Darknet.JS

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

Prerequisites

• Linux, Mac, Windows (Linux sub-system),
• Node
• Build tools (make, gcc, etc.)

Examples

To run the examples, run the following commands:

# Clone the repositorys
git clone https://github.com/bennetthardwick/darknet.js.git darknet && cd darknet
# Install dependencies and build Darknet
npm install
# Compile Darknet.js library
npx tsc
# Run examples
./examples/example

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

Installation

You can install darknet with npm using the following command:

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

You can enable OpenMP by also exporting the flag DARKNET_BUILD_WITH_OPENMP=1;

You can also build for a different architecture by using the DARKNET_BUILD_WITH_ARCH flag.

Usage

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(darknet.detect(frame));
} while (!frame.empty);

Example Configuration

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.

Built-With

• Darknet