https://github.com/cloud-annotations/node-red-contrib-cloud-annotations-gpu
https://github.com/cloud-annotations/node-red-contrib-cloud-annotations-gpu
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/cloud-annotations/node-red-contrib-cloud-annotations-gpu
- Owner: cloud-annotations
- License: mit
- Created: 2020-11-18T21:52:16.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-20T00:09:50.000Z (over 5 years ago)
- Last Synced: 2025-03-18T07:08:21.960Z (12 months ago)
- Language: HTML
- Size: 305 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# node-red-contrib-cloud-annotations-gpu
[](http://www.npmjs.org/package/node-red-contrib-cloud-annotations-gpu)
[](https://david-dm.org/cloud-annotations/node-red-contrib-cloud-annotations-gpu)
A [Node-RED](http://nodered.org) node for making predictions with Cloud Annotations models.
## Install
If you plan to use this Node-RED node on a Nvidia Jetson Nano, follow these [official TensorFlow 1.x release instructions for Jetson Nano](https://forums.developer.nvidia.com/t/official-tensorflow-for-jetson-nano/71770) before installing this node.
Run the following command in your Node-RED user directory - typically `~/.node-red`:
```
npm install node-red-contrib-cloud-annotations-gpu
```
You will notice that npm installs a ```@cloud-annotations/models-node-gpu``` dependency. That package requires a dependency on ```@tensorflow/tfjs-node-gpu```
After a lengthy build process, it will download a set of libtensorflow*.so libraries.
If you are running on a Jetson Nano, that's where the Nvidia Jetson package installed initially becomes important. You may need to copy the libtensorflow.so and libtensorflow_framework.so from the Nvidia Jetson python package to
```~/.node-red/node_modules/@tensorflow/tfjs-node-gpu/deps/lib```
## Usage
Feed the model an image and get a list of predictions.
### Configuration
- `Model path`: The path to the Cloud Annotations model.
### Input
- `msg.payload`: The payload can either be a string path to an image file or a buffer object holding the image bytes.
### Output
- `msg.payload`: A list of predictions.
The prediction array varies depending on the type of model loaded.
Object detection model example:
```js
[{
label: 'dog',
bbox: [
330.9269714355469, // x coordinate
281.0324728488922, // y coordinate
374.56329345703125, // width
550.1768910884857 // height
],
score: 0.92
},
{
label: 'cat',
bbox: [
1168.9867401123047, // x coordinate
431.68705701828003, // y coordinate
412.3546600341797, // width
522.2745037078857 // height
],
score: 0.72
}]
```
Classification model example:
```js
[
{ label: 'dog', score: 0.92 },
{ label: 'cat', score: 0.72 }
]
```