Add drawing boxes example

Author: bennetthardwick

.gitignore

@@ -8,6 +8,7 @@ build/
 *.d.ts
 
 examples/dog.jpg
+examples/dog-with-boxes.png
 examples/eagle.jpg
 examples/giraffe.jpg
 examples/yolov3-tiny.cfg

README.md

@@ -1,13 +1,17 @@
 # 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:
+
 ```sh
 # Clone the repositorys
 git clone https://github.com/bennetthardwick/darknet.js.git darknet && cd darknet
@@ -18,14 +22,19 @@ 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
@@ -35,50 +44,56 @@ npm rebuild darknet
 You can enable OpenMP by also exporting the flag `DARKNET_BUILD_WITH_OPENMP=1`;
 
 ## 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.
+
 ```typescript
-import { Darknet } from 'darknet';
+import { Darknet } from "darknet";
 
 // Init
 let darknet = new Darknet({
-    weights: './cats.weights',
-    config: './cats.cfg',
-    names: [ 'dog', 'cat' ]
+  weights: "./cats.weights",
+  config: "./cats.cfg",
+  names: ["dog", "cat"],
 });
 
 // Detect
-console.log(darknet.detect('/image/of/a/dog.jpg'));
+console.log(darknet.detect("/image/of/a/dog.jpg"));
 ```
 
 In conjuction with [opencv4nodejs](https://github.com/justadudewhohacks/opencv4nodejs), Darknet.js can also be used to detect objects inside videos.
+
 ```javascript
-const fs = require('fs');
-const cv = require('opencv4nodejs');
-const { Darknet } = require('darknet');
+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'
+  weights: "yolov3.weights",
+  config: "cfg/yolov3.cfg",
+  namefile: "data/coco.names",
 });
 
-const cap = new cv.VideoCapture('video.mp4');
+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);
+} 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: 
+
+You can download pre-trained weights and configuration from pjreddie's website. The latest version (yolov3-tiny) is linked below:
+
 - [weights](https://pjreddie.com/media/files/yolov3-tiny.weights)
 - [config](https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg)
 - [names](https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.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](https://github.com/pjreddie/darknet)