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Commit 6ec7ff81 authored by Martin Genet's avatar Martin Genet
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Setting up Docker image and Jupyter book build and deploy on GitHub

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Stage: deploy
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.binder/Dockerfile
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### ###
################################################################################
FROM registry.gitlab.inria.fr/mgenet/dolfin_warp-tutorials:latest
# FROM registry.gitlab.inria.fr/mgenet/dolfin_warp-tutorials:latest
FROM ghcr.io/mgenet/dolfin_warp-tutorials:latest
# Copy repo into the image, cf. https://mybinder.readthedocs.io/en/latest/tutorials/dockerfile.html. MG20230531: OMG this copies from the "build context", cf. https://stackoverflow.com/questions/73156067/where-does-the-copy-command-in-docker-copy-from; here it seems to be the repo itself.
ARG NB_USER=jovyan
......@@ -14,3 +15,6 @@ ARG NB_UID=1000
USER ${NB_USER}
ENV HOME /home/${NB_USER}
COPY --chown=${NB_UID} . ${HOME}
# MG20250418: Somehow repo2docker does not seem to take into account the environment variable defined in environment.yml…
ENV CPATH /srv/conda/envs/notebook/include/vtk-9.2
.github/workflows/build_and_deploy_jupyter_book.yml 0 → 100644
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on:
- push
jobs:
build_and_deploy_jupyter_book:
runs-on: ubuntu-latest
permissions:
pages: write
id-token: write
steps:
- name: Checkout repository
uses: actions/checkout@main
- name: Build Jupyter Book
run: |
pip install -U jupyter-book
jupyter-book clean .
jupyter-book build .
- name: Upload artifact
uses: actions/upload-pages-artifact@main
with:
path: "_build/html"
- name: Deploy Jupyter Book to GitHub Pages
id: deployment
uses: actions/deploy-pages@main
.github/workflows/build_and_push_docker_image.yml 0 → 100644
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on:
- push
jobs:
build_and_push_docker_image:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@main
- name: Prepare build
run: |
rm -rf ${{github.workspace}}/.binder
cp ${{github.workspace}}/.repo2docker/* ${{github.workspace}}/.
- name: Build and push docker image
uses: jupyterhub/repo2docker-action@master
with:
DOCKER_USERNAME: ${{github.actor}}
DOCKER_PASSWORD: ${{secrets.GITHUB_TOKEN}}
DOCKER_REGISTRY: ghcr.io
.github/workflows/mirror_to_gitlab.yml 0 → 100644
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on:
- push
- delete
jobs:
mirror_to_gitlab:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@main
with:
fetch-depth: 0
- name: Sync repo
uses: wangchucheng/git-repo-sync@master
with:
target-url: https://gitlab.inria.fr/mgenet/dolfin_warp-tutorials
target-username: mgenet
target-token: ${{secrets.GITLAB_TOKEN}}
.gitlab-ci.yml
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tags:
- ci.inria.fr
- large
# image: registry.gitlab.inria.fr/inria-ci/docker/ubuntu:20.04
image: ubuntu:20.04
script:
- apt update; DEBIAN_FRONTEND=noninteractive apt install -y ca-certificates curl git gnupg lsb-release mercurial python3 python3-pip tzdata
......
.repo2docker/environment.yml
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- defaults
dependencies:
# python
- mpi4py=3.1.3 # MG20220906: To avoid mpi warnings in FEniCS, cf. https://fenicsproject.discourse.group/t/runtimewarning-mpi4py-mpi-file-size-changed-may-indicate-binary-incompatibility-expected-32-from-c-header-got-40-from-pyobject-def-compile-class-cpp-data-mpi-comm-mpi-comm-world/6496 # MG20220906: Note that 3.0.3 leads to a `RuntimeError: Error when importing mpi4py`, but 3.1.3 seems to work.
