https://github.com/rajkamalsah/flow-hpc-shocktrack
GPU-accelerated, fault-tolerant Schlieren/PIV shock tracking with interactive ROI, 1-px edges, and resumable training.
https://github.com/rajkamalsah/flow-hpc-shocktrack
ai-ml computer-vision cuda fluid-dynamics hpc mlsystem opencv piv pytorch schlieren scientific-ml smalldata transformer
Last synced: 6 days ago
JSON representation
GPU-accelerated, fault-tolerant Schlieren/PIV shock tracking with interactive ROI, 1-px edges, and resumable training.
- Host: GitHub
- URL: https://github.com/rajkamalsah/flow-hpc-shocktrack
- Owner: Rajkamalsah
- License: other
- Created: 2025-09-12T07:50:34.000Z (27 days ago)
- Default Branch: main
- Last Pushed: 2025-09-12T08:28:11.000Z (27 days ago)
- Last Synced: 2025-09-12T10:24:39.206Z (26 days ago)
- Topics: ai-ml, computer-vision, cuda, fluid-dynamics, hpc, mlsystem, opencv, piv, pytorch, schlieren, scientific-ml, smalldata, transformer
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# flow-hpc-shocktrack
**GPU-accelerated, fault-tolerant Schlieren/PIV shock tracking with interactive ROI, 1-px edges, and resumable training.**
End-to-end pipeline to extract shock angle vs time and dominant oscillation frequency from Schlieren frames (PIV-friendly too).
Designed for **small datasets** and **Spyder**. PyTorch is optional.
## Features
- Interactive ROI (OpenCV; Matplotlib fallback)
- 1‑px thin edges (skeletonization) + sub‑pixel `cv2.fitLine` angle
- (Optional) tiny U‑Net: synthetic pretrain + fine‑tune
- Resumable training (epoch checkpoints, PAUSE file or Ctrl+C)
- Outputs: overlays, time series, FFT/Strouhal, JSON summary
## Quickstart
1. `pip install -r requirements.txt`
2. Unzip `demo_schlieren_dataset.zip` → set `CONFIG["data_dir"]` to `demo_schlieren/images/` (and `mask_dir` to `demo_schlieren/masks/`).
3. Run `python shocktrack_roi.py` (or in Spyder).
ROI keys: click points → **c** close → **y** confirm (u=undo, r=reset, q=cancel).
4. Results in `runs/exp_roi/` → `pred/` overlays, `analysis/*.png`, `analysis/summary.json`.
## Pause / Resume
- Create empty `PAUSE` file in the training dir (e.g., `runs/exp_roi/finetune/PAUSE`) to stop after current epoch.
- Or press **Ctrl+C**; a checkpoint is saved.
- Resume: set `CONFIG["resume_finetune_dir"]` (or pretrain) to that folder and run again.
## Files
- `shocktrack_roi.py` — main script (Spyder-friendly)
- `requirements.txt` — core deps (no PyTorch)
- `requirements-torch.txt` — optional torch deps
- `demo_schlieren_dataset.zip` — tiny synthetic dataset (30 frames with masks)
- `LICENSE` (MIT), `.gitignore`, `.gitattributes`