AI Object Removal Using Python: A Practical Guide

Last Updated on 07/12/2025 by Eran Feit

Introduction

AI Object Removal Using Python has quickly become one of the most practical and exciting applications of modern computer vision. It allows anyone—from developers to digital artists—to automatically erase unwanted objects from images without manually editing pixels. Instead of relying on traditional tools like clone stamping or masking, deep learning models can now detect objects with precision, remove them, and intelligently fill in the missing regions. The result is a clean, natural-looking image produced with just a few lines of code.

This approach combines two powerful techniques: semantic segmentation and image inpainting. Segmentation identifies exactly which pixels belong to the objects you want to remove, while inpainting reconstructs the background as if the object was never there. Together, they create a smooth workflow that is both accurate and efficient, even when dealing with complex textures, lighting, and shapes in the original photo.

With AI Object Removal Using Python, the process becomes fully automated. You no longer need to manually draw regions or select shapes for removal. The model recognizes people, animals, vehicles, furniture, and many other categories based on pretrained datasets. Once identified, these objects can be erased with a single command, making it possible to process hundreds or thousands of images in batch mode.

This workflow is extremely useful for photographers, e-commerce sellers, content creators, and data scientists who need clean, distraction-free images. Whether you are removing a passerby from a street photo, deleting an unwanted product from a catalog image, or preparing cleaner datasets for machine learning, Python provides a flexible and powerful foundation for automating this task.

Exploring How AI Object Removal Using Python Works

AI object removal may appear almost magical when you see the final result, but behind the scenes it relies on well-structured steps that work together seamlessly. The process starts by analyzing the image and locating the objects that need to be removed. This is achieved through semantic segmentation, where a model assigns each pixel to a category such as person, dog, bottle, car, or background. Once the model identifies the target regions, it generates a mask that highlights exactly which pixels should be removed.

After segmentation, the next step is inpainting—an advanced technique that reconstructs the missing areas in a visually coherent way. Instead of leaving a hole in the image, the inpainting model looks at surrounding textures, colors, and edges to infer what the hidden background should look like. This allows the final image to appear untouched, even if large objects have been removed.

Python provides an ideal environment for this workflow because of its rich ecosystem of machine learning libraries such as PyTorch, OpenCV, and NumPy. These tools make it easy to preprocess images, apply segmentation models, load pretrained weights, visualize results, and run inpainting networks. With command-line scripts or automation pipelines, users can process one image or an entire directory with minimal effort.

This capability becomes particularly powerful when working with datasets or production environments where consistency matters. AI Object Removal Using Python ensures that results remain stable, scalable, and reproducible across many images. It provides a high-level, automated approach that blends deep learning with practical real-world use cases, making it one of the most accessible and valuable image-processing techniques available today.

AI Object Removal Using Python is not just a cool demo – it’s a complete, reproducible coding workflow you can run on your own machine.
In this tutorial-style approach, the focus is on turning abstract concepts like segmentation and inpainting into a practical Python pipeline that you can understand, modify, and reuse for your own projects.
You see exactly how the environment is created, which libraries are installed, what each command does, and how everything connects together to remove objects from real images.

The idea is to start from a clean Conda environment, install the correct version of PyTorch with GPU support, and then clone the repository that contains all the object removal logic.
From there, the Python code loads pretrained models, reads your input images from a folder, generates masks for the objects you want to remove, and feeds them into an inpainting network that reconstructs the missing regions.
By following the code step by step, you learn not only how to run the tool, but also how the models are wired together behind the scenes.

Because the code supports different object classes, AI Object Removal Using Python becomes a flexible tool rather than a one-off script.
You can tell the program to remove just people, or people and dogs, or perhaps bottles from product shots, simply by changing the numeric IDs passed to the --remove argument.
This makes the tutorial especially useful if you want to adapt it for your own datasets, automate image cleanup, or integrate object removal into a larger computer vision pipeline.

Most importantly, the tutorial shows you how to handle both GPU and CPU execution.
If you have a CUDA-enabled GPU, you can process large batches of images quickly; if not, you can still run the same Python code on CPU, just with longer processing times.
This makes the whole workflow accessible, whether you are a beginner experimenting on a laptop or a more advanced user working on a dedicated deep learning machine.