# dolfin_warp
- expat=2.5
- fenics=2019.1.0
- gnuplot=5.4
- matplotlib=3.5
- meshio=5.3
- mpi4py=3.1.3
- numpy=1.21.6 # MG20221220: later numpy versions seem incompatible with itkwidgets 0.32
- python=3.8
- pandas=1.3
- python=3.10
- scipy=1.9
- sympy=1.9
- vtk=9.2
# jupyter
- ipywidgets=7.6 # MG20221226: cf. https://github.com/InsightSoftwareConsortium/itkwidgets/pull/590/files # MG20230601: Somehow this is still needed, despite the updated itkwidgets version…
- itkwidgets=0.32
- ipydatawidgets=4.3.2
- ipywidgets=7.6.5 # MG20221226: cf. https://github.com/InsightSoftwareConsortium/itkwidgets/pull/590/files # MG20230601: Somehow this is still needed, despite the updated itkwidgets version…
- itkwidgets=0.32.6
# MG20240123: Specified all these versions based on the 2023 build to get ipywidgets and itkwidgets to properly interact
- jupyter=1.0.0
- jupyter_client=7.4.8
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- jupyter_server=2.0.6
- jupyter_server_terminals=0.4.3
# MG20240123: Specified all these versions based on the 2023 build to get ipywidgets and itkwidgets to properly interact
- jupyterlab=3.5 # MG20230602: This seems to be needed as well, otherwise jupyter does not start on binder
- notebook=6.4 # MG20221226: cf. https://github.com/InsightSoftwareConsortium/itkwidgets/pull/590/files # MG20230601: Somehow this is still needed, despite the updated itkwidgets version…
# coding stuff
- git=2.39
# computing stuff
- expat=2.5
- fenics=2019.1.0
- vtk=9.0 # MG20201223: Need to correspond to the environment variable CPATH # MG20220628: Apparently there is some kind of conflict between vtk9.1 & libstdc++.so.6 in Ubuntu 20.04… # MG20221220: Apparently there is some kind of conflict between vtk9.1 & itkwidgets 0.32…
# plotting stuff
- matplotlib=3.5
- gnuplot=5.4
- jupyterlab=3.5.2 # MG20230602: This seems to be needed as well, otherwise jupyter does not start on binder
- notebook=6.4.6 # MG20221226: cf. https://github.com/InsightSoftwareConsortium/itkwidgets/pull/590/files # MG20230601: Somehow this is still needed, despite the updated itkwidgets version…
- traitlets=5.6.0 # MG20221226: cf. https://github.com/InsightSoftwareConsortium/itkwidgets/pull/590/files # MG20230601: Somehow this is still needed, despite the updated itkwidgets version…
- pip
- pip:
# python
- numpy==1.21.6 # MG20240605: meshio seems to be installing a newer numpy…
- zstd
# computing stuff
- gmsh==4.9.0
- meshio==5.3.4
# tracking stuff
- dolfin_warp
variables:
CPATH: /srv/conda/envs/notebook/include/vtk-9.0
CPATH: /srv/conda/envs/notebook/include/vtk-9.2 # MG20250418: Somehow repo2docker does not seem to take into account the environment variable defined in environment.yml…
.repo2docker/postBuild
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echo "alias ll=\"ls -l\"" >> ~/.bash_aliases
echo "alias la=\"ls -la\"" >> ~/.bash_aliases
export CPATH=$CPATH:"/srv/conda/envs/notebook/include/vtk-9.2" # MG20250418: Somehow repo2docker does not seem to take into account the environment variable defined in environment.yml, but this does not work…
rm ~/apt.txt
rm ~/environment.yml
rm ~/postBuild
README.md
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# Welcome to the dolfin_warp tutorials!
Main library can be found at https://gitlab.inria.fr/mgenet/dolfin_warp
Main library can be found at https://github.com/mgenet/dolfin_warp
Interactive tutorials can be found at https://mgenet.gitlabpages.inria.fr/dolfin_warp-tutorials/index.html.
Static and interactive tutorials can be found at https://mgenet.github.io/dolfin_warp-tutorials.
<!--, or directly on [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mgenet/dolfin_warp-tutorials/master?urlpath=lab/tree/./tutos). MG20250418: itkwidget does not work in JupyterLab… -->
index.md
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# Welcome to the dolfin_warp tutorials!
Main library can be found at [https://gitlab.inria.fr/mgenet/dolfin_warp](https://gitlab.inria.fr/mgenet/dolfin_warp).
Main library can be found at [https://github.com/mgenet/dolfin_warp](https://github.com/mgenet/dolfin_warp).
Tutorials can be browsed statically but also interactively—to start a session and run the code just click on the rocket icon at the top of a tutorial page and then click on Binder.
