/node-fetch": {
@ -1962,9 +1962,9 @@
} }
}, },
"node_modules/google-protobuf": { "node_modules/google-protobuf": {
"version": "3.15.5", "version": "3.15.6",
"resolved": "https://registry.npmjs.org/google-protobuf/-/google-protobuf-3.15.5.tgz", "resolved": "https://registry.npmjs.org/google-protobuf/-/google-protobuf-3.15.6.tgz",
"integrity": "sha512-6bLpAI4nMIQODlegR7OevgkCoyOj5frLVDArUpeuBWad7XWUNWMGP0v5lz1/aeUI6Yf3cG9XA6acZkPxom4SEw==", "integrity": "sha512-p65NyhIZFHFUxbIPOm6cygg2rCjK+2uDCxruOG3RaWKM9R4rBGX0STmlJoSOhoyAG8Fha7U8FP4pQomAV1JXsA==",
"dev": true "dev": true
}, },
"node_modules/graceful-fs": { "node_modules/graceful-fs": {
@ -4236,9 +4236,9 @@
"dev": true "dev": true
}, },
"@types/node": { "@types/node": {
"version": "14.14.33", "version": "14.14.34",
"resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.33.tgz", "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.34.tgz",
"integrity": "sha512-oJqcTrgPUF29oUP8AsUqbXGJNuPutsetaa9kTQAQce5Lx5dTYWV02ScBiT/k1BX/Z7pKeqedmvp39Wu4zR7N7g==", "integrity": "sha512-dBPaxocOK6UVyvhbnpFIj2W+S+1cBTkHQbFQfeeJhoKFbzYcVUGHvddeWPSucKATb3F0+pgDq0i6ghEaZjsugA==",
"dev": true "dev": true
}, },
"@types/node-fetch": { "@types/node-fetch": {
@ -5408,9 +5408,9 @@
} }
}, },
"google-protobuf": { "google-protobuf": {
"version": "3.15.5", "version": "3.15.6",
"resolved": "https://registry.npmjs.org/google-protobuf/-/google-protobuf-3.15.5.tgz", "resolved": "https://registry.npmjs.org/google-protobuf/-/google-protobuf-3.15.6.tgz",
"integrity": "sha512-6bLpAI4nMIQODlegR7OevgkCoyOj5frLVDArUpeuBWad7XWUNWMGP0v5lz1/aeUI6Yf3cG9XA6acZkPxom4SEw==", "integrity": "sha512-p65NyhIZFHFUxbIPOm6cygg2rCjK+2uDCxruOG3RaWKM9R4rBGX0STmlJoSOhoyAG8Fha7U8FP4pQomAV1JXsA==",
"dev": true "dev": true
}, },
"graceful-fs": { "graceful-fs": {

6
package.json
View File

@ -1,6 +1,6 @@
{ {
"name": "@vladmandic/human", "name": "@vladmandic/human",
"version": "1.0.4", "version": "1.1.0",
"description": "Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition", "description": "Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",
"sideEffects": false, "sideEffects": false,
"main": "dist/human.node.js", "main": "dist/human.node.js",
@ -54,13 +54,13 @@
"@tensorflow/tfjs-layers": "^3.3.0", "@tensorflow/tfjs-layers": "^3.3.0",
"@tensorflow/tfjs-node": "^3.3.0", "@tensorflow/tfjs-node": "^3.3.0",
"@tensorflow/tfjs-node-gpu": "^3.3.0", "@tensorflow/tfjs-node-gpu": "^3.3.0",
"@types/node": "^14.14.33", "@types/node": "^14.14.34",
"@typescript-eslint/eslint-plugin": "^4.17.0", "@typescript-eslint/eslint-plugin": "^4.17.0",
"@typescript-eslint/parser": "^4.17.0", "@typescript-eslint/parser": "^4.17.0",
"@vladmandic/pilogger": "^0.2.14", "@vladmandic/pilogger": "^0.2.14",
"chokidar": "^3.5.1", "chokidar": "^3.5.1",
"dayjs": "^1.10.4", "dayjs": "^1.10.4",
"esbuild": "^0.9.0", "esbuild": "=0.9.0",
"eslint": "^7.21.0", "eslint": "^7.21.0",
"eslint-config-airbnb-base": "^14.2.1", "eslint-config-airbnb-base": "^14.2.1",
"eslint-plugin-import": "^2.22.1", "eslint-plugin-import": "^2.22.1",

