Computer vision routines suitable for nucleate pool boiling bubble analysis. See the documentation for more detail, including a Binder for running code live. Click the "launch binder" badge above and you will be able to run the notebooks in docs/experiments in your browser with different parameters, and even create your own notebooks.

Overlay of the external contours detected in one frame of a high-speed video. Represents output from the "fill" step of the data process.

The data process graph shows the data process, and allows for individual steps in the process to be defined indpendently as Python scripts, Jupyter notebooks, or even in other languages like Matlab. The process is defined in dvc.yaml as as series of "stages". Each stage has dependencies and outputs. This structure allows the data process graph to be constructed, directly from the dvc.yaml file. This separates the concerns of data management, process development, and pipeline orchestration. This project reflects the application of data science best practices to modern engineering wokflows.

If you would like to adopt this approach to processing your own data, you may clone this repository and begin swapping logic for your own, or use a similar architecture for your data processing. To run a working example with some actual data from this study, perform the following steps:

1. Clone this repository and open it in your terminal or IDE (e.g. git clone https://github.com/blakeNaccarato/boilercv.git boilercv).
2. Navigate to the clone directory in a terminal window (e.g. cd boilercv).
3. Create a Python 3.10 virtual environment (e.g. py -3.11 -m venv .venv on Windows w/ Python 3.11 installed from python.org).
4. Activate the virtual environment (e.g. .venv/scripts/activate on Windows).
5. Run pip install --editable . to install boilercv package in an editable fashion. This step may take awhile.
6. Delete the top-level data directory, then copy the cloud folder inside tests/data to the root directory. Rename it to data.
7. Copy the local folder from tests/data to ~/.local where ~ is your user/home folder (e.g. C:/Users/<you>/.local on Windows). Rename it to boilercv.
8. Run dvc repro to execute the data process up to that stage.

The data process should run the following stages: contours, fill, filled_preview, binarized_preview and gray_preview. You may inspect the actual code that runs during these stages in src/boilercv/stages, e.g. contours.py contains the logic for the contours stage. This example happens to use Python scripts, but you could define a stage in dvc.yaml that instead runs Matlab scripts, or any arbitrary action. This approach allows for the data process to be reliably reproduced over time, and for the process to be easily modified and extended in a collaborative effort.

There are other details of this process, such as the hosting of data in the data folder in a Google Cloud Bucket (alternatively it can be hosted on Google Drive), and more. This has to do with the need to store data (especially large datasets) outside of the repository, and access it in an authenticated fashion.

This data process graph is derived from the code itself. It is automatically generated by dvc. This self-documenting process improves reproducibility and reduces documentation overhead. Nodes currently being implemented are highlighted.

flowchart TD
 node1["binarized_preview"]
 node2["contours"]
 node3["correlations"]
 node4["data\rois.dvc"]
 node5["data\sources.dvc"]
 node6["fill"]
 node7["filled_preview"]
 node8["gray_preview"]
 node9["lifetimes"]
 node10["tracks"]
 node11["unobstructed"]
 node2-->node6
 node2-->node11
 node3-->node9
 node4-->node1
 node5-->node1
 node5-->node2
 node5-->node6
 node5-->node7
 node5-->node8
 node6-->node7
 node10-->node9
 node11-->node10
 node12["data\examples.dvc"]
 node13["data\samples.dvc"]
 style node3 fill:#000784
 style node9 fill:#000784
 style node10 fill:#000784
 style node11 fill:#000784