Getting Comfortable with the AI Object Removal Code

The heart of this tutorial is the Python code that glues everything together into a single, easy-to-run command.
Instead of writing dozens of lines from scratch, you work with a well-structured script (test.py) that already knows how to load models, prepare images, generate masks, and inpaint removed regions.
Your main job is to understand how the script is structured and which arguments you need to pass so it behaves exactly the way you want.

At a high level, the target of the code is simple: take all images from an input folder, automatically detect specific object classes using a semantic segmentation model, cut those objects out, and fill the gaps using a deep learning inpainting network.
The segmentation model is responsible for recognizing which pixels belong to targets like “person”, “dog”, “bottle”, and other everyday categories.
Once those masks are created, the inpainting model uses context from the surrounding pixels to recreate a plausible background where the removed objects used to be.

The command-line interface of the script is designed to make this process very intuitive.
You specify --input to point to your folder of images, --output to choose where the processed images should be saved, and --remove followed by one or more class IDs to tell the model which objects to erase.
Behind the scenes, the code loops through each image, runs the segmentation model, applies the mask, calls the inpainting network, and finally writes the cleaned image to the output directory.
This pattern makes it very easy to batch-process an entire dataset with a single command.

Another important aspect of the code is the flexibility around hardware.
By default, the script will try to use the GPU if available, which significantly speeds up the object removal process.
However, you can switch to CPU mode simply by adding an argument like --cpu yes, which instructs the code to run all computations on the processor instead of the graphics card.
This design ensures that the tutorial remains usable regardless of your hardware capabilities, while still taking advantage of acceleration when it is available.

Finally, the codebase is built to be extendable.
Because the logic for loading models, reading class IDs from a text file, and handling image folders is clearly separated, you can tweak individual parts without breaking the whole pipeline.
You might add new object classes, change the inpainting model, or integrate extra preprocessing steps, all while keeping the original command-line interface.
In this way, the tutorial becomes both a learning resource and a solid starting point for your own custom AI object removal projects in Python.

Link to the video tutorial : https://youtu.be/GmqsPQNBScA

You can find the instructions and the all the demo and images files here : https://eranfeit.lemonsqueezy.com/buy/64e55bd4-a165-4f0b-b46d-a7562b27ab7f or here : https://ko-fi.com/s/8e7da11588

Link for Medium users : https://medium.com/cool-python-pojects/ai-object-removal-using-python-a-practical-guide-6490740169f1

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 Want to get started with Computer Vision or take your skills to the next level ?

If you’re just beginning, I recommend this step-by-step course designed to introduce you to the foundations of Computer Vision – Complete Computer Vision Bootcamp With PyTorch & TensorFlow

If you’re already experienced and looking for more advanced techniques, check out this deep-dive course – Modern Computer Vision GPT, PyTorch, Keras, OpenCV4

AI Object Removal Using Python: A Practical Guide

AI Object Removal Using Python has become one of the most powerful ways to clean and enhance images without manually editing them. By combining semantic segmentation with deep-learning-based inpainting, Python makes it possible to remove people, animals, vehicles, or any unwanted objects automatically. This tutorial walks through the full workflow—from setting up the environment, to running predictions—so you can start removing objects from images at scale.

Whether you’re processing travel photos, preparing a dataset, or cleaning product images, this guide will show you how to install the tools, download the models, and run the commands that bring AI-powered object removal to life.

Setting Up the Environment and Installing Dependencies

Before running object removal, we first prepare a clean Conda environment and install the right Python version, CUDA-compatible PyTorch, and the project repository. This environment ensures the models run smoothly, especially when using GPU acceleration.

Below is the full setup process, with each command explained.

