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argument/README.md

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+## Argument
+base: v0. 
+
+scale + fuzz modifier: v1. 
+
+helix modifier profile1: v2. 
+
+helix modifier profile2: v3.
+
+The strands is generated as 60000x 64x3 saved as point cloud (npz) at `float16` in order.
+
+data layout: place the dataset e.g. `v0`, `v1` on the parent level outside the project.
+It should looks like:
+```
+PARENT_FOLDER/                     # parent folder = dataset root (any location)
+├── v0/                            # base
+│   ├── 00000.npz                  # frame 0  —  P: (60000, 64, 3) float16
+│   ├── 00001.npz
+│   └── …                          # one .npz per frame (00000 … )
+├── v1/                            # scale + fuzz modifier
+├── v2/                            # helix modifier profile1
+├── v3/                            # helix modifier profile2
+└── argument/                      # ← this project, beside the versions
+    ├── run_viewer.py
+    ├── meta/head_water_tight_boundary.obj
+    └── viewer/
+```
+
+### Load strands data
+```python
+import numpy as np
+
+data = np.load("../v0/00000.npz")     # one frame, relative to the project folder
+P = data["P"].astype(np.float32)      # (60000, 64, 3): 60000 strands, 64 samples, xyz
+root, tip = P[:, 0], P[:, -1]         # samples are ordered root -> tip
+```
+
+### Viewer
+
+`python run_viewer.py` on current project root path.
+
+### Strand surface
+
+Wrap a frame's strands in a watertight mesh (voxel rasterise → EDT/SDF →
+marching cubes → isotropic remesh). Loads `.npz` (key `P`) or an l-polyline OBJ
+by extension; knobs live in `conf_strand2surface.toml`.
+
+```sh
+poetry install --with surface     # one-time: open3d, scipy, skimage, pymeshlab + cupy (GPU)
+python run_strand2surface.py --config conf_strand2surface.toml --skip-viz   # uses [data] dataset/asset
+python run_strand2surface.py --config conf_strand2surface.toml --dataset 3 --asset 100
+```
+
+Frame = `[data].dataset` (→ `vN`) + `[data].asset` (→ `{M:05d}.npz`), overridable
+with `--dataset`/`--asset`; `--strands <path>` takes a raw `.npz`/`.obj` instead.
+Output: `output/strand2surface/<version>_<frame>.obj` (+ `.ply`).
+
+**GPU**: `[strand_surface].gpu = true` runs the voxel→SDF stages (rasterise, the
+two EDTs, Gaussian) on the GPU via CuPy — ~**44× faster** for those stages at
+`radius=0.001` (≈29 s → 0.7 s), bit-identical isosurface, auto-fallback to CPU.
+After that the isotropic remesh is the bottleneck (vectorized Surface Nets is the
+next lever).
+
+### Batch the running
+
+`run_batch_strand2surface.py` surfaces every frame of a version with N parallel
+workers, writing `output/<version>/<frame>.obj`.
+
+```sh
+python run_batch_strand2surface.py --dataset v0 --config conf_strand2surface.toml --parallel 6
+python run_batch_strand2surface.py --dataset 3  --config conf_strand2surface.toml --parallel 4 --overwrite
+python run_batch_strand2surface.py --dataset 0  --config conf_strand2surface.toml --parallel 2 --limit 20
+```
+
+`--dataset` takes `v0` or `0`. Existing `.obj` files are skipped (resumable)
+unless `--overwrite`; `--limit N` does just the first N frames. Each worker runs
+the GPU SDF with CPU fallback, so with `gpu = true` at fine `radius` keep
+`--parallel` modest (≈ GPU memory / ~3 GB per frame); the remesh is CPU-bound, so
+parallelism scales with cores.

argument/conf_strand2surface.toml

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+# ---------------------------------------------------------------------------
+# Standalone config for run_strand2surface.py
+#
+# Generates a watertight triangle mesh from hair strands only (no card
+# geometry). Pipeline: rasterise -> interior-chamber fill -> EDT SDF ->
+# Gaussian smooth -> marching cubes -> (optional) isotropic remesh.
+# ---------------------------------------------------------------------------
+
+[data]
+# Pick the frame to surface by dataset (version) + asset (frame) number:
+#   dataset = N  ->  vN              asset = M  ->  {M:05d}.npz
+# Built path: <dataset_root>/v{dataset}/{asset:05d}.npz, where dataset_root is
+# the parent folder that holds v0, v1, …. --dataset / --asset override these.
+dataset = 0
+asset   = 0
+
+# Optional explicit override (a dataset .npz with key P, or an l-polyline OBJ).
+# Wins over dataset/asset. Absolute, else relative to dataset root, else project.
+# strands = "v0/00000.npz"
+
+[output]
+# Written under the project folder as <base>/<version>/<frame>.obj, e.g.
+# output/v1/00000.obj. (OBJ only -- no PLY.)
+base_path = "output"
+
+[strand_surface]
+# --- Speed knobs ---
+# Run the voxel -> SDF stages (rasterise, EDT, Gaussian) on the GPU via CuPy.
+# These dominate as `radius` shrinks. Auto-falls back to CPU if CuPy/CUDA is
+# missing. (Marching cubes + remesh always run on the CPU.)
+gpu                = true
+
+# The rasteriser DDA-walks every voxel along every edge already, so external
+# resampling is usually pure overhead. Leave false unless you need the
+# densified strands for viz/export.
+resample           = false
+target_edge_length = 0.002    # only used when resample = true
+
+# Isotropic remesh is the slowest step after rasterisation. Set remesh=false
+# to skip entirely (raw marching-cubes mesh is already usable, just noisier
+# triangulation). Lower remesh_iterations for faster runs with looser
+# uniformity.
+remesh             = true
+remesh_iterations  = 5        # (5 = default, 1-2 for fast, 0/false to skip)
+remesh_edge_length = 0.004    # final triangle size; defaults to radius
+# Extra remesh speed (trade a little surface accuracy for time):
+remesh_checksurfdist = false  # true = constrain to original surface each iter (slow)
+remesh_reproject     = true   # reproject verts onto original surface (false = faster)
+remesh_pre_decimate  = 0      # quadric-decimate raw MC mesh to N faces first (0 = off)
+
+# --- Voxelisation / SDF ---
+radius              = 0.0015   # voxel size, remesh target, default isovalue source
+point_radius_scale  = 0.5     # VDB point-radius multiplier
+min_radius_in_voxel = 1     # minimum particle radius, in voxel units
+smooth_iterations   = 2       # Gaussian SDF smoothing passes
+isovalue            = 0.0018   # marching-cubes isovalue (~= radius * 0.5)
+padding_voxels      = 1       # bbox padding (voxels)
+closing_radius      = 3       # morphological closing (0 disables interior fill)
+
+
+##Optional: less details config:
+#radius              = 0.0025   # voxel size, remesh target, default isovalue source
+#point_radius_scale  = 0.5     # VDB point-radius multiplier
+#min_radius_in_voxel = 1       # minimum particle radius, in voxel units
+#smooth_iterations   = 2       # Gaussian SDF smoothing passes
+#isovalue            = 0.002  # marching-cubes isovalue (~= radius * 0.5)
+#padding_voxels      = 2       # bbox padding (voxels)
+#closing_radius      = 3       # morphological closing (0 disables interior fill)
+
+# --- Output mesh cleanup ---
+mesh_smooth_iterations = 30    # Laplacian smooth passes on the final mesh
+mesh_smooth_lambda     = 0.5   # Laplacian step size (0-1)

argument/gpu_sdf.py

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+"""GPU voxel -> SDF pipeline for run_strand2surface (CuPy / CUDA).
+
+Mirrors the CPU ``rasterise -> interior-fill -> EDT-SDF -> Gaussian-smooth``
+path, but runs the dense regular-grid work on the GPU. Returns a host (NumPy)
+SDF so marching cubes + remeshing continue on the CPU unchanged.
+
+These stages are the part that scales with voxel count (the two Euclidean
+distance transforms dominate as ``radius`` shrinks), and they map almost 1:1
+onto ``cupyx.scipy.ndimage`` -- which is why CuPy beats hand-written kernels
+here. The isotropic remesh is *not* here: it is serial topological editing and
+does not belong on the GPU.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+
+def cupy_available() -> bool:
+    """True if CuPy imports and a CUDA device is visible."""
+    try:
+        import cupy as cp
+
+        return cp.cuda.runtime.getDeviceCount() > 0
+    except Exception:  # noqa: BLE001 - any failure means "no usable GPU"
+        return False
+
+
+def _rasterise_gpu(cp, strands, bbox_min, voxel_size, grid_dims):
+    """Boolean occupancy grid from strand polylines, on the GPU.
+
+    Same bucket-by-step-count DDA as the CPU path: every voxel along every edge
+    is visited so no voxel is skipped. Setting True is idempotent, so the
+    scattered writes need no atomics.
+    """
+    gx, gy, gz = int(grid_dims[0]), int(grid_dims[1]), int(grid_dims[2])
+    occ = cp.zeros((gx, gy, gz), dtype=cp.bool_)
+    inv_vs = 1.0 / voxel_size
+    bmin = cp.asarray(np.asarray(bbox_min, dtype=np.float64))
+
+    def splat(pts):  # pts: (M, 3) float64 on device
+        vi = cp.floor((pts - bmin) * inv_vs).astype(cp.int32)
+        vx = cp.clip(vi[:, 0], 0, gx - 1)
+        vy = cp.clip(vi[:, 1], 0, gy - 1)
+        vz = cp.clip(vi[:, 2], 0, gz - 1)
+        occ[vx, vy, vz] = True
+
+    singles = [s for s in strands if len(s) == 1]
+    if singles:
+        splat(cp.asarray(np.concatenate(singles, axis=0), dtype=cp.float64))
+
+    p0_list = [s[:-1] for s in strands if len(s) >= 2]
+    p1_list = [s[1:] for s in strands if len(s) >= 2]
+    if not p0_list:
+        return occ
+
+    p0 = cp.asarray(np.concatenate(p0_list, axis=0), dtype=cp.float64)
+    p1 = cp.asarray(np.concatenate(p1_list, axis=0), dtype=cp.float64)
+    diffs = p1 - p0
+    edge_len_vox = cp.abs(diffs * inv_vs).max(axis=1)
+    n_steps = cp.maximum(cp.ceil(edge_len_vox).astype(cp.int64), 1)
+    max_ns = int(n_steps.max())
+    chunk_pts = 20_000_000
+
+    for ns in range(1, max_ns + 1):
+        mask = n_steps == ns
+        count = int(mask.sum())
+        if count == 0:
+            continue
+        sub_p0 = p0[mask]
+        sub_diffs = diffs[mask]
+        pts_per_edge = ns + 1
+        edges_per_chunk = max(chunk_pts // pts_per_edge, 1)
+        t = cp.linspace(0.0, 1.0, pts_per_edge, dtype=cp.float64)
+        for i in range(0, count, edges_per_chunk):
+            cp0 = sub_p0[i:i + edges_per_chunk]
+            cd = sub_diffs[i:i + edges_per_chunk]
+            pts = cp0[:, None, :] + t[None, :, None] * cd[:, None, :]
+            splat(pts.reshape(-1, 3))
+    return occ
+
+
+def _fill_interior_gpu(cp, cndi, struct, occ, closing_radius, eff_radius_vox):
+    """Morphological close + flood-fill interior chambers, on the GPU."""
+    if closing_radius > 0:
+        # cupyx only implements iterated morphology with brute_force=True.
+        occ = cndi.binary_closing(
+            occ, structure=struct, iterations=closing_radius, brute_force=True,
+        )
+    edt = cndi.distance_transform_edt(~occ)
+    solid = edt <= eff_radius_vox
+    empty = ~solid
+    labelled, n_labels = cndi.label(empty, structure=struct)
+    if n_labels <= 1:
+        return occ
+
+    boundary: set[int] = set()
+    for ax in range(3):
+        for s in (0, solid.shape[ax] - 1):
+            sl = [slice(None)] * 3
+            sl[ax] = s
+            boundary.update(cp.asnumpy(cp.unique(labelled[tuple(sl)])).tolist())
+    boundary.discard(0)
+
+    interior_labels = sorted(set(range(1, n_labels + 1)) - boundary)
+    if not interior_labels:
+        return occ
+    interior_mask = cp.isin(labelled, cp.asarray(interior_labels))
+    return occ | interior_mask
+
+
+def occupancy_to_sdf(
+    strands,
+    bbox_min,
+    voxel_size,
+    grid_dims,
+    eff_radius_vox,
+    closing_radius,
+    smooth_iterations,
+    radius,
+) -> np.ndarray:
+    """Run rasterise -> fill -> EDT/SDF -> smooth on the GPU; return host SDF.
+
+    Output is a float32 NumPy array matching the CPU ``build_sdf_edt`` +
+    Gaussian-smooth result, ready for ``skimage.marching_cubes``.
+    """
+    import cupy as cp
+    import cupyx.scipy.ndimage as cndi
+    from scipy.ndimage import generate_binary_structure
+
+    struct = cp.asarray(generate_binary_structure(3, 1))
+
+    occ = _rasterise_gpu(cp, strands, bbox_min, voxel_size, grid_dims)
+    if closing_radius > 0:
+        occ = _fill_interior_gpu(
+            cp, cndi, struct, occ, closing_radius, eff_radius_vox,
+        )
+
+    edt = cndi.distance_transform_edt(~occ).astype(cp.float32)
+    sdf = edt * voxel_size - (eff_radius_vox * voxel_size)
+    del occ, edt
+
+    sigma = (radius * 0.5) / voxel_size
+    if smooth_iterations > 0 and sigma > 0:
+        for _ in range(smooth_iterations):
+            sdf = cndi.gaussian_filter(sdf, sigma=sigma)
+
+    cp.cuda.Stream.null.synchronize()
+    out = cp.asnumpy(sdf)
+    del sdf
+    cp.get_default_memory_pool().free_all_blocks()
+    return out

