File size: 5,141 Bytes
08764e9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | """Visual comparison of reconstructed meshes vs ground truth for held-out cases.
Produces, per case, a PNG with GT (grey) and prediction (tooth=blue, canal=red)
overlaid, plus an error-colored view (vertices colored by distance to GT).
python -m toothcanal.compare --config configs/default.yaml
python -m toothcanal.compare --config configs/default.yaml --case 031-
"""
import os, glob, argparse
import numpy as np
import torch
from .utils import load_config, ensure_dir
from .splits import make_split
from .models import ImplicitNet
from .infer import reconstruct_instance
from .evaluate import gt_meshes
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 _nearest_dist(pred, gt, n=8000):
from scipy.spatial import cKDTree
import trimesh
pp, _ = trimesh.sample.sample_surface(pred, n)
gp, _ = trimesh.sample.sample_surface(gt, n)
d, _ = cKDTree(gp).query(pp)
return pp, d
def render_case(net, d, ids, cfg, dev, out_png, do_tto=True, max_teeth=None):
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
fig = plt.figure(figsize=(16, 6))
axO = fig.add_subplot(131, projection="3d"); axO.set_title("Overlay (grey=GT)")
axP = fig.add_subplot(132, projection="3d"); axP.set_title("Prediction")
axE = fig.add_subplot(133, projection="3d"); axE.set_title("Error (mm, vs GT)")
allv = []
errs = []
teeth = ids if max_teeth is None else ids[:max_teeth]
for iid in teeth:
rec = reconstruct_instance(net, d, iid, cfg, dev, do_tto=do_tto)
gt_t, gt_c = gt_meshes(d, iid, cfg)
if rec is None or rec["tooth"] is None or gt_t is None:
continue
# place meshes in world by ROI origin so all teeth sit in the arch
off = rec["roi"]["lo_world_mm"]
pt = rec["tooth"].copy(); pt.vertices += off
gt = gt_t.copy(); gt.vertices += off
allv.append(pt.vertices)
# GT grey
axO.add_collection3d(Poly3DCollection(gt.vertices[gt.faces], facecolor=(.6,.6,.6),
alpha=.25, linewidths=0))
axO.add_collection3d(Poly3DCollection(pt.vertices[pt.faces], facecolor=(.2,.4,.8),
alpha=.5, linewidths=0))
axP.add_collection3d(Poly3DCollection(pt.vertices[pt.faces], facecolor=(.2,.4,.8),
alpha=.7, linewidths=0))
if rec["canal"] is not None:
pc = rec["canal"].copy(); pc.vertices += off
axP.add_collection3d(Poly3DCollection(pc.vertices[pc.faces], facecolor=(.85,.15,.15),
alpha=.95, linewidths=0))
axO.add_collection3d(Poly3DCollection(pc.vertices[pc.faces], facecolor=(.85,.15,.15),
alpha=.8, linewidths=0))
# error coloring
pp, dd = _nearest_dist(pt, gt)
errs.append(dd)
axE.scatter(pp[:,0], pp[:,1], pp[:,2], c=dd, cmap="jet", s=1, vmin=0, vmax=1.0)
if not allv:
plt.close(fig); return False
V = np.vstack(allv)
for ax in (axO, axP, axE):
ax.set_xlim(V[:,0].min(), V[:,0].max())
ax.set_ylim(V[:,1].min(), V[:,1].max())
ax.set_zlim(V[:,2].min(), V[:,2].max())
ax.set_axis_off(); ax.view_init(elev=15, azim=-70)
if errs:
e = np.concatenate(errs)
fig.suptitle(f"mean surface error = {e.mean():.3f} mm "
f"(blue<{0.0:.1f} red>{1.0:.1f} mm)", fontsize=12)
fig.savefig(out_png, dpi=120, bbox_inches="tight"); plt.close(fig)
return True
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--config", default="configs/default.yaml")
ap.add_argument("--case", default=None)
ap.add_argument("--no_tto", action="store_true")
ap.add_argument("--max_teeth", type=int, default=None,
help="limit teeth per case for a faster preview")
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
out_dir = ensure_dir(os.path.join(cfg["paths"]["out_dir"], "compare"))
for cid in cases:
d = dict(np.load(os.path.join(cfg["paths"]["proc_dir"], f"{cid}.npz")))
ids = [int(v) for v in np.unique(d["inst"]) if v > 0]
out = os.path.join(out_dir, f"{cid}_compare.png")
ok = render_case(net, d, ids, cfg, dev, out, do_tto=not args.no_tto,
max_teeth=args.max_teeth)
print(f"[compare] {cid}: {'wrote '+out if ok else 'no meshes'}")
if __name__ == "__main__":
main()
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