Tutorials can be browsed statically but also interactively—to start a session and run the code just click on the rocket icon at the top of a tutorial page and then click on "Binder".
<!-- You can also launch a JupyterLab session with all tutorials: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mgenet/dolfin_warp-tutorials/master?urlpath=lab/tree/./tutos). MG20250418: itkwidget does not work in JupyterLab… -->
\ No newline at end of file
tutos/tuto_warp.ipynb
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%% Cell type:markdown id: tags:
# Generate & track synthetic images
%% Cell type:markdown id: tags:
We start with some imports…
%% Cell type:code id: tags:
``` python
import dolfin # https://fenicsproject.org
import dolfin_warp as dwarp # https://gitlab.inria.fr/mgenet/dolfin_warp
import dolfin_warp as dwarp # https://github.com/mgenet/dolfin_warp
import lib_viewer
```
%% Cell type:markdown id: tags:
## Synthetic data generation
%% Cell type:markdown id: tags:
### Image generation
%% Cell type:markdown id: tags:
Let us introduce a small tool to generate simple synthetic images.
Here we define all the images parameters.
%% Cell type:code id: tags:
``` python
# Image parameters
n_dim = 2 # dimension (2 or 3)
L = [1.]*n_dim # spatial extent
n_voxels = [100]*n_dim # spatial discretization
T = 1. # temporal extent
n_frames = 21 # temporal discretization
# Structure (i.e., object) parameters
# (For now we will consider a simple squared object.
# (For the translation case it is convenient to start with an uncentered square,
# (whereas for the other cases it is convenient if the square is centered.
structure_type = "box"; structure_Xmin = [0.1, 0.3]; structure_Xmax = [0.5, 0.7]
# structure_type = "box"; structure_Xmin = [0.3, 0.3]; structure_Xmax = [0.7, 0.7]
# Texture parameters
# ("no" means the image will be the characteristic function of the object in its current position,
# (which is the simplest model for standard (i.e., untagged) MRI images,
# (whereas "tagging" means the signal over the object will be sqrt(abs(sin(pi*x/s))*abs(sin(pi*y/s))),
# (which is the simplest model for tagged MRI images.
texture_type = "tagging"; texture_s = 0.1
# texture_type = "no"
# Noise parameters
noise_level = 0
# noise_level = 0.1
# noise_level = 0.2
# noise_level = 0.3
# Transformation parameters
# (For now we consider basis transformations of the object.
deformation_type = "translation"; deformation_Dx = 0.4; deformation_Dy = 0.
# deformation_type = "rotation"; deformation_Cx = 0.5; deformation_Cy = 0.5; deformation_Rz = 45.
# deformation_type = "compression"; deformation_Cx = 0.5; deformation_Cy = 0.5; deformation_Exx = -0.20
# deformation_type = "shear"; deformation_Cx = 0.5; deformation_Cy = 0.5; deformation_Fxy = +0.20
# Evolution parameters
# ("linear" means the object will transform linearly in time,
# (reaching its presribed deformation on the last frame.
evolution_type = "linear"
# Case
case = deformation_type
case += "-"+texture_type
if (noise_level > 0):
case += "-"+str(noise_level)
```
%% Cell type:markdown id: tags:
And then actually generate the images.
%% Cell type:code id: tags:
``` python
# Image properties
images = {
"n_dim":n_dim,
"L":L,
"n_voxels":n_voxels,
"T":T,
"n_frames":n_frames,
"data_type":"float",
"folder":"E1",
"basename":"images-"+case}
# Structure (i.e., object) properties
if (structure_type == "box"):
structure = {"type":"box", "Xmin":structure_Xmin, "Xmax":structure_Xmax}
# Texture properties
if (texture_type == "no"):
texture = {"type":"no"}
elif (texture_type == "tagging"):
texture = {"type":"tagging", "s":texture_s}
# Noise properties
if (noise_level == 0):
noise = {"type":"no"}
else:
noise = {"type":"normal", "stdev":noise_level}
# Deformation properties
if (deformation_type == "translation"):
deformation = {"type":"translation", "Dx":deformation_Dx, "Dy":deformation_Dy}
elif (deformation_type == "rotation"):
deformation = {"type":"rotation", "Cx":deformation_Cx, "Cy":deformation_Cy, "Rz":deformation_Rz}
elif (deformation_type == "compression"):
deformation = {"type":"homogeneous", "X0":deformation_Cx, "Y0":deformation_Cy, "Exx":deformation_Exx}
elif (deformation_type == "shear"):
deformation = {"type":"homogeneous", "X0":deformation_Cx, "Y0":deformation_Cy, "Fxy":deformation_Fxy}
# Evolution properties
if (evolution_type == "linear"):
evolution = {"type":"linear"}
# Generate images
dwarp.generate_images(
images=images,
structure=structure,
texture=texture,
noise=noise,
deformation=deformation,
evolution=evolution,
verbose=0)
```
%% Cell type:markdown id: tags:
We visualize them using [itkwidgets](https://github.com/InsightSoftwareConsortium/itkwidgets).
