From 4336f1a0de46618455c6028ff649db60c1928294 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 29 Mar 2023 08:11:14 +0000
Subject: [PATCH 01/37] Update README.md
---
README.md | 19 ++++++++++++++++---
1 file changed, 16 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index b2746c4..da618f7 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,18 @@
-# NAISR
-NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+
+This is the official implenmentation for,
+**NAISR: A 3D Neural Additive Model for Interpretable Shape Representation.**
+Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer. https://arxiv.org/abs/2303.09234
-Codes on the way...
+## Cite this work
+```
+@article{jiao2023naisr,
+ title={NAISR: A 3D Neural Additive Model for Interpretable Shape Representation},
+ author={Jiao, Yining and Zdanski, Carlton and Kimbell, Julia and Prince, Andrew and Worden, Cameron and Kirse, Samuel and Rutter, Christopher and Shields, Benjamin and Dunn, William and Mahmud, Jisan and others},
+ journal={arXiv preprint arXiv:2303.09234},
+ year={2023}
+}
+
+## Model
+Instructions on the way...
From 636cec0368a8816b2ec2f31bb2289b7be25846e4 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 29 Mar 2023 08:11:53 +0000
Subject: [PATCH 02/37] Update README.md
---
README.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/README.md b/README.md
index da618f7..991ea89 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
This is the official implenmentation for,
+
+
**NAISR: A 3D Neural Additive Model for Interpretable Shape Representation.**
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer. https://arxiv.org/abs/2303.09234
From 7abe38d9b477c535decbea2c4028a53ddb4c666a Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 29 Mar 2023 08:40:46 +0000
Subject: [PATCH 03/37] Update README.md
---
README.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/README.md b/README.md
index 991ea89..56f708d 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,8 @@ This is the official implenmentation for,
**NAISR: A 3D Neural Additive Model for Interpretable Shape Representation.**
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer. https://arxiv.org/abs/2303.09234
+Codes to be revised..
+
## Cite this work
```
@article{jiao2023naisr,
From dfb6b3ac86d80fb12bb65e7a424e0e85f04544e3 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 29 Mar 2023 08:42:23 +0000
Subject: [PATCH 04/37] Update README.md
---
README.md | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 56f708d..b2a7a47 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,10 @@ Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samu
Codes to be revised..
+## Usage
+
+Instructions on the way...
+
## Cite this work
```
@article{jiao2023naisr,
@@ -18,5 +22,4 @@ Codes to be revised..
year={2023}
}
-## Model
-Instructions on the way...
+
From a25caaaf3b0190a31233b6a221a449c50ee8f026 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 29 Mar 2023 08:42:43 +0000
Subject: [PATCH 05/37] Update README.md
---
README.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/README.md b/README.md
index b2a7a47..98b994b 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@ Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samu
Codes to be revised..
+## Installation
+
## Usage
Instructions on the way...
From 739a6196f37a121cbc542502a1728109dd7f4485 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Fri, 22 Mar 2024 13:07:42 -0400
Subject: [PATCH 06/37] Update README.md
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 98b994b..86edacf 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ This is the official implenmentation for,
**NAISR: A 3D Neural Additive Model for Interpretable Shape Representation.**
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer. https://arxiv.org/abs/2303.09234
-Codes to be revised..
+An update is coming soon
## Installation
From 548e9217f072623e5437a172ed8f08fcfd65ff1c Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Tue, 23 Apr 2024 12:06:21 -0400
Subject: [PATCH 07/37] Create NAISRicon.svg
---
NAISRicon.svg | 1 +
1 file changed, 1 insertion(+)
create mode 100644 NAISRicon.svg
diff --git a/NAISRicon.svg b/NAISRicon.svg
new file mode 100644
index 0000000..959a07d
--- /dev/null
+++ b/NAISRicon.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
From b91f2067143ce8cc8ec4dfde81674951f41e4def Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 12:12:13 -0400
Subject: [PATCH 08/37] Update README.md
---
README.md | 8 ++++++++
1 file changed, 8 insertions(+)
diff --git a/README.md b/README.md
index 86edacf..7f6cea0 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,11 @@
+# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+Pytorch implementation for our `ICLR 2024 Spotlight` paper
+[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024.
+Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
+UNC-Chapel Hill
+#### [Paper](https://arxiv.org/abs/2303.09234) | [Project Page](https://uncbiag.github.io/NAISR/) | [Demos](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg?usp=sharing)
+
+
# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
From e9f4406ae6c6c68cc39993bc53f76220a6e766e3 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 12:13:08 -0400
Subject: [PATCH 09/37] Update README.md
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 7f6cea0..a3002d2 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Pytorch implementation for our `ICLR 2024 Spotlight` paper
[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024.
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
From 89af98e8a3cf9191c4c1c7a0296de3e4eea56fc5 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 12:36:26 -0400
Subject: [PATCH 10/37] Update README.md
---
README.md | 69 ++++++++++++++++++++++++++++++++++++++++++++-----------
1 file changed, 56 insertions(+), 13 deletions(-)
diff --git a/README.md b/README.md
index a3002d2..b8d0961 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,78 @@
# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
+
Pytorch implementation for our `ICLR 2024 Spotlight` paper
[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024.
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
UNC-Chapel Hill
+
#### [Paper](https://arxiv.org/abs/2303.09234) | [Project Page](https://uncbiag.github.io/NAISR/) | [Demos](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg?usp=sharing)
-# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
-This is the official implenmentation for,
-
+Please cite as:
+```
+@inproceedings{
+jiao2024naisr,
+title={\texttt{NAISR}: A 3D Neural Additive Model for Interpretable Shape Representation},
+author={Yining Jiao and Carlton Jude ZDANSKI and Julia S Kimbell and Andrew Prince and Cameron P Worden and Samuel Kirse and Christopher Rutter and Benjamin Shields and William Alexander Dunn and Jisan Mahmud and Marc Niethammer},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=wg8NPfeMF9}
+}
+```
-**NAISR: A 3D Neural Additive Model for Interpretable Shape Representation.**
-Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer. https://arxiv.org/abs/2303.09234
An update is coming soon
+
## Installation
+The code is tested with ``python=3.9``, ``torch=2.1.0``, ``torchvision=0.15.2``.
