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https://github.com/mobimeo/node-yolo

Node bindings for YOLO/Darknet image recognition library
https://github.com/mobimeo/node-yolo

darknet image-recognition moovel-eu nodejs non-prod yolo

Last synced: 12 days ago
JSON representation

Node bindings for YOLO/Darknet image recognition library

Host: GitHub
URL: https://github.com/mobimeo/node-yolo
Owner: mobimeo
License: mit
Created: 2017-01-20T15:58:33.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2019-11-01T05:10:59.000Z (over 4 years ago)
Last Synced: 2024-02-29T07:37:03.075Z (4 months ago)
Topics: darknet, image-recognition, moovel-eu, nodejs, non-prod, yolo
Language: C++
Homepage: https://lab.moovel.com/blog/what-you-get-is-what-you-see-nodejs-yolo
Size: 1.8 MB
Stars: 370
Watchers: 68
Forks: 51
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - mobimeo/node-yolo - Node bindings for YOLO/Darknet image recognition library (C++)
awesome-yolo-object-detection - mobimeo/node-yolo - yolo?style=social"/> : Node bindings for YOLO/Darknet image recognition library. (Other Versions of YOLO)

README

# Node.js Wrapper for YOLO/Darknet recognition framework

Darknet is [an open source neural network framework written in C and CUDA](https://github.com/pjreddie/darknet).
This project wraps this framework in a Node.js native addon.

- [darknet](http://pjreddie.com/darknet/)
- [YOLO (You only look once)](http://pjreddie.com/darknet/yolo/)

## Status

Currently, the wrapper implements a demo method to run a recognition from the webcam/video or an image file. Equivalent to

```sh
./darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights
./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg
```

## Prerequisites

Requires:

- Modern Node and NPM versions (tested with latest LTS release Node v6.*.*, npm 3.*.*, supposed to work with the newer versions as well)
- OpenCV (version 2, for example, 2.4.9.1) to be installed on your system.

Installing with Brew on MacOS:

brew install opencv@2
sudo chown -R $(whoami):admin /usr/local
brew link --force opencv@2

- If you are on a mac: macOS 10.12 Sierra or newer, Apple LLVM version 8.0.0 (xcode 8.2, check version with `clang -v`). For GPU support, Nvidia [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) _(you need to have Nvidia CUDA GPU graphic card)_

First, you need to compile [this fork](https://github.com/OrKoN/darknet) of darknet with OpenCV support (optionally, with CUDA support):

```sh
git clone https://github.com/OrKoN/darknet
cd darknet
make OPENCV=1 # optionally GPU=1
make install # by default installed to /usr/local
```

After that you can process with the installation via NPM.

The fork is required because it contains [a few important changes](https://github.com/pjreddie/darknet/compare/master...OrKoN:master) to the origin source code which allow using darknet with NodeJS:

- Makefile is extended to build a static library (darknet.a).
- Makefile is extended with `install` and `uninstall` commands which install the library globally so that this module can easily find and link it.
- All darknet functions in header files are marked with `extern "C"` if they are included in a C++ program (such as a NodeJS module).

## Installation

```sh
npm install @moovel/yolo --save
```

## Usage

Either download your own `cfg`, `data` folders and `.weight` files from the darknet project or use the ones included in `test` folder (see also [test/readme.md](test/README.md)). You also can find there the examples from below.

Detect a video from camera or a file:

```js
const darknet = require('@moovel/yolo');

darknet.detect({
cfg: './cfg/yolo.cfg',
weights: './yolo.weights',
data: './cfg/coco.data',
cameraIndex: 0, // optional, default: 0,
video: './test.mp4', // optional, forces to use the video file instead of a camera
thresh: 0.24, // optional, default: 0.24
hierThresh: 0.5, // optional, default: 0.5
}, function(modified, original, detections, dimensions) {
/**

modified - raw frame with detections drawn, rgb24 format
original - raw frame, as captured by the webcam/video, rgb24 format,
detections - array of detections
dimenstions - image width and height

Example detections:

[ { x: 0.8602103590965271,
y: 0.20008485019207,
w: 0.13895535469055176,
h: 0.39782464504241943,
prob: 0.2408987432718277,
name: 'tvmonitor' },
{ x: 0.26072466373443604,
y: 0.4977818727493286,
w: 0.10842404514551163,
h: 0.22796104848384857,
prob: 0.3290732204914093,
name: 'person' },
{ x: 0.2568981349468231,
y: 0.5765896439552307,
w: 0.12322483211755753,
h: 0.2544059157371521,
prob: 0.2738085687160492,
name: 'chair' },
{ x: 0.6593853235244751,
y: 0.8188746571540833,
w: 0.06210440397262573,
h: 0.100614033639431,
prob: 0.3225017189979553,
name: 'clock' } ]
*/
});
```

Detect on a single image:

```js
const darknet = require('@moovel/yolo');

darknet.detectImage({
cfg: './cfg/yolo.cfg',
weights: './yolo.weights',
data: './cfg/coco.data',
image: './data/dog.jpg',
thresh: 0.24, // optional, default: 0.24
hierThresh: 0.5, // optional, default: 0.5,
}, function(modified, original, detections, dimensions) {
/**

modified - raw frame with detections drawn, rgb24 format
original - raw frame, as captured by the webcam/video, rgb24 format,
detections - array of detections
dimenstions - image width and height

Example detections:

After that you can convert the original frames with ffmpeg to an image. Play with the parameter `bgr24`/`rgb24` if the color channels are swapped e.g. blueish image:

```sh
ffmpeg -f rawvideo -s 768x576 -pix_fmt bgr24 -i data.raw data.png
```