https://github.com/thomasjo/nemo
Detection and classification of microscopic foraminifera
https://github.com/thomasjo/nemo
climatology deep-learning foraminifera geology geoscience machine-learning oceanography research tensorflow
Last synced: 7 months ago
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Detection and classification of microscopic foraminifera
- Host: GitHub
- URL: https://github.com/thomasjo/nemo
- Owner: thomasjo
- Created: 2019-03-18T08:39:57.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T03:37:51.000Z (almost 3 years ago)
- Last Synced: 2025-01-21T04:41:40.038Z (9 months ago)
- Topics: climatology, deep-learning, foraminifera, geology, geoscience, machine-learning, oceanography, research, tensorflow
- Language: Python
- Homepage:
- Size: 409 KB
- Stars: 3
- Watchers: 6
- Forks: 0
- Open Issues: 5
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Metadata Files:
- Readme: README.md
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README
# Project Nemo
Foraminifera (forams for short) classification via deep feature extraction.
## Image dataset
All models have been trained on a dataset of large, high-resolution images of
forams. The dataset has been produced by our research group, and will be made
publically available in the near future. Each of the source images consist of
a single class of forams. From these images, patches of _224x224_ pixels are
extracted using combinations of Gaussian smoothing, binary image generation
via thresholding, and connected components. The first step removes the metallic
border present in all source images, and the second step extracts candidate
patches. Each patch that passes a defined selection critera is extracted by
placing a _224x224_ crop at the centroid of the candidate region. The entire
process is automated in the `preprocess_data.py` script.
Once the source images have been preprocessed by extracting patches, datasets
for training, validation, and testing are generated automatically by using the
`build_datasets.py` script.
### Caveat regarding `raw-halves` source images
The `raw-halves` source images are slightly different in nature, and requires
that the `preprocess_data.py` script be invoked with `--border-threshold=50`.
Patches from this dataset must be manually copied to the `preprocessed` folder
built by process outlined above. In the future, we should find a way to fully
automate this step as well.