+```
+git clone https://github.com/uncbiag/NAISR
+cd NAISR
+```
+Now, create a new conda environment and install required packages accordingly.
+```
+conda create -n naisr python=3.9
+conda activate naisr
+pip install -r requirements.txt
+
+## Getting Started
+First, download three model weights: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M). These weights will be automatically saved to the ``weights`` folder.
+```
+python download.py
+```
+```
+Run interactive GUI with the downloaded weights. The ``assets`` contains images for demo.
+```
+./run_demo.sh
+```
+
+We train and test our method on three datasets: DAVIS, COCO+LVIS, and HQSeg-44K.
+
+| Dataset | Description | Download Link |
+|-----------|----------------------------------------------|:------------------------------------:|
+|DAVIS | 345 images with one object each (test) | [DAVIS.zip (43 MB)][DAVIS] |
+|HQSeg-44K | 44320 images (train); 1537 images (val) | [official site][HQSeg] |
+|COCO+LVIS* | 99k images with 1.5M instances (train) | [original LVIS images][LVIS] + [combined annotations][COCOLVIS_annotation] |
+
-## Usage
Instructions on the way...
+
+
+
+
## Cite this work
```
-@article{jiao2023naisr,
- title={NAISR: A 3D Neural Additive Model for Interpretable Shape Representation},
- author={Jiao, Yining and Zdanski, Carlton and Kimbell, Julia and Prince, Andrew and Worden, Cameron and Kirse, Samuel and Rutter, Christopher and Shields, Benjamin and Dunn, William and Mahmud, Jisan and others},
- journal={arXiv preprint arXiv:2303.09234},
- year={2023}
+@inproceedings{
+jiao2024naisr,
+title={\texttt{NAISR}: A 3D Neural Additive Model for Interpretable Shape Representation},
+author={Yining Jiao and Carlton Jude ZDANSKI and Julia S Kimbell and Andrew Prince and Cameron P Worden and Samuel Kirse and Christopher Rutter and Benjamin Shields and William Alexander Dunn and Jisan Mahmud and Marc Niethammer},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=wg8NPfeMF9}
}
-
-
+```
From 6862fb56649bba9c376b1d1b8db5f50649730f91 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 12:47:14 -0400
Subject: [PATCH 11/37] Update README.md
---
README.md | 15 +++++++++------
1 file changed, 9 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index b8d0961..bb955de 100644
--- a/README.md
+++ b/README.md
@@ -49,16 +49,19 @@ Run interactive GUI with the downloaded weights. The ``assets`` contains images
./run_demo.sh
```
-We train and test our method on three datasets: DAVIS, COCO+LVIS, and HQSeg-44K.
+We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pedeatric Airway.
-| Dataset | Description | Download Link |
-|-----------|----------------------------------------------|:------------------------------------:|
-|DAVIS | 345 images with one object each (test) | [DAVIS.zip (43 MB)][DAVIS] |
-|HQSeg-44K | 44320 images (train); 1537 images (val) | [official site][HQSeg] |
-|COCO+LVIS* | 99k images with 1.5M instances (train) | [original LVIS images][LVIS] + [combined annotations][COCOLVIS_annotation] |
+| Dataset | Description | Dataset Link | Model Download Link |
+|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
+|Starman | simulated 2D Starman shapes | [simulation code)][Starman] |
+|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNI] |
+|Pedeatric Airway | 357 pediatric airway shapes | NA |
+[ADNI]: https://ida.loni.usc.edu/login.jsp?project=ADNI
+[Starman]:
+
Instructions on the way...
From c06fc7b396583477f8d02891e71b9a8810ec7730 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:16:31 -0400
Subject: [PATCH 12/37] Update README.md
---
README.md | 16 ++++++++++------
1 file changed, 10 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index bb955de..b391fe4 100644
--- a/README.md
+++ b/README.md
@@ -51,16 +51,20 @@ Run interactive GUI with the downloaded weights. The ``assets`` contains images
We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pedeatric Airway.
-| Dataset | Description | Dataset Link | Model Download Link |
+| Dataset | Description | Dataset Link | Model Link |
|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
-|Starman | simulated 2D Starman shapes | [simulation code)][Starman] |
-|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNI] |
-|Pedeatric Airway | 357 pediatric airway shapes | NA |
+|Starman | simulated 2D Starman shapes | [simulation code)][StarmanData] | [weights][StarmanModel] |
+|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNIData] | [weights][ADNIModel] |
+|Pedeatric Airway | 357 pediatric airway shapes | NA | [weights][AirwayModel] |
-[ADNI]: https://ida.loni.usc.edu/login.jsp?project=ADNI
-[Starman]:
+[ADNIData]: https://ida.loni.usc.edu/login.jsp?project=ADNI
+[StarmanData]:
+
+[ADNIModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_adni.pth
+[StarmanModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_starman.pth
+[AirwayModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_pediatric_airway.pth
Instructions on the way...
From fb9761ac4b1a80ebff0d79c338548a0ce6bb0b77 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:18:09 -0400
Subject: [PATCH 13/37] Update README.md
---
README.md | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index b391fe4..97df119 100644
--- a/README.md
+++ b/README.md
@@ -37,13 +37,14 @@ Now, create a new conda environment and install required packages accordingly.
conda create -n naisr python=3.9
conda activate naisr
pip install -r requirements.txt
+```
## Getting Started
First, download three model weights: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M). These weights will be automatically saved to the ``weights`` folder.
```
python download.py
```
-```
+
Run interactive GUI with the downloaded weights. The ``assets`` contains images for demo.
```
./run_demo.sh
From 5dcb26a56da6fa43813afa7a5001d5eeee3cf6b8 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:43:32 -0400
Subject: [PATCH 14/37] Update README.md
---
README.md | 14 +++++++++++---
1 file changed, 11 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 97df119..4f443cf 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
-Pytorch implementation for our `ICLR 2024 Spotlight` paper
-[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024.
