How to quantify the volume of a Volume
ttsesm opened this issue · comments
Hi @marcomusy,
I have the following question, for meshes it is possible to quantify the volume of a mesh by calling the volume()
function. Though as I understood this is dependent to the triangulation of the meshes if I am not wrong.
My ultimate goal was to quantify the result of the "intersection" boolean operation between two meshes. But I've noticed that this can become quite slow, and also I know that in vtk the boolean operations are on the weak side of the library. Thus, I thought to do the same using the volumes of two objects. However, I couldn't figure out how to quantify the output.
So for example let's say that we have two boxes:
from vedo import Box, show
vol1 = Box(size=(35,10, 5)).binarize()
vol2 = Box(size=( 5,10,35)).binarize()
vol = vol1.operation("and", vol2)
print(vol)
show(vol1.c('g'), vol2.c('r'), vol.c('magenta'), interactive=True).close()
How can I quantify the vol
which is a class Volume()
, there is no .volume()
function to call on a volume. Then I thought that I can transform it back to mesh by using .tomesh()
but this seems to be slow and then calling .volume()
gives 0, which I figured out that I can fix by calling the triangulate()
. However, the output from that also looks quite high, and then I realized that the volume()
output on a mesh probably is related to its triangulation.
Any idea is welcome, thanks.
Well I have not tested this but you can try with some specific configuration:
from vedo import *
vol1 = Box(size=(35,10, 5)).binarize()
vol2 = Box(size=( 5,10,35)).binarize()
vol = vol1.operation("and", vol2)
dx, dy, dz = vol.spacing() # voxel size
counts = np.unique(vol.pointdata[0], return_counts=True)
n0, n1 = counts[1]
vol_value = dx*dy*dz * n1
vol1.cmap('g')
vol2.cmap('r')
vol.cmap('m')
show(vol1, vol2, vol, f"volume = {vol_value}", axes=1).close()
Hi @marcomusy,
Lovely, this works fine. The point is now I want to apply this on two point clouds.
However, the issue that I face now is that when I try to extract the volume from the point clouds directly I get nothing. I tried the different parameters to the .tovolume()
function but it doesn't seem to give me something that I can use:
pcd1.tovolume(kernel='shepard', n=4)
<vedo.volume.Volume object at 0x7fc247ea2800>
pcd1.pointdata
Point Data is empty.
The same with pcd2. Then I tried first to make them as meshes by using the .reconstruct_surface()
method and use .binarize()
on them and then extract overlap but the reconstructed meshes are really bad.
Any idea how to handle this.
Thanks.
pcds.zip
Hi, what about this alternative solution
from vedo import *
pcd1 = Points("pcd1.ply").color("blue5")
pcd2 = Points("pcd2.ply").color("red5")
ug1 = pcd1.generate_delaunay3d(radius=0.01)
surf1 = ug1.tomesh().compute_normals()
surf1.color("blue5").alpha(0.1)
ug2 = pcd2.generate_delaunay3d(radius=0.01)
surf2 = ug2.tomesh().compute_normals()
surf2.color("red5").alpha(0.1)
s12 = surf1.boolean("intersect", surf2)
s12.color("green5").lw(1)
vol = s12.volume()
show(pcd1, pcd2, surf1, surf2, s12, f"volume = {vol}", axes=1)
PS : creating a volume from a pointcloud is not possible because there is no face normal which defines the "inside" from the "outside".
Using the meshes is good as alternative the only issue that I have though with this approach is that radius needs to change each time because I am loading different point clouds each time so using 0.01
as a fixed number it doesn't always work, e.g.:
that's why I wanted to go volumes instead.
Do you have any idea how I can dynamically specify the radius value for the triangulation, depending on the point cloud that I have each time... 🤔
You can automatize the radius value by histogramming the point relative distances, something like
from vedo import *
from vedo.pyplot import histogram
pcd1 = Points("pcd1.ply").color("blue5")
pcd2 = Points("pcd2.ply").color("red5")
dists1 = []
for p1 in pcd1.coordinates:
q1 = pcd1.closest_point(p1, n=2)[1]
dists1.append(mag(p1 - q1))
histo1 = histogram(dists1, bins=25).clone2d()
radius = histo1.mean * 10
tetm1 = pcd1.generate_delaunay3d(radius=radius)
surf1 = tetm1.tomesh().compute_normals()
surf1.color("blue5").alpha(0.1)
tetm2 = pcd2.generate_delaunay3d(radius=radius)
surf2 = tetm2.tomesh().compute_normals()
surf2.color("red5").alpha(0.1)
s12 = surf1.boolean("intersect", surf2)
s12.color("green5").lw(1)
print(s12.volume())
show(pcd1, pcd2, surf1, surf2, s12, histo1, axes=1)
Thanks @marcomusy, as always really helpful 👍
For the moment, it seems to do the job so I will be closing this issue.