File size: 4,168 Bytes
3799002 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | """Export predictions as a single .nii.gz label volume per test case, aligned to
the processed CBCT, so you can open it in ITK-SNAP ON TOP of the image and scroll
through slices (canal = 1..28, tooth body = 29..56), exactly like the GT labels.
python -m toothcanal.export_nifti --config configs/default.yaml
Outputs, per test case, in outputs/nifti/:
<case>_image.nii.gz the (normalized) CBCT used
<case>_pred_label.nii.gz predicted labels (open as 'Segmentation' in ITK-SNAP)
<case>_gt_label.nii.gz ground-truth labels (for side-by-side comparison)
"""
import os, argparse
import numpy as np
import torch
from scipy import ndimage as ndi
from .utils import load_config, ensure_dir, save_nii
from .splits import make_split
from .models import ImplicitNet
from .infer import reconstruct_instance
def _load_net(cfg, dev):
ckpt = torch.load(os.path.join(cfg["paths"]["out_dir"], "stage2.pt"),
map_location=dev)
n_lat = ckpt["model"]["latents.weight"].shape[0]
class _N(ImplicitNet):
def __init__(s): super().__init__(n_lat, cfg)
net = _N().to(dev); net.load_state_dict(ckpt["model"]); net.eval()
return net
def export_case(cfg, net, cid, dev):
d = dict(np.load(os.path.join(cfg["paths"]["proc_dir"], f"{cid}.npz")))
img = d["image"].astype(np.float32)
sp = d["spacing"].astype(np.float32)
origin = d.get("origin", np.zeros(3)).astype(np.float32)
full_shape = img.shape
pred = np.zeros(full_shape, np.int16)
gt = np.zeros(full_shape, np.int16)
# GT directly from processed volumes
inst = d["inst"]; cinst = d["cinst"]
gt[inst > 0] = inst[inst > 0].astype(np.int16) + 28 # body 29..56
gt[cinst > 0] = cinst[cinst > 0].astype(np.int16) # canal 1..28
ids = [int(v) for v in np.unique(inst) if v > 0]
for iid in ids:
rec = reconstruct_instance(net, d, iid, cfg, dev, do_tto=False)
if rec is None:
continue
roi = rec["roi"]
g = rec["grid"]
lo_world = roi["lo_world_mm"]
# per-axis grid spacing from the ACTUAL physical extent the ROI covered
# (handles teeth clipped at the volume border, which previously stamped wrong).
actual = roi.get("actual_size_mm", None)
if actual is None:
actual = np.array([cfg["stage2"]["roi_mm"]] * 3, np.float32)
roi_grid_sp = actual / g
for name, occ, value in [("tooth", rec["occ_tooth"], iid + 28),
("canal", rec["occ_canal"], iid)]:
pts = np.argwhere(occ > 0)
if len(pts) == 0:
continue
world = lo_world[None, :] + pts * roi_grid_sp[None, :]
vox = np.round((world - origin[None, :]) / sp[None, :]).astype(int)
ok = np.all((vox >= 0) & (vox < np.array(full_shape)), axis=1)
vox = vox[ok]
if len(vox) == 0:
continue
if name == "canal":
pred[vox[:, 0], vox[:, 1], vox[:, 2]] = value
else:
cur = pred[vox[:, 0], vox[:, 1], vox[:, 2]]
vv = vox[cur == 0]
pred[vv[:, 0], vv[:, 1], vv[:, 2]] = value
meta = dict(spacing=sp, origin=origin, direction=np.eye(3).flatten())
out_dir = ensure_dir(os.path.join(cfg["paths"]["out_dir"], "nifti"))
save_nii(img, meta, os.path.join(out_dir, f"{cid}_image.nii.gz"))
save_nii(pred, meta, os.path.join(out_dir, f"{cid}_pred_label.nii.gz"))
save_nii(gt, meta, os.path.join(out_dir, f"{cid}_gt_label.nii.gz"))
print(f"[nifti] {cid}: wrote image + pred_label + gt_label to {out_dir}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--config", default="configs/default.yaml")
ap.add_argument("--case", default=None)
args = ap.parse_args()
cfg = load_config(args.config)
dev = "cuda" if torch.cuda.is_available() else "cpu"
net = _load_net(cfg, dev)
_, test = make_split(cfg["paths"]["proc_dir"], cfg)
cases = [args.case] if args.case else test
for cid in cases:
export_case(cfg, net, cid, dev)
if __name__ == "__main__":
main()
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