%% Cell type:code id: tags:
``` python
viewer = lib_viewer.Viewer(
images="E1/images-"+case+"_*.vti")
viewer.view()
```
%% Cell type:markdown id: tags:
### Ground truth generation
%% Cell type:markdown id: tags:
Later on we will track the object throughout the images using finite elements.
To assess the quality of the tracking, it is convenient to have a ground truth, in the form of a finite element solution over a mesh.
We now create the mesh, and generate this ground truth.
%% Cell type:markdown id: tags:
#### Mesh
%% Cell type:code id: tags:
``` python
# Mesh parameter
n_elems = 1
```
%% Cell type:code id: tags:
``` python
# Generate mesh
mesh = dolfin.RectangleMesh(
dolfin.Point(structure_Xmin),
dolfin.Point(structure_Xmax),
n_elems,
n_elems,
"crossed")
```
%% Cell type:markdown id: tags:
#### Ground truth
%% Cell type:code id: tags:
``` python
# Generate ground truth
dwarp.compute_warped_mesh(
working_folder="E1",
working_basename="ground_truth"+"-"+deformation_type,
images=images,
structure=structure,
deformation=deformation,
evolution=evolution,
mesh=mesh,
verbose=0)
```
%% Cell type:markdown id: tags:
We can now visualize the images and meshes.
(To better see the mesh on top of the images, select the "Wireframe" mode for the mesh).
%% Cell type:code id: tags:
``` python
viewer = lib_viewer.Viewer(
images="E1/images-"+case+"_*.vti",
meshes="E1/ground_truth-"+deformation_type+"_*.vtu")
viewer.view()
```
%% Cell type:markdown id: tags:
## Motion tracking
%% Cell type:markdown id: tags:
### Motion tracking
%% Cell type:markdown id: tags:
We now introduce a motion tracking tool.
Here we define all the tracking parameters.
%% Cell type:code id: tags:
``` python
# Regularization strength
# (It must be ≥ 0 and < 1.
regul_level = 0.
# Regularization type
# ("continuous-elastic" means the image term is penalized with the strain energy of the displacement field.
# ("continuous-equilibrated" means the image term is penalized with the equilibrium gap of the displacement field.
# ("discrete-equilibrated" means the image term is penalized with the discrete linear equilibrium gap of the displacement field.
# regul_type = "continuous-linear-elastic"
# regul_type = "continuous-linear-equilibrated"
regul_type = "continuous-elastic"
# regul_type = "continuous-equilibrated"
# regul_type = "discrete-simple-elastic"
# regul_type = "discrete-simple-equilibrated"
# regul_type = "discrete-linear-equilibrated"
# regul_type = "discrete-linear-equilibrated-tractions-normal"
# regul_type = "discrete-linear-equilibrated-tractions-tangential"
# regul_type = "discrete-linear-equilibrated-tractions-normal-tangential"
# regul_type = "discrete-equilibrated"
# regul_type = "discrete-equilibrated-tractions-normal"
# regul_type = "discrete-equilibrated-tractions-tangential"
# regul_type = "discrete-equilibrated-tractions-normal-tangential"
# Regularization model
# ("hooke" means the linear isotropic Hooke law (infinitesimal strain/linearized elasticity).
# ("ciarletgeymonatneohookean" means the hyperelastic potential made of Ciarlet-Geymonat & neo-Hookean terms (finite strain/nonlinear elasticity).
if any([_ in regul_type for _ in ["linear", "simple"]]):
regul_model = "hooke"
else:
regul_model = "ciarletgeymonatneohookean"
```
%% Cell type:markdown id: tags:
And then actually run the tracking.
