2nd notebook
2nd notebook#
[47]:
%pip install git+https://github.com/nndt-team/nndt.git
%pip install ipyvtklink
Collecting git+https://github.com/nndt-team/nndt.git
Cloning https://github.com/nndt-team/nndt.git to /tmp/pip-req-build-vxhlf_29
Running command git clone -q https://github.com/nndt-team/nndt.git /tmp/pip-req-build-vxhlf_29
Resolved https://github.com/nndt-team/nndt.git to commit c4850b38a5d82ce379a145e17fcb6a547af2c5f1
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Building wheels for collected packages: NNDT
Building wheel for NNDT (setup.py) ... done
Created wheel for NNDT: filename=NNDT-0.0.3b1-py3-none-any.whl size=49113 sha256=bcd7d7d129401444fd214b6cbb8982858e19319aab9f346bdeb3be0f9c58a875
Stored in directory: /tmp/pip-ephem-wheel-cache-609gvw8d/wheels/48/e6/b0/eed564ae0a50dab01b1cf11f639b92028e56be7ec1205789dc
Successfully built NNDT
Installing collected packages: NNDT
Attempting uninstall: NNDT
Found existing installation: NNDT 0.0.3a3
Can't uninstall 'NNDT'. No files were found to uninstall.
Successfully installed NNDT-0.0.3b1
Note: you may need to restart the kernel to use updated packages.
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Note: you may need to restart the kernel to use updated packages.
[48]:
import nndt
import nndt.space2 as spc
from nndt.datasets import ACDC
nndt.__version__
[48]:
'0.0.3b1'
[49]:
import pyvista as pv
import jax.numpy as jnp
nndt.init_colab() turns on interactive plots in google collaboratory. This string is not necessary in py-scripts or jupyter notebook.
[50]:
# nndt.init_colab()
Load data
[51]:
ACDC().load()
Downloading...
From: https://drive.google.com/uc?export=download&id=1UzC2WPkjMQSxzI5sj1rMT47URuZbQhYb
To: /home/kostanew/PycharmProjects/nndt/tutorials/.datasets/ACDC_5/temp.7z
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 9.01M/9.01M [00:01<00:00, 4.74MB/s]
Loading complete
[52]:
space = spc.load_from_path("./.datasets/ACDC_5")
space.preload("shift_and_scale", ns_padding=(0.1, 0.1, 0.1))
print(space.print())
S:space 0.0.3b1
├── O3D:patient009 ((-0.85, -0.81, -0.87), (0.85, 0.81, 0.87))
│ ├── FS:colored_obj mesh_obj^ ./.datasets/ACDC_5/patient009/colored.obj
│ ├── FS:sdf_npy sdt^ ./.datasets/ACDC_5/patient009/sdf.npy
│ ├── TR:transform shift_and_scale
│ ├── MS:mesh
│ ├── MS:mesh_colors
│ ├── MS:sampling
│ ├── MS:sdt
│ ├── MS:train_task
│ ├── plot(default, filepath=None)
│ ├── print(default|source|full)
│ ├── sampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])
│ ├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
│ ├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
│ ├── sampling_uniform(key, N) -> ns_xyz[N,3]
│ ├── save_mesh(filepath, {name, array})
│ ├── surface_ind2rgba(ind[..,1]) -> rgba[..,4]
│ ├── surface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]
│ ├── surface_rgba() -> xyz[N,4]
│ ├── surface_xyz() -> xyz[N,3]
│ ├── surface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]
│ ├── surface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]
│ ├── surface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]
│ ├── surface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]
│ ├── train_task_sdt2sdf(filename, **kwargs)
│ ├── transform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]
│ ├── transform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]
│ ├── transform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]
│ ├── transform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]
│ └── unload_from_memory()
├── O3D:patient029 ((-0.85, -0.88, -0.93), (0.85, 0.88, 0.93))
│ ├── FS:colored_obj mesh_obj^ ./.datasets/ACDC_5/patient029/colored.obj
│ ├── FS:sdf_npy sdt^ ./.datasets/ACDC_5/patient029/sdf.npy
│ ├── TR:transform shift_and_scale
│ ├── MS:mesh
│ ├── MS:mesh_colors
│ ├── MS:sampling