+Pytorch implementation for our `NAISR` paper
+[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024 Spotlight.
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
UNC-Chapel Hill
@@ -39,7 +39,13 @@ conda activate naisr
pip install -r requirements.txt
```
+>[!NOTE]
+>For the three datasets used in `NAISR` paper, we provided this [Colab demo](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg) to investigate the shapes learned with `NAISR`.
+We also provide the instructions on this page to apply `NAISR` to your customized shape analysis questions.
+
## Getting Started
+
+First
First, download three model weights: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M). These weights will be automatically saved to the ``weights`` folder.
```
python download.py
@@ -50,7 +56,9 @@ Run interactive GUI with the downloaded weights. The ``assets`` contains images
./run_demo.sh
```
-We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pedeatric Airway.
+
+
+We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pediatric Airways.
| Dataset | Description | Dataset Link | Model Link |
|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
From da80e3b2770fa4fae3934453f0ee5acd249dc36a Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:55:28 -0400
Subject: [PATCH 15/37] Update README.md
---
README.md | 51 ++++++++++++++++++++++++++++++++++-----------------
1 file changed, 34 insertions(+), 17 deletions(-)
diff --git a/README.md b/README.md
index 4f443cf..8429588 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,10 @@ url={https://openreview.net/forum?id=wg8NPfeMF9}
An update is coming soon
+>[!NOTE]
+>For the three datasets used in `NAISR` paper, we provided this [Colab demo](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg) to investigate the shapes learned with `NAISR`.
+We also provide the instructions on this page to apply `NAISR` to your customized shape analysis questions.
+
## Installation
The code is tested with ``python=3.9``, ``torch=2.1.0``, ``torchvision=0.15.2``.
@@ -39,14 +43,31 @@ conda activate naisr
pip install -r requirements.txt
```
->[!NOTE]
->For the three datasets used in `NAISR` paper, we provided this [Colab demo](https://colab.research.google.com/drive/1OudGynEydIXpAgfA9lvi5d0L86kK-uwg) to investigate the shapes learned with `NAISR`.
-We also provide the instructions on this page to apply `NAISR` to your customized shape analysis questions.
-## Getting Started
+## Data and Model Weights
+We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pediatric Airways.
+
+| Dataset | Description | Dataset Link | Model Link |
+|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
+|Starman | simulated 2D Starman shapes | [simulation code)][StarmanData] | [weights][StarmanModel] |
+|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNIData] | [weights][ADNIModel] |
+|Pedeatric Airway | 357 pediatric airway shapes | NA | [weights][AirwayModel] |
+
+
+
+[ADNIData]: https://ida.loni.usc.edu/login.jsp?project=ADNI
+[StarmanData]:
+
+[ADNIModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_adni.pth
+[StarmanModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_starman.pth
+[AirwayModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_pediatric_airway.pth
+
-First
-First, download three model weights: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M). These weights will be automatically saved to the ``weights`` folder.
+## Visualization of Existing Shape Space
+To get the shape space like the following figures for the three datasets used in the paper, one just need to apply the model weights with the following command,
+
+First, download the `NAISR model weights`: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M).
+These weights will be automatically saved to the ``weights`` folder.
```
python download.py
```
@@ -58,22 +79,18 @@ Run interactive GUI with the downloaded weights. The ``assets`` contains images
-We train and test our method on three datasets: Starman, ADNI Hippocampus, and Pediatric Airways.
-| Dataset | Description | Dataset Link | Model Link |
-|-----------|----------------------------------------------|:------------------------------------:|:------------------------------------:|
-|Starman | simulated 2D Starman shapes | [simulation code)][StarmanData] | [weights][StarmanModel] |
-|ADNI Hippocampus | 1632 hippocampus from ADNI | [official site][ADNIData] | [weights][ADNIModel] |
-|Pedeatric Airway | 357 pediatric airway shapes | NA | [weights][AirwayModel] |
+## Customize
+### Data Preprocessing
+### Training
+
+### Testing
+
+### Application - Visualization of Your Shape Space
-[ADNIData]: https://ida.loni.usc.edu/login.jsp?project=ADNI
-[StarmanData]:
-[ADNIModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_adni.pth
-[StarmanModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_starman.pth
-[AirwayModel]: https://github.com/uncbiag/NAISR/releases/download/naisr_weights_v0/naisr_weights_pediatric_airway.pth
Instructions on the way...
From efb32927958432e2eb8f0b3d8a7cf0b4916deefb Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 14:59:06 -0400
Subject: [PATCH 16/37] Update README.md
---
README.md | 67 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 file changed, 66 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 8429588..12ba801 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@ python download.py
Run interactive GUI with the downloaded weights. The ``assets`` contains images for demo.
```
-./run_demo.sh
+python evolution_shapematrix.py -examples/hippocampus/naigsr_0920_base.json
```
@@ -83,6 +83,71 @@ Run interactive GUI with the downloaded weights. The ``assets`` contains images
## Customize
### Data Preprocessing
+#### Alignment with Rigid Transformation
+The shapes to explore need to be registered with a rigid transformation (translation + rotation).
+If paired point clouds are available, we recommend to use ; otherwise, we recommend to use ICP to register the point clouds.
+In our case, we use airway landmarks to learn the rigid transformation; and ICP algorithms to register the point clouds of the hippocampi.
