https://github.com/manik2000/unsupervised-ml-image-compression

Web application enabling compression of images using unsupervised machine learning algorithm.
https://github.com/manik2000/unsupervised-ml-image-compression

clustering docker pca python streamlit-application unsupervised-machine-learning

Last synced: 3 months ago
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Web application enabling compression of images using unsupervised machine learning algorithm.

• Host: GitHub
• URL: https://github.com/manik2000/unsupervised-ml-image-compression
• Owner: Manik2000
• Created: 2023-01-19T16:37:44.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-23T11:46:55.000Z (9 months ago)
• Last Synced: 2025-01-30T04:25:28.473Z (5 months ago)
• Topics: clustering, docker, pca, python, streamlit-application, unsupervised-machine-learning
• Language: Python
• Homepage:
• Size: 20.1 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Images compression using unsupervised ML

This project is focused on applying PCA and clustering methods (mini batch K-means) to image compression problem.

`scikit-learn` implementations of both algorithms are used. More on how these methods can be used to compress images can be found in the [notebooks](notebooks/) directory. 

**Note:** Python >= 3.10 is required!

### Web application

Web application is deployed on Streamlit Community Cloud.

You can experiment and have fun on your own [here :fire:](https://unsupervised-ml-image-compression.streamlit.app/).

### Results

Below you can see an examplary output of using clustering for image compression.

|Original image | Compressed image with K = 10 | Compressed image with K = 50 |

|---| --- | ---|

|![](images/drops.jpg)| ![](images/k_10.png)| ![](images/k_50.png)|

||||

### Project structure

```

.

├── Dockerfile <- Dockerfile for building the image

├── Main_page.py <- main Python file defining Streamlit app

├── README.md

├── images <- directory with sample images

├── notebooks 

│   ├── clustering.ipynb <- notebook showcasing clustering compression

│   └── pca.ipynb <- notebook showcasing PCA compression

├── pages

│   ├── Clustering_compression.py <- sybpage with clustering compression

│   └── PCA_compression.py <- subpage with PCA compression

├── requirements.txt <- required packages

└── scripts

    ├── __init__.py <- makes scripts a module

    ├── cluster_compression.py <- ClusterCompressor class

    ├── pca_compression.py <- PCACompressor class

    └── utils.py <- uitilty functions for image to array and array to image conversion

```

### Running the application

Clone this repository and navigate to the root directory of the project.

* Python virtual environment

    1. Create a virtual environment (below named env) and activate it

    ```bash

    python3 -m venv env

    source env/Scripts/activate # bash

    env\Scripts\activate # on Windows

    ```

    2. Install required packages

    ```bash

    pip install -r requirements.txt

    ```

    3. Run the application

    ```bash

    streamlit run Main_page.py

    ```

Alternatilvely, if you have Make installed, you can use use command `make streamlit`.

* Docker

    1. Build docker image (named `my_app_image`)

    ```bash

    docker build -t my_app_image .

    ```

    2. Run the container

    ```bash

    docker run -p 8501:8501 my_app_image

    ```

Alternatilvely, if you have Make installed, you can use use command `make docker-all`.