│ ├── MS:sdt
│ ├── MS:train_task
│ ├── plot(default, filepath=None)
│ ├── print(default|source|full)
│ ├── sampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])
│ ├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
│ ├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
│ ├── sampling_uniform(key, N) -> ns_xyz[N,3]
│ ├── save_mesh(filepath, {name, array})
│ ├── surface_ind2rgba(ind[..,1]) -> rgba[..,4]
│ ├── surface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]
│ ├── surface_rgba() -> xyz[N,4]
│ ├── surface_xyz() -> xyz[N,3]
│ ├── surface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]
│ ├── surface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]
│ ├── surface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]
│ ├── surface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]
│ ├── train_task_sdt2sdf(filename, **kwargs)
│ ├── transform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]
│ ├── transform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]
│ ├── transform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]
│ ├── transform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]
│ └── unload_from_memory()
├── O3D:patient049 ((-0.86, -0.83, -0.60), (0.86, 0.83, 0.60))
│ ├── FS:colored_obj mesh_obj^ ./.datasets/ACDC_5/patient049/colored.obj
│ ├── FS:sdf_npy sdt^ ./.datasets/ACDC_5/patient049/sdf.npy
│ ├── TR:transform shift_and_scale
│ ├── MS:mesh
│ ├── MS:mesh_colors
│ ├── MS:sampling
│ ├── MS:sdt
│ ├── MS:train_task
│ ├── plot(default, filepath=None)
│ ├── print(default|source|full)
│ ├── sampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])
│ ├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
│ ├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
│ ├── sampling_uniform(key, N) -> ns_xyz[N,3]
│ ├── save_mesh(filepath, {name, array})
│ ├── surface_ind2rgba(ind[..,1]) -> rgba[..,4]
│ ├── surface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]
│ ├── surface_rgba() -> xyz[N,4]
│ ├── surface_xyz() -> xyz[N,3]
│ ├── surface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]
│ ├── surface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]
│ ├── surface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]
│ ├── surface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]
│ ├── train_task_sdt2sdf(filename, **kwargs)
│ ├── transform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]
│ ├── transform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]
│ ├── transform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]
│ ├── transform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]
│ └── unload_from_memory()
├── O3D:patient069 ((-0.68, -0.58, -0.58), (0.68, 0.58, 0.58))
│ ├── FS:colored_obj mesh_obj^ ./.datasets/ACDC_5/patient069/colored.obj
│ ├── FS:sdf_npy sdt^ ./.datasets/ACDC_5/patient069/sdf.npy
│ ├── TR:transform shift_and_scale
│ ├── MS:mesh
│ ├── MS:mesh_colors
│ ├── MS:sampling
│ ├── MS:sdt
│ ├── MS:train_task
│ ├── plot(default, filepath=None)
│ ├── print(default|source|full)
│ ├── sampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])
│ ├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
│ ├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
│ ├── sampling_uniform(key, N) -> ns_xyz[N,3]
│ ├── save_mesh(filepath, {name, array})
│ ├── surface_ind2rgba(ind[..,1]) -> rgba[..,4]
│ ├── surface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]
│ ├── surface_rgba() -> xyz[N,4]
│ ├── surface_xyz() -> xyz[N,3]
│ ├── surface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]
│ ├── surface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]
│ ├── surface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]
│ ├── surface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]
│ ├── train_task_sdt2sdf(filename, **kwargs)