+
+#### SDF Extraction
+
+###
+```json
+ {
+ "Description" : [ "This experiment learns a shape representation for starman dataset." ],
+ "Device":0,
+ "DataSource": {"train": "/home/jyn/NAISR/examples/starman/2dshape_train_with_temp.csv",
+ "test": "/home/jyn/NAISR/examples/starman/2dshape_test_with_temp.csv"},
+ "Split": null,
+ "Network": "DeepNAIGSR",
+ "NumEpochs": 300,
+ "LoggingRoot": "/playpen-raid/jyn/NAISR/log",
+ "ExperimentName": "DeepNAIGSR_STARMAN3D_0222_256_base",
+
+ "EpochsTilCkpt": 10,
+ "StepsTilSummary": 1000,
+ "UseLBFGS": false,
+ "DoublePrecision": false,
+ "CheckpointPath": "",
+ "CodeLength": 256,
+
+ "AdditionalSnapshots" : [ 50, 100, 200, 300, 400, 500 ],
+ "LearningRateSchedule" : [
+ {
+ "Type": "Step",
+ "Initial": 0.00005,
+ "Interval": 1000,
+ "Factor": 0.5
+ },
+ {
+ "Type": "Step",
+ "Initial": 0.001,
+ "Interval": 1000,
+ "Factor": 0.5
+ }],
+ "SamplesPerScene" : 750,
+ "BatchSize": 64,
+ "DataLoaderThreads": 4,
+ "ClampingDistance": 1,
+
+ "Articulation": true,
+ "NumAtcParts": 1,
+ "TrainWithParts": false,
+ "Class": "starman",
+ "Attributes": ["cov_1", "cov_2"],
+ "TemplateAttributes": {"cov_1": 0, "cov_2": 0},
+ "Backbone": "siren",
+ "PosEnc": false,
+ "InFeatures": 2,
+ "HiddenFeatures": 256,
+ "HidenLayers": 6,
+ "OutFeatures": 1,
+ "Loss": {
+ "whether_sdf": true,
+ "whether_normal_constraint": true,
+ "whether_inter_constraint": true,
+ "whether_eikonal": true,
+ "whether_code_regularization": true}
+```
### Training
### Testing
From ae28d489320b9d8d80ba5605e8601c2e39d1b6ba Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Tue, 23 Apr 2024 15:00:17 -0400
Subject: [PATCH 17/37] Update README.md
---
README.md | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 12ba801..8ea1aed 100644
--- a/README.md
+++ b/README.md
@@ -66,8 +66,7 @@ We train and test our method on three datasets: Starman, ADNI Hippocampus, and P
## Visualization of Existing Shape Space
To get the shape space like the following figures for the three datasets used in the paper, one just need to apply the model weights with the following command,
-First, download the `NAISR model weights`: vitb_sax1 (408M), vitb_sax2 (435M), and vitb_sax2_ft (435M).
-These weights will be automatically saved to the ``weights`` folder.
+First, download the `NAISR model weights`. These weights will be automatically saved to the ``checkpoints`` folder.
```
python download.py
```
From b3e68d882970923882156297944d0c4f28c8200d Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 10:42:08 -0400
Subject: [PATCH 18/37] Create workspace.xml
---
.idea/workspace.xml | 24 ++++++++++++++++++++++++
1 file changed, 24 insertions(+)
create mode 100644 .idea/workspace.xml
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 0000000..02cb1fa
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,24 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
From e0b6c73f8a529a373aa5ca0b320cbc0300d5333a Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 10:53:26 -0400
Subject: [PATCH 19/37] a
---
visualizer.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/visualizer.py b/visualizer.py
index b403183..521ef80 100755
--- a/visualizer.py
+++ b/visualizer.py
@@ -16,7 +16,6 @@
from naisr import diff_operators
import pickle
-import pymeshfix as pmf
import pandas as pd
From 10366ab6f1df48eac463760ab0e7710d1b25ab78 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 11:13:58 -0400
Subject: [PATCH 20/37] Update workspace.xml
---
.idea/workspace.xml | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 02cb1fa..a6f839d 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -4,7 +4,9 @@
-
+
+
+
@@ -13,11 +15,11 @@
- {
+ "keyToString": {
+ "node.js.selected.package.tslint": "(autodetect)"
}
-}]]>
+}
From 8858fffda51e8c3444268d160e3c6d2c20968b4a Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 11:17:54 -0400
Subject: [PATCH 21/37] a
---
.idea/workspace.xml | 4 +---
naisr/__init__.py | 5 ++++-
2 files changed, 5 insertions(+), 4 deletions(-)
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index a6f839d..c588152 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -4,9 +4,7 @@
-
-
-
+
diff --git a/naisr/__init__.py b/naisr/__init__.py
index 7e84edf..09ad2dd 100755
--- a/naisr/__init__.py
+++ b/naisr/__init__.py
@@ -10,4 +10,7 @@
#from naisr.model import NAISiren, NAIVF, BaselineVF, FCBaseline, NAIVF_withtempl, SirenlatentVF, NAIlatentVF_withtempl, LipNAIVF_withtempl, Baseline, BaselineVF, ICVF, NAIVF_with3dtempl, NAIVF_autotempl, NAIVF_fixedtempl, NAIVF_fixed, DeepSDF #NAISR, HyperSirenBaseline, SirenBaseline,
from naisr.diff_operators import *
from naisr.metrics import *
-from naisr.model_naigsr import DeepNAIGSR
\ No newline at end of file
+from naisr.model_naigsr import DeepNAIGSR
+from naisr.starman_dataset import *
+from naisr.adni_dataset import *
+from naisr.airway_dataset import *
\ No newline at end of file
From cad4c529a7aa7c0d0f41151054165a403bdcda04 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 11:53:07 -0400
Subject: [PATCH 22/37] a
---
.DS_Store | Bin 8196 -> 10244 bytes
.idea/workspace.xml | 5 +-
examples/starman/naigsr_0222_base.json | 3 +-
publicdata/.DS_Store | Bin 0 -> 6148 bytes
publicdata/__init__.py | 2 +
publicdata/deformation.py | 103 ++++++++
publicdata/demo.vtk | Bin 0 -> 5672 bytes