argument/pyproject.toml

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+# pyproject for the `argument` project.
+#
+# Two things live here:
+#   1. The hair-strand VIEWER (run_viewer.py)  -> main dependencies below.
+#   2. The standalone strand-surface script    -> optional "surface" group.
+#
+# The viewer needs VTK/Qt wheels, which only exist for Python <= 3.12, so the
+# project is pinned to >=3.11,<3.13. Point Poetry at 3.11 and install:
+#
+#     poetry env use 3.11
+#     poetry install                # viewer only (fast)
+#     poetry install --with surface # + open3d/scipy/skimage/pymeshlab/toml/cupy(GPU)
+#
+# Run the viewer:
+#
+#     poetry run python run_viewer.py                  # default: parent of project
+#     poetry run python run_viewer.py <dataset_root>   # explicit dataset root
+#
+# Or with pip into a 3.11 venv:
+#
+#     py -3.11 -m venv .venv311
+#     .venv311\Scripts\pip install "numpy<2" pyvista pyvistaqt pyside6
+
+[tool.poetry]
+name         = "argument"
+version      = "0.1.0"
+description  = "Hair-strand dataset viewer (PyVista + Qt) and strand-surface tools."
+authors      = ["birdpeople1984@gmail.com"]
+readme       = "README.md"
+package-mode = false
+
+[tool.poetry.dependencies]
+python    = ">=3.11,<3.13"
+numpy     = ">=1.24,<2"
+pyvista   = ">=0.43,<1.0"
+pyvistaqt = ">=0.11,<1.0"
+pyside6   = ">=6.6,<7.0"
+
+# Dependencies for the standalone strand-surface script (run_strand2surface.py).
+# Install with:  poetry install --with surface
+[tool.poetry.group.surface]
+optional = true
+
+[tool.poetry.group.surface.dependencies]
+scipy        = "^1.11"
+scikit-image = "^0.21"
+open3d       = "^0.18"
+pymeshlab    = ">=2023.12"
+toml         = "^0.10.2"
+tqdm         = "^4.66"
+# Optional GPU acceleration for the voxel->SDF stages ([strand_surface].gpu).
+# Pinned <14: cupy 14 requires numpy>=2, which breaks the surface stack.
+# Imports lazily and auto-falls back to CPU when CUDA is unavailable.
+cupy-cuda12x = ">=13,<14"
+
+[build-system]
+requires      = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

argument/run_batch_strand2surface.py

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+"""Batch strand -> surface over every frame of a dataset version.
+
+Runs ``run_strand2surface.generate_strand_surface`` on each ``<frame>.npz`` in a
+version folder and writes ``output/<version>/<frame>.obj``, using N parallel
+worker processes.
+
+Usage:
+    python run_batch_strand2surface.py --dataset v0 --config conf_strand2surface.toml --parallel 6
+    python run_batch_strand2surface.py --dataset 3  --config conf_strand2surface.toml --parallel 4 --overwrite
+    python run_batch_strand2surface.py --dataset 0  --config conf_strand2surface.toml --parallel 2 --limit 20
+
+* ``--dataset`` accepts ``v0`` or ``0``.
+* Frames are read from ``<dataset_root>/v{n}/`` (dataset_root = the parent of
+  this project folder); output goes to ``output/v{n}/<frame>.obj``.
+* Existing ``.obj`` files are skipped unless ``--overwrite`` (so the batch is
+  resumable).
+* Each frame uses the GPU SDF path when ``[strand_surface].gpu = true``, with
+  automatic CPU fallback -- GPU-memory contention degrades to CPU, it never
+  fails the frame.
+"""
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import io
+import re
+import time
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+
+import toml
+
+try:
+    from tqdm import tqdm
+except ImportError:  # progress bar is optional
+    tqdm = None
+
+
+def _frame_key(p: Path):
+    m = re.search(r"(\d+)", p.stem)
+    return (0, int(m.group(1))) if m else (1, 0)
+
+
+def _normalize_version(raw: str) -> str:
+    """'v0' / '0' / 'V3' -> 'v0' / 'v0' / 'v3'. Raises ValueError on garbage."""
+    s = str(raw).strip().lower()
+    if s.startswith("v"):
+        s = s[1:]
+    return f"v{int(s)}"
+
+
+def process_frame(task):
+    """Worker: one npz -> one obj. Returns a picklable result tuple.
+
+    Stdout/stderr of the (chatty) surface pipeline are captured so the parent
+    process can print one clean line per frame; on failure the tail is returned.
+    """
+    npz_path, obj_path, kwargs, do_resample, target_edge = task
+    import run_strand2surface as R
+
+    t0 = time.time()
+    buf = io.StringIO()
+    try:
+        with contextlib.redirect_stdout(buf), contextlib.redirect_stderr(buf):
+            strands = R.load_strands(npz_path)
+            if do_resample:
+                strands = [
+                    R.resample_strand_by_arc(s, target_edge) for s in strands
+                ]
+            mesh = R.generate_strand_surface(strands, **kwargs)
+            R.save_mesh_obj(mesh, obj_path)
+            n_faces = len(mesh.triangles)
+        return (npz_path, obj_path, True, n_faces, time.time() - t0, "")
+    except Exception as exc:  # noqa: BLE001 - report, never kill the batch
+        return (npz_path, obj_path, False, 0, time.time() - t0,
+                f"{exc!r}\n{buf.getvalue()[-400:]}")
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(
+        description="Batch strand -> surface over a dataset version.",
+    )
+    ap.add_argument("--dataset", required=True,
+                    help="dataset/version: 'v0' or '0'")
+    ap.add_argument("--config", required=True, help="config .toml")
+    ap.add_argument("--parallel", type=int, default=1,
+                    help="number of worker processes (default 1)")
+    ap.add_argument("--overwrite", action="store_true",
+                    help="re-generate frames whose .obj already exists")
+    ap.add_argument("--limit", type=int, default=0,
+                    help="process only the first N frames (0 = all)")
+    args = ap.parse_args()
+
+    script_dir = Path(__file__).resolve().parent
+    dataset_root = script_dir.parent
+
+    try:
+        version = _normalize_version(args.dataset)
+    except ValueError:
+        print(f"Invalid --dataset {args.dataset!r}; use e.g. 'v0' or '0'.")
+        return 2
+
+    cfg_path = Path(args.config)
+    if not cfg_path.is_absolute():
+        cfg_path = script_dir / cfg_path
+    cfg = toml.load(cfg_path)
+    surf_cfg = cfg.get("strand_surface", {})
+    output_cfg = cfg.get("output", {})
+
+    in_dir = dataset_root / version
+    if not in_dir.is_dir():
+        print(f"Dataset folder not found: {in_dir}")
+        return 2
+    frames = sorted(in_dir.glob("*.npz"), key=_frame_key)
+    if args.limit > 0:
+        frames = frames[:args.limit]
+    if not frames:
+        print(f"No .npz frames in {in_dir}")
+        return 1
+
+    out_base = Path(output_cfg.get("base_path", "output"))
+    if not out_base.is_absolute():
+        out_base = script_dir / out_base
+    out_dir = out_base / version
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    import run_strand2surface as R
+    kwargs = R.build_surface_kwargs(surf_cfg)
+    do_resample = bool(surf_cfg.get("resample", False))
+    target_edge = float(
+        surf_cfg.get("target_edge_length", kwargs["radius"] * 0.5)
+    )
+
+    tasks, skipped = [], 0
+    for f in frames:
+        obj = out_dir / f"{f.stem}.obj"
+        if not args.overwrite and obj.exists():
+            skipped += 1
+            continue
+        tasks.append((str(f), str(obj), kwargs, do_resample, target_edge))
+
+    workers = max(1, args.parallel)
+    print(f"Dataset {version}: {len(frames)} frame(s) in {in_dir}")
+    print(f"  output -> {out_dir}")
+    print(f"  gpu={kwargs['use_gpu']}  parallel={workers}  "
+          f"radius={kwargs['radius']}  remesh={kwargs['remesh']}")
+    print(f"  to process: {len(tasks)}   skipped existing: {skipped}")
+    if not tasks:
+        print("Nothing to do.")
+        return 0
+    if kwargs["use_gpu"] and workers > 5:
+        print(f"  NOTE: gpu=true with {workers} workers may exhaust GPU memory "
+              f"at fine radius; OOM frames fall back to CPU automatically.")
+
+    t_total = time.time()
+    ok = fail = 0
+    failures: list[tuple[str, str]] = []
+    n = len(tasks)
+    bar = tqdm(total=n, unit="frame", dynamic_ncols=True, desc=version) \
+        if tqdm is not None else None
+    with ProcessPoolExecutor(max_workers=workers) as ex:
+        futs = [ex.submit(process_frame, t) for t in tasks]
+        for i, fut in enumerate(as_completed(futs), 1):
+            npz, obj, success, n_faces, elapsed, msg = fut.result()
+            name = Path(npz).name
+            if success:
+                ok += 1
+                line = (f"[{i}/{n}] ok   {name} -> {Path(obj).name}  "
+                        f"{n_faces:,} faces  {elapsed:.1f}s")
+            else:
+                fail += 1
+                failures.append((name, msg))
+                line = (f"[{i}/{n}] FAIL {name}: "
+                        f"{msg.splitlines()[0] if msg else '?'}")
+            if bar is not None:
+                bar.update(1)
+                bar.set_postfix(ok=ok, fail=fail,
+                                last=f"{name} {elapsed:.0f}s", refresh=False)
+                if not success:
+                    bar.write(line)   # keep failures visible above the bar
+            else:
+                print(line)
+    if bar is not None:
+        bar.close()
+
+    dt = time.time() - t_total
+    print(f"\nDone: {ok} ok, {fail} failed in {dt:.1f}s "
+          f"(wall {dt / n:.1f}s/frame across {workers} workers)")
+    if failures:
+        print("Failures:")
+        for name, msg in failures[:10]:
+            print(f"  {name}: {msg.splitlines()[0] if msg else '?'}")
+    return 0 if fail == 0 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

