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

Done.

Updated extractor: garmin_img_to_osmand_v6.py

Validation sample for the blue unnamed marker:

• near_blue_marker_02235051.csv
• near_blue_marker_02235051.json

What changed in v6

• Added point-focused parametrization so unnamed/raw symbols can be kept on purpose instead of disappearing behind semantic filters.

• Added near-coordinate capture:

  • --near lon,lat[,radius_m]

• Added point discovery profiles:

  • --point-profile all
  • --point-profile named
  • --point-profile semantic
  • --point-profile symbols
  • --point-profile interesting
  • --point-profile named_or_symbol

• Added raw type inclusion/exclusion:

  • --include-raw-point-type 0x65:0x00
  • --exclude-raw-point-type ...

• Added grouping key output for landmarks:

  • --point-group-by auto|semantic|raw_type|image|mapset

• Landmark CSV/GeoJSON now includes:

  • point_group_key
  • point_interest_reasons_json
  • distance_m when --near is used
  • has_name
  • has_semantic

• Kept the previous fixes:

  • better POI-indirected label decoding
  • image id / image blob best-effort extraction
  • all mapsets traversal
  • segment stats and error previews in metadata

Most important test result

The previously missing point is now capturable with parameters.

Using:

• mapsets 02235051 and 02235063
• --near 25.1401,42.3391,60
• --point-profile interesting
• --include-unnamed
• --include-raw-point-type 0x65:0x00

the extractor returns:

• mapset: 02235051
• lon: 25.14019489
• lat: 42.33907700
• type: point / 0x65 / 0x00
• name: empty
• group key: raw:0x65:0x00
• reasons: ["unnamed_raw_symbol", "raw_type_include"]

So that class of point is no longer lost.

Useful commands

Targeted check for a suspicious map symbol:

python garmin_img_to_osmand_v6.py gmapsupp.img ^
  --mapset 02235051 ^
  --mapset 02235063 ^
  --near 25.1401,42.3391,60 ^
  --point-profile interesting ^
  --include-unnamed ^
  --include-raw-point-type 0x65:0x00 ^
  --landmarks-csv near_check.csv ^
  --meta-json near_check.json

Maximal “interesting spots” extraction from one IMG:

python garmin_img_to_osmand_v6.py gmapsupp.img ^
  --point-profile interesting ^
  --include-unnamed ^
  --point-group-by auto ^
  --landmarks-csv interesting_spots.csv ^
  --landmarks-geojson interesting_spots.geojson ^
  --meta-json interesting_spots.json

Same, but grouped by raw Garmin type:

python garmin_img_to_osmand_v6.py gmapsupp.img ^
  --point-profile interesting ^
  --include-unnamed ^
  --point-group-by raw_type ^
  --landmarks-csv interesting_raw_groups.csv

Same, but force-include raw symbol classes you discover:

python garmin_img_to_osmand_v6.py gmapsupp.img ^
  --point-profile interesting ^
  --include-unnamed ^
  --include-raw-point-type 0x65:0x00 ^
  --include-raw-point-type 0x64:0x14 ^
  --include-raw-point-type 0x66:0x16 ^
  --landmarks-csv interesting_plus_whitelist.csv

If you want every point, no heuristics:

python garmin_img_to_osmand_v6.py gmapsupp.img ^
  --point-profile all ^
  --include-unnamed ^
  --landmarks-csv all_points.csv

What I consider improved beyond your direct request

• The extractor now supports a discovery workflow, not just a clean export workflow.

• The output has enough metadata to build downstream grouping logic without reparsing:

  • semantic group
  • raw type group
  • image group
  • mapset group
  • interest reasons

• Near-coordinate debugging is now a first-class capability, which is crucial for validating “I see this on the map, why isn’t it in the export?”

What is still not fully solved

• Not every Garmin symbol is semantically understood yet.
• Image/icon extraction is still best-effort, because some symbol rendering in Garmin maps is style/TYP-driven rather than directly stored as easy blobs.
• Some “interesting” raw point types will still need iterative whitelisting as you discover them.

The next strong upgrade would be adding a discovery summary mode that automatically ranks raw point types/subtypes by:

• unnamed count
• image-backed count
• frequency
• clustering
• proximity to trails/water/peaks

That would make the whitelist-building process much faster.