kneejerk

A package for scoring image data with your quick, kneejerk reactions, then organizing them for ingestion to Data Science pipelines

Contents

• Tutorial
  • Example Project
  • Scoring
  • Transferring
  • Loading
• On Image Dimensions
  • Resolving Rectangles
  • Resolving Image Sizes
  • Rescaling Images
• Developing

kneejerk

Image data can be messy.

Especially when considering the time it takes to label, persist, load, and operate– generating datasets for user-preference Machine Learning projects can be a costly task.

The main goal of kneejerk is to allow users to quickly key in scores as they’re served images, persist those scores, and formulate a way to quickly load everything into a format consumable by any number of Data Science libraries.

Motivation

Ultimately, this library is intended to facilitate getting into the clean workflow outlined in François Chollet’s excellent book Deep Learning with Python.

Link to one of his notebooks outlining this workflow on a neat, pre-labeled dataset.

In particular, we want to go from “big, unified directory of a ton of images” to “well-organized directories of images sorted into test/train/validation sets, by class.” Crucially, though, we want to do all of this file organization based on preferences that a user quickly generated using this library.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Using the tool is as easy as pip installing it and leveraging the command line utility

pip install kneejerk

Using the Package

Generating user preferences is as easy as using the command-line tool you just pip install ‘ed.

kneejerk score --input_dir im_dir --output_dir . --file_name preferences.csv

After you’ve generated your preferences.csv you can transfer all of the images to the proper directory structure via

kneejerk transfer --file_name preferences.csv

From there, you’re all set to use the ImageDataGenerator.flow_from_directory() functionality in keras, or any similar library.

See the Tutorial section in the documentation for more clarification on how this all works as well as some of the customization options.

Project Goals

Done

• Quick command line interface that:

  • Points at a directory and combs through all images
  • Allows user to key in preference scores
  • Saves results to .csv of (filepath, score)
  • Allow for random shuffling of the order of images shown

• Loader that converts from the .csv and image files to numpy

• Handle necessary data cleaning to resolve size mismatches

• Published on PyPI

ToDo

• Unit tests
• Documentation :)

Contributing

Bugs and Feature Requests should come in the form of Issues in the project

Contributions should only be made via Pull Requests, after an appropriate Issue has been opened.

Please see our contribution guide if you’ve got more questions than that!

Running the tests

This project uses a simple combination of the unittest.TestCase object and pytest. All code should be tested, and all tests should be run from the root of the project via the simple call:

pytest

Authors

Huge shout-out to avlaskin on GitHub for early collaboration via his slick library quickLabel, a really cool TkInter interface that does a very similar task. My data processing extended beyond the scope of his library and so I figured I’d start from scratch instead of blow up his PR feed :)