argument/run_strand2surface.py

@@ -0,0 +1,770 @@
+"""Strand -> watertight surface mesh (fully standalone).
+
+Zero dependency on the ``codebase/`` package — only third-party libs:
+numpy, scipy, skimage, open3d, pymeshlab, toml.
+
+Pipeline: OBJ polylines -> voxel rasterisation (vectorised, bucketed by
+Chebyshev DDA step count) -> morphological closing + interior chamber fill
+-> Euclidean Distance Transform SDF -> Gaussian smooth -> marching cubes
+-> (optional) PyMeshLab isotropic remesh -> write OBJ/PLY.
+
+Usage:
+    python run_strand2surface.py --config conf_strand2surface.toml
+    python run_strand2surface.py --config <cfg> --dataset 3 --asset 100
+    python run_strand2surface.py --config <cfg> --strands <guides.obj> --skip-viz
+
+The frame is selected by dataset (version) + asset (frame) number ->
+``<dataset_root>/v{dataset}/{asset:05d}.npz``, or by an explicit ``--strands``
+path. Input may be a dataset ``.npz`` (key ``P``, shape (S, N, 3)) or an
+l-polyline OBJ; the loader dispatches on the file extension.
+"""
+
+import argparse
+import os
+import time
+
+import numpy as np
+import open3d as o3d
+import toml
+from scipy.ndimage import (
+    binary_closing,
+    distance_transform_edt,
+    gaussian_filter,
+    generate_binary_structure,
+    label,
+)
+from skimage.measure import marching_cubes
+
+
+# ===========================================================================
+# OBJ polyline loader
+# ===========================================================================
+
+def load_guides_obj(obj_path: str) -> list[np.ndarray]:
+    """Load ``l``-polylines from an OBJ file into a list of (N, 3) arrays."""
+    all_vertices: list[list[float]] = []
+    guides: list[np.ndarray] = []
+    with open(obj_path, "r") as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("#"):
+                continue
+            if line.startswith("v "):
+                parts = line.split()
+                all_vertices.append(
+                    [float(parts[1]), float(parts[2]), float(parts[3])]
+                )
+            elif line.startswith("l "):
+                idx = [int(x) - 1 for x in line.split()[1:]]
+                guides.append(
+                    np.array([all_vertices[i] for i in idx], dtype=np.float64)
+                )
+    print(f"  Loaded {len(guides)} polylines "
+          f"({len(all_vertices):,} vertices) from {obj_path}")
+    return guides
+
+
+def load_strands_npz(npz_path: str) -> list[np.ndarray]:
+    """Load strands from a dataset ``.npz`` (key ``P``, shape ``(S, N, 3)``).
+
+    fp16 on disk is returned as fp32; strands with any non-finite sample are
+    dropped. Returns a list of ``(N, 3)`` arrays, one per strand.
+    """
+    data = np.load(npz_path)
+    if "P" not in data.files:
+        raise RuntimeError(
+            f"{npz_path}: no 'P' array (found {list(data.files)})"
+        )
+    P = np.asarray(data["P"])
+    if P.ndim != 3 or P.shape[-1] != 3:
+        raise RuntimeError(
+            f"{npz_path}: expected P shape (S, N, 3), got {tuple(P.shape)}"
+        )
+    P = P.astype(np.float32, copy=False)
+    finite = np.isfinite(P).all(axis=(1, 2))
+    if not finite.all():
+        P = P[finite]
+    if P.shape[0] == 0:
+        raise RuntimeError(f"{npz_path}: no finite strands")
+    print(f"  Loaded {P.shape[0]:,} strands x {P.shape[1]} samples "
+          f"from {npz_path}")
+    return list(P)
+
+
+def load_strands(path: str) -> list[np.ndarray]:
+    """Dispatch on extension: ``.npz`` -> key ``P``; anything else -> OBJ."""
+    if os.path.splitext(path)[1].lower() == ".npz":
+        return load_strands_npz(path)
+    return load_guides_obj(path)
+
+
+# ===========================================================================
+# Optional resampling (off by default; the rasteriser handles voxel walk)
+# ===========================================================================
+
+def resample_strand_by_arc(strand: np.ndarray, max_step: float) -> np.ndarray:
+    """Densify a polyline so every segment is at most ``max_step`` long."""
+    if len(strand) < 2 or max_step <= 0:
+        return strand
+    diffs = np.diff(strand, axis=0)
+    seg = np.linalg.norm(diffs, axis=1)
+    total = float(seg.sum())
+    if total < 1e-12 or float(seg.max()) <= max_step:
+        return strand
+    n = max(int(np.ceil(total / max_step)), len(strand) - 1)
+    cum = np.concatenate([[0.0], np.cumsum(seg)])
+    targets = np.linspace(0.0, total, n + 1)
+    idx = np.searchsorted(cum, targets, side="right") - 1
+    idx = np.clip(idx, 0, len(strand) - 2)
+    t = (targets - cum[idx]) / np.maximum(seg[idx], 1e-12)
+    t = np.clip(t, 0.0, 1.0)
+    return strand[idx] * (1.0 - t)[:, None] + strand[idx + 1] * t[:, None]
+
+
+# ===========================================================================
+# Voxel rasterisation (vectorised DDA, bucketed by step count)
+# ===========================================================================
+
+def rasterise_strands_to_grid(
+    strands: list[np.ndarray],
+    bbox_min: np.ndarray,
+    voxel_size: float,
+    grid_dims: np.ndarray,
+) -> np.ndarray:
+    """Rasterise all strand edges into a boolean occupancy grid.
+
+    All edges across all strands are flattened into one batch and
+    processed bucketed by n_steps so each bucket becomes a single
+    broadcast — no gather, no np.repeat. Adjacent voxels along every
+    edge are visited at sub-voxel steps so no voxel is skipped.
+    """
+    gx, gy, gz = int(grid_dims[0]), int(grid_dims[1]), int(grid_dims[2])
+    occupancy = np.zeros((gx, gy, gz), dtype=bool)
+    inv_vs = 1.0 / voxel_size
+    bbox_min64 = np.asarray(bbox_min, dtype=np.float64)
+
+    def splat(pts: np.ndarray) -> None:
+        vi = np.floor((pts - bbox_min64) * inv_vs).astype(np.int32)
+        np.clip(vi[:, 0], 0, gx - 1, out=vi[:, 0])
+        np.clip(vi[:, 1], 0, gy - 1, out=vi[:, 1])
+        np.clip(vi[:, 2], 0, gz - 1, out=vi[:, 2])
+        occupancy[vi[:, 0], vi[:, 1], vi[:, 2]] = True
+
+    # Single-point strands: splat directly
+    single_pts = [s for s in strands if len(s) == 1]
+    if single_pts:
+        splat(np.concatenate(single_pts, axis=0))
+
+    p0_list = [s[:-1] for s in strands if len(s) >= 2]
+    p1_list = [s[1:] for s in strands if len(s) >= 2]
+    if not p0_list:
+        return occupancy
+
+    p0 = np.concatenate(p0_list, axis=0)
+    p1 = np.concatenate(p1_list, axis=0)
+    diffs = p1 - p0
+
+    edge_len_voxels = np.abs(diffs * inv_vs).max(axis=1)
+    n_steps = np.maximum(np.ceil(edge_len_voxels).astype(np.int64), 1)
+    max_ns = int(n_steps.max())
+    CHUNK_PTS = 5_000_000
+
+    for ns in range(1, max_ns + 1):
+        mask = n_steps == ns
+        if not mask.any():
+            continue
+        sub_p0 = p0[mask]
+        sub_diffs = diffs[mask]
+        pts_per_edge = ns + 1
+        edges_per_chunk = max(CHUNK_PTS // pts_per_edge, 1)
+
+        if ns == 1:
+            # Fast path: stack the two endpoints (no interpolation)
+            for i in range(0, len(sub_p0), edges_per_chunk):
+                chunk_p0 = sub_p0[i:i + edges_per_chunk]
+                chunk_p1 = chunk_p0 + sub_diffs[i:i + edges_per_chunk]
+                splat(np.concatenate([chunk_p0, chunk_p1], axis=0))
+        else:
+            t = np.linspace(0.0, 1.0, pts_per_edge, dtype=np.float64)
+            for i in range(0, len(sub_p0), edges_per_chunk):
+                cp0 = sub_p0[i:i + edges_per_chunk]
+                cd = sub_diffs[i:i + edges_per_chunk]
+                pts = cp0[:, None, :] + t[None, :, None] * cd[:, None, :]
+                splat(pts.reshape(-1, 3))
+
+    return occupancy
+
+
+# ===========================================================================
+# Interior chamber fill (morph closing + flood fill)
+# ===========================================================================
+
+def fill_interior_chambers(
+    occupancy: np.ndarray,
+    closing_radius: int,
+    effective_radius_voxels: float,
+) -> np.ndarray:
+    if closing_radius > 0:
+        struct = generate_binary_structure(3, 1)
+        occupancy = binary_closing(
+            occupancy, structure=struct, iterations=closing_radius,
+        )
+
+    edt_occ = distance_transform_edt(~occupancy)
+    solid = edt_occ <= effective_radius_voxels
+
+    empty = ~solid
+    labelled, n_labels = label(empty, structure=generate_binary_structure(3, 1))
+    if n_labels <= 1:
+        return occupancy
+
+    boundary_labels: set[int] = set()
+    for ax in range(3):
+        for s in (0, solid.shape[ax] - 1):
+            slc = [slice(None)] * 3
+            slc[ax] = s
+            boundary_labels.update(np.unique(labelled[tuple(slc)]))
+    boundary_labels.discard(0)
+
+    interior_mask = np.zeros_like(solid, dtype=bool)
+    for lbl in range(1, n_labels + 1):
+        if lbl not in boundary_labels:
+            interior_mask |= labelled == lbl
+
+    n_filled = int(interior_mask.sum())
+    if n_filled > 0:
+        occupancy = occupancy | interior_mask
+        print(f"    Filled {n_filled:,} interior chamber voxels")
+    else:
+        print("    No interior chambers detected")
+    return occupancy
+
+
+# ===========================================================================
+# EDT -> signed distance field
+# ===========================================================================
+
+def build_sdf_edt(
+    occupancy: np.ndarray,
+    voxel_size: float,
+    effective_radius_voxels: float,
+) -> np.ndarray:
+    edt = distance_transform_edt(~occupancy).astype(np.float32)
+    return edt * voxel_size - (effective_radius_voxels * voxel_size)
+
+
+# ===========================================================================
+# PyMeshLab isotropic remesh
+# ===========================================================================
+
+def remesh_isotropic(
+    mesh: o3d.geometry.TriangleMesh,
+    target_edge_length: float,
+    iterations: int = 5,
+    keep_largest: bool = True,
+    check_surf_dist: bool = False,
+    reproject: bool = True,
+    pre_decimate_faces: int = 0,
+) -> o3d.geometry.TriangleMesh:
+    """Isotropic remesh via PyMeshLab.
+