30
src/blazeface/facemesh.ts
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@ -18,19 +18,42 @@ export class MediaPipeFaceMesh {
const results: Array<{}> = []; const results: Array<{}> = [];
for (const prediction of (predictions || [])) { for (const prediction of (predictions || [])) {
if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
const mesh = prediction.coords ? prediction.coords.arraySync() : null; const mesh = prediction.coords ? prediction.coords.arraySync() : [];
const meshRaw = prediction.rawCoords; const meshRaw = mesh.map((pt) => [
pt[0] / input.shape[2],
pt[1] / input.shape[1],
pt[2] / this.facePipeline.meshSize,
]);
const annotations = {}; const annotations = {};
if (mesh && mesh.length > 0) { if (mesh && mesh.length > 0) {
for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]); for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);
} }
const boxRaw = (prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null; // const boxRaw = (prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;
const box = prediction.box ? [ const box = prediction.box ? [
Math.max(0, prediction.box.startPoint[0]), Math.max(0, prediction.box.startPoint[0]),
Math.max(0, prediction.box.startPoint[1]), Math.max(0, prediction.box.startPoint[1]),
Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0], Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],
Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1], Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],
] : 0; ] : 0;
const boxRaw = prediction.box ? [
Math.max(0, prediction.box.startPoint[0] / input.shape[2]),
Math.max(0, prediction.box.startPoint[1] / input.shape[1]),
Math.min(input.shape[2], (prediction.box.endPoint[0]) - prediction.box.startPoint[0]) / input.shape[2],
Math.min(input.shape[1], (prediction.box.endPoint[1]) - prediction.box.startPoint[1]) / input.shape[2],
] : [];
/*
let offsetRaw = <any>[];
if (meshRaw.length > 0 && boxRaw.length > 0) {
const dimX = meshRaw.map((pt) => pt[0]);
const dimY = meshRaw.map((pt) => pt[1]);
offsetRaw = [
Math.max(0, 0 + Math.min(...dimY) - boxRaw[0]), // distance of detected face border to box top edge
Math.max(0, 0 + Math.min(...dimX) - boxRaw[1]), // distance of detected face border to box left edge
Math.min(1, 1 - Math.max(...dimY) + boxRaw[2]), // distance of detected face border to box bottom edge
Math.min(1, 1 - Math.max(...dimX) + boxRaw[3]), // distance of detected face border to box right edge
];
}
*/
results.push({ results.push({
confidence: prediction.faceConfidence || prediction.boxConfidence || 0, confidence: prediction.faceConfidence || prediction.boxConfidence || 0,
boxConfidence: prediction.boxConfidence, boxConfidence: prediction.boxConfidence,
@ -39,6 +62,7 @@ export class MediaPipeFaceMesh {
mesh, mesh,
boxRaw, boxRaw,
meshRaw, meshRaw,
// offsetRaw,
annotations, annotations,
image: prediction.image ? tf.clone(prediction.image) : null, image: prediction.image ? tf.clone(prediction.image) : null,
}); });

4
src/blazeface/facepipeline.ts
View File

@ -197,6 +197,8 @@ export class Pipeline {
} }
let results = tf.tidy(() => this.storedBoxes.map((box, i) => { let results = tf.tidy(() => this.storedBoxes.map((box, i) => {
const boxConfidence = box.confidence;
// The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box). // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).
let face; let face;
let angle = 0; let angle = 0;
@ -282,7 +284,7 @@ export class Pipeline {
coords: transformedCoords, coords: transformedCoords,
box, box,
faceConfidence, faceConfidence,
boxConfidence: box.confidence, boxConfidence,
image: face, image: face,
rawCoords, rawCoords,
}; };

