A Node wrapper of pjreddie's open source neural network framework Darknet, using the Foreign Function Interface Library. Read: YOLOv3 in JavaScript.

• Linux, Mac, Windows (Linux sub-system),
• Node (most versions will work, darknet.js <=1.1.5 only works on node <=8.11.2)
• Build tools (make, gcc, etc.)

To run the examples, run the following commands:

git clone https://github.com/bennetthardwick/darknet.js.git darknet && cd darknet
npm install
./examples/example

Note: The example weights are quite large, the download might take some time

Super easy, just install it with npm:

npm install darknet

If you'd like to enable CUDA and/or CUDANN, export the flags DARKNET_BUILD_WITH_GPU=1 for CUDA, and DARKNET_BUILD_WITH_CUDNN=1 for CUDANN, and rebuild:

export DARKNET_BUILD_WITH_GPU=1
export DARKNET_BUILD_WITH_CUDNN=1
npm rebuild darknet

To create an instance of darknet.js, you need a three things. The trained weights, the configuration file they were trained with and a list of the names of all the classes.

import { Darknet } from 'darknet';

// Init
let darknet = new Darknet({
    weights: './cats.weights',
    config: './cats.cfg',
    names: [ 'dog', 'cat' ]
});

// Detect
console.log(darknet.detect('/image/of/a/dog.jpg'));

In conjuction with opencv4nodejs, Darknet.js can also be used to detect objects inside videos.

const fs = require('fs');
const cv = require('opencv4nodejs');
const { Darknet } = require('darknet');

const darknet = new Darknet({
  weights: 'yolov3.weights',
  config: 'cfg/yolov3.cfg',
  namefile: 'data/coco.names'
});

const cap = new cv.VideoCapture('video.mp4');

let frame;
let index = 0;
do {
  frame = cap.read().cvtColor(cv.COLOR_BGR2RGB);
  console.log('frame', index++); 
  console.log(darknet.detect({
    b: frame.getData(),
    w: frame.cols,
    h: frame.rows,
    c: frame.channels
  }));
} while(!frame.empty);

You can download pre-trained weights and configuration from pjreddie's website. The latest version (yolov3-tiny) is linked below:

• weights
• config
• names

If you don't want to download that stuff manually, navigate to the examples directory and issue the ./example command. This will download the necessary files and run some detections.

By default, darknet.js will run the detections synchronously. If this isn't your style, you can run detections asynchronously, using the detectAsync method.

darknet.detectAsync('/image/of/a/dog.jpg')
    .then(detections => console.log(detections));

At this time, async detections cannot be run in parallel and attempting to will cause your detections to be incorrect. The DarknetExperimental class has serial async. It is intended to eventually replace the original Darknet class:

import { DarknetExperimental } from 'darknet';

const darknet = new DarknetExperimental(config);

darknet.detectAsync('/image/of/a/dog.jpg')
  .then(detections => console.log(detections));
  
darknet.detectAsync('/image/of/a/cat.jpg')
  .then(detections => console.log(detections));

darknet.detectAsync('/image/of/an/eagle.jpg')
  .then(detections => console.log(detections));

• Node FFI
• Ref
• Darknet