Brand Color Analyzer

A comprehensive tool for analyzing brand colors based on empirical research in color psychology and marketing. The tool provides detailed analysis of colors including brand personality dimensions, emotional responses, and cultural associations.

Features

• Research-based color analysis using established theories
• Brand personality dimension analysis (sincerity, excitement, competence, sophistication, ruggedness)
• Emotional response evaluation (arousal, pleasure, dominance, warmth, calmness)
• Cultural association analysis (trust, quality, premium, innovation, tradition)
• Support for multiple image formats: PNG, JPG, JPEG, TIFF, WebP
• Batch processing capabilities
• Multiple output formats (JSON, TXT)
• Progress tracking and detailed summaries
• Comprehensive error handling

Research Foundation

The tool implements research findings and methodologies from:

• Labrecque & Milne (2013): "Exciting Red and Competent Blue: The Importance of Color in Marketing"
• Singh (2006): "Impact of Color on Marketing"
• Elliot & Maier (2014): "Color Psychology: Effects of Perceiving Color on Psychological Functioning in Humans"

Installation

1. Clone the repository:

git clone https://github.com/MichailSemoglou/brand-color-analyzer.git
cd brand-color-analyzer

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install requirements:

pip install -r requirements.txt

Usage

Python API

from brand_color_analyzer import ColorAnalyzer, ImageAnalyzer
from pathlib import Path

# 1. Analyze a specific RGB color
analyzer = ColorAnalyzer()
color_results = analyzer.analyze_color((255, 0, 0))  # Analyze red
print(color_results)

# 2. Analyze an image
img_analyzer = ImageAnalyzer(
    min_frequency=0.5,    # Analyze colors appearing in at least 0.5% of pixels
    max_frequency=100.0   # Include colors up to 100% of pixels
)

# Analyze and get results in text format
analysis = img_analyzer.analyze_image("path/to/image.jpg", output_format="txt")

# Save the analysis
output_dir = Path("output")
output_dir.mkdir(exist_ok=True)
img_analyzer.save_analysis(analysis, output_dir / "color_analysis")  # Will add .txt extension

Command Line Interface

Single image analysis:

python brand_color_analyzer.py path/to/image.jpg output/directory

Batch processing:

python brand_color_analyzer.py path/to/image/directory output/directory

Options

• --min-frequency: Minimum color frequency to analyze (default: 5.0)
• --max-frequency: Maximum color frequency percentage (default: 100.0)
• --format: Output format: json or txt (default: json)
• -v, --verbose: Increase output verbosity
• --version: Show program version
• -h, --help: Show help message

Output Examples

Text Output

Color Analysis Results
======================

Color Information:
  Name: Red
  RGB: (255, 0, 0)
  HSV: (0, 1.000, 1.000)
  HEX: #FF0000
  CMYK: (0.00, 100.00, 100.00, 0.00)

Brand Personality Dimensions:
  Sincerity: 25.0%
  Excitement: 85.0%
  Competence: 30.0%
  Sophistication: 40.0%
  Ruggedness: 45.0%

JSON Output

{
  "color_attributes": {
    "rgb": [255, 0, 0],
    "hsv": [0, 1, 1],
    "hex": "#FF0000",
    "cmyk": [0, 100, 100, 0],
    "name": "Red"
  },
  "brand_personality": {
    "sincerity": 25.0,
    "excitement": 85.0,
    "competence": 30.0,
    "sophistication": 40.0,
    "ruggedness": 45.0
  }
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

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

Citation

If you use this tool in your work, please cite:

@software{semoglou2025brandcolor,
    author = {Semoglou, Michail},
    title = {Brand Color Analyzer: A Research-Based Tool for Color Psychology in Marketing},
    year = {2025},
    url = {https://github.com/MichailSemoglou/brand-color-analyzer},
    version = {1.0.0}
}

Contact

• Author: Michail Semoglou
• Email: [email protected]
• GitHub: https://github.com/MichailSemoglou