77
src/embedding/embedding.ts
View File

@ -2,10 +2,6 @@ import { log } from '../log';
import * as tf from '../../dist/tfjs.esm.js'; import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile'; import * as profile from '../profile';
// original: https://github.com/sirius-ai/MobileFaceNet_TF
// modified: https://github.com/sirius-ai/MobileFaceNet_TF/issues/46
// download: https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip
let model; let model;
export async function load(config) { export async function load(config) {
@ -26,32 +22,63 @@ export function simmilarity(embedding1, embedding2, order = 2) {
.map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared
.reduce((sum, now) => (sum + now), 0) // sum all distances .reduce((sum, now) => (sum + now), 0) // sum all distances
** (1 / order); // get root of ** (1 / order); // get root of
const res = Math.max(Math.trunc(1000 * (1 - (50 * distance))) / 1000, 0); const res = Math.max(Math.trunc(1000 * (1 - (1 * distance))) / 1000, 0);
return res; return res;
} }
export function enhance(input) {
const image = tf.tidy(() => {
// input received from detector is already normalized to 0..1
// input is also assumed to be straightened
// const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model
// do a tight crop of image and resize it to fit the model
// maybe offsets are already prepared by face model, if not use empirical values
const box = input.offsetRaw
? [input.offsetRaw] // crop based on face mesh borders
: [[0.05, 0.15, 0.85, 0.85]]; // fixed crop for top, left, bottom, right
const tensor = input.image || input.tensor;
const crop = tensor.shape.length === 3
? tf.image.cropAndResize(tensor.expandDims(0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch if missing
: tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
// convert to black&white to avoid colorization impact
const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
const [red, green, blue] = tf.split(crop, 3, 3);
const redNorm = tf.mul(red, rgb[0]);
const greenNorm = tf.mul(green, rgb[1]);
const blueNorm = tf.mul(blue, rgb[2]);
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);
// normalize brightness from 0..1
const darken = merge.sub(merge.min());
const lighten = darken.div(darken.max());
return lighten;
});
return image;
}
export async function predict(input, config) { export async function predict(input, config) {
if (!model) return null; if (!model) return null;
return new Promise(async (resolve) => { return new Promise(async (resolve) => {
const image = tf.tidy(() => { const image = enhance(input);
const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // input is already normalized to 0..1 // let data: Array<[]> = [];
const box = [[0.05, 0.15, 0.90, 0.85]]; // top, left, bottom, right let data: Array<number> = [];
const crop = tf.image.cropAndResize(data, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]); // optionally do a tight box crop
// const norm = crop.sub(crop.min()).sub(0.5); // trick to normalize around image mean value
const norm = crop.sub(0.5);
return norm;
});
let data: Array<[]> = [];
if (config.face.embedding.enabled) { if (config.face.embedding.enabled) {
if (!config.profile) { if (!config.profile) {
const res = await model.predict({ img_inputs: image }); const res = await model.predict(image);
// optional normalize outputs with l2 normalization
/*
const scaled = tf.tidy(() => { const scaled = tf.tidy(() => {
const l2 = res.norm('euclidean'); const l2 = res.norm('euclidean');
const scale = res.div(l2); const scale = res.div(l2);
return scale; return scale;
}); });
data = scaled.dataSync(); // convert object array to standard array */
tf.dispose(scaled); data = res.dataSync();
// tf.dispose(scaled);
tf.dispose(res); tf.dispose(res);
} else { } else {
const profileData = await tf.profile(() => model.predict({ img_inputs: image })); const profileData = await tf.profile(() => model.predict({ img_inputs: image }));
@ -64,3 +91,19 @@ export async function predict(input, config) {
resolve(data); resolve(data);
}); });
} }
/*
git clone https://github.com/becauseofAI/MobileFace
cd MobileFace/MobileFace_Identification
mmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved
saved_model_cli show --dir saved/
tensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/
~/dev/detector/signature.js graph/
2021-03-12 08:25:12 DATA: created on: 2021-03-12T13:17:11.960Z
2021-03-12 08:25:12 INFO: graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json
2021-03-12 08:25:12 INFO: size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }
2021-03-12 08:25:12 INFO: model inputs based on signature
2021-03-12 08:25:12 INFO: model outputs based on signature
2021-03-12 08:25:12 DATA: inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]
2021-03-12 08:25:12 DATA: outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]
*/

