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UV: improve performance for lightmap unwrap

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Replaces this search by representing the 3 edge lengths of each triangle
as 3d points and searching with a KD-tree.

On a mesh with ~20k Tris the old method runs in 40s while the new method
takes 0.23s with a difference of <0.001%.

Ref !113720
This commit is contained in:
SebastianWitt
2024-01-11 10:30:04 +11:00
committed by Campbell Barton
parent 7fa2a1071e
commit 406a2fde91
1 changed files with 40 additions and 16 deletions
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56
scripts/startup/bl_operators/uvcalc_lightmap.py
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@@ -302,28 +302,52 @@ def lightmap_uvpack(
tri_lengths = [trylens(f) for f in face_sel if f.loop_total == 3]
del trylens
def trilensdiff(t1, t2):
return (abs(t1[1][t1[2][0]] - t2[1][t2[2][0]]) +
abs(t1[1][t1[2][1]] - t2[1][t2[2][1]]) +
abs(t1[1][t1[2][2]] - t2[1][t2[2][2]]))
# To add triangles into the light-map pack triangles are grouped in pairs to fill rectangular areas.
# In the following for each triangle we add the sorted triangle edge lengths (3d point) into a KD-Tree
# then iterate over all triangles and search for pairs of triangles by looking for the closest
# sorted triangle point.
# Additionally clusters of similar/equal triangles are parsed by searching for ranges in a second step.
kd = mathutils.kdtree.KDTree(len(tri_lengths))
for i, (f, lens, o) in enumerate(tri_lengths):
vector = (lens[o[0]], lens[o[1]], lens[o[2]])
kd.insert(vector, i)
kd.balance()
while tri_lengths:
tri1 = tri_lengths.pop()
added_ids = [False] * len(tri_lengths)
pairs_added = 0
tri_equality_threshold = 0.00001 # Add multiple pairs at once that are within this threshold.
for i in range(len(tri_lengths)):
if added_ids[i]:
continue
tri1 = tri_lengths[i]
f1, lens1, lo1 = tri1
if not tri_lengths:
sorted_l = (lens1[lo1[0]], lens1[lo1[1]], lens1[lo1[2]])
added_ids[i] = True
vec, nearest, dist = kd.find(sorted_l, filter=lambda idx: not added_ids[idx])
if not nearest or nearest < 0:
pretty_faces.append(prettyface((tri1, None)))
break
tri2 = tri_lengths[nearest]
pretty_faces.append(prettyface((tri1, tri2)))
pairs_added = pairs_added + 1
added_ids[nearest] = True
best_tri_index = -1
best_tri_diff = 100000000.0
# Look in threshold proximity to add all similar/equal triangles in one go.
# This code is not necessary but acts as a shortcut (~9% performance improvement).
if dist < tri_equality_threshold:
cluster_tri_ids = [
idx for _, idx, _ in kd.find_range(sorted_l, tri_equality_threshold)
if not added_ids[idx]
]
for i, tri2 in enumerate(tri_lengths):
diff = trilensdiff(tri1, tri2)
if diff < best_tri_diff:
best_tri_index = i
best_tri_diff = diff
pretty_faces.append(prettyface((tri1, tri_lengths.pop(best_tri_index))))
if len(cluster_tri_ids) > 1:
for ci in range(0, len(cluster_tri_ids) - (len(cluster_tri_ids) % 2), 2):
pretty_faces.append(
prettyface((tri_lengths[cluster_tri_ids[ci]], tri_lengths[cluster_tri_ids[ci + 1]]))
)
added_ids[cluster_tri_ids[ci]] = added_ids[cluster_tri_ids[ci + 1]] = True
pairs_added = pairs_added + 1
# Get the min, max and total areas
max_area = 0.0