Addressing 3D and 2D Bubbles In Rock (subchallenge)
Please note: this project focused entirely on solving the included subchallenge, quoted below:
Find the frame in the tomography - what plane in the full tomography volume best matches the BSE map of a flat polished surface cut from the original rock?
- Chuck Bassett [chucksmash]
I created a small software library for correlating BSE maps to frames from a computed tomography scan. The library uses a technique known as perceptual hashing, or phashing, (editor's note: not p-hacking) to compute a measure of visual similarity.
Cryptographically strong hashing algorithms exhibit a property known as the avalanche effect, where a small change in the input value results in a large change in the hashed output. For general applications, this is a desirable property.
Perceptual hashes are a family of hash algorithms where a small change in the input value only results in a small change in the hashed output. Because of this, comparing the output of a phash function applied to image data is useful for quantifying the degree of similarity between images and also for quickly finding similar images.
This solution is written in Python 3.7 and uses the imagehash
package available from PyPI.
No attempts were made to pre-process or normalize input data. Normalizing inputs prior to algorithm application could potentially improve results in the future.
The project includes a Dockerfile. To run the project, you'll need to
install docker; to follow the run steps below, you'll also need to
install docker-compose.
- Checkout the project:
$ git clone [email protected]:hackthesolarsystem/frame-seeker.git
$ cd frame-seeker- Download the
Sem2_YZ_8a.zipfile from the problem's resources page. Save it under thepythondirectory asdata.zip:
$ cp Sem2_YZ_8a.zip python/data.zip- Build the Docker image:
$ cd path/to/project/root
$ docker-compose build- Run the Docker image:
$ docker-compose run seek_frame # TBD