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aadc8c0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | """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
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