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updated readme

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Vladimir Mandic 2020-10-12 11:05:29 -04:00
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README.md
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@ -25,7 +25,7 @@ This is an amalgamation of multiple existing models:
## Installation
There are several ways to use Human:
There are several ways to use `Human` library:
**Important**
*This version of `Human` includes `TensorFlow/JS (TFJS) 2.6.0` library which can be accessed via `human.tf`*
@ -49,7 +49,7 @@ Script is distributed in minified form with attached sourcemap
#### 2.1 With Bundler
If you're using bundler *(such as rollup, webpack, esbuild)* to package your client application, you can import ESM version of `Human` which supports full tree shaking
If you're using bundler *(such as rollup, webpack, esbuild)* to package your client application, you can import ESM version of `Human` library which supports full tree shaking
```js
import human from 'dist/human.esm.js';
@ -140,7 +140,7 @@ human.defaults = {
face: {
enabled: true,
detector: {
modelPath: '/models/human/blazeface/model.json',
modelPath: '../models/human/blazeface/model.json',
maxFaces: 10,
skipFrames: 5,
minConfidence: 0.8,
@ -149,25 +149,25 @@ human.defaults = {
},
mesh: {
enabled: true,
modelPath: '/models/human/facemesh/model.json',
modelPath: '../models/human/facemesh/model.json',
},
iris: {
enabled: true,
modelPath: '/models/human/iris/model.json',
modelPath: '../models/human/iris/model.json',
},
age: {
enabled: true,
modelPath: '/models/human/ssrnet-imdb-age/model.json',
modelPath: '../models/human/ssrnet-imdb-age/model.json',
skipFrames: 5,
},
gender: {
enabled: true,
modelPath: '/models/human/ssrnet-imdb-gender/model.json',
modelPath: '../models/human/ssrnet-imdb-gender/model.json',
},
},
body: {
enabled: true,
modelPath: '/models/human/posenet/model.json',
modelPath: '../models/human/posenet/model.json',
maxDetections: 5,
scoreThreshold: 0.75,
nmsRadius: 20,
@ -179,11 +179,11 @@ human.defaults = {
iouThreshold: 0.3,
scoreThreshold: 0.75,
detector: {
anchors: '/models/human/handdetect/anchors.json',
modelPath: '/models/human/handdetect/model.json',
anchors: '../models/human/handdetect/anchors.json',
modelPath: '../models/human/handdetect/model.json',
},
skeleton: {
modelPath: '/models/human/handskeleton/model.json',
modelPath: '../models/human/handskeleton/model.json',
},
},
};
@ -228,10 +228,12 @@ result = {
],
hand: // <array of detected objects>
[
confidence: // <number>,
box: // <array [x, y, width, height]>,
landmarks: // <array of points [x, y,z]> (21 points)
annotations: // <array of landmarks [ landmark: <array of points> ]> (5 annotated landmakrs)
{
confidence: // <number>,
box: // <array [x, y, width, height]>,
landmarks: // <array of points [x, y,z]> (21 points)
annotations: // <array of landmarks [ landmark: <array of points> ]> (5 annotated landmakrs)
}
]
}
```
@ -240,8 +242,9 @@ result = {
## Performance
Of course, performance will vary depending on your hardware, but also on number of enabled modules as well as their parameters.
For example, on a low-end nVidia GTX1050 it can perform face detection at 50+ FPS, but drop to <5 FPS if all modules are enabled.
Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters
For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 50+ FPS, but drops to 5-10 FPS on a medium complex images if all modules are enabled
Library can also be used on mobile devices
<hr>