+    Speed knobs (trade a little accuracy for a lot of time):
+      * ``check_surf_dist`` -- reject ops that drift past ``maxsurfdist`` of the
+        original surface each iteration (octree queries; usually the biggest
+        single cost). Off by default.
+      * ``reproject``       -- reproject vertices back onto the original surface.
+      * ``pre_decimate_faces`` -- quadric-decimate the (heavily over-tessellated)
+        marching-cubes mesh to this face count first, so the remesher starts
+        from far fewer triangles (0 = skip).
+    """
+    import pymeshlab
+
+    verts = np.asarray(mesh.vertices).copy()
+    faces = np.asarray(mesh.triangles).copy()
+    ms = pymeshlab.MeshSet()
+    ms.add_mesh(pymeshlab.Mesh(verts, faces))
+
+    if pre_decimate_faces and len(faces) > pre_decimate_faces:
+        try:
+            ms.meshing_decimation_quadric_edge_collapse(
+                targetfacenum=int(pre_decimate_faces),
+                preservenormal=True,
+                planarquadric=True,
+            )
+        except Exception as exc:  # noqa: BLE001 - decimation is optional
+            print(f"    pre-decimate skipped: {exc}")
+
+    ms.meshing_isotropic_explicit_remeshing(
+        targetlen=pymeshlab.PureValue(target_edge_length),
+        iterations=iterations,
+        adaptive=False,
+        checksurfdist=check_surf_dist,
+        maxsurfdist=pymeshlab.PureValue(target_edge_length * 0.5),
+        splitflag=True,
+        collapseflag=True,
+        swapflag=True,
+        smoothflag=True,
+        reprojectflag=reproject,
+    )
+    ms.apply_coord_taubin_smoothing(stepsmoothnum=3, lambda_=0.5, mu=-0.53)
+    if keep_largest:
+        try:
+            ms.meshing_remove_connected_component_by_face_number(
+                mincomponentsize=100,
+            )
+        except Exception:
+            pass
+
+    result = ms.current_mesh()
+    out_v = result.vertex_matrix()
+    out_f = result.face_matrix()
+    out = o3d.geometry.TriangleMesh(
+        o3d.utility.Vector3dVector(out_v),
+        o3d.utility.Vector3iVector(out_f),
+    )
+    out.compute_vertex_normals()
+    print(f"    Isotropic remesh: {len(out_v):,} verts, "
+          f"{len(out_f):,} faces (edge~={target_edge_length:.4f})")
+    return out
+
+
+# ===========================================================================
+# Mesh I/O
+# ===========================================================================
+
+def save_mesh_obj(mesh: o3d.geometry.TriangleMesh, path: str) -> None:
+    verts = np.asarray(mesh.vertices)
+    nan_mask = ~np.isfinite(verts).all(axis=1)
+    if nan_mask.any():
+        keep = ~nan_mask
+        remap = np.full(len(verts), -1, dtype=np.int64)
+        remap[keep] = np.arange(int(keep.sum()))
+        faces = np.asarray(mesh.triangles)
+        new_faces = remap[faces]
+        valid = (new_faces >= 0).all(axis=1)
+        mesh.vertices = o3d.utility.Vector3dVector(verts[keep])
+        mesh.triangles = o3d.utility.Vector3iVector(new_faces[valid])
+        if mesh.has_vertex_normals():
+            mesh.vertex_normals = o3d.utility.Vector3dVector(
+                np.asarray(mesh.vertex_normals)[keep]
+            )
+        print(f"  WARNING: removed {int(nan_mask.sum())} NaN verts before save")
+    o3d.io.write_triangle_mesh(path, mesh, write_vertex_normals=True)
+    print(f"  Saved OBJ -> {path}")
+
+
+def save_mesh_ply(mesh: o3d.geometry.TriangleMesh, path: str) -> None:
+    o3d.io.write_triangle_mesh(path, mesh, write_vertex_normals=True)
+    print(f"  Saved PLY -> {path}")
+
+
+def keep_largest_component(
+    mesh: o3d.geometry.TriangleMesh,
+) -> o3d.geometry.TriangleMesh:
+    """Drop every connected component except the largest one.
+
+    Removes interior bubbles and floating islands so the result is a single,
+    watertight shell. Also strips duplicate/degenerate triangles first so the
+    clustering is clean.
+    """
+    mesh.remove_duplicated_vertices()
+    mesh.remove_duplicated_triangles()
+    mesh.remove_degenerate_triangles()
+
+    cluster_ids, cluster_sizes, _ = mesh.cluster_connected_triangles()
+    cluster_ids = np.asarray(cluster_ids)
+    cluster_sizes = np.asarray(cluster_sizes)
+    if cluster_sizes.size <= 1:
+        mesh.remove_unreferenced_vertices()
+        return mesh
+
+    largest = int(cluster_sizes.argmax())
+    drop = cluster_ids != largest
+    mesh.remove_triangles_by_mask(drop)
+    mesh.remove_unreferenced_vertices()
+    print(f"    Kept largest of {cluster_sizes.size} components "
+          f"(dropped {int(cluster_sizes.size) - 1}, "
+          f"{int(drop.sum()):,} faces removed)")
+    return mesh
+
+
+# ===========================================================================
+# Main surface generation
+# ===========================================================================
+
+def generate_strand_surface(
+    strands: list[np.ndarray],
+    *,
+    radius: float = 0.003,
+    point_radius_scale: float = 1.5,
+    min_radius_in_voxel: float = 1.5,
+    smooth_iterations: int = 1,
+    isovalue: float | None = None,
+    padding_voxels: int = 3,
+    closing_radius: int = 3,
+    mesh_smooth_iterations: int = 10,
+    mesh_smooth_lambda: float = 0.5,
+    remesh: bool = True,
+    remesh_edge_length: float | None = None,
+    remesh_iterations: int = 5,
+    remesh_check_surf_dist: bool = False,
+    remesh_reproject: bool = True,
+    remesh_pre_decimate_faces: int = 0,
+    use_gpu: bool = False,
+) -> o3d.geometry.TriangleMesh:
+    """Build a watertight triangle mesh enveloping hair strands."""
+    if isovalue is None:
+        isovalue = radius * 0.5
+    voxel_size = radius
+
+    all_verts = np.concatenate(strands, axis=0)
+    effective_radius_voxels = max(point_radius_scale, min_radius_in_voxel)
+    effective_radius_world = effective_radius_voxels * voxel_size
+    pad = voxel_size * padding_voxels + effective_radius_world
+    bbox_min = all_verts.min(axis=0) - pad
+    bbox_max = all_verts.max(axis=0) + pad
+    grid_dims = (np.ceil((bbox_max - bbox_min) / voxel_size) + 1).astype(int)
+    n_voxels = int(np.prod(grid_dims))
+    total_verts = sum(len(s) for s in strands)
+    total_edges = sum(max(0, len(s) - 1) for s in strands)
+    print(f"  {total_verts:,} vertices, {total_edges:,} edges in "
+          f"{len(strands):,} strands")
+    print(f"  Grid {grid_dims} ({n_voxels:,} voxels), "
+          f"voxel_size={voxel_size:.6f}")
+
+    sdf = None
+    if use_gpu:
+        try:
+            import gpu_sdf
+
+            print("  [GPU] Rasterise -> fill -> EDT/SDF -> smooth on device ...")
+            t0 = time.time()
+            sdf = gpu_sdf.occupancy_to_sdf(
+                strands, bbox_min, voxel_size, grid_dims,
+                effective_radius_voxels, closing_radius,
+                smooth_iterations, radius,
+            )
+            print(f"    [GPU] done in {time.time()-t0:.1f}s")
+        except Exception as exc:  # noqa: BLE001 - any GPU error -> CPU fallback
+            print(f"  [GPU] unavailable ({exc}); using CPU path")
+            sdf = None
+
+    if sdf is None:
+        print("  Rasterising strand edges ...")
+        t0 = time.time()
+        occupancy = rasterise_strands_to_grid(
+            strands, bbox_min, voxel_size, grid_dims,
+        )
+        n_occupied = int(occupancy.sum())
+        print(f"    {n_occupied:,} occupied voxels "
+              f"({100.0 * n_occupied / n_voxels:.1f}%) in {time.time()-t0:.1f}s")
+
+        if closing_radius > 0:
+            print(f"  Filling interior chambers (closing_radius={closing_radius}) ...")
+            t0 = time.time()
+            occupancy = fill_interior_chambers(
+                occupancy, closing_radius, effective_radius_voxels,
+            )
+            print(f"    done in {time.time()-t0:.1f}s")
+
+        print("  Computing EDT -> SDF ...")
+        t0 = time.time()
+        sdf = build_sdf_edt(occupancy, voxel_size, effective_radius_voxels)
+        del occupancy
+        print(f"    done in {time.time()-t0:.1f}s")
+
+        smooth_sigma_voxels = (radius * 0.5) / voxel_size
+        if smooth_iterations > 0 and smooth_sigma_voxels > 0:
+            print(f"  Smoothing SDF (sigma={smooth_sigma_voxels:.2f} vox, "
+                  f"{smooth_iterations} iter) ...")
+            t0 = time.time()
+            for _ in range(smooth_iterations):
+                sdf = gaussian_filter(sdf, sigma=smooth_sigma_voxels)
+            print(f"    done in {time.time()-t0:.1f}s")
+
+    print(f"  Extracting isosurface (isovalue={isovalue:.6f}) ...")
+    t0 = time.time()
+    if not np.all(np.isfinite(sdf)):
+        sdf = np.where(np.isfinite(sdf), sdf, 0.0)
+    try:
+        verts, faces, _, _ = marching_cubes(
+            -sdf,
+            level=-isovalue,
+            spacing=(voxel_size, voxel_size, voxel_size),
+        )
+    except ValueError as exc:
+        raise RuntimeError(
+            f"Marching cubes failed -- isovalue {isovalue} may not "
+            "intersect the SDF. Try increasing radius or padding."
+        ) from exc
+    verts = verts + bbox_min
+    nan_verts = ~np.isfinite(verts).all(axis=1)
+    if nan_verts.any():
+        keep = ~nan_verts
+        remap = np.full(len(verts), -1, dtype=np.int64)
+        remap[keep] = np.arange(int(keep.sum()))
+        verts = verts[keep]
+        faces = remap[faces]
+        faces = faces[(faces >= 0).all(axis=1)]
+    print(f"    Raw mesh: {len(verts):,} verts, {len(faces):,} faces "
+          f"({time.time()-t0:.1f}s)")
+    del sdf
+
+    mesh = o3d.geometry.TriangleMesh(
+        o3d.utility.Vector3dVector(verts),
+        o3d.utility.Vector3iVector(faces),
+    )
+    mesh.compute_vertex_normals()
+
+    if remesh and remesh_iterations > 0:
+        tgt_edge = remesh_edge_length if remesh_edge_length is not None else radius
+        print(f"  Isotropic remesh (edge~={tgt_edge:.6f}, "
+              f"{remesh_iterations} iter) ...")
+        t0 = time.time()
+        mesh = remesh_isotropic(
+            mesh,
+            target_edge_length=tgt_edge,
+            iterations=remesh_iterations,
+            keep_largest=True,
