<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 .
```


{}
region:us
TAGS
#region-us 

<h1 align="center" >Remove Objects Server</h1>



<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:


After creating the virtual environment, you can install the required libraries using pip:



## Usage

To use this project, first start the server by running URL:

 

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 'URL' 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




## 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:


After creating the virtual environment, you can install the required libraries using pip: ## Usage

To use this project, first start the server by running URL:

 

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 'URL' 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
TAGS
#region-us 
 ## 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:


After creating the virtual environment, you can install the required libraries using pip: ## Usage

To use this project, first start the server by running URL:

 

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 'URL' 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
TAGS
#region-us 
## 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:


After creating the virtual environment, you can install the required libraries using pip:## Usage

To use this project, first start the server by running URL:

 

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 'URL' 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