NP's Color Classifier 🎨

A sophisticated color analysis tool that uses machine learning to identify and analyze the distribution of colors in images. This application provides a detailed breakdown of color composition in any uploaded image.

🌟 Features

• Smart Color Analysis: Uses machine learning to accurately identify and classify colors
• Real-time Processing: Instant color distribution analysis
• Visual Color Swatches: See the actual colors alongside their percentages
• User-friendly Interface: Clean and intuitive design
• High Accuracy: Trained on a diverse dataset of color samples
• Support for Multiple Formats: Works with JPG, JPEG, and PNG images

📸 Screenshots

Main Interface

The main interface of NP's Color Classifier showing the upload section and welcome message

Color Analysis Results

Example of color analysis results showing the distribution of colors in an uploaded image

Upload Process

Demonstration of the image upload process and real-time analysis

🛠️ Technical Details

Technologies Used

• Frontend: Streamlit
• Machine Learning: scikit-learn
• Image Processing: OpenCV, PIL
• Data Processing: NumPy, Pandas
• Color Analysis: Matplotlib

Model Architecture

• Random Forest Classifier
• Color histogram extraction
• HSV color space analysis
• Patch-based color distribution

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/color-classifier.git
cd color-classifier

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the application:

streamlit run app.py

📁 Project Structure

color-classifier/
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── ColorClassification_dataset/  # Training dataset
│   ├── red/              # Red color samples
│   ├── blue/             # Blue color samples
│   └── ...               # Other color samples
├── uploads/              # Temporary storage for uploaded images
└── screenshots/          # Application screenshots
    ├── main_interface.png
    ├── color_analysis.png
    └── upload_example.png

💡 How It Works

1. Image Upload: Users can upload any JPG, JPEG, or PNG image
2. Color Extraction: The application extracts color histograms from the image
3. Analysis: The machine learning model analyzes the color distribution
4. Results: A detailed breakdown of colors is displayed with percentages

🎯 Model Performance

• Accuracy: ~99.3% on test dataset
• Color Categories: 9 distinct color classes
• Processing Time: Near real-time analysis

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

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

👤 Author

Namraa Patel

• GitHub: @yourusername

🙏 Acknowledgments

• Streamlit team for the amazing framework
• scikit-learn community for the machine learning tools
• OpenCV team for image processing capabilities