publicdata/demo_adni_dataset_info.py | 256 ++++++++++++++++++
publicdata/demo_investigate_adni.py | 51 ++++
publicdata/demo_pair_adni.py | 349 +++++++++++++++++++++++++
publicdata/demo_random_deform.py | 104 ++++++++
publicdata/demo_split_adni_dataset.py | 154 +++++++++++
publicdata/demo_starman.py | 172 ++++++++++++
publicdata/demo_vis_starman.py | 37 +++
publicdata/generate_shapes_and_sdfs.py | 127 +++++++++
publicdata/run_generation.py | 132 ++++++++++
publicdata/test_2dsdf.py | 86 ++++++
publicdata/utils_2d.py | 305 +++++++++++++++++++++
publicdata/utils_3d.py | 62 +++++
19 files changed, 1946 insertions(+), 2 deletions(-)
create mode 100644 publicdata/.DS_Store
create mode 100644 publicdata/__init__.py
create mode 100644 publicdata/deformation.py
create mode 100644 publicdata/demo.vtk
create mode 100644 publicdata/demo_adni_dataset_info.py
create mode 100644 publicdata/demo_investigate_adni.py
create mode 100644 publicdata/demo_pair_adni.py
create mode 100644 publicdata/demo_random_deform.py
create mode 100644 publicdata/demo_split_adni_dataset.py
create mode 100644 publicdata/demo_starman.py
create mode 100644 publicdata/demo_vis_starman.py
create mode 100644 publicdata/generate_shapes_and_sdfs.py
create mode 100644 publicdata/run_generation.py
create mode 100644 publicdata/test_2dsdf.py
create mode 100644 publicdata/utils_2d.py
create mode 100644 publicdata/utils_3d.py
diff --git a/.DS_Store b/.DS_Store
index fb3ddd84b4a7e0a27d6ef8bef20e406b29c70abf..6bed6f588c851a8da43be2765962f8916f8344a5 100644
GIT binary patch
delta 624
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index c588152..97a3c2a 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -4,7 +4,10 @@
-
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diff --git a/examples/starman/naigsr_0222_base.json b/examples/starman/naigsr_0222_base.json
index 71b3f70..f5ae11e 100644
--- a/examples/starman/naigsr_0222_base.json
+++ b/examples/starman/naigsr_0222_base.json
@@ -1,7 +1,8 @@
{
"Description" : [ "This experiment learns a shape representation for starman dataset." ],
"Device":0,
- "DataSource": {"train": "/home/jyn/NAISR/examples/starman/2dshape_train_with_temp.csv", "test": "/home/jyn/NAISR/examples/starman/2dshape_test_with_temp.csv"},
+ "DataSource": {"train": "examples/starman/2dshape_train_with_temp.csv",
+ "test": "examples/starman/2dshape_test_with_temp.csv"},
"Split": null,
"Network" : "DeepNAIGSR",
"NumEpochs" : 300,
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F`X4mv(? 0)
+ current_sex = ith_dict['sex']
+
+ #
+
+ #generate_shapes_and_sdfs(current_path, ROOT_DATASET)
+ #os.system('python create_sdf_from_mesh.py --subj ' + str(source_subj))
+
+ savepath_on_npy = os.path.join(ROOT_DATASET, '3dshape', current_scan.split('.')[0] + '_on.npy')
+ savepath_off_npy = os.path.join(ROOT_DATASET, '3dsdf', current_scan.split('.')[0] + '_off.npy')
+ savepath_on_pv = os.path.join(ROOT_DATASET, '3dvis', current_scan.split('.')[0] + '_on_aligned.stl')
+
+
+ list_scan_data.append({"id": current_scan,
+ "PID": current_pid,
+ 'age': current_age,
+ 'sex':current_sex,
+ 'edu': current_edu_level,
+ 'AD': current_AD,
+ 'MCI': current_MCI,
+ 'path': current_path,
+ '3dshape': savepath_on_npy,
+ '3dsdf': savepath_off_npy,
+ '3dvis': savepath_on_pv})
+
+ return list_scan_data
+
+def read_mesh_as_trimesh(path_mesh):
+ # unpacking
+ pv_data = pv.read(path_mesh)
+ #current_airway.faces = np.hstack((np.ones((faces.shape[0], 1)) * 3, faces)).ravel().astype('int')
+ faces = np.reshape(pv_data.faces, (-1, 4))[:, [1, 2, 3]]
+ surf_airway = trimesh.Trimesh(vertices=pv_data.points, faces=faces)
+ #surf_airway.show()
+ return surf_airway
+
+
+
+
+# def generate_shapes_and_sdfs(path_vtk, root_dataset):
+#
+#
+# # get id
+# scan_id = path_vtk.split('/')[-1]
+#
+# # get savepath
+# savepath_on_npy = os.path.join(root_dataset, '3dshape', scan_id.split('.')[0] + '_on.npy')
+# savepath_off_npy = os.path.join(root_dataset, '3dsdf', scan_id.split('.')[0] + '_off.npy')
+# savepath_on_pv = os.path.join(root_dataset, '3dshape', scan_id.split('.')[0] + '_on.vtk')
+#
+# #path_examples = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data/sub-ADNI002S0729_ses-M00.vtk"
+#
+# # off surface points
+# mesh = read_mesh_as_trimesh(path_vtk)
+# # off surface points
+# arr_off = calculate_sdf_from_mesh(mesh)
+# # on surface points
+# pv_data = pv.read(path_vtk)
+# pv_data.save(savepath_on_pv)
+#
+# np.save(savepath_on_npy, np.concatenate((np.array(mesh.vertices), np.array(mesh.vertex_normals)), axis=-1))
+# np.save(savepath_off_npy, arr_off)
+#
+# del arr_off, mesh, pv_data
+#
+# return
+
+
+
+
+
+
+if __name__ == "__main__":
+
+ path_seg = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/ADNI" #"/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI"
+ list_subjects = os.listdir(path_seg)
+
+
+ root_adni_dataset = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/ADNI" #"/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI" #"/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/playpen-raid/jyn/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ ROOTPATH_DEMMOG = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv" #"/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv" # "/home/jyn/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv"