│ ├── transform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]
│ ├── transform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]
│ ├── transform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]
│ ├── transform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]
│ └── unload_from_memory()
├── O3D:patient089 ((-0.83, -0.72, -0.55), (0.83, 0.72, 0.55))
│ ├── FS:colored_obj mesh_obj^ ./.datasets/ACDC_5/patient089/colored.obj
│ ├── FS:sdf_npy sdt^ ./.datasets/ACDC_5/patient089/sdf.npy
│ ├── TR:transform shift_and_scale
│ ├── MS:mesh
│ ├── MS:mesh_colors
│ ├── MS:sampling
│ ├── MS:sdt
│ ├── MS:train_task
│ ├── plot(default, filepath=None)
│ ├── print(default|source|full)
│ ├── sampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])
│ ├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
│ ├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
│ ├── sampling_uniform(key, N) -> ns_xyz[N,3]
│ ├── save_mesh(filepath, {name, array})
│ ├── surface_ind2rgba(ind[..,1]) -> rgba[..,4]
│ ├── surface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]
│ ├── surface_rgba() -> xyz[N,4]
│ ├── surface_xyz() -> xyz[N,3]
│ ├── surface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]
│ ├── surface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]
│ ├── surface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]
│ ├── surface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]
│ ├── train_task_sdt2sdf(filename, **kwargs)
│ ├── transform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]
│ ├── transform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]
│ ├── transform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]
│ ├── transform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]
│ └── unload_from_memory()
├── MS:sampling
├── init()
├── plot(default, filepath=None)
├── preload(identity|shift_and_scale|to_cube, scale, keep_in_memory=True)
├── print(default|source|full)
├── sampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]
├── sampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]
├── sampling_uniform(key, N) -> ns_xyz[N,3]
├── save_space_to_file(filepath)
├── to_json()
└── unload_from_memory()
Examples for save_3D_slices with data arrays
[53]:
cube_xyz = space.patient009.sampling_grid((32, 32, 32))
cube_std = space.patient009.surface_xyz2sdt(cube_xyz)
cube_std = cube_std[:, :, :, 0]
cube_xyz.shape, cube_std.shape
[53]:
((32, 32, 32, 3), (32, 32, 32))
[54]:
nndt.save_3D_slices(cube_std, "./viz1.png", include_boundary=False)
[55]:
nndt.save_3D_slices(
cube_std, "./viz1.png", levels=(), level_colors=(), include_boundary=False
)
Examples for save_3D_slices with RGB/RGBA colors
[56]:
cube_rgba = space.patient009.surface_xyz2rgba(cube_xyz)
cube_rgba = cube_rgba
cube_rgba.shape
[56]:
(32, 32, 32, 4)
[57]:
nndt.save_3D_slices(cube_rgba, include_boundary=True)
Examples for save_sdt_as_obj
[58]:
nndt.save_sdt_as_obj(cube_std, "./model.obj")
[59]:
mesh = pv.read("./model.obj")
cpos = mesh.plot()
Load data and put values into .vtp output using .save_mesh()
[60]:
vertex = space.patient009.surface_xyz()
vertex_rgba = space.patient009.surface_rgba()
vertex_from_full_sampling = space.patient009.sampling_eachN_from_mesh(len(vertex), 1, 0)
len(vertex), len(vertex_rgba), len(vertex_from_full_sampling[1])
[60]:
(2502, 2502, 2502)
[61]:
jnp.allclose(vertex, vertex_from_full_sampling[1])
[61]:
DeviceArray(True, dtype=bool)
[62]:
space.patient009.save_mesh(
"./output.vtp",
{
"red": vertex_rgba[:, 0],
"green": vertex_rgba[:, 1],
"blue": vertex_rgba[:, 2],
"alpha": vertex_rgba[:, 2],
"x": vertex[:, 0],
"y": vertex[:, 1],
"z": vertex[:, 2],
"target_class": jnp.argmax(vertex_rgba[:, :3], axis=1),
},
)
[63]:
mesh = pv.read("./output.vtp")
cpos = mesh.plot(scalars="target_class", show_edges=True)