What the x,y,z coordinates locations denotes (mid point of voxel or anything else)?

[ashishgupta2598]

What are these x,y,z coordinates locations, outputting by binvox_rw.read_as_coord_array(f) function. Are they mid point of any voxel or any vertices of a particular voxel. I want to find all the 8 vertices of a voxel. So please let me know the conversion formulas.

[jingnanshi]

See pull request #11 . If you are using python 3, you need to fix the division to the correct integer division operator. The coordinates returned by read_as_coord_array should be the indices of each vox in 3D.

[jingnanshi]

I'll reply your question here. The indices are just integer indices of the voxels. So for a 32 by 32 by 32 model, the indices i,j,k will be from 0 to 31. They just represent the indices of each voxel in the model.

To find all 8 vertices of a voxel, you can do something like this:

# lower left corner
x_n = i/dims[0]
y_n = j/dims[1]
z_n = k/dims[2]
x = scale*x_n + translate[0]
y = scale*y_n + translate[1]
z = scale*z_n + translate[2]

# upper right corner
x_n = (i+1)/dims[0]
y_n = (j+1)/dims[1]
z_n = (k+1)/dims[2]
x = scale*x_n + translate[0]
y = scale*y_n + translate[1]
z = scale*z_n + translate[2]

# and do something similar for the other 6 ...

[ashishgupta2598]

Got it. Thanks a lot. You literally save my work.

…

On Mon, 18 Nov 2019, 11:54 p.m. Jingnan Shi ***@***.*** wrote: I'll reply your question here. The indices are just integer indices of the voxels. So for a 32 by 32 by 32 model, the indices i,j,k will be from 0 to 31. They just represent the indices of each voxel in the model. To find all 8 vertices of a voxel, you can do something like this: # lower left corner x_n = i/dims[0] y_n = j/dims[1] z_n = k/dims[2] x = scale*x_n + translate[0] y = scale*y_n + translate[1] z = scale*z_n + translate[2] # upper right corner x_n = (i+1)/dims[0] y_n = (j+1)/dims[1] z_n = (k+1)/dims[2] x = scale*x_n + translate[0] y = scale*y_n + translate[1] z = scale*z_n + translate[2] # and do something similar for the other 6 ... — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#10?email_source=notifications&email_token=AJVES2WYKG7LFY3C47W4WZDQULMWPA5CNFSM4JODVBQ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEELNOMQ#issuecomment-555145010>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AJVES2URDQQP4M623CB7DSTQULMWPANCNFSM4JODVBQQ> .

[jingnanshi]

This might also help you: https://www.patrickmin.com/binvox/binvox.html I'm glad I can help.

[ashishgupta2598]

Thanks a lot for this also.

…

On Mon, 18 Nov 2019, 11:59 p.m. Jingnan Shi ***@***.*** wrote: This might also help you: https://www.patrickmin.com/binvox/binvox.html I'm glad I can help. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#10?email_source=notifications&email_token=AJVES2QZWNJ4EUXLF3KB3CDQULNJXA5CNFSM4JODVBQ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEELN7NI#issuecomment-555147189>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AJVES2X3IV3ZOGRTA2CEQALQULNJXANCNFSM4JODVBQQ> .