+ ROOTPATH_DATASET_INFO = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/DATASET_INFO.csv"
+ list_subjects, dict_subj_scans, list_data = extract_patient_convariates(root_adni_dataset)
+
+ ROOT_DATASET = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/" #"/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/
+
+ list_scan_data = create_adni_dataset_sheet(list_data)
+
+ # save the data set info to a csv
+ savepath_dataset = "/home/jyn/NAISR/examples/hippocampus/3dshape.csv" #"/home/jyn/NAISR/examples/starman/2dshape_train.csv"
+ # save data
+ a = pd.DataFrame.from_records(list_scan_data)
+ a.loc[np.isnan(a['edu'].values), 'edu'] = a.loc[np.isfinite(a['edu'].values), 'edu'].mean()
+ a.to_csv(savepath_dataset)
+
+ print('finished')
+
+
diff --git a/publicdata/demo_investigate_adni.py b/publicdata/demo_investigate_adni.py
new file mode 100644
index 0000000..be76071
--- /dev/null
+++ b/publicdata/demo_investigate_adni.py
@@ -0,0 +1,51 @@
+import os
+
+root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" #"/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+list_scans = os.listdir(root_dataset)
+list_subjects = []
+for ith_scan in list_scans:
+ if '.vtk' in ith_scan and 'sub' in ith_scan:
+ list_subjects.append(int(ith_scan.split('_')[0][-4::]))
+
+import xml.etree.ElementTree as ET
+from bs4 import BeautifulSoup
+
+# Reading the data inside the xml
+# file to a variable under the name
+# data
+path ='/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data_set.xml'
+with open(path, 'r') as f:
+ data = f.read()
+
+# Passing the stored data inside
+# the beautifulsoup parser, storing
+# the returned object
+Bs_data = BeautifulSoup(data, "xml")
+
+# Finding all instances of tag
+# `unique`
+b_unique = Bs_data.find_all('unique')
+
+print(b_unique)
+
+# Using find() to extract attributes
+# of the first instance of the tag
+b_name = Bs_data.find('child', {'name': 'Frank'})
+
+print(b_name)
+
+
+# Parse the XML file
+tree = ET.parse('/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data_set.xml') # Replace with your XML file path
+root = tree.getroot()
+a = []
+b = []
+# Access elements and attributes
+for child in root:
+ print(child.tag, child.attrib)
+ a.append(child.attrib)
+
+# Access specific element data
+for element in root.findall('your_element'): # Replace 'your_element' with the desired element's tag name
+ value = element.text
+ print(value)
diff --git a/publicdata/demo_pair_adni.py b/publicdata/demo_pair_adni.py
new file mode 100644
index 0000000..7b0de72
--- /dev/null
+++ b/publicdata/demo_pair_adni.py
@@ -0,0 +1,349 @@
+import os
+import numpy as np
+import pandas as pd
+import math
+import pyvista as pv
+import trimesh
+from utils_3d import *
+import skimage.measure as measure
+import datetime
+# def extract_subj_list(root_dataset):
+# #root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" #"/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+# list_scans = os.listdir(root_dataset)
+# list_subjects = []
+# dict_subj_scans = {}
+# for ith_scan in list_scans:
+# if '.vtk' in ith_scan and 'sub' in ith_scan:
+# ith_subj = int(ith_scan.split('_')[0][-4::])
+# list_subjects.append(ith_subj)
+# # attach scans to subjects
+# if ith_subj not in list(dict_subj_scans.keys()):
+# dict_subj_scans[ith_subj] = []
+# dict_subj_scans[ith_subj].append(ith_scan)
+#
+# list_subjects = np.unique(np.array(list_subjects))
+# return list_subjects, dict_subj_scans
+
+
+def extract_subj_list(root_dataset):
+ #root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" #"/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ list_all_subjects = os.listdir(root_dataset)
+ list_of_subjects = []
+ dict_subj_scans = {}
+ for ith_scan in list_all_subjects:
+ # current patient
+ if os.path.isdir(os.path.join(root_dataset, ith_scan)):
+ ith_subj = int(ith_scan.split('_')[-1])
+ # current folder
+ current_scan_folder = os.path.join(root_dataset, ith_scan, "Hippocampal_Mask")
+ # attach scans to subjects
+ dict_subj_scans[ith_scan] = os.listdir(current_scan_folder)
+ list_of_subjects.append(ith_scan)
+ return list_of_subjects, dict_subj_scans
+
+
+def transform_group_to_num(str_class):
+ if str_class == 'CN':
+ return 0.
+ elif str_class == 'MCI':
+ return 0.5
+ elif str_class == 'AD':
+ return 1.
+def extract_patient_convariates(root_adni_dataset):
+ #root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ list_subjects, dict_subj_scans = extract_subj_list(root_adni_dataset)
+ pd_demmog = pd.read_csv(ROOTPATH_DEMMOG)
+ pd_dataset_info = pd.read_csv(ROOTPATH_DATASET_INFO)
+ pd_dataset_info['Study Date'] = pd.to_datetime(pd_dataset_info['Study Date'], yearfirst=True)
+ list_data = []
+
+ for ith_subject in list_subjects:
+ # print("processing " + str(ith_subject))
+ RID = int(ith_subject.split('_')[-1])
+ df_current_subj = pd_demmog[pd_demmog['RID'] == RID]
+ list_edu_level = []
+ # get ages
+ for i in range(len(df_current_subj)):
+ # get education
+ list_edu_level.append(df_current_subj.iloc[i]["PTEDUCAT"])
+
+ edu_level = np.array(list_edu_level).max()
+
+ list_current_scans = dict_subj_scans[ith_subject]
+
+
+ current_datasetinfo = pd_dataset_info[pd_dataset_info['Subject ID']==ith_subject]
+ # id,PID,cov_1,cov_2,cov_3,cov_4,2dsdf,2dshape
+ for ith_scan in list_current_scans:
+ scandate = ith_scan.split("_")[0]
