| <h1 align="center" >Remove Objects Server</h1> | |
| <!-- TABLE OF CONTENTS --> | |
| <details> | |
| <summary>Table of Contents</summary> | |
| <ol> | |
| <li><a href="#about-the-project">About</a></li> | |
| <li><a href="#built-with">Installation</a></li> | |
| <li><a href="#usage">Usage</a></li> | |
| <li><a href="#license">License</a></li> | |
| </ol> | |
| </details> | |
| ## About | |
| This is a Python project for removing unwanted objects from images using the inpainting technique. It includes a server implemented with FastAPI and an endpoint for processing images by applying inpainting techniques. This project uses a deep learning library, PyTorch, for training and testing the inpainting model. | |
| <p align="center"> | |
| <img src="lama_cleaner_video.gif" /> | |
| </p> | |
| ## Installation | |
| To install this project, you should first create a virtual environment using the following commands: | |
| ```bash | |
| python3 -m venv venv | |
| source venv/bin/activate | |
| ``` | |
| After creating the virtual environment, you can install the required libraries using pip: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Usage | |
| To use this project, first start the server by running main.py: | |
| ```bash | |
| python main.py | |
| ``` | |
| After the server has started, you can test following endpoints: | |
| - `http://{localhost}:{port}/lama/paint` | |
| - This endpoint accepts an image file in the `file` parameter and applies inpainting techniques to remove unwanted objects. | |
| - `http://{localhost}:{port}/mask` | |
| - Mask endpoint is used to apply a mask to an image. The route accepts `img` and `mask` as input parameters. Then, it applies a mask on an image. | |
| - You can use `testX.png` image and `testX_mask.png` mask in image folder for testing. | |
| ## License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| Other command | |
| ```bash | |
| docker build -t zest . | |
| ``` | |