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TintTrove

TintTrove is a simple and elegant image color picker tool that extracts dominant colors from uploaded images. Designed to help designers, artists, and developers explore color palettes with ease. The project is powered by a KMeans clustering algorithm and deployed on Heroku. Watch the video

Features

  • Extracts dominant colors from any uploaded image.
  • Provides HEX and RGB values of colors.
  • Responsive and user-friendly design.
  • Built for seamless performance with minimal load times.

Algorithm Details

The core functionality relies on the following algorithm:

Color Clustering Algorithm

def get_colors(image, number_of_colors, show_chart=False):
    if image.shape[2] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    elif image.shape[2] == 1:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)

    reshaped_image = cv2.resize(image, (600, 400))
    reshaped_image = reshaped_image.reshape(reshaped_image.shape[0] * reshaped_image.shape[1], 3)
    clf = KMeans(n_clusters=number_of_colors)
    labels = clf.fit_predict(reshaped_image)
    counts = Counter(labels)
    center_colors = clf.cluster_centers_
    ordered_colors = [center_colors[i] for i in counts.keys()]
    hex_colors = [RGB_HEX(ordered_colors[i]) for i in counts.keys()]
    return hex_colors

Key Concepts:

  • KMeans Clustering: Groups pixels in the image into number_of_colors clusters.
  • OpenCV: Used for image preprocessing (e.g., resizing, color conversion).
  • HEX Conversion: Converts RGB values to HEX codes for easy usage in design tools.

Deployment

TintTrove is deployed on Heroku, making it accessible anywhere.

Live Demo

Check out TintTrove live on Heroku

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/tinttrove.git

  2. Navigate to the project directory:

    cd tinttrove

  3. Install dependencies:

    pip install -r requirements.txt

  4. Run the application locally:

    python app.py

  5. Open the application in your browser at http://localhost:5000.

Technologies Used

  • Frontend: HTML, Tailwind CSS, GSAP
  • Backend: Python, Flask
  • Algorithm: KMeans clustering for color extraction
  • Image Processing: OpenCV
  • Deployment: Heroku

Contributing

Contributions are welcome! Please fork this repository and submit a pull request.