+            check_surf_dist=remesh_check_surf_dist,
+            reproject=remesh_reproject,
+            pre_decimate_faces=remesh_pre_decimate_faces,
+        )
+        print(f"    done in {time.time()-t0:.1f}s")
+    else:
+        print("  Skipping remesh.")
+
+    print("  Keeping largest connected component ...")
+    mesh = keep_largest_component(mesh)
+
+    # Cheap watertight indicator: closed (no boundary edges) + manifold.
+    # NB: avoid mesh.is_watertight() -- it runs an O(faces^2) self-intersection
+    # test that effectively hangs on large meshes.
+    closed = (mesh.is_edge_manifold(allow_boundary_edges=False)
+              and mesh.is_vertex_manifold())
+    print(f"  Final: {len(mesh.vertices):,} verts, "
+          f"{len(mesh.triangles):,} faces, closed={closed}")
+    return mesh
+
+
+# ===========================================================================
+# Script entry
+# ===========================================================================
+
+def build_surface_kwargs(surf_cfg: dict) -> dict:
+    """Translate a ``[strand_surface]`` config table into the keyword args of
+    :func:`generate_strand_surface`. Shared by the single-frame and batch
+    runners so both honor the same knobs.
+    """
+    radius = float(surf_cfg.get("radius", 0.003))
+    return dict(
+        radius=radius,
+        point_radius_scale=float(surf_cfg.get("point_radius_scale", 1.5)),
+        min_radius_in_voxel=float(surf_cfg.get("min_radius_in_voxel", 1.5)),
+        smooth_iterations=int(surf_cfg.get("smooth_iterations", 1)),
+        isovalue=float(surf_cfg.get("isovalue", radius * 0.5)),
+        padding_voxels=int(surf_cfg.get("padding_voxels", 3)),
+        closing_radius=int(surf_cfg.get("closing_radius", 3)),
+        mesh_smooth_iterations=int(surf_cfg.get("mesh_smooth_iterations", 10)),
+        mesh_smooth_lambda=float(surf_cfg.get("mesh_smooth_lambda", 0.5)),
+        remesh=bool(surf_cfg.get("remesh", True)),
+        remesh_edge_length=float(surf_cfg.get("remesh_edge_length", radius)),
+        remesh_iterations=int(surf_cfg.get("remesh_iterations", 5)),
+        remesh_check_surf_dist=bool(surf_cfg.get("remesh_checksurfdist", False)),
+        remesh_reproject=bool(surf_cfg.get("remesh_reproject", True)),
+        remesh_pre_decimate_faces=int(surf_cfg.get("remesh_pre_decimate", 0)),
+        use_gpu=bool(surf_cfg.get("gpu", False)),
+    )
+
+
+def resolve_strands_path(
+    dataset_root: str,
+    project_dir: str,
+    data_cfg: dict,
+    cli_strands: str | None,
+    cli_dataset: int | None,
+    cli_asset: int | None,
+) -> str:
+    """Resolve the input strands path (``.npz`` or ``.obj``).
+
+    Priority:
+      1. An explicit path -- ``--strands`` then ``[data].strands`` -- resolved
+         absolute, else against the dataset root (the parent folder holding
+         ``v0``, ``v1``, …), else against the project folder.
+      2. Otherwise built from a dataset/version number + asset/frame id:
+         ``<dataset_root>/v{dataset}/{asset:05d}.npz`` (CLI overrides config).
+    """
+    raw = cli_strands or data_cfg.get("strands")
+    if raw:
+        if os.path.isabs(raw):
+            return raw
+        by_dataset = os.path.join(dataset_root, raw)
+        if os.path.exists(by_dataset):
+            return by_dataset
+        return os.path.join(project_dir, raw)
+
+    dataset = cli_dataset if cli_dataset is not None else data_cfg.get("dataset")
+    asset = cli_asset if cli_asset is not None else data_cfg.get("asset")
+    if dataset is None or asset is None:
+        raise FileNotFoundError(
+            "No input strands. Set [data].dataset + [data].asset (or "
+            "[data].strands) in the config, or pass --dataset/--asset (or "
+            "--strands)."
+        )
+    return os.path.join(
+        dataset_root, f"v{int(dataset)}", f"{int(asset):05d}.npz"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Standalone strands -> watertight surface mesh",
+    )
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--strands", type=str, default=None,
+                        help="Explicit .npz or .obj path (wins over dataset/asset)")
+    parser.add_argument("--dataset", type=int, default=None,
+                        help="Dataset/version number: N -> vN (overrides config)")
+    parser.add_argument("--asset", type=int, default=None,
+                        help="Asset/frame id: M -> {M:05d}.npz (overrides config)")
+    parser.add_argument("--skip-viz", action="store_true")
+    args = parser.parse_args()
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))   # the project folder
+    dataset_root = os.path.dirname(script_dir)                 # parent: holds v0, v1…
+    cfg_path = args.config if os.path.isabs(args.config) \
+        else os.path.join(script_dir, args.config)
+    cfg = toml.load(cfg_path)
+
+    data_cfg = cfg.get("data", {})
+    output_cfg = cfg.get("output", {})
+    surf_cfg = cfg.get("strand_surface", {})
+
+    output_base = output_cfg.get("base_path", "output")
+    if not os.path.isabs(output_base):
+        output_base = os.path.join(script_dir, output_base)
+    os.makedirs(output_base, exist_ok=True)
+
+    t_total = time.time()
+
+    strands_path = resolve_strands_path(
+        dataset_root, script_dir, data_cfg,
+        args.strands, args.dataset, args.asset,
+    )
+    print(f"Loading strands: {strands_path}")
+    t0 = time.time()
+    strands = load_strands(strands_path)
+    print(f"  Loaded in {time.time()-t0:.1f}s")
+    if not strands:
+        raise RuntimeError(f"No strands found in {strands_path}")
+
+    radius = float(surf_cfg.get("radius", 0.003))
+    target_edge_length = float(surf_cfg.get("target_edge_length", radius * 0.5))
+    point_radius_scale = float(surf_cfg.get("point_radius_scale", 1.5))
+    min_radius_in_voxel = float(surf_cfg.get("min_radius_in_voxel", 1.5))
+    smooth_iterations = int(surf_cfg.get("smooth_iterations", 1))
+    isovalue = float(surf_cfg.get("isovalue", radius * 0.5))
+    padding_voxels = int(surf_cfg.get("padding_voxels", 3))
+    closing_radius = int(surf_cfg.get("closing_radius", 3))
+    mesh_smooth_iterations = int(surf_cfg.get("mesh_smooth_iterations", 10))
+    mesh_smooth_lambda = float(surf_cfg.get("mesh_smooth_lambda", 0.5))
+    do_resample = bool(surf_cfg.get("resample", False))
+    do_remesh = bool(surf_cfg.get("remesh", True))
+    remesh_iterations = int(surf_cfg.get("remesh_iterations", 5))
+    remesh_edge_length = float(surf_cfg.get("remesh_edge_length", radius))
+    remesh_check_surf_dist = bool(surf_cfg.get("remesh_checksurfdist", False))
+    remesh_reproject = bool(surf_cfg.get("remesh_reproject", True))
+    remesh_pre_decimate_faces = int(surf_cfg.get("remesh_pre_decimate", 0))
+    use_gpu = bool(surf_cfg.get("gpu", False))
+
+    if do_resample:
+        t0 = time.time()
+        n_before = sum(len(s) for s in strands)
+        strands = [
+            resample_strand_by_arc(s, target_edge_length) for s in strands
+        ]
+        n_after = sum(len(s) for s in strands)
+        print(f"Resampled (max step={target_edge_length:.6f}): "
+              f"{n_before:,} -> {n_after:,} verts "
+              f"({time.time()-t0:.1f}s)")
+    else:
+        print("Skipping resample (rasteriser walks every voxel per edge).")
+
+    print("=" * 60)
+    print(f"Generating surface: {len(strands):,} strands, radius={radius}, "
+          f"remesh={do_remesh} (iter={remesh_iterations})")
+    print("=" * 60)
+    t_surf = time.time()
+    mesh = generate_strand_surface(
+        strands,
+        radius=radius,
+        point_radius_scale=point_radius_scale,
+        min_radius_in_voxel=min_radius_in_voxel,
+        smooth_iterations=smooth_iterations,
+        isovalue=isovalue,
+        padding_voxels=padding_voxels,
+        closing_radius=closing_radius,
+        mesh_smooth_iterations=mesh_smooth_iterations,
+        mesh_smooth_lambda=mesh_smooth_lambda,
+        remesh=do_remesh,
+        remesh_edge_length=remesh_edge_length,
+        remesh_iterations=remesh_iterations,
+        remesh_check_surf_dist=remesh_check_surf_dist,
+        remesh_reproject=remesh_reproject,
+        remesh_pre_decimate_faces=remesh_pre_decimate_faces,
+        use_gpu=use_gpu,
+    )
+    print(f"Surface generated in {time.time()-t_surf:.1f}s")
+
+    stem = os.path.splitext(os.path.basename(strands_path))[0]
+    version = os.path.basename(os.path.dirname(strands_path))
+    out_dir = os.path.join(output_base, version) if version else output_base
+    os.makedirs(out_dir, exist_ok=True)
+    obj_path = os.path.join(out_dir, f"{stem}.obj")
+    save_mesh_obj(mesh, obj_path)
+    print(f"Total: {time.time()-t_total:.1f}s")
+
+    if not args.skip_viz:
+        print("Visualising (close window to continue) ...")
+        tris = np.asarray(mesh.triangles)
+        mesh.triangles = o3d.utility.Vector3iVector(
+            np.vstack([tris, tris[:, ::-1]]),
+        )
+        mesh.compute_vertex_normals()
+
+        mat = o3d.visualization.rendering.MaterialRecord()
+        mat.shader = "defaultLitTransparency"
+        mat.base_color = [0.7, 0.85, 1.0, 0.4]
+
+        all_pts, all_lines, offset = [], [], 0
+        for s in strands:
+            if len(s) < 2:
+                continue
+            all_pts.append(s)
+            all_lines.extend(
+                [offset + j, offset + j + 1] for j in range(len(s) - 1)
+            )
+            offset += len(s)
+        strand_ls = o3d.geometry.LineSet()
+        if all_pts:
+            strand_ls.points = o3d.utility.Vector3dVector(
+                np.concatenate(all_pts)
+            )
+            strand_ls.lines = o3d.utility.Vector2iVector(all_lines)
+            strand_ls.paint_uniform_color([0.0, 0.8, 0.2])
+
+        o3d.visualization.draw(
+            [
+                {"name": "surface", "geometry": mesh, "material": mat},
+                {"name": "strands", "geometry": strand_ls},
+            ],
+            title="Strand -> Surface",
+            width=1280, height=720,
+            bg_color=(1.0, 1.0, 1.0, 1.0),
+        )
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