Create a new Conda environment with Python 3.8.5

conda create --name OBJRemove python=3.8.5 

Activate the newly created environment

conda activate OBJRemove 

Check which CUDA version your system supports

nvcc --version 

Install PyTorch 2.3.1 with CUDA 11.8 support

conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=11.8 -c pytorch -c nvidia 

Clone the object-removal repository from GitHub

git clone https://github.com/sujaykhandekar/Automated-objects-removal-inpainter.git 

Enter the project folder :

cd Automated-objects-removal-inpainter 

Install all required Python libraries from the project

pip install -r requirements.txt 

Install a compatible Pillow version for image handling

pip install Pillow==9.5.0 

Copy the pretrained model checkpoints into the project folder

Download the model files and place them inside the checkpoints directory. These weights allow the segmentation and inpainting models to run.

Summary:
At this point, your environment is fully prepared and ready to run AI Object Removal Using Python.

Running Object Removal on Your Images

Now that everything is installed, the next step is to run predictions. The command-line interface of the project makes it easy to pick which objects you want removed from your images using numeric class IDs from the segmentation model.

Remove all people from images

python test.py --input My-images --output resultsfinal --remove 15 

Remove bottles, dogs, and people

python test.py --input My-images --output resultsfinal --remove 5 12 15 

Explanation of IDs

Each number represents a segmentation class:

0: Background   1: Aeroplane   2: bicycle   3: bird   4: boat   5: bottle   6: bus   7: car   8: cat   9: chair   10: cow   11: dining table   12: dog   13: horse   14: motorbike   15: person   16: potted plant   17: sheep   18: sofa   19: train   20: tv/monitor 

When you pass a list of class IDs to --remove, the program automatically removes all those objects across every image in your input folder.

Summary:
This part of the workflow gives you full control over which objects should disappear from your images—whether it’s one object or multiple categories at once.

Running Object Removal on CPU or GPU

Many users run this pipeline on machines without a dedicated GPU. The script supports CPU mode, but runs slower due to the heavy computation required for segmentation and inpainting.

Run object removal on CPU

python test.py --input My-images --output resultsfinal --remove 3 15 --cpu yes 

This command will remove birds (3) and people (15) using CPU-based inference.

Summary:
Use --cpu yes when working on a non-GPU system. The results remain identical—the only difference is processing speed.

FAQ — Understanding AI Object Removal Using Python

Conclusion

AI Object Removal Using Python opens up a wide range of possibilities for cleaning, enhancing, and transforming images automatically. With just a few commands, you can remove people from photos, hide distracting objects, improve datasets, or generate clean professional visuals for projects and presentations.

The workflow you explored in this post demonstrates how segmentation and inpainting work together to deliver realistic results. Once your environment is set up, the object removal pipeline is easy to use and scalable across hundreds or thousands of images. Whether you’re editing images for fun or preparing a machine learning dataset, this tutorial gives you the foundation to explore even more advanced computer vision tools in Python.

 Automated-objects-removal-inpainter : =====================================  conda create --name OBJRemove python=3.8.5 conda activate OBJRemove   # -> get cuda version nvcc --version  Install Pytorch 2.3.1 for for Cuda 11.18 : conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=11.8 -c pytorch -c nvidia  git clone https://github.com/sujaykhandekar/Automated-objects-removal-inpainter.git cd Automated-objects-removal-inpainter  # Install Python libraries pip install -r requirements.txt  pip install Pillow==9.5.0  copy the files from this link to the "checkpoints" directory : https://drive.google.com/drive/folders/1qjieeThyse_iwJ0gZoJr-FERkgD5sm4y   Run the Predictions : =====================  Remove person : python test.py --input My-images --output resultsfinal --remove 15  Remove : person , dog , bottle  python test.py --input My-images --output resultsfinal --remove 5 12 15   numbers after --remove specifies objects to be removed in the images.  ABove command will remove 5bottle and 12 (dog) and 15(people) from the images.   Check segmentation-classes.txt for all removal options along with it's number.  0: Background 1: Aeroplane 2: bicycle 3: bird 4: boat 5: bottle 6: bus 7: car 8: cat 9: chair 10: cow 11: dining table 12: dog 13: horse 14: motorbike 15: person 16: potted plant 17: sheep 18: sofa 19: train 20: tv/monitor   If you do not have a gpu card you can use cpu . It will be slower python test.py --input My-images --output resultsfinal --remove 3 15 --cpu yes 

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