- has: searches what segmentation found, by the whole class name: has:dog, has:"dining table"
(has:table is a table, not a dining table). Plain words do not match segments.
- The catalog keeps each capture's segments (class, score, area) and its classes between newlines,
so a has: term is one contains per capture.
- Stats: "What is in the images", how many of the segmented images hold each class, each one a
has: search; shown even when nothing has been read by OCR. -stats logs it.
- Tests: has: (whole names, quoted names, case, combinations, plain words not matching).
- .library-folders in the library (src/app/library_folders.jai): each library folder's options,
starting with segment: true. Every folder starts off; unknown keys are kept when it is rewritten.
- The Segmentation window (Segments, above the grid): each folder's Segment checkbox, how many of
its images are segmented, and Erase per folder; a button to segment the folders set to segment.
- The segmentation scan (src/app/segment_ui.jai) runs on the OCR thread as its own task, with the
OCR scan's progress, pause and cancel: images only (never videos), not yet segmented, in folders
set to segment. The engine loads with the first scan (2.4 GB of GPU memory). Each image's segs and
mask are stored in one rewrite and its catalog entry loaded again.
- Importing into a folder set to segment segments the new images; the Import window's checkbox is
the folder's setting.
- -segment segments and exits; -erase segments, and -erase all now includes segments.
- The catalog reads each capture's segs (class list hash, segment count). The exit overlay names the
segmentation scan and model. segment_mcap_chunks moved into modules/Segment, shared with
segment-probe. The quoting helpers of Capture_File's text formats are public.
- Tests: .library-folders read and written back (quoted names, unknown keys, versions).
- modules/Capture_File/segment_chunks.jai: a front "segs" chunk (text: engine, date, the image read
and its size, the class list's hash, and each segment's id, score, area, box and class) and a back
"mask" chunk (a segment id per pixel, run-length encoded with LEB128 lengths, longest side at most
1024), keyed by model. No new file version: chunks came in version 3.
- mcap_store takes back chunks too, so a model's summary and mask are stored in one rewrite.
- Tests: the text round trip, the runs' edge cases (one segment over everything, none, runs across
rows, multi-byte lengths, id 255), bad masks refused, and storing, replacing and erasing both
chunks in a file with other chunks kept.
- segment-probe takes .mcap files (the image each is shown as; videos skipped) and with -store stores
its segments in them. On 27 images a mask is 0.8 to 6.4 KB.
- The format README's "Segmentation chunks" section; the plan's phase 3 results.
- segmentation-plan.html: the plan for panoptic segmentation of images, per library folder, stored
in .mcap chunks (no file version bump), in six phases.
- tools/export_mask2former.py: exports facebook/mask2former-swin-base-coco-panoptic to ONNX (opset 17,
dynamic batch, 384 x 384 input) and checks it against PyTorch: on 25 images, all 180 segments
identical (IoU 1.000). Writes the class list with names for people ("sky-other-merged" is "sky"),
and PyTorch's own segmentations for segment-probe -check.
- modules/Segment: runs the model with onnxruntime (CUDA, else the CPU) and makes segments of its
outputs as transformers' post_process_panoptic_segmentation does: a segment id map (longest side at
most 1024) and each segment's class, score, area and bounds. Matches PyTorch on 99.8% of pixels,
every segment scored 0.6 or more found. 57 ms an image at batch 4 on an RTX 5080 laptop.
- tools/segment_probe.jai (jai first.jai - segment_probe): segments image files; -out draws them,
-check compares with PyTorch, -dump-input writes the model's input.
- modules/onnxruntime: ort_run_float_outputs, for a model with several outputs.
- first.jai copies the model to bin/segment-models. The model itself is not in git
(modules/Segment/models, 462 MB); tools/export_mask2former.py makes it.
