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Ipyplot

screenshot of Ipyplot

IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big numbers of images.

Github

Overview

IPyPlot is an innovative Python package designed to enhance the plotting experience of images in Jupyter Notebooks and other similar platforms. With its roots in leveraging IPython and HTML, this package fundamentally transforms how users can display a large volume of images, making the process faster, more interactive, and far less frustrating. The traditional approach using matplotlib can often lead to long waiting times and inefficient workflows, especially when dealing with a high number of images. IPyPlot addresses these pain points with a straightforward yet powerful solution.

By simplifying the plotting of extensive image datasets, IPyPlot is particularly beneficial for projects that demand a swift, responsive interface for visualizing content—perfect for data scientists, researchers, and educators working with image data. This tool is not just about speed; it also offers a wealth of functionality that can enrich the user experience further, making it a must-have addition to your Python toolkit.

Features

  • Fast and Efficient Plotting: Quickly plot images in Python notebooks without the sluggishness associated with traditional libraries.

  • Multiple Plotting Functions:

    • plot_images: Easily plots all images in a grid format.
    • plot_class_representations: Displays the first image for each label/class based on provided labels, maintaining clarity in analysis.
    • plot_class_tabs: Organizes images into separate tabs for each label/class, enhancing navigability.

  • Diverse Image Format Support: Works with local storage URLs, remote URLs, PIL.Image objects, and numpy.ndarray formats, making it flexible for various data inputs.

  • Customizable Display Options: Use parameters like max_images and img_width to control the number and size of displayed images.

  • Interactive Features: Click any image to enlarge it, providing a closer look with just a simple interaction.

  • HTML Code Visibility: A "show HTML" button reveals the underlying HTML used for generating plots, promoting transparency and customization.

  • Extensive Notebook Compatibility: Fully supported on popular platforms including Jupyter, Google Colab, Azure Notebooks, and Kaggle Notebooks.

  • Extra Information Option: The custom_texts parameter allows users to display additional information like confidence scores alongside each image, adding context to the visual data.