+ current_folder = os.path.join(root_adni_dataset, ith_subject, "Hippocampal_Mask", ith_scan)
+ current_scanfile_folder = os.path.join(current_folder, os.listdir(current_folder)[0])
+ current_scanfile = os.listdir(current_scanfile_folder)[0]
+ current_path = os.path.join(current_scanfile_folder, current_scanfile)
+
+ #date = str(int(scandate.split('-')[1])) + '/' + str(scandate.split('-')[2]) + '/' + str(scandate.split('-')[0])
+ #try:
+
+ #current_age = float(current_datasetinfo[current_datasetinfo['Study Date'] == scandate]['Age'].values[0])
+ time_diff = (current_datasetinfo['Study Date'] - pd.to_datetime(scandate)) / pd.Timedelta(1, 'D')
+ current_age = float(current_datasetinfo[time_diff.abs()<=1]['Age'].values[0])
+ #except:
+ # print('a')
+ #try:
+ current_group = current_datasetinfo[time_diff.abs()<=1]['Research Group'].values[0]
+ current_group = float(transform_group_to_num(current_group))
+ #except:
+ # print('a')
+
+ #datetime.strftime(format)
+ list_data.append({'ID': ith_subject,
+ 'age': current_age,
+ "edu": edu_level,
+ 'group': current_group,
+ "scan": current_scanfile,
+ "path": current_path
+ })
+
+ print("Done")
+ return list_subjects, dict_subj_scans, list_data
+
+
+
+
+# def pair_covariates_with_shapes(list_subjects, dict_subj_scans, list_subjects_data):
+#
+# dict_subjects_data = pd.DataFrame.from_records(list_subjects_data)
+# list_scan_data = []
+# for ith_subject in list_subjects:
+# print("processing " + str(ith_subject))
+# current_scans = list(dict_subjects_data[dict_subjects_data['ID'] == ith_subject]['scans'])[0]
+# current_paths = list(dict_subjects_data[dict_subjects_data['ID'] == ith_subject]['paths'])[0]
+#
+# for ith_scan in dict_subj_scans[ith_subject]:
+# if '.vtk' in ith_scan and 'sub' in ith_scan:
+# scantime = float(str(ith_scan.split("-")[2][1:3]))
+#
+# # current scan with covariates
+# current_age = scantime / 12 + float(dict_subjects_data[dict_subjects_data['ID'] == ith_subject]['age'])
+# current_edu_level = float(dict_subjects_data[dict_subjects_data['ID'] == ith_subject]['edu'])
+# current_path = np.array(current_paths)[ith_scan == np.array(current_scans)][0]
+# current_pid = ith_subject
+# current_scan = ith_scan
+#
+# #
+# generate_shapes_and_sdfs(current_path, ROOT_DATASET)
+#
+# list_scan_data.append({"ID": current_scan,
+# "PID": current_pid,
+# 'age': current_age,
+# 'edu': current_edu_level,
+# 'path': current_path})
+#
+# return list_scan_data
+
+def pair_covariates_with_shapes(list_data):
+
+ dict_subjects_data = pd.DataFrame.from_records(list_data)
+ list_scan_data = []
+ for ith_dict in list_data:
+ current_path = ith_dict['path']
+
+
+ # current scan with covariates
+ current_age = ith_dict['age']
+ current_edu_level = ith_dict['edu']
+ current_pid = ith_dict['ID']
+ current_scan = ith_dict['scan']
+
+ #
+ from generate_shapes_and_sdfs import generate_shape_and_sdf_for_a_seg
+ #generate_shapes_and_sdfs(current_path, ROOT_DATASET)
+ #os.system('python create_sdf_from_mesh.py --subj ' + str(source_subj))
+
+ savepath_on_npy = os.path.join(ROOT_DATASET, '3dshape', current_scan.split('.')[0] + '_on.npy')
+ savepath_off_npy = os.path.join(ROOT_DATASET, '3dsdf', current_scan.split('.')[0] + '_off.npy')
+ savepath_on_pv_source = os.path.join(ROOT_DATASET, '3dvis', current_scan.split('.')[0] + '_on_source.stl')
+ savepath_on_pv_aligned = os.path.join(ROOT_DATASET, '3dvis', current_scan.split('.')[0] + '_on_aligned.stl')
+
+ #generate_shape_and_sdf_for_a_seg(path_seg=current_path, root_dataset=ROOT_DATASET, path_target=PATH_TARGET)
+
+
+ if os.path.exists(savepath_on_npy) and os.path.exists(savepath_off_npy):
+ try:
+ a = np.load(savepath_off_npy)
+ except:
+ print("processing " + str(current_path))
+ os.system('python generate_shapes_and_sdfs.py --path_seg ' + str(current_path) + ' --root_dataset ' + str(ROOT_DATASET) + " --path_target " + str(PATH_TARGET))
+ print("Failure OF Extraction, re-extraction")
+ list_scan_data.append({"ID": current_scan,
+ "PID": current_pid,
+ 'age': current_age,
+ 'edu': current_edu_level,
+ 'path': current_path,
+ '3dshape': savepath_on_npy,
+ '3dsdf': savepath_off_npy,
+ '3dvis_source': savepath_on_pv_source,
+ '3dvis': savepath_on_pv_aligned})
+ #continue
+ else:
+ #print("processing " + str(current_path))
+ print("missing " + str(current_path))
+ os.system('python generate_shapes_and_sdfs.py --path_seg ' + str(current_path) + ' --root_dataset ' + str(ROOT_DATASET) + " --path_target " + str(PATH_TARGET))
+ #generate_shape_and_sdf_for_a_seg(path_seg=current_path, root_dataset=ROOT_DATASET, path_target=PATH_TARGET)
+
+
+ list_scan_data.append({"ID": current_scan,
+ "PID": current_pid,
+ 'age': current_age,
+ 'edu': current_edu_level,
+ 'path': current_path,
+ '3dshape': savepath_on_npy,
+ '3dsdf': savepath_off_npy,
+ '3dvis_source': savepath_on_pv_source,
+ '3dvis': savepath_on_pv_aligned})
+
+ return list_scan_data
+
+def read_mesh_as_trimesh(path_mesh):
+ # unpacking
+ pv_data = pv.read(path_mesh)
+ #current_airway.faces = np.hstack((np.ones((faces.shape[0], 1)) * 3, faces)).ravel().astype('int')
+ faces = np.reshape(pv_data.faces, (-1, 4))[:, [1, 2, 3]]
+ surf_airway = trimesh.Trimesh(vertices=pv_data.points, faces=faces)
+ #surf_airway.show()
+ return surf_airway
+
+
+
+
+# def generate_shapes_and_sdfs(path_vtk, root_dataset):
