read_marker_id (map_vision.py) reads each marker's own small "#<N>" id
label via template correlation, same approach as read_cell_label and
for the same documented reason (this text sits over the same aerial-
photo backdrop that defeated detection-based approaches for grid
labels). Wired end-to-end: find_markers -> Proposal.detected_id ->
save_marker_ground_truth's JSON. Reads against ScreenshotImport's
full_image when available, since the id text is tiny. Crop region and
threshold are a single-screenshot calibration, not yet validated
against real ground truth (documented as such).
Also switches save_marker_ground_truth/save_grid_correction to use
full_image over the WORK_W-downscaled image, so a human reviewing a
capture can actually read the small id text well enough to judge it.
Logs unit_score/unit_margin on every Proposal too (previously only
pass/fail `unit` was recorded), and measured current type-detection
reliability against the 6 existing ground-truth captures: 0/72 (0%)
accepted proposals had any confident detected_unit at all, not just
wrong guesses -- classify_marker never clears its own confidence floor
against real screenshots. Findings and next steps in TODO.md.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
