🎨 Art and Art History

Bokeh

• Description: An interactive visualization library for modern web browsers.

• Use Case: Creating interactive visualizations and plots of art historical data for analysis and presentation.

• Documentation: Bokeh Documentation

• GitHub Repository: Bokeh GitHub

Matplotlib

• Description: A library for creating static, animated, and interactive visualizations in Python.

• Use Case: Generating charts and graphs for art historical analysis and data visualization.

• Documentation: Matplotlib Documentation

• GitHub Repository: Matplotlib GitHub

NumPy

• Description: Fundamental package for scientific computing with Python.

• Use Case: Handling numerical computations for quantitative analysis in art history research.

• Documentation: NumPy Documentation

• GitHub Repository: NumPy GitHub

OpenCV

• Description: Open Source Computer Vision Library, designed for computational efficiency and with a strong focus on real-time applications.

• Use Case: Image processing and analysis for art restoration, feature detection in artworks.

• Documentation: OpenCV Documentation

• GitHub Repository: OpenCV GitHub

Pandas

• Description: A data analysis and manipulation library.

• Use Case: Managing and analyzing datasets in art history, including cataloging and archival research.

• Documentation: Pandas Documentation

• GitHub Repository: Pandas GitHub

Pillow (PIL Fork)

• Description: The Python Imaging Library adds image processing capabilities to your Python interpreter.

• Use Case: Image manipulation tasks such as opening, manipulating, and saving many different image file formats in art research.

• Documentation: Pillow Documentation

• GitHub Repository: Pillow GitHub

Plotly

• Description: A graphing library that makes interactive, publication-quality graphs online.

• Use Case: Creating interactive visualizations for art history data.

• Documentation: Plotly Documentation

• GitHub Repository: Plotly GitHub

PyTorch

• Description: An open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.

• Use Case: Advanced applications like neural style transfer, and pattern recognition in art history studies.

• Documentation: PyTorch Documentation

• GitHub Repository: PyTorch GitHub

Scikit-image

• Description: A collection of algorithms for image processing in Python.

• Use Case: Used in art analysis for tasks like image segmentation, geometric transformations, color space manipulation.

• Documentation: Scikit-image Documentation

• GitHub Repository: Scikit-image GitHub

Scikit-learn

• Description: Simple and efficient tools for predictive data analysis.

• Use Case: Machine learning for pattern recognition in art history research, clustering artworks, and stylistic analysis.

• Documentation: Scikit-learn Documentation

• GitHub Repository: Scikit-learn GitHub

Seaborn

• Description: A Python data visualization library based on Matplotlib.

• Use Case: Creating informative and attractive statistical graphics in art research.

• Documentation: Seaborn Documentation

• GitHub Repository: Seaborn GitHub

TensorFlow

• Description: An end-to-end open source platform for machine learning.

• Use Case: Deep learning applications in art, such as style transfer, image recognition, and exploring AI-generated art.

• Documentation: TensorFlow Documentation

• GitHub Repository: TensorFlow GitHub

Vega

• Description: A visualization grammar for creating, saving, and sharing interactive visualization designs.

• Use Case: Advanced data visualization in art history and visual arts research.

• Documentation: Vega Documentation

• GitHub Repository: Vega GitHub