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:
git clone https://github.com/softboiler/boilercv.git boilercv
).cd boilercv
).py -3.11 -m venv .venv
on Windows w/ Python 3.11 installed from python.org)..venv/scripts/activate
on Windows).pip install --editable .
to install boilercv
package in an editable fashion. This step may take awhile.data
directory, then copy the cloud
folder inside tests/data
to the root directory. Rename it to data
.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
.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.
Blake Naccarato 💻 |
asennain 📖 |