argument/run_viewer.py

@@ -0,0 +1,44 @@
+"""Launch the hair-strand viewer.
+
+Usage:
+    python run_viewer.py                  # default: the folder containing this
+                                          #          project (scans v0, v1, …)
+    python run_viewer.py <dataset_root>   # explicit root (scans v* sub-folders)
+    python run_viewer.py <dataset_root>/v3  # a single version / frame folder
+    python run_viewer.py <path>/0001.npz    # a single frame
+
+The default root is derived at runtime from this file's location, so it follows
+the project if you move it -- nothing is hard-coded. The head mesh is read from
+``meta/head_water_tight_boundary.obj`` next to this script.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+from PySide6 import QtWidgets
+
+from viewer.app import ViewerWindow
+
+
+def main() -> int:
+    project_dir = Path(__file__).resolve().parent
+
+    if len(sys.argv) > 1:
+        target = Path(sys.argv[1])
+    else:
+        # Default to the folder that contains this project folder, derived from
+        # this file's path (portable -- not a hard-coded absolute path).
+        target = project_dir.parent
+
+    head_path = project_dir / "meta" / "head_water_tight_boundary.obj"
+
+    app = QtWidgets.QApplication(sys.argv)
+    window = ViewerWindow(dataset_root=target, head_path=head_path)
+    window.show()
+    return app.exec()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

argument/viewer/__init__.py

@@ -0,0 +1,8 @@
+"""Hair-strand dataset viewer (PyVista + Qt).
+
+Modules:
+    strands_io       -- load .npz frames, list frames/versions
+    head             -- load the transparent head mesh
+    strand_geometry  -- build line / variable-width-tube PolyData from strands
+    app              -- the Qt main window
+"""