%% Cell type:code id: tags:
``` python
# Perform tracking
dwarp.warp(
working_folder="E1",
working_basename="tracking-"+case,
#
images_folder="E1",
images_basename="images-"+case,
#
mesh=mesh,
#
regul_type=regul_type,
regul_model=regul_model,
regul_level=regul_level,
#
relax_type="constant",
tol_dU=1e-2,
#
write_VTU_files=True,
write_VTU_files_with_preserved_connectivity=True)
```
%% Cell type:markdown id: tags:
We can now visualize the images and tracked meshes.
(To better see the mesh on top of the images, select the "Wireframe" mode for the mesh).
%% Cell type:code id: tags:
``` python
viewer = lib_viewer.Viewer(
images="E1/images-"+case+"_*.vti",
meshes="E1/tracking-"+case+"_*.vtu")
viewer.view()
```
%% Cell type:markdown id: tags:
### Tracking error
%% Cell type:markdown id: tags:
We can compute a normalized tracking error by comparing the tracked displacement $\underline{U}$ and the ground truth displacement $\underline{\bar U}$:
$$
e := \dfrac{\sqrt{\frac{1}{T}\int_{t=0}^{T} \frac{1}{\left|\Omega_0\right|}\int_{\Omega_0} \left\|\underline{U} - \underline{\bar U}\right\|^2 d\Omega_0~dT}}{\sqrt{\frac{1}{T}\int_{t=0}^{T} \frac{1}{\left|\Omega_0\right|}\int_{\Omega_0} \left\|\underline{\bar U}\right\|^2 d\Omega_0~dT}}
$$
(Watch out, the meshes should match as the error is actually computed at the discrete level!)
%% Cell type:code id: tags:
``` python
dwarp.compute_displacement_error(
working_folder="E1",
working_basename="tracking-"+case,
ref_folder="E1",
ref_basename="ground_truth-"+deformation_type,
verbose=0)
```
%% Cell type:markdown id: tags:
## References
[[Claire, Hild & Roux (2004). A finite element formulation to identify damage fields: The equilibrium gap method. International Journal for Numerical Methods in Engineering, 61(2), 189–208.]](https://doi.org/10.1002/nme.1057)
[[Veress, Gullberg & Weiss (2005). Measurement of Strain in the Left Ventricle during Diastole with cine-MRI and Deformable Image Registration. Journal of Biomechanical Engineering, 127(7), 1195.]](https://doi.org/10.1115/1.2073677)
[[Réthoré, Roux & Hild (2009). An extended and integrated digital image correlation technique applied to the analysis of fractured samples: The equilibrium gap method as a mechanical filter. European Journal of Computational Mechanics.]](https://doi.org/10.3166/ejcm.18.285-306)
[[Leclerc, Périé, Roux & Hild (2010). Voxel-Scale Digital Volume Correlation. Experimental Mechanics, 51(4), 479–490.]](https://doi.org/10.1007/s11340-010-9407-6)
[[Genet, Stoeck, von Deuster, Lee & Kozerke (2018). Equilibrated Warping: Finite Element Image Registration with Finite Strain Equilibrium Gap Regularization. Medical Image Analysis, 50, 1–22.]](https://doi.org/10.1016/j.media.2018.07.007)
[[Lee & Genet (2019). Validation of Equilibrated Warping—Image Registration with Mechanical Regularization—On 3D Ultrasound Images. In Coudière, Ozenne, Vigmond & Zemzemi (Eds.), Functional Imaging and Modeling of the Heart (FIMH) (Vol. 11504, pp. 334–341). Springer International Publishing.]](https://doi.org/10.1007/978-3-030-21949-9_36)
[[Berberoğlu, Stoeck, Moireau, Kozerke & Genet (2019). Validation of Finite Element Image Registration‐based Cardiac Strain Estimation from Magnetic Resonance Images. PAMM, 19(1).]](https://doi.org/10.1002/pamm.201900418)
[[Genet (2023). Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking. Comptes Rendus Mécanique, 351, 429-458.]](https://doi.org/10.5802/crmeca.228)
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