๐จ 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
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