Package Exports

darknet

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (darknet) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

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:

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