React UI for Python Scripts on Node.JS

If you are familiar with node.js, you know that it is

1. Ultra Fast ⚡
2. Ultra Scalable ⚖️
3. Ultra Powerful 💥
4. Ultra Simple 😁

and python has great scientific computing libraries [NumPy,Pandas,etc] that make it the go to choice for academics, data scientists, deep learning engineers, etc.

Some time ago, I wanted to explore computer vision, something that I had been really fascinated for quite a while.

So I started learning CV and wrote a python script that would take an image and remove color channels to make it look like as if a color filter had been applied to it.

It was super cool and I wanted to make a fun little website/webUI out of it so I could share it to the rest of the world.

Being a self-taught MERN Stack Developer, I started to research upon how one could combine python and javascript.

A Week or Two Later, I Did It.

And this blog is a documentation of how I solved this challenge.

I have also including here, the full code I used to deploy my application to Heroku {% youtube i1QW52spBD4 %} Live Deployment: https://color-filter.netlify.app Source Code: https://github.com/LucidMach/ColorFilter

How Does It Work

The Projecct has 4 phases

1. Webcam -> React -> NodeJS
2. NodeJS Py Child Process
3. Actual Python Program
4. NodeJS -> React -> Canvas

Phase 1: Webcam -> React -> NodeJS

We begin by first extracting an image from the webcam, we can use plain HTML5’s navigator.getUserMedia API but there’s an react package that simplifies the whole process.

yarn add react-webcam

we can use getScreenshot({width: 1920, height: 1080}) to take a 1080p snapshot of the user.

🔗: React-WebCam Docs

Now that we have a snapshot (as a base64 string), we’ve to send it to the server

Any browser can only run javascript on the client, so we’ve to run python on the server

we make a post request

axios.post(url, { image: imageSrc, color: selectedColor })

I also send the selected color, as I need it for the application that I’m building

By default the server(bodyParser middleware) limits the size of data it can get(post) to 1MB and pictures are usually way big

Unless you used an image optimizer like I did in a previous project

Let’s Push the Limits

app.use(bodyParser.json({ limit: "5mb" }));

Also we need to extract the image from the base64 string

Example base64 PNG String data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKsAAADVCAMAAAAfHvCaAAAAGFBMVEVY

Actual base64 Image iVBORw0KGgoAAAANSUhEUgAAAKsAAADVCAMAAAAfHvCaAAAAGFBMVEVY

const base64Image = req.body.image.split(";base64,").pop();

Phase 2: NodeJS Py Child Process

Now that we have the image back on the server, we need to run the python script

If you’ve ever passed parameters(argv) to a python script / built a CLI tool, what we’re going to be doing is very similar

Before that let’s save the image temporarily cuz we can’t pass images as argv(script parameter)

const fs = require("fs");

fs.writeFileSync("input/image.png", base64Image, { encoding: "base64" });

Now, we spawn a python child process we do this my representing terminal commands to an array

const { spawn } = require("child_process");

const py = spawn("python", ["color-filter.py", body.color]);

Every python script probabily sends data back to the terminal/console

To read py console log, we create a callback function

var data2send

py.stdout.on("data", (data) => {
    data2send = data.toString();
});

console.log(data2send);

Phase 3: Actual Python Program

The python script gets executed, in my case it’s a numpy script that conditionally removes color channels

If you’re interested you can check out the source-code on github

Phase 4: NodeJS -> React -> Canvas

now when the py child process terminates we need to encode the image back to base64 and send back a response

we can do that by latching a callback to when the child process ends

py.on("close", () => {
  // Adding Heading and converting image to base64
  const image = `data:image/png;base64,${fs.readFileSync("output/image.png", {
    encoding: "base64",
  })}`;

  // sending image to client
    res.json({ image });
  });

BONUS PHASE: Heroku Deployment

This most important part of any project

It no longer only “works on your machine”

The process is basically the exact same as you deploy vanilla node apps + config for python childprocess

1. Standard Deploy Node to Heroku Heroku Node App Deployment Docs

2. Add Python Packages In the JavaScript World we have a package.json which tells every node instance all the packages required to run

We make something similar for python called requirements.txt to replicate that behavior.

It would look sorta like a .gitignore file

// requirements.txt

numpy
cv2
matplotlib

when Heroku notices the requirements.txt file it runs pip install -r requirements.txt, hence installing all the required packages

3. Configure Buildpacks Heroku Node App Deployment Docs Here’s the TL:DR; version

// terminal


// This command will set your default buildpack to Node.js
heroku buildpacks:set heroku/nodejs

// This command will set it up so that the Heroku Python buildpack will run first
heroku buildpacks:add --index 1 heroku/python

If You ❤️ This Blog Post Be Sure To Drop a DM on Twitter

✌️, LucidMach

untill next time !️ ✌

or you could spot me in the wild 🤭 i mean instagram, twitter, linkedin and maybe even youtube where i excalidraw those diagrams