Processing Snapshot
This directory contains a source snapshot of the scripts used for the released BubbleID-Flow data products. It accompanies, but does not replace, the canonical software repository:
https://github.com/UARK-NED3/BubbleID-Flow
The repository baseline at the time of this release snapshot was commit
051c07dd8016cc27861509ac203b59b45b29e170. The files under scripts/ are the
archived processing snapshot for this dataset release. Run them from this
directory with python -m scripts.<module_name> so intra-script imports resolve
from the snapshot.
Environment
environment.yml records the environment versions observed during release
preparation: Python 3.10.19, PyTorch 2.9.1 CPU, Detectron2 0.6, OpenCV 4.11.0,
NumPy 1.23.0, and pandas 2.3.3. The environment file is a dependency
specification, not a platform-independent binary lockfile.
Main entry points
labelme_to_coco.py: convert LabelMe annotations to ROI-aware COCO format.train_detectron2.py: fine-tune the one-class Mask R-CNN model.evaluate_detectron2.py: calculate COCO and union-mask metrics.analyze_segmentation_robustness.py: reproduce score-threshold and segmentation-robustness analyses.audit_thermal_reduction.py: calculate traceable thermal state summaries.analyze_optical_thermal_association.py: perform state-level optical-thermal screening.analyze_multimodal_case.py: prepare per-case optical, thermal, and AE summaries subject to the timing limits in../timing_reconciliation.csv.create_bubbleid_flow_showcase_video.py: regenerate the derived presentation videos under../Videos.
Reproduction boundary
The release contains the 130 LabelMe files and checkpoint but does not include a frozen file-level 104/26 train/holdout split manifest, original training seed, augmentation record, or checkpoint-selection history. It therefore supports reanalysis and new split construction, but does not independently reconstruct the reported same-sequence evaluation exactly.