Crack segmentation produces a region. Many inspection tasks need a path. They need to trace where the crack runs, smooth the trace without erasing meaningful bends, compare it with a reference, and export results that can be analyzed outside a notebook.
The fine-crack tracing toolkit packages that work as an installable Python project with a command-line interface. It consumes raw frames and pre-segmented masks, extracts candidate crack points, orders them, optionally fits smoother curves, generates overlays, and exports metrics in CSV and JSON.
The repository exposes several ordering strategies rather than pretending one heuristic fits every geometry: a classic approach, a minimum-spanning-tree path, and a greedy alternative. ORB or Shi-Tomasi features can support corner detection where local structure matters. The output can be evaluated with precision, recall, F1, IoU, mean and maximum distance, and RMS error.
Why packaging matters
Computer-vision experiments often stop in the state where only the original author can reproduce them. Paths are hard-coded, configuration lives in notebook cells, and evaluation is a collection of plots with no machine-readable record.
Turning the work into a package changes the engineering question. Inputs and outputs need contracts. Configuration needs predictable precedence. Runs need named output directories. Metrics need stable serialization. A command such as `fine-tracing run` or `fine-tracing metrics` becomes a repeatable interface rather than a memory of which cells to execute.
The toolkit is not a crack detector. It begins after segmentation. That boundary is useful: it keeps the package focused on geometry and evaluation while allowing different segmentation models to feed it.
The next serious validation step is dataset-level comparison across crack types, widths, branching patterns, and imaging conditions. The current value is the reproducible bridge from mask to path—the layer required before a thin visual defect can become a measurement.