/ColorSense

Primary LanguageCSS

ColorSense

ColorSense is an image processing application that allows users to upload an image, apply a segmentation algorithm based on k-means clustering, and customize the color of each segment using RGB values. The result is a transformed image with a unique and personalized look.

Features

  • Image Upload: Users can easily upload their images through the user-friendly interface.

  • K-Means Segmentation: The application utilizes the k-means clustering algorithm to segment the uploaded image into a specified number of clusters.

  • Color Customization: Users can interactively customize the color of each segment by entering RGB values.

  • Preview: A preview is provided to visualize the changes made to the image.

  • Dockerized: The entire application is containerized using Docker, ensuring easy deployment and consistent behavior across different environments.

  • Kubernetes Provisioning: For those interested in deploying the application on Kubernetes, instructions are provided in the Kubernetes section.

Prerequisites

Before running ColorSense, ensure you have the following dependencies installed:

  • Docker
  • Docker Compose

Getting Started

  1. Clone the Repository:

    git clone https://github.com/sanchi-t/ColorSense.git

  2. Navigate to the Project Directory:

    cd colorsense

  3. Build and Run the Docker Containers:

    docker-compose up --build

  4. Access the Application:

    Open your web browser and go to http://localhost:5000 to use ColorSense.

Kubernetes

If you are interested in deploying ColorSense on Kubernetes, follow the steps below:

  1. Apply Kubernetes Manifests:

    cd kubernetes
    kubectl apply -f kubernetes/

  2. Access the Application:

    Once the pods are running, the application will be accessible. You can find the service details using:

    kubectl get svc colorsense-service

    Access the application using the provided EXTERNAL-IP.

Usage

  1. Segmentation:

    • Enter the number of segments for k-means clustering.

  2. Upload Image:

    • Click on the "Upload" button to select and upload an image.

  3. Color Customization:

    • For each segment, enter RGB values to customize the color.
    • Changes will be reflected after you click on detect object button.

  4. Save or Download:

    • Once satisfied with the customization, save or download the transformed image by right-clicking it.

Images

brave_MPUnUfwsGs brave_qZvbnggbQH brave_6gFIHJ2sLJ brave_2LO3VTzI9z brave_uSjozZkz1v

Contributing

If you want to contribute to ColorSense, follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature: git checkout -b feature-name.
  3. Make your changes and commit them: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature-name.
  5. Submit a pull request.

Acknowledgments

  • The ColorSense project is built using Flask, k-means clustering, and Docker.

Feel free to customize this README according to your specific project details and needs.