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Simple package that leverages IPython and HTML for more efficient, reach and interactive plotting of images in Jupyter Notebooks

Project description

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

Displaying huge numbers of images with Python in Notebooks always was a big pain for me as I always used matplotlib for that task and never have I even considered if it can be done faster, easier or more efficiently.
Especially in one of my recent projects I had to work with a vast number of document images in a very interactive way which led me to forever rerunning notebook cells and waiting for countless seconds for matplotlib to do it's thing..
My frustration grew up to a point were I couldn't stand it anymore and started to look for other options..
Best solution I found involved using IPython.display function in connection with simple HTML. Using that approach I built a simple python package called IPyPlot which finally helped me cure my frustration and saved a lot of my time

Getting Started

Checkout the examples below and gear-images-examples.ipynb notebook which holds end to end examples for using IPyPlot.

Installation

IPyPlot can be installed directly from this repo using pip:

pip install git+https://github.com/karolzak/ipyplot

or through PyPI

pip install ipyplot

Usage examples

IPyPlot offers 3 main functions which can be used for displaying images in notebooks:

To start working with IPyPlot you need to simply import it like this:

import ipyplot

and use any of the available plotting functions shown below (notice execution times).
images - should be a numpy array of either string (image file paths), PIL.Image objects or numpy.array objects representing images
labels - should be a numpy array of string

Display images in separate, interactive tabs for each class

Display a collection of images

Display class representations (first image for each class)

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