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339 | def run_segmentation(
*,
tiles_dir: Path | None = None,
img: Path | None = None,
chm: Path | None = None,
den: Path | None = None,
rgb_bands: tuple[int, int, int] | None = None,
out_dir: Path,
sam_ckpt: Path,
device: str = "cpu",
stardist_model: Path | None = None,
stardist_input_key: str = "index",
stardist_use_aux: bool = True,
stardist_prob_thr: float | None = None,
stardist_nms_thr: float | None = None,
stardist_window_size_m: float | None = None,
stardist_window_overlap_m: float = 0.0,
stardist_dedup_iou_thr: float = 0.0,
stardist_cpu: bool = False,
stardist_resume: bool = False,
stardist_resume_from_labels: bool = False,
stardist_skip_sources: str | None = None,
stardist_skip_sources_file: Path | None = None,
run_sam: bool = True,
run_stardist: bool = True,
skip_existing_sam: bool = True,
log_refinement: bool = False,
fast: bool = True,
fast_batch_size: int = 32,
crop_smooth_sigma: float = 2.0,
smooth_sigma: float = 4.0,
post_smooth_sigma: float = 0.0,
) -> dict:
"""Run end-to-end segmentation from prepared tiles or one raw input scene."""
out_dir = Path(out_dir)
resolved_sam_ckpt: Path | None = None
resolved_stardist_model: Path | None = None
raw_inputs = (img, chm, den)
if tiles_dir is not None and any(v is not None for v in raw_inputs):
raise ValueError("Provide either tiles_dir or img/chm/den, not both.")
if tiles_dir is None:
if any(v is None for v in raw_inputs):
raise ValueError("Provide either tiles_dir or all of img/chm/den.")
prepared_root = out_dir / "prep" / "scene"
tiles_dir = build_single_tile(
img=Path(img),
chm=Path(chm),
den=Path(den),
rgb_bands=rgb_bands,
out=prepared_root,
skip_existing=True,
)
tiles_dir = Path(tiles_dir)
records_root = out_dir / "run_records"
proposals_root = out_dir / "proposals"
crowns_dir = out_dir / "crowns"
proposal_records = proposals_root / "proposal_records.json"
proposal_rasters_dir = proposals_root / "rasters"
tile_records = records_root / "tiles.json"
pending_tile_records_path = records_root / "tiles.pending.json"
recs, _ = build_tile_records(
tiles_dir=tiles_dir,
out_root=records_root,
)
pending_count = len(recs)
active_tile_records = tile_records
resolved_sam_ckpt = _resolve_sam_ckpt(sam_ckpt=sam_ckpt)
if run_sam and skip_existing_sam:
proposal_rasters_dir.mkdir(parents=True, exist_ok=True)
_, _, pending_count = build_pending_tile_records(
records_path=tile_records,
proposal_rasters_dir=proposal_rasters_dir,
out_path=pending_tile_records_path,
)
if pending_count > 0:
active_tile_records = pending_tile_records_path
if run_sam and (not skip_existing_sam or pending_count > 0):
from samstars.segmentation.proposals import run_tile_proposals
run_tile_proposals(
active_tile_records,
proposals_root,
resolved_sam_ckpt,
device=device,
rgb_lo_hi=None,
fast=fast,
fast_batch=fast_batch_size,
crop_smooth_sigma=crop_smooth_sigma,
smooth_sigma=smooth_sigma,
post_smooth_sigma=post_smooth_sigma,
)
_normalize_record_paths(proposal_records)
proposal_records_rebuilt = False
if not run_stardist:
return {
"records_root": records_root,
"proposals_root": proposals_root,
"crowns_dir": crowns_dir,
"proposal_records": proposal_records,
}
if proposal_rasters_dir.exists():
partial = _complete_proposal_records_from_rasters(proposal_rasters_dir)
existing_count = _record_count(proposal_records)
if partial and existing_count != len(partial):
proposal_records.parent.mkdir(parents=True, exist_ok=True)
json.dump(partial, open(proposal_records, "w"))
_normalize_record_paths(proposal_records)
proposal_records_rebuilt = True
if not proposal_records.exists():
raise RuntimeError(f"Missing proposal records: {proposal_records}")
stardist_model_dir, stardist_model_name = _resolve_stardist_model(stardist_model=stardist_model)
resolved_stardist_model = Path(stardist_model_dir) / stardist_model_name
scale_stats = out_dir / "proposal_scale_stats.json"
if proposal_records_rebuilt and scale_stats.exists():
scale_stats.unlink()
if not scale_stats.exists():
build_scale_stats_from_proposal_records(
proposal_records=proposal_records,
out=scale_stats,
percentiles=(2.0, 98.0),
chm_key="chm",
nodata_values=(-9999.0,),
)
refinement_py = Path(__file__).resolve().parent / "refinement.py"
cmd = [
sys.executable,
str(refinement_py),
"--proposal-records",
str(proposal_records),
"--model-dir",
str(Path(stardist_model_dir)),
"--model-name",
stardist_model_name,
"--out-dir",
str(crowns_dir),
"--input-key",
stardist_input_key,
"--preds-name",
"crowns.gpkg",
"--write-labels",
]
if stardist_use_aux:
cmd.extend(
[
"--use-aux",
"--scale-stats",
str(scale_stats),
]
)
if (stardist_prob_thr is None) ^ (stardist_nms_thr is None):
raise ValueError("Provide both stardist_prob_thr and stardist_nms_thr, or neither.")
if stardist_window_size_m is not None and stardist_window_size_m <= 0:
raise ValueError("stardist_window_size_m must be > 0.")
if stardist_window_overlap_m < 0:
raise ValueError("stardist_window_overlap_m must be >= 0.")
if (
stardist_window_size_m is not None
and stardist_window_overlap_m >= stardist_window_size_m
):
raise ValueError("stardist_window_overlap_m must be smaller than stardist_window_size_m.")
if not (0.0 <= stardist_dedup_iou_thr < 1.0):
raise ValueError("stardist_dedup_iou_thr must be in [0, 1).")
if stardist_resume_from_labels and not stardist_resume:
raise ValueError("stardist_resume_from_labels requires stardist_resume.")
if stardist_prob_thr is not None and stardist_nms_thr is not None:
cmd.extend(
[
"--prob-thr",
str(stardist_prob_thr),
"--nms-thr",
str(stardist_nms_thr),
]
)
if stardist_window_size_m is not None:
cmd.extend(
[
"--window-size-m",
str(stardist_window_size_m),
"--window-overlap-m",
str(stardist_window_overlap_m),
]
)
if stardist_dedup_iou_thr > 0:
cmd.extend(
[
"--dedup-iou-thr",
str(stardist_dedup_iou_thr),
]
)
if log_refinement:
cmd.append("--log")
if stardist_resume:
cmd.append("--resume")
if stardist_resume_from_labels:
cmd.append("--resume-from-labels")
if stardist_skip_sources:
cmd.extend(["--skip-sources", stardist_skip_sources])
if stardist_skip_sources_file is not None:
cmd.extend(["--skip-sources-file", str(stardist_skip_sources_file)])
env = None
if stardist_cpu:
env = dict(**os.environ)
env["CUDA_VISIBLE_DEVICES"] = ""
subprocess.run(cmd, check=True, env=env)
return {
"records_root": records_root,
"proposals_root": proposals_root,
"crowns_dir": crowns_dir,
"proposal_records": proposal_records,
"scale_stats": scale_stats,
"sam_ckpt": resolved_sam_ckpt,
"stardist_model": resolved_stardist_model,
}
|