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

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.