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https://github.com/cinex10/img2img_ui
https://github.com/cinex10/img2img_ui
Last synced: 19 days ago
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- Host: GitHub
- URL: https://github.com/cinex10/img2img_ui
- Owner: Cinex10
- Created: 2024-03-02T20:27:29.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-07T15:49:59.000Z (7 months ago)
- Last Synced: 2024-11-14T02:10:16.004Z (3 months ago)
- Language: Python
- Size: 119 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Image to image translation GUI
As part of my end of study insternship, I built this Streamlit UI application that allows users to perform various image-to-image translation tasks using their custom images.
## Installation
1. **Create a new virtual environment** (recommended):
```bash
python -m venv myenv
source myenv/bin/activate # On Windows, use `myenv\Scripts\activate`
```
2. **Install required packages**:
```bash
pip install -r requirements.txt
```
This will install all the necessary packages specified in the `requirements.txt` file.
## Usage
1. **Run the Streamlit app**:
```bash
streamlit run ui_1.py # for the first ui
streamlit run ui_2.py # for the second ui (only edges)
```
This will launch the Streamlit UI in your default web browser.
2. **Use the UI**:
- Select the desired image-to-image translation task from the dropdown menu.
- Upload the input image(s) required for the selected task or you can draw in case of edges2shoes task.
- Click the "Translate" button to perform the translation.
- The translated output image(s) will be displayed in the UI.
## Tasks
The following image-to-image translation tasks are currently supported:
- **Edge-to-Shoes**: Generate a realistic shoe image from an edge map.
- **Other tasks will be added soon.**
## Models
The project currently uses the following deep learning models:
- **Pix2Pix**
- **CycleGAN**
## Contributing
Contributions are welcome! If you would like to add more features or improve the existing functionality, please feel free to submit a pull request.
## Acknowledgments
- The Pix2Pix and CycleGAN models were built using the [Official pix2pix and cyclegan repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) deep learning framework.
- The Streamlit UI was created using the [Streamlit](https://streamlit.io/) library.