argument/viewer/app.py

@@ -0,0 +1,410 @@
+"""Qt main window for the hair-strand viewer.
+
+Loads a transparent head, then lets the user pick a dataset version / folder or
+drag-drop a ``.npz`` and scrub or play through frames. Strands draw as fast
+colored lines (random per-strand color + root->tip brightness ramp) or, with
+the Tubes toggle, true variable-width tubes honoring the Houdini ``@width`` ramp.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+from PySide6 import QtCore, QtWidgets
+from pyvistaqt import QtInteractor
+
+from . import head as head_mod
+from . import strand_geometry as sg
+from . import strands_io
+
+
+class ViewerWindow(QtWidgets.QMainWindow):
+    DEFAULT_TUBE_BUDGET = 4000
+
+    def __init__(self, dataset_root=None, head_path=None, parent=None):
+        super().__init__(parent)
+        self.setWindowTitle("Hair Strand Viewer")
+        self.resize(1280, 820)
+        self.setAcceptDrops(True)
+
+        # --- state ---
+        self.head_path = Path(head_path) if head_path else None
+        self.versions: list[Path] = []
+        self.frames: list[Path] = []
+        self.cur_index = 0
+        self.cur_P: np.ndarray | None = None
+        self.colors: np.ndarray | None = None
+        self.line_mesh = None
+        self.line_actor = None
+        self.tube_actor = None
+        self.head_actor = None
+        self._tube_shown: int | None = None
+        self._framed = False
+        self._seed = 0
+        self._sync = False  # guards slider<->spin<->set_frame recursion
+
+        # --- layout: controls on top, 3D view below ---
+        central = QtWidgets.QWidget(self)
+        self.setCentralWidget(central)
+        vbox = QtWidgets.QVBoxLayout(central)
+        vbox.setContentsMargins(6, 6, 6, 6)
+        vbox.setSpacing(6)
+        vbox.addWidget(self._build_controls())
+
+        self.plotter = QtInteractor(central)
+        vbox.addWidget(self.plotter, stretch=1)
+
+        self.status = self.statusBar()
+        self._init_scene()
+
+        # playback timer
+        self.timer = QtCore.QTimer(self)
+        self.timer.setInterval(60)
+        self.timer.timeout.connect(self._advance_frame)
+
+        if dataset_root is not None:
+            self.open_target(Path(dataset_root))
+        else:
+            self._set_status("Drop a .npz or a folder, or use Open…")
+
+    # ------------------------------------------------------------------ UI
+    def _build_controls(self) -> QtWidgets.QWidget:
+        bar = QtWidgets.QWidget()
+        outer = QtWidgets.QVBoxLayout(bar)
+        outer.setContentsMargins(0, 0, 0, 0)
+        outer.setSpacing(4)
+
+        row1 = QtWidgets.QHBoxLayout()
+        btn_folder = QtWidgets.QPushButton("Open Folder…")
+        btn_folder.clicked.connect(self._pick_folder)
+        btn_npz = QtWidgets.QPushButton("Open NPZ…")
+        btn_npz.clicked.connect(self._pick_npz)
+        row1.addWidget(btn_folder)
+        row1.addWidget(btn_npz)
+
+        row1.addSpacing(12)
+        row1.addWidget(QtWidgets.QLabel("Version:"))
+        self.version_combo = QtWidgets.QComboBox()
+        self.version_combo.setMinimumWidth(90)
+        self.version_combo.setEnabled(False)
+        self.version_combo.currentIndexChanged.connect(self._on_version_changed)
+        row1.addWidget(self.version_combo)
+
+        row1.addSpacing(12)
+        self.tube_check = QtWidgets.QCheckBox("Tubes")
+        self.tube_check.toggled.connect(self._on_tube_toggled)
+        row1.addWidget(self.tube_check)
+        row1.addWidget(QtWidgets.QLabel("budget:"))
+        self.budget_spin = QtWidgets.QSpinBox()
+        self.budget_spin.setRange(100, 200000)
+        self.budget_spin.setSingleStep(500)
+        self.budget_spin.setValue(self.DEFAULT_TUBE_BUDGET)
+        self.budget_spin.editingFinished.connect(self._on_budget_changed)
+        row1.addWidget(self.budget_spin)
+
+        row1.addSpacing(12)
+        self.head_check = QtWidgets.QCheckBox("Show head")
+        self.head_check.setChecked(True)
+        self.head_check.toggled.connect(self._on_head_toggled)
+        row1.addWidget(self.head_check)
+
+        btn_reset = QtWidgets.QPushButton("Reset camera")
+        btn_reset.clicked.connect(self.reset_camera)
+        row1.addWidget(btn_reset)
+        row1.addStretch(1)
+
+        row2 = QtWidgets.QHBoxLayout()
+        self.play_btn = QtWidgets.QToolButton()
+        self.play_btn.setText("▶")  # play
+        self.play_btn.setCheckable(True)
+        self.play_btn.toggled.connect(self._on_play_toggled)
+        row2.addWidget(self.play_btn)
+        row2.addWidget(QtWidgets.QLabel("Frame:"))
+        self.frame_slider = QtWidgets.QSlider(QtCore.Qt.Horizontal)
+        self.frame_slider.setRange(0, 0)
+        self.frame_slider.valueChanged.connect(self._on_slider)
+        row2.addWidget(self.frame_slider, stretch=1)
+        self.frame_spin = QtWidgets.QSpinBox()
+        self.frame_spin.setRange(0, 0)
+        self.frame_spin.valueChanged.connect(self._on_spin)
+        row2.addWidget(self.frame_spin)
+        self.total_label = QtWidgets.QLabel("/ 0")
+        row2.addWidget(self.total_label)
+
+        outer.addLayout(row1)
+        outer.addLayout(row2)
+        return bar
+
+    def _init_scene(self) -> None:
+        self.plotter.set_background([0.11, 0.12, 0.15], top=[0.18, 0.20, 0.25])
+        try:
+            self.plotter.enable_depth_peeling(4)
+        except Exception:  # noqa: BLE001 - depth peeling is best-effort
+            pass
+        self.plotter.add_axes()
+        if self.head_path is not None:
+            mesh = head_mod.load_head(self.head_path)
+            if mesh is not None:
+                self.head_actor = self.plotter.add_mesh(
+                    mesh,
+                    color=head_mod.HEAD_COLOR,
+                    opacity=head_mod.HEAD_OPACITY,
+                    name="head",
+                    smooth_shading=True,
+                    specular=0.3,
+                    specular_power=15,
+                )
+                self.plotter.reset_camera()
+            else:
+                self._set_status(f"Head not loaded: {self.head_path}")
+
+    # ----------------------------------------------------------- targets
+    def open_target(self, path: Path) -> None:
+        """Dispatch a dropped/opened path: single npz, version root, or folder."""
+        path = Path(path)
+        if path.is_file() and path.suffix.lower() == ".npz":
+            self._clear_versions()
+            self._set_frames([path], source=path.name)
+            return
+        if path.is_dir():
+            versions = strands_io.list_versions(path)
+            if versions:
+                self._set_versions(versions)
+                return
+            frames = strands_io.list_frames(path)
+            if frames:
+                self._clear_versions()
+                self._set_frames(frames, source=str(path))
+                return
+            self._set_status(f"No .npz files or v* folders in {path}")
+            return
+        self._set_status(f"Not found: {path}")
+
+    def _set_versions(self, versions: list[Path]) -> None:
+        self.versions = versions
+        self.version_combo.blockSignals(True)
+        self.version_combo.clear()
+        for v in versions:
+            self.version_combo.addItem(v.name)
+        self.version_combo.setCurrentIndex(0)
+        self.version_combo.setEnabled(True)
+        self.version_combo.blockSignals(False)
+        self._load_version(0)
+
+    def _clear_versions(self) -> None:
+        self.versions = []
+        self.version_combo.blockSignals(True)
+        self.version_combo.clear()
+        self.version_combo.setEnabled(False)
+        self.version_combo.blockSignals(False)
+
+    def _on_version_changed(self, idx: int) -> None:
+        if 0 <= idx < len(self.versions):
+            self._load_version(idx)
+
+    def _load_version(self, idx: int) -> None:
+        folder = self.versions[idx]
+        self._set_frames(strands_io.list_frames(folder), source=folder.name)
+
+    def _set_frames(self, frames: list[Path], source: str = "") -> None:
+        self.frames = frames
+        n = len(frames)
+        self._sync = True
+        self.frame_slider.setRange(0, max(0, n - 1))
+        self.frame_spin.setRange(0, max(0, n - 1))
+        self._sync = False
+        self.total_label.setText(f"/ {max(0, n - 1)}")
+        if n == 0:
+            self._set_status(f"{source}: no frames")
+            return
+        # New dataset -> force color + mesh rebuild (strand count may change).
+        self.colors = None
+        self.line_mesh = None
+        self.set_frame(0)
+
+    # ------------------------------------------------------------ frames
+    def set_frame(self, i: int) -> None:
+        if not self.frames:
+            return
+        i = int(max(0, min(i, len(self.frames) - 1)))
+        self.cur_index = i
+        try:
+            P = strands_io.load_npz(self.frames[i])
+        except strands_io.StrandLoadError as exc:
+            self._set_status(str(exc))
+            QtWidgets.QMessageBox.warning(self, "Load error", str(exc))
+            return
+
+        self.cur_P = P
+        n_strands = P.shape[0]
+        if self.colors is None or len(self.colors) != n_strands:
+            self.colors = sg.strand_colors(n_strands, seed=self._seed)
+            self.line_mesh = None  # strand count changed -> rebuild
+
+        self._refresh_strands()
+
+        self._sync = True
+        self.frame_slider.setValue(i)
+        self.frame_spin.setValue(i)
+        self._sync = False
+        self._update_status()
+
+        if not self._framed:
+            self.reset_camera()
+            self._framed = True
+        else:
+            self.plotter.render()
+
+    def _refresh_strands(self) -> None:
+        P = self.cur_P
+        if P is None:
+            return
+        # Keep the persistent line mesh current (cheap point swap when possible).
+        if self.line_mesh is None:
+            self.line_mesh = sg.build_lines(P, self.colors)
+            self.line_actor = self.plotter.add_mesh(
+                self.line_mesh,
+                scalars="rgb",
+                rgb=True,
+                line_width=1.0,
+                name="strands_lines",
+                lighting=False,
+            )
+        else:
+            self.line_mesh.points = np.ascontiguousarray(P.reshape(-1, 3))
+
+        if self.tube_check.isChecked():
+            tube, shown = sg.build_tubes(
+                P, self.colors, budget=self.budget_spin.value()
+            )
+            self._tube_shown = shown
+            self.tube_actor = self.plotter.add_mesh(
+                tube,
+                scalars="rgb",
+                rgb=True,
+                name="strands_tubes",
+                smooth_shading=True,
+                specular=0.25,
+                specular_power=12,
+            )
+            if self.line_actor is not None:
+                self.line_actor.visibility = False
+        else:
+            self._tube_shown = None
+            if self.tube_actor is not None:
+                self.plotter.remove_actor("strands_tubes")
+                self.tube_actor = None
+            if self.line_actor is not None:
+                self.line_actor.visibility = True
+
+    # ----------------------------------------------------------- toggles
+    def _on_tube_toggled(self, _on: bool) -> None:
+        if self.cur_P is None:
+            return
+        self._refresh_strands()
+        self._update_status()
+        self.plotter.render()
+
+    def _on_budget_changed(self) -> None:
+        if self.cur_P is not None and self.tube_check.isChecked():
+            self._refresh_strands()
+            self._update_status()
+            self.plotter.render()
+
+    def _on_head_toggled(self, on: bool) -> None:
+        if self.head_actor is not None:
+            self.head_actor.visibility = on
+            self.plotter.render()
+
+    def reset_camera(self) -> None:
+        # Face-on / front view: the face is on the -Z side, so look along +Z.
+        self.plotter.view_vector((0.0, 0.0, 1.0), viewup=(0.0, 1.0, 0.0))
+        self.plotter.reset_camera()
+        self.plotter.render()
+
+    # ---------------------------------------------------------- playback
+    def _on_play_toggled(self, on: bool) -> None:
+        if on and len(self.frames) > 1:
+            self.play_btn.setText("⏸")  # pause
+            self.timer.start()
+        else:
+            self.play_btn.setText("▶")
+            self.timer.stop()
+            if on:  # nothing to play; pop the button back out
+                self.play_btn.setChecked(False)
+
+    def _advance_frame(self) -> None:
+        if not self.frames:
+            return
+        self.set_frame((self.cur_index + 1) % len(self.frames))
+
+    def _on_slider(self, val: int) -> None:
+        if not self._sync:
+            self.set_frame(val)
+
+    def _on_spin(self, val: int) -> None:
+        if not self._sync:
+            self.set_frame(val)
+
+    # ------------------------------------------------------------ pickers
+    def _pick_folder(self) -> None:
+        d = QtWidgets.QFileDialog.getExistingDirectory(self, "Open frame folder")
+        if d:
+            self.open_target(Path(d))
+
+    def _pick_npz(self) -> None:
+        f, _ = QtWidgets.QFileDialog.getOpenFileName(
+            self, "Open .npz", "", "NumPy archives (*.npz)"
+        )
+        if f:
+            self.open_target(Path(f))
+
+    # --------------------------------------------------------- status bar
+    def _update_status(self) -> None:
+        if self.cur_P is None:
+            return
+        n_strands, n_samples = self.cur_P.shape[0], self.cur_P.shape[1]
+        name = self.frames[self.cur_index].name if self.frames else "-"
+        last = len(self.frames) - 1 if self.frames else 0
+        extra = ""
+        if self.tube_check.isChecked():
+            if self._tube_shown is not None and self._tube_shown < n_strands:
+                extra = (
+                    f"    |    tubes: {self._tube_shown:,} of "
+                    f"{n_strands:,} strands (decimated)"
+                )
+            else:
+                extra = "    |    tubes"
+        self._set_status(
+            f"{name}    strands {n_strands:,}    samples {n_samples}    "
+            f"frame {self.cur_index}/{last}{extra}"
+        )
+
+    def _set_status(self, msg: str) -> None:
+        self.status.showMessage(msg)
+
+    # ----------------------------------------------------------- drag/drop
+    def dragEnterEvent(self, event) -> None:  # noqa: N802 (Qt signature)
+        if event.mimeData().hasUrls():
+            for url in event.mimeData().urls():
+                p = Path(url.toLocalFile())
+                if p.is_dir() or p.suffix.lower() == ".npz":
+                    event.acceptProposedAction()
+                    return
+        event.ignore()
+
+    def dropEvent(self, event) -> None:  # noqa: N802 (Qt signature)
+        for url in event.mimeData().urls():
+            p = Path(url.toLocalFile())
+            if p.is_dir() or p.suffix.lower() == ".npz":
+                self.open_target(p)
+                return
+
+    def closeEvent(self, event) -> None:  # noqa: N802 (Qt signature)
+        self.timer.stop()
+        try:
+            self.plotter.close()
+        except Exception:  # noqa: BLE001 - best-effort VTK teardown
+            pass
+        super().closeEvent(event)

argument/viewer/head.py

@@ -0,0 +1,28 @@
+"""Load the head boundary mesh that the strands sit inside."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pyvista as pv
+
+# Transparent-blue look for the head, shared by the app.
+HEAD_COLOR = (0.45, 0.62, 1.0)
+HEAD_OPACITY = 0.28
+
+
+def load_head(obj_path: str | Path) -> pv.PolyData | None:
+    """Read the head OBJ, or return ``None`` if missing/unreadable.
+
+    Returning ``None`` lets the viewer run without a head rather than crash.
+    """
+    obj_path = Path(obj_path)
+    if not obj_path.exists():
+        return None
+    try:
+        mesh = pv.read(obj_path)
+    except Exception:  # noqa: BLE001 - missing head is non-fatal
+        return None
+    if mesh.n_points == 0:
+        return None
+    return mesh