13
src/human.ts
View File

@ -151,6 +151,11 @@ class Human {
return 0; return 0;
} }
enhance(input: any): any {
if (this.config.face.embedding.enabled) return embedding.enhance(input);
return null;
}
// preload models, not explicitly required as it's done automatically on first use // preload models, not explicitly required as it's done automatically on first use
async load(userConfig = null) { async load(userConfig = null) {
this.state = 'load'; this.state = 'load';
@ -359,11 +364,11 @@ class Human {
// run emotion, inherits face from blazeface // run emotion, inherits face from blazeface
this.#analyze('Start Embedding:'); this.#analyze('Start Embedding:');
if (this.config.async) { if (this.config.async) {
embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : []; embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face, this.config) : [];
} else { } else {
this.state = 'run:embedding'; this.state = 'run:embedding';
timeStamp = now(); timeStamp = now();
embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : []; embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face, this.config) : [];
this.#perf.embedding = Math.trunc(now() - timeStamp); this.#perf.embedding = Math.trunc(now() - timeStamp);
} }
this.#analyze('End Emotion:'); this.#analyze('End Emotion:');
@ -388,6 +393,8 @@ class Human {
// combine results // combine results
faceRes.push({ faceRes.push({
...face,
/*
confidence: face.confidence, confidence: face.confidence,
faceConfidence: face.faceConfidence, faceConfidence: face.faceConfidence,
boxConfidence: face.boxConfidence, boxConfidence: face.boxConfidence,
@ -395,7 +402,9 @@ class Human {
mesh: face.mesh, mesh: face.mesh,
boxRaw: face.boxRaw, boxRaw: face.boxRaw,
meshRaw: face.meshRaw, meshRaw: face.meshRaw,
offsetRaw: face.offsetRaw,
annotations: face.annotations, annotations: face.annotations,
*/
age: ageRes.age, age: ageRes.age,
gender: genderRes.gender, gender: genderRes.gender,
genderConfidence: genderRes.confidence, genderConfidence: genderRes.confidence,

1
src/posenet/posenet.ts
View File

@ -32,6 +32,7 @@ export class PoseNet {
constructor(model) { constructor(model) {
this.baseModel = model; this.baseModel = model;
this.inputSize = model.model.inputs[0].shape[1]; this.inputSize = model.model.inputs[0].shape[1];
if (this.inputSize < 128) this.inputSize = 257;
} }
async estimatePoses(input, config) { async estimatePoses(input, config) {

1
types/embedding/embedding.d.ts vendored
View File

@ -1,3 +1,4 @@
export declare function load(config: any): Promise<any>; export declare function load(config: any): Promise<any>;
export declare function simmilarity(embedding1: any, embedding2: any, order?: number): number; export declare function simmilarity(embedding1: any, embedding2: any, order?: number): number;
export declare function enhance(input: any): any;
export declare function predict(input: any, config: any): Promise<unknown>; export declare function predict(input: any, config: any): Promise<unknown>;

1
types/human.d.ts vendored
View File

@ -53,6 +53,7 @@ declare class Human {
largestKernelOps: any; largestKernelOps: any;
} | {}; } | {};
simmilarity(embedding1: any, embedding2: any): number; simmilarity(embedding1: any, embedding2: any): number;
enhance(input: any): any;
load(userConfig?: null): Promise<void>; load(userConfig?: null): Promise<void>;
detect(input: any, userConfig?: {}): Promise<{ detect(input: any, userConfig?: {}): Promise<{
face: any; face: any;