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Naïve Bees: Image Loading and Processing

Load, transform, and understand images of honey bees and bumble bees in Python.

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  • 10 tasks
  • 3,193 participants
  • 1,500 XP

Project Description

Can a machine distinguish between a honey bee and a bumble bee? Being able to identify bee species from images, while challenging, would allow researchers to more quickly and effectively collect field data. In this Project, you will use the Python image library Pillow to load and manipulate image data. You'll learn common transformations of images and how to build them into a pipeline.

This project is the first part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision. The second project in the series is Naïve Bees: Predict Species from Images.

The recommended prerequisites for this project are Intermediate Python for Data Science and Introduction to Data Visualization with Python.

Project Tasks

  • 1Import Python libraries
  • 2Opening images with PIL
  • 3Image manipulation with PIL
  • 4Images as arrays of data
  • 5Explore the color channels
  • 6Honey bees and bumble bees (i)
  • 7Honey bees and bumble bees (ii)
  • 8Simplify, simplify, simplify
  • 9Save your work!
  • 10Make a pipeline
Instructor Avatar
Peter Bull

Co-founder of DrivenData

Peter is a co-founder of DrivenData. He earned his master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. His work lies at the intersection of statistics and computer science, and he wants to help bring powerful new modeling techniques to the organizations that need them most. He previously worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.

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Emily Miller

Data Scientist at DrivenData

Emily is a data scientist at DrivenData. With a background in international development, her interests lie in using data science to make poverty alleviation efforts more effective. She previously worked at the Bill & Melinda Gates Foundation, Stanford Center for International Development, and Brookings Institution. She holds a master's in International Development from The New School.

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Technology

  • Python LogoPython
  • Topics

    Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data