For Free, Remove the Background from a Picture With Python!

For Free, Remove the Background from a Picture With Python!

I was scouring the internet for AI tools and strategies to generate money online when I came across Remove, a Picture Background Removal service. Bg.

It works perfectly. But the cost astonished me. It costs between $0.02 and $1.99 per image!?

So I decided to take on the challenge and reverse engineer this website in order to create my own Free Picture Background Remover that does not rely on any APIs or third-party services.

After a few days of hunting, I came upon a fantastic Python project called U2Net on GitHub.

A big shout-out and thank you to the project's writers!

I tried to keep things as basic as possible so that even beginners could use and run the python script I created.

I have made a short video showcasing the script and how to use it, as well as three strategies and secrets that may help you generate an online revenue stream with this script. Here's a link to the video:


You might, for example, transform this into an online utility that users pay to use on a monthly subscription basis. I used BoostCTR, FreeImageAI, LargeFileSender, and H-supertools in a similar manner.

There is something else you can do. You may utilize the script, transform it into an API, post it on RapidAPI, and sell it for regular monthly money. This is also something I do with my Domain Authority API.

Remove Background from Image with Python

So, open our source code in Visual Studio Code.

The essential component of our script is the removeBg magic function (). The Image Path will be sent as an argument.

As a result, two methods are critical in this Python script. The first preserves the result, while the second creates the mask of the picture as well as the image without the backdrop and returns.

Thus, in the first section, we load the model. I will also provide you with the trained model so that you may utilize it immediately.

We use the removebg() method to remove the background picture after it has been loaded.

Let us see how it goes. I'll go back to and get an example image from their website.

As a consequence, we may compare the outcomes.

This cat image will be saved on my desktop.

I'll copy the name and reopen the script. And then copy and paste the picture path here.

And then execute the script.

To view the outcomes. Go to the Python Project Folder.

Look for a folder called'static.'

Go to the inputs folder in the static folder.

As a result, this is the input (this is the main image.)

The identical image will be represented as a mask in the Masks folder.

Finally, this is the end result. It's fantastic!

Let's go on to The outcome is nearly same in this case.

Thank you to everyone who contributed to the creation of this fantastic machine-learning model.

The Source Code
You can get the whole source code here or on GitHub.

You may get the API version here.

Here's the script, by the way!

Additionally, if you download the Complete Project Folder, you can get some assistance here.

File Organization

when you navigate to the Downloads folder. This is what you'll discover inside:

Saved models


And here's the trained model, which you can use right away.

And the script file is right here.

We also have the inputs, masks, and outcomes in the static folder. You may also find them here. It's quite simple to use.

You may use the pip command to import and install all the prerequisites straight from this requirements text file.

Please leave your questions in the comments section below.

Please share this with your friends if you find it useful!




CEO / Co-Founder

Enjoy the little things in life. For one day, you may look back and realize they were the big things. Many of life's failures are people who did not realize how close they were to success when they gave up.