argument/viewer/strand_geometry.py

@@ -0,0 +1,167 @@
+"""Build PyVista geometry from a strand array ``P`` of shape ``(S, N, 3)``.
+
+Two representations:
+
+* :func:`build_lines` -- one GL polyline per strand, random color per strand
+  with a root->tip brightness ramp baked into per-point RGB. Fast enough for
+  60k strands and frame scrubbing.
+* :func:`build_tubes` -- true variable-radius tubes honoring the Houdini
+  ``@width`` ramp (root 0.5 mm -> tip 0.2 mm), evenly decimated to a strand
+  budget so it stays interactive.
+
+No Qt here; everything is pure NumPy + PyVista so it can be unit/smoke tested
+head-less.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pyvista as pv
+
+# Houdini width ramp, used as tube *radius* in meters (matches the data scale).
+ROOT_WIDTH = 0.0005
+TIP_WIDTH = 0.0002
+
+# How much darker the tip is than the root in line mode (brightness factor).
+_TIP_BRIGHTNESS = 0.35
+
+# Consecutive samples closer than this (meters) are treated as duplicates and
+# collapsed before tubing -- strands resampled to a fixed length often pad the
+# tail with repeated points, which the VTK tube filter cannot frame.
+_DEGENERATE_EPS = 1e-7
+
+# Cache the VTK line-connectivity array per (n_strands, n_samples): it never
+# changes between frames of the same dataset, so we build it once.
+_CONN_CACHE: dict[tuple[int, int], np.ndarray] = {}
+
+
+def _line_connectivity(n_strands: int, n_samples: int) -> np.ndarray:
+    """VTK ``lines`` array: ``[N, i0..iN-1, N, ...]`` for each strand."""
+    key = (n_strands, n_samples)
+    conn = _CONN_CACHE.get(key)
+    if conn is None:
+        idx = np.arange(n_strands * n_samples, dtype=np.int64)
+        idx = idx.reshape(n_strands, n_samples)
+        counts = np.full((n_strands, 1), n_samples, dtype=np.int64)
+        conn = np.hstack([counts, idx]).ravel()
+        _CONN_CACHE[key] = conn
+    return conn
+
+
+def _u_param(n_samples: int) -> np.ndarray:
+    """Root->tip parameter in ``[0, 1]`` along a strand (root = 0)."""
+    if n_samples < 2:
+        return np.zeros(1, dtype=np.float32)
+    return np.linspace(0.0, 1.0, n_samples, dtype=np.float32)
+
+
+def strand_colors(n_strands: int, seed: int = 0) -> np.ndarray:
+    """Deterministic vivid random RGB per strand, ``(S, 3) uint8``.
+
+    Seeded so a strand keeps the same color across frames (temporal coherence).
+    Uses high-saturation HSV so tips stay distinguishable after the ramp.
+    """
+    rng = np.random.default_rng(seed)
+    h = rng.random(n_strands)
+    s = 0.55 + 0.45 * rng.random(n_strands)
+    v = 0.80 + 0.20 * rng.random(n_strands)
+    rgb = _hsv_to_rgb(h, s, v)
+    return (rgb * 255.0 + 0.5).astype(np.uint8)
+
+
+def _hsv_to_rgb(h: np.ndarray, s: np.ndarray, v: np.ndarray) -> np.ndarray:
+    """Vectorized HSV->RGB for arrays in ``[0, 1]``; returns ``(n, 3)`` float."""
+    i = np.floor(h * 6.0).astype(np.int64)
+    f = h * 6.0 - i
+    p = v * (1.0 - s)
+    q = v * (1.0 - f * s)
+    t = v * (1.0 - (1.0 - f) * s)
+    i = i % 6
+    r = np.choose(i, [v, q, p, p, t, v])
+    g = np.choose(i, [t, v, v, q, p, p])
+    b = np.choose(i, [p, p, t, v, v, q])
+    return np.stack([r, g, b], axis=1)
+
+
+def _point_rgb(colors: np.ndarray, n_samples: int) -> np.ndarray:
+    """Per-point RGB ``(S*N, 3) uint8`` with a root->tip brightness ramp."""
+    u = _u_param(n_samples)
+    bright = _TIP_BRIGHTNESS + (1.0 - _TIP_BRIGHTNESS) * (1.0 - u)  # (N,)
+    rgb = colors[:, None, :].astype(np.float32) * bright[None, :, None]
+    return rgb.reshape(-1, 3).astype(np.uint8)
+
+
+def build_lines(P: np.ndarray, colors: np.ndarray) -> pv.PolyData:
+    """One polyline per strand with per-point RGB (root brighter than tip)."""
+    n_strands, n_samples = P.shape[0], P.shape[1]
+    mesh = pv.PolyData(np.ascontiguousarray(P.reshape(-1, 3)))
+    mesh.lines = _line_connectivity(n_strands, n_samples)
+    u = np.broadcast_to(_u_param(n_samples), (n_strands, n_samples)).reshape(-1)
+    mesh.point_data["u"] = np.ascontiguousarray(u, dtype=np.float32)
+    mesh.point_data["rgb"] = _point_rgb(colors, n_samples)
+    return mesh
+
+
+def select_strands(n_strands: int, budget: int) -> np.ndarray:
+    """Indices of up to *budget* strands, evenly spaced across ``[0, S)``."""
+    if budget <= 0 or n_strands <= budget:
+        return np.arange(n_strands)
+    return np.linspace(0, n_strands - 1, budget).astype(np.int64)
+
+
+def build_tubes(
+    P: np.ndarray,
+    colors: np.ndarray,
+    budget: int = 4000,
+    n_sides: int = 6,
+) -> tuple[pv.PolyData, int]:
+    """Variable-radius tubes honoring the ``@width`` ramp.
+
+    Returns ``(tube_polydata, n_shown)``. ``n_shown`` < S means the result was
+    decimated to *budget* strands -- the caller should surface that, never hide
+    it.
+    """
+    sel = select_strands(P.shape[0], budget)
+    n_samples = P.shape[1]
+    u_full = _u_param(n_samples)
+    bright = _TIP_BRIGHTNESS + (1.0 - _TIP_BRIGHTNESS) * (1.0 - u_full)  # (N,)
+
+    pts_chunks: list[np.ndarray] = []
+    conn_chunks: list[np.ndarray] = []
+    width_chunks: list[np.ndarray] = []
+    rgb_chunks: list[np.ndarray] = []
+    offset = 0
+    n_shown = 0
+    for s in sel:
+        strand = P[s]
+        # Keep the first sample plus any sample that actually moved; this drops
+        # duplicate consecutive points that would break the tube filter.
+        seg = np.linalg.norm(np.diff(strand, axis=0), axis=1)
+        keep = np.concatenate(([True], seg > _DEGENERATE_EPS))
+        m = int(keep.sum())
+        if m < 2:
+            continue  # fully degenerate strand -> cannot be tubed
+        ku = u_full[keep]
+        pts_chunks.append(strand[keep])
+        conn_chunks.append(np.concatenate(([m], np.arange(offset, offset + m))))
+        width_chunks.append(ROOT_WIDTH + (TIP_WIDTH - ROOT_WIDTH) * ku)
+        rgb_chunks.append(colors[s].astype(np.float32) * bright[keep][:, None])
+        offset += m
+        n_shown += 1
+
+    if n_shown == 0:
+        return pv.PolyData(), 0
+
+    mesh = pv.PolyData(np.ascontiguousarray(np.concatenate(pts_chunks, axis=0)))
+    mesh.lines = np.concatenate(conn_chunks).astype(np.int64)
+    mesh.point_data["width"] = np.concatenate(width_chunks).astype(np.float32)
+    mesh.point_data["rgb"] = np.concatenate(rgb_chunks, axis=0).astype(np.uint8)
+
+    tube = mesh.tube(
+        scalars="width",
+        absolute=True,
+        radius=ROOT_WIDTH,
+        n_sides=n_sides,
+        capping=True,
+    )
+    return tube, n_shown

argument/viewer/strands_io.py

@@ -0,0 +1,91 @@
+"""Dataset / frame I/O for the hair-strand viewer.
+
+A frame is an ``.npz`` holding key ``P`` of shape ``(S, N, 3)`` (fp16 on disk,
+returned here as fp32). Versions are sibling folders matching ``v<number>``.
+Nothing in this module touches PyVista or Qt.
+"""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+import numpy as np
+
+
+class StrandLoadError(Exception):
+    """Raised when an ``.npz`` cannot be read or has an unexpected layout.
+
+    The message is safe to show to the user.
+    """
+
+
+def load_npz(path: str | Path) -> np.ndarray:
+    """Load strand points from an ``.npz`` as ``(S, N, 3) float32``.
+
+    Validates the ``P`` key and trailing dimension, converts fp16 -> fp32, and
+    drops any strand containing non-finite samples.
+    """
+    path = Path(path)
+    try:
+        data = np.load(path)
+    except Exception as exc:  # noqa: BLE001 - surface any read error uniformly
+        raise StrandLoadError(f"Could not read {path.name}: {exc}") from exc
+
+    if "P" not in data.files:
+        raise StrandLoadError(
+            f"{path.name}: no 'P' array (found {list(data.files)})"
+        )
+
+    P = np.asarray(data["P"])
+    if P.ndim != 3 or P.shape[-1] != 3:
+        raise StrandLoadError(
+            f"{path.name}: expected P shape (S, N, 3), got {tuple(P.shape)}"
+        )
+
+    P = P.astype(np.float32, copy=False)
+    finite = np.isfinite(P).all(axis=(1, 2))
+    if not finite.all():
+        P = P[finite]
+    if P.shape[0] == 0:
+        raise StrandLoadError(f"{path.name}: no finite strands to show")
+    return np.ascontiguousarray(P)
+
+
+_NUM_RE = re.compile(r"(\d+)")
+
+
+def _stem_key(p: Path):
+    """Sort key that orders ``00010.npz`` after ``00009.npz`` numerically."""
+    m = _NUM_RE.search(p.stem)
+    return (0, int(m.group(1)), "") if m else (1, 0, p.stem.lower())
+
+
+def list_frames(folder: str | Path) -> list[Path]:
+    """Return ``*.npz`` files in *folder*, sorted by numeric stem."""
+    folder = Path(folder)
+    if not folder.is_dir():
+        return []
+    return sorted(folder.glob("*.npz"), key=_stem_key)
+
+
+_VER_RE = re.compile(r"^v(\d+)$", re.IGNORECASE)
+
+
+def list_versions(root: str | Path) -> list[Path]:
+    """Return ``v<number>`` sub-folders of *root* that contain at least one npz.
+
+    Sorted numerically (v0, v1, ... v10).
+    """
+    root = Path(root)
+    if not root.is_dir():
+        return []
+    versions = [
+        child
+        for child in root.iterdir()
+        if child.is_dir()
+        and _VER_RE.match(child.name)
+        and next(child.glob("*.npz"), None) is not None
+    ]
+    versions.sort(key=lambda p: int(_VER_RE.match(p.name).group(1)))
+    return versions

data/v4/00012.npz

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label/v0.csv

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+00000,long
+00001,long
+00002,long
+00003,bob
+00004,long
+00005,short
+00006,long
+00007,long
+00008,shoulder
+00009,long
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+00020,short
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+00026,long
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label/v1.csv

@@ -0,0 +1,1077 @@
+number,class
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label/v2.csv

@@ -0,0 +1,819 @@
+number,class
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label/v3.csv

@@ -0,0 +1,845 @@
+number,class
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label/v4.csv

@@ -0,0 +1,651 @@
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label/v5.csv

@@ -0,0 +1,1540 @@
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label/v6.csv

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render/v4.zip

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