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https://github.com/snototter/pycamcalib

WIP: Intrinsic camera calibration with custom patterns & a simple UI
https://github.com/snototter/pycamcalib

Last synced: 16 days ago
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WIP: Intrinsic camera calibration with custom patterns & a simple UI

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README 

        
# pycamcalib

Camera calibration UI



* Goal of this project: Replace our MATLAB calibration toolbox.

* Desired functionality:

  * Simple UI to load images & visualize calibration results

  * Option to skip images with large reconstruction error

  * Image enhancement

    * Apply to all images

    * Apply on per-image basis ("live fine-tuning")

* Feasibility:

  * Bouguet's toolbox is fully integrated in OpenCV, so we "only" need the marker detection and correspondence search.

  * Result plots (e.g. marker/camera poses) can be done with matplotlib 3d plots

  * Interactive 3d plots can be shown via Qt + matplotlib (create a custom widget)

* Caveats:

  * Correspondence search is obviously tricky

  * For sub-pixel accuracy, we need inverse compositional image alignment



## Setup

TODO mention opencv-python/opencv/qt issue  

either use opencv-python + CLI, or install system package opencv + link their bindings (simple `cv2.pth`)



## Status

* [ ] Use configurable template

  * [x] Render to SVG

  * [x] Render to PNG

  * [ ] Refactor eddie (pattern_specs is quite cluttered & uses ambiguous names - e.g. "grid")

* [x] Detect eddie center marker

  * [x] Contour-based detection, estimate initial homography

  * [x] Support clipped center markers (near image borders)

  * [ ] Refactoring

* [ ] Correspondence search for eddie

  * [ ] Initial point correspondences

  * [ ] Refine matches via FCA/ICA

  * [ ] Refactoring

* [ ] Extensions

  * [x] Integrate standard checkerboard & shifted checkerboard

  * [ ] Preprocessing submodule (histeq, binarization fixed/adaptive, normalization, cutoff bi-ended-slider, ...)

  * [ ] Each pattern should provide config widget (import/export config JSON or TOML, export SVG/PNG/PDF)

  * [ ] Each pattern should provide preconfigured calibration boards

  * [ ] Rethink extensions (each pattern submodule could provide a "Specification" and "Detector") - `pcc.patterns` could iterate submodules and create a table of known patterns

  * [ ] `pcc.patterns` should provide mechanism to register default patterns (e.g. A4 checkerboard, A0 something)

  * [ ] Each detector should be able to `visualize(image, img_pts)`, compute and visualize coverage (convex hull over list of img_pts)

* [ ] UI

  * [ ] Image loading

  * [ ] Visualize marker & point matches 

  * [ ] Visualize calibration results

  * [ ] De-select "bad" images

  * [ ] Visualize camera/marker poses (3d plot)

  * [ ] Export calibration

  * [ ] Parallelization

* UI elements

  * [x] Folder selection

  * [ ] Board selection (pre-configured vs custom)

  * [ ] Board config widget checkerboard std/shifted

  * [ ] Board config widget eddie

  * [ ] Preproc UI (list view)

  * [ ] image board/gallery (show original, preprocessed, detected, coverage)

  * [ ] matplotlib 3d plot

  * [ ] Export/save widget

* TODO Documentation

  * Conventions (`NxM` means number of squares, NOT internal corners)

* Change opencv-python install

  * Issue: https://stackoverflow.com/questions/52337870/python-opencv-error-current-thread-is-not-the-objects-thread

  * Potential solution (setup.py): https://stackoverflow.com/questions/68436434/how-to-instruct-pip-to-install-a-requirement-with-no-binary-flag

  * Don't want to fix opencv version yet (maybe after deployment, to ensure major version is 2 or 3), thus for now: 

    1) set up venv

    2) pip install -r requirements.txt

    3) grab a coffee, go for a walk...

    4) pip install -e .

    

    opencv-python --no-binary opencv-python # FIXME this only works for GUI (CLI then no longer works...)

    use system-wide opencv (with official pybindings instead, see setup scripts)