vipy 1.8.22

Visym Python Tools for Visual Dataset Transformation

[](https://badge.fury.io/py/vipy)

VIPY

VIPY: Visym Python Tools for Visual Dataset Transformation URL: https://github.com/visym/vipy/

VIPY is a python package for representation, transformation and visualization of annotated videos and images. Annotations are the ground truth provided by labelers (e.g. object bounding boxes, face identities, temporal activity clips), suitable for training computer vision systems. VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels. This enables a clean interface for transforming complex datasets for input to your computer vision training and testing pipeline.

VIPY provides:

• Representation of videos with labeled activities that can be resized, clipped, rotated, scaled and cropped

• Representation of images with object bounding boxes that can be manipulated as easily as editing an image

• Clean visualization of annotated images and videos

• Lazy loading of images and videos suitable for distributed procesing (e.g. dask, spark)

• Straightforward integration into machine learning toolchains (e.g. torch, numpy)

• Fluent interface for chaining operations on videos and images

• Dataset download, unpack and import (e.g. Charades, AVA, ActivityNet, Kinetics, Moments in Time)

• Video and image web search tools with URL downloading and caching

• Minimum dependencies for easy installation (e.g. AWS Lambda)

[](https://youtu.be/_jixHQr5dK4)

Requirements

python 3.* ffmpeg (required for videos)

Installation

`python pip install vipy `

This package has the following required dependencies which are installed by default `python pip install numpy matplotlib dill pillow ffmpeg-python `

The following packages are optional. You will receive a friendly warning if attempting to use these dependencies: `python pip install scipy opencv-python torch ipython scikit-learn boto3 youtube-dl dask distributed h5py nltk bs4 dropbox pyyaml pytest `

Quickstart

`python import vipy vipy.image.owl().mindim(512).show(figure=1).fliplr().show(figure=2).minsquare().show(figure=3) `

The [demos](https://github.com/visym/vipy/tree/master/demo) provide useful notebook tutorials to help you get started.