This web application utilizes K Means algorithm to extract a color palette from an uploaded image. The extracted colors are carefully analyzed and selected to create a visually appealing and harmonious color scheme. In addition, this application generates a segmented image that showcases the areas where the colors were picked in the original image. This feature provides users with a deeper understanding of the color palette and allows them to use it more effectively in their designs. This application is perfect for designers, artists, photographers, and organizations who want to simplify the color selection process and elevate their visual content.

https://dipitvasdev-color-pick-image-segmentation-run-evfp9a.streamlit.app/

Clone the project

  git clone https://github.com/dipitvasdev/Color-Pick-Image-Segmentation.git

Go to the project directory

  cd Color-Pick-Image-Segmentation/

Install requirements

  pip install -r requirements.txt

Start the streamlit server

  streamlit run run.py

The working code for this project can be found at:-

https://colab.research.google.com/drive/1s9aSE83Us0JEHeCTpnaNo7bWYV5jEkqs

I am Dipit Vasdev, a highly motivated problem solver with a passion for neural networks and machine learning. I am currently pursuing a Master's degree in Computer Engineering at New York University, and my greatest strength lies in my drive for solving complex problems in computer science. I possess a wealth of technical skills in machine learning, deep learning, Android development, and more, and I have taken part in various projects and internships to continuously improve my skills and knowledge.

If you have any feedback, please reach out to me at dipit.vasdev@nyu.edu