High-Level Project Summary
The purpose of our project is to develop an easy and user-friendly way to query and interact with data from JunoCam.The program also allows for image processing of JunoCam image data as well as displaying metadata from JunoCam, such as the orbital path around Jupiter during a time interval.
Link to Final Project
Link to Project "Demo"
Detailed Project Description
The user can perform rudimentary image processing such as merging r, g, b channel, brightness adjustment, contrast enhancement. In our working solution, the user will have to upload the images to the Colab notebook, however future plans involve a query using the api to get JunoCam image data directly from the official website. For a final solution, we hope to create an interactive website that would allow users to select from specific times/locations of Juno's orbit or any other satellite around a planet, in addition to other celestial objects. We used astronomical Python packages such as matplotlib, cv2, PIL & numpy in order to apply image processing techniques as well as visualising the JunoCam image data. Finally, for a final solution, we would like to fully implement a front and back end using React.js to handle user input as well as helping the user share science and art images created from the image processing functionality to the public.
Space Agency Data
Data made available by NASA, JunoCam operated by JDEA (JunoCam Digital Electronics Assembly )
- Images were used as an example for a proof of concept of being able to visualise mission data from specific longitude and latitude data of the Juno spacecraft and allow the user to choose from a variety of image processing techniques to create art and science images
- The JunoCam specifications influenced the specific image processing techniques featured in the notebook based on the properties of it's raw images, which have a prominent red band
- The JunoCam also has specific filters such as the methane, red, green, blue filters in its camera, hence filtering and adjusting the color channels is important in processing images effectively
Hackathon Journey
The Space Apps experience was a great learning opportunity for our team because we were able to design a solution that we would personally as well as the larger amateur astronomy community be interested in using. We learned how to apply our experiences in Astronomy research into a project deliverable.
Our team members have experience in Python astronomical packages, We used multiple Colab notebook blocks for documentation, debugging, trial and error and functionality.
Our team encountered difficulties in balancing time with academic work as well as the hackathon, however we came into learn and create a useful solution to the challenge.
We would like to give special thanks to our mentors Tom and Nick for providing invaluable insight and advice in not only the application of certain packages or possible ideas to pursue in our project, but the encouragement that our project is impactful.
References
Image Processing: https://www.missionjuno.swri.edu/junocam/processing/
Understanding JunoCam's operating specifications: https://www.missionjuno.swri.edu/pub/e/downloads/JunoCam_Junos_Outreach_Camera.pdf
Diving deep into the nasa api for pdf imaging for experimenting with querying our image data: https://pds-imaging.jpl.nasa.gov/tools/atlas/api/#parameters
Jovian storms: https://www.missionjuno.swri.edu/news/swirling-jovian-storm
Tags
#Juno, #JunoCam, #Jupiter, #image_processing, #orbit, #science, #art