2026a
# smooth3
平滑处理三维数据
函数库: TyVolumeVisualization
# 语法
W = smooth3(V)
W = smooth3(V,filter)
W = smooth3(V,filter,sz)
W = smooth3(V,filter,sz;sd=0.65)
# 说明
W = smooth3(V) 对输入数据 V 进行平滑处理并在 W 中返回经过平滑处理的数据。
W = smooth3(V,filter) filter 确定卷积核，可以为下列值之一：
"gaussian"
"box"（默认值）
W = smooth3(V,filter,size) 设置卷积核的大小（默认值为 [3 3 3]）。如果 size 为标量，则 size 解释为 [size, size, size]。
W = smooth3(V,filter,size;sd=0.65) 设置卷积核的属性。当 filter 为 gaussian 时，sd 是标准差（默认值为 0.65）。
# 示例
对等值面数据进行平滑处理
此示例对 10×10×10 数组进行平滑处理。
using TyVolumeVisualization
pkg_dir = pkgdir(TyVolumeVisualization)
source_path = pkg_dir * "/examples/smooth3/0-data.jl"
include(source_path)
data = smooth3(v, "box", 5)
data =
10×10×10 Array{Float64, 3}:
[:, :, 1] =
0.44251 0.448596 0.479544 0.496295 0.576818 0.566313 0.599346 0.60231 0.619472 0.606595
0.454127 0.447846 0.464406 0.485826 0.544362 0.537069 0.563011 0.570073 0.572276 0.566575
0.496092 0.474774 0.457595 0.48074 0.510999 0.517032 0.529394 0.553639 0.537561 0.551448
0.516051 0.497001 0.449305 0.470191 0.472794 0.486727 0.492709 0.544494 0.527696 0.565942
0.461902 0.434583 0.40777 0.425745 0.425065 0.44513 0.456254 0.496476 0.49191 0.53868
0.452505 0.431035 0.417294 0.447914 0.44383 0.46024 0.485601 0.506208 0.482454 0.516383
0.454232 0.429908 0.423372 0.432714 0.427382 0.436741 0.462347 0.460304 0.442382 0.472173
0.476988 0.455519 0.455982 0.441476 0.432518 0.415509 0.431821 0.418326 0.4198 0.446665
0.52175 0.485831 0.490072 0.455701 0.442496 0.404545 0.404363 0.369059 0.372367 0.391535
0.596694 0.557235 0.553231 0.500561 0.46932 0.432513 0.418532 0.366312 0.362758 0.380685
[:, :, 2] =
0.455228 0.441422 0.45219 0.457362 0.528218 0.537253 0.582378 0.589669 0.602555 0.574912
0.461865 0.447258 0.449775 0.460954 0.508025 0.511746 0.54331 0.556885 0.561798 0.550056
0.485498 0.467902 0.454777 0.471114 0.490894 0.504723 0.517514 0.538318 0.529658 0.538654
0.503836 0.495176 0.458418 0.473101 0.467822 0.481723 0.474723 0.513542 0.50092 0.532318
0.472043 0.455106 0.435344 0.448867 0.439857 0.463522 0.459022 0.488365 0.484422 0.525131
0.465245 0.458374 0.450723 0.475549 0.458734 0.468685 0.466699 0.479009 0.458842 0.492225
0.467585 0.455582 0.451349 0.465562 0.45751 0.459167 0.463469 0.454642 0.427368 0.445489
0.476431 0.466881 0.469574 0.467781 0.463426 0.441788 0.441973 0.427751 0.418015 0.43697
0.490572 0.471551 0.489159 0.478174 0.475286 0.440386 0.43747 0.407069 0.405261 0.423386
0.53516 0.519847 0.53915 0.513087 0.49208 0.45729 0.44474 0.399023 0.398671 0.423537
⋮
[:, :, 10] =
0.446341 0.498311 0.492746 0.497233 0.517588 0.470505 0.456715 0.482279 0.49779 0.535518
0.481818 0.505636 0.4932 0.487672 0.482637 0.44115 0.441223 0.450687 0.453243 0.478503
0.463517 0.477316 0.480941 0.480263 0.448664 0.440025 0.447633 0.432532 0.421734 0.438873
0.459022 0.470612 0.479993 0.48649 0.457072 0.451511 0.464453 0.43708 0.412591 0.396345
0.481476 0.493754 0.524918 0.537816 0.505983 0.481794 0.48819 0.453582 0.437239 0.441614
0.551237 0.558237 0.569567 0.586839 0.533468 0.490752 0.483168 0.445111 0.401113 0.407321
0.471035 0.50915 0.547625 0.595099 0.563263 0.524644 0.510716 0.476075 0.432434 0.432525
0.453454 0.493834 0.546328 0.603015 0.592975 0.533318 0.520806 0.492876 0.460754 0.461272
0.422065 0.456901 0.53372 0.597296 0.580306 0.529381 0.520485 0.486974 0.466706 0.492917
0.422187 0.440136 0.519784 0.575012 0.563732 0.532105 0.529397 0.48711 0.469242 0.477414