Color Palette Extractor

This Python script extracts a color palette from an image and generates various color harmonies. It’s designed for design students and professionals to analyze and utilize color schemes in their projects.

Features

• Extract dominant colors from any image
• Generate color harmonies (Complementary, Analogous, Triadic, Tetradic, Tints, and Shades)
• Create a PDF report with visual representation of colors and harmonies
• Save color information in a text file for easy reference

Installation

1. Clone this repository:

   git clone https://github.com/MichailSemoglou/color-palette-extractor.git
   cd color-palette-extractor
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Download the Inter font files (Inter-Bold.ttf and Inter-Regular.ttf) and place them in the same directory as the script.

Usage

Run the script from the command line as follows:

python color_palette_extractor.py path/to/your/image.jpg -n 6

• path/to/your/image.jpg: Path to the input image file
• -n 6: Number of colors to extract (optional, default is 6)

The script will generate two output files in the same directory as the script:

1. color_info.txt: A text file containing detailed color information, including:

   • HEX, RGB, and CMYK values for each color in the extracted palette
   • Color harmony information (Complementary, Analogous, Triadic, Tetradic, Tints, and Shades)

2. color_palette.pdf: A visual report in PDF format, including:

   • The original image
   • The extracted color palette
   • Visual representations of each color harmony

Troubleshooting

Here are some common issues you might encounter and how to resolve them:

1. ModuleNotFoundError: If you see an error like ModuleNotFoundError: No module named 'numpy', it means the required dependencies are not installed. Make sure you’ve run pip install -r requirements.txt in your virtual environment.

2. FileNotFoundError for font files: If you see an error mentioning Inter-Bold.ttf or Inter-Regular.ttf, ensure these font files are in the same directory as the script. You can download them from the Inter font website.

3. Permission denied when saving output files: Ensure you have write permissions in the directory where you’re running the script.

4. Image file not found: Double-check the path to your image file. Use the full path if the image is not in the same directory as the script.

5. Unexpected color results: For very large images, try increasing the max_dimension value in the extract_color_palette function for potentially more accurate results, at the cost of increased processing time.

If you encounter any other issues, please open an issue on the GitHub repository with a detailed description of the problem and the steps to reproduce it.

Color Harmonies Explanation

1. Complementary: Colors opposite each other on the color wheel, creating a high-contrast effect.
2. Analogous: Colors adjacent to each other on the color wheel, creating a harmonious and cohesive look.
3. Triadic: Three colors evenly spaced on the color wheel, offering a balanced and vibrant color scheme.
4. Tetradic: Four colors arranged into two complementary pairs, providing a rich and varied palette.
5. Tints: Lighter variations of a color, created by adding white.
6. Shades: Darker variations of a color, created by adding black.

Dependencies

• Python 3.6+
• numpy (1.19.5)
• scikit-learn (0.24.2)
• Pillow (8.2.0)
• reportlab (3.5.67)

For a complete list of dependencies with version information, see requirements.txt.

Potential Use Cases

1. Brand Identity Development: Analyze existing logos or brand imagery to extract key colors and develop complementary palettes.

2. Web Design: Generate color schemes for websites based on a key visual element or photograph.

3. Interior Design: Extract colors from inspiration images to create cohesive room color schemes.

4. Fashion Design: Analyze fabric patterns or inspiration images to develop coordinated clothing collections.

5. Data Visualization: Create harmonious color palettes for charts, graphs, and infographics.

6. Digital Art: Generate color palettes from reference images for digital illustrations or concept art.

7. Photography: Analyze color composition in photographs and develop presets or filters based on extracted palettes.

8. Print Design: Ensure color consistency across various printed materials by extracting and using a consistent palette.

9. Product Design: Develop color schemes for product lines based on trend images or competitor analysis.

10. Marketing Materials: Create visually cohesive marketing campaigns by extracting colors from key visuals.

License

This project is licensed under the MIT License – see the LICENSE file for details.