garmin-img-format-parsing/stage-1-read-garmin-img/readme.md

I reworked the extractor around the weak spots you pointed out and validated it against the uploaded gmapsupp.img, using GPXSee’s Garmin IMG support and source tree as the reference model for the label and local-field paths. GPXSee officially supports Garmin IMG/GMAP maps, and its public source is the right place to mirror parsing behavior from. (gpxsee.org)

Updated script: garmin_img_to_osmand_v5.py

What changed:

• better name extraction

  • fixed the big one: standard point labels now respect the Garmin POI indirection bit, instead of always treating the label offset as a direct string pointer
  • codepage handling is kept for your map’s Cyrillic labels
  • uppercase labels are normalized more cleanly
  • control-character cleanup added

• all mapsets / segments

  • still traverses all mapsets
  • now records segment stats and segment error previews into metadata so you can see where parsing is still rough

• image/icon grouping

  • extended points can now expose garmin_image_id when present in local fields

  • landmark CSV/GeoJSON now includes:

    • mapset
    • garmin_image_id
    • garmin_image_file

  • best-effort embedded image dumping is supported with --extract-images-dir

  • image grouping can be listed/exported with:

    • --list-image-groups
    • --image-groups-csv

• validation against your upload

  • the broken/truncated names improved a lot on the tested mapset

  • examples that now decode sensibly include:

    • Полски ясен
    • Андзова чешма
    • Дубрава (310)
    • Св. Димитър
    • Паметник на Мико Нинов

A concrete test I ran on your uploaded mapset 02234010:

• parsed 52686 features
• water-source landmark export produced 1404 point rows
• segment stats reported parsed points/polylines/polygons instead of only points

Use it like this.

Better landmark CSV with mapset + image fields:

python garmin_img_to_osmand_v5.py gmapsupp.img ^
  --mapset 02234010 ^
  --category water_sources ^
  --landmarks-csv water_sources.csv ^
  --meta-json water_sources.meta.json

List landmark types with better names:

python garmin_img_to_osmand_v5.py gmapsupp.img ^
  --mapset 02234010 ^
  --list-landmark-types

List image/icon groups:

python garmin_img_to_osmand_v5.py gmapsupp.img ^
  --mapset 02234010 ^
  --list-image-groups

Export image/icon group summary:

python garmin_img_to_osmand_v5.py gmapsupp.img ^
  --mapset 02234010 ^
  --image-groups-csv image_groups.csv

Best-effort dump embedded Garmin image blobs:

python garmin_img_to_osmand_v5.py gmapsupp.img ^
  --mapset 02234010 ^
  --extract-images-dir dumped_images ^
  --landmarks-csv landmarks.csv

The landmark CSV now has these extra columns:

• mapset
• garmin_image_id
• garmin_image_file

So even if a real image blob is not recoverable for a given point, you can still group by image id or by dumped image file when present.

One important note: on the tested mapset, I did not see a rich population of extracted image-backed point icons yet. The support is there now, but this particular IMG seems to benefit much more from the fixed label path than from embedded-image extraction. So the image grouping path is now available, but the biggest win on your sample was clearly the label repair.

What is still not fully okay:

• some segment errors remain, and the script now reports them in metadata instead of silently hiding them
• extended/local/class Garmin fields are still only partially decoded
• raw embedded icon extraction is best-effort, not guaranteed for every IMG flavor

The next best refinement is to push one more level into GPXSee-style local/class field parsing so more special POIs get richer metadata instead of only names and coordinates.