petercorke / petercorke/machinevision-toolbox-python
FiducialCollection.estimatePose: ids/cornerss zip misaligns after ID filtering
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- Dominant language
- Python
- Stars
- 219
- Forks
- 30
- Avg merge
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Description
Found 2026-08-03 while fixing the OpenCV 4/5 `ids` shape compat issue in the same method (see #44). `ImageFiducials.py`'s `FiducialCollection.estimatePose` does:
```python
cornerss = [
corners.T.squeeze()
for corners, id in zip(cornerss, ids)
if id in self._ids
]
ids = [id for corners, id in zip(cornerss, ids) if id in self._ids]
```
The second line's `zip(cornerss, ids)` pairs the already-filtered `cornerss` (reassigned on the line above) against the original, unfiltered `ids`. If any detected marker's ID isn't in `self._ids` (exactly the scenario the docstring calls out -- "filter the markers, useful if there are several ArUco boards in the scene"), the two lists have different lengths and `zip` silently pairs mismatched corners/ids from that point on. Only harmless when every detected marker happens to belong to this board (no actual filtering occurs), which is presumably why it hasn't been caught yet.
Fix
Compute the filtered `cornerss`/`ids` together in a single pass (e.g. one list comprehension producing tuples, then unzip), so both lists are always built from the same original, unfiltered pairing. Needs a test with multiple boards/extraneous marker IDs in view to catch a regression -- there's currently no coverage for the multi-board filtering case at all.
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in ImageFiducials.py at FiducialCollection.estimatePose and inspect how detected corners and IDs are filtered. Add regression coverage for multiple boards or extraneous marker IDs, then verify that filtered corners and IDs remain aligned and the existing test suite passes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- opencv, python
- Domain
- computer-vision
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Clearly specified
- Newbie friendliness
- 76/100