+#
+#
+# # get id
+# scan_id = path_vtk.split('/')[-1]
+#
+# # get savepath
+# savepath_on_npy = os.path.join(root_dataset, '3dshape', scan_id.split('.')[0] + '_on.npy')
+# savepath_off_npy = os.path.join(root_dataset, '3dsdf', scan_id.split('.')[0] + '_off.npy')
+# savepath_on_pv = os.path.join(root_dataset, '3dshape', scan_id.split('.')[0] + '_on.vtk')
+#
+# #path_examples = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data/sub-ADNI002S0729_ses-M00.vtk"
+#
+# # off surface points
+# mesh = read_mesh_as_trimesh(path_vtk)
+# # off surface points
+# arr_off = calculate_sdf_from_mesh(mesh)
+# # on surface points
+# pv_data = pv.read(path_vtk)
+# pv_data.save(savepath_on_pv)
+#
+# np.save(savepath_on_npy, np.concatenate((np.array(mesh.vertices), np.array(mesh.vertex_normals)), axis=-1))
+# np.save(savepath_off_npy, arr_off)
+#
+# del arr_off, mesh, pv_data
+#
+# return
+
+
+
+
+
+
+if __name__ == "__main__":
+
+ path_seg = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI"
+ list_subjects = os.listdir(path_seg)
+
+
+ # path_examples = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data/sub-ADNI002S0729_ses-M00.vtk"
+
+ # # off surface points
+ # mesh = read_mesh_as_trimesh(path_examples)
+ # arr_off = calculate_sdf_from_mesh(mesh)
+
+
+ root_adni_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI" #"/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/playpen-raid/jyn/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ ROOTPATH_DEMMOG = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv" # "/home/jyn/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv"
+ ROOTPATH_DATASET_INFO = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/DATASET_INFO.csv"
+ list_subjects, dict_subj_scans, list_data = extract_patient_convariates(root_adni_dataset)
+
+ ROOT_DATASET = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/" #"/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/
+ PATH_TARGET = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI/941_S_1202/Hippocampal_Mask/2007-01-30_09_16_45.0/I147765/ADNI_941_S_1202_MR_Hippocampal_Mask_Hi_20090702092745323_S25680_I147765.nii"
+ #"/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/ADNI/002_S_0295/Hippocampal_Mask/2006-04-18_08_20_30.0/I93328/ADNI_002_S_0295_MR_Hippocampal_Mask_Hi_20080228111448800_S13408_I93328.nii"
+
+
+ list_scan_data = pair_covariates_with_shapes(list_data)
+
+ # save the data set info to a csv
+ savepath_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/3dshape.csv" #"/home/jyn/NAISR/examples/starman/2dshape_train.csv"
+
+
+ # save data
+ a = pd.DataFrame.from_records(list_scan_data)
+ a.loc[np.isnan(a['edu'].values), 'edu'] = a.loc[np.isfinite(a['edu'].values), 'edu'].mean()
+ pd.DataFrame.from_records(list_scan_data).to_csv(savepath_dataset)
+
+ print('finished')
+
+
+
+#
+# root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" #"/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+# list_subjects, dict_subj_scans = extract_subj_list(root_dataset)
+# rootpath_demog = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv"
+# pd_demmog = pd.read_csv(rootpath_demog)
+#
+#
+# list_data = []
+# dict_patient_cov = {}
+# for ith_subject in list_subjects:
+# #print("processing " + str(ith_subject))
+# df_current_subj = pd_demmog[pd_demmog['RID']==ith_subject]
+#
+# list_ages_current_subject = []
+# list_birthday = []
+# list_edu_level = []
+# # get ages
+# for i in range(len(df_current_subj)):
+# # get age and birthday
+# if (not pd.isna(df_current_subj.iloc[i]['PTDOBYY'])) and \
+# (not pd.isna(df_current_subj.iloc[i]['USERDATE'])):
+#
+# scantdate = int(str(df_current_subj.iloc[i]['USERDATE'])[0:4])
+# birthdate = int(str(df_current_subj.iloc[i]['PTDOBYY'])[0:4])
+# age = scantdate - birthdate
+# #print(age)
+# list_ages_current_subject.append(age)
+# list_birthday.append(birthdate)
+#
+# # get education
+# list_edu_level.append(df_current_subj.iloc[i]["PTEDUCAT"])
+#
+#
+# if len(np.unique(np.array(list_birthday))) > 1:
+# print('insistent birthday for subject')
+# print(ith_subject)
+#
+#
+# age = np.array(list_subjects).min()
+# edu_level = np.array(list_edu_level).max()
+# # id,PID,cov_1,cov_2,cov_3,cov_4,2dsdf,2dshape
+# dict_patient_cov[ith_subject] = age
+# list_data.append({'ID': ith_subject,
+# 'age': age,
+# "edu": edu_level,
+# })
+#
+#
+# print("Done")
+
+
+
+
+
+
+# def load_dempgraphics_info():
+# rootpath_demog = "/Users/jyn/jyn/research/projects/NAISR/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv"
+# return
+
+import xml.etree.ElementTree as ET
diff --git a/publicdata/demo_random_deform.py b/publicdata/demo_random_deform.py
new file mode 100644
index 0000000..aa3342b
--- /dev/null
+++ b/publicdata/demo_random_deform.py
@@ -0,0 +1,104 @@
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+
+def rotate_polygon(polygon, rotation_deg=30):
+ """
+ Rotate the polygon.
+ """
+ theta = np.deg2rad(rotation_deg)
+
+ rotation_matrix = np.array([
+ [np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]
+ ])
+
+ # Apply the rotation
+ rotated_polygon = np.dot(polygon, rotation_matrix.T)
+
+ return rotated_polygon
+
+
+def translate_polygon(polygon, translation=(10, 10)):
+ """
+ Translate the polygon.
+ """
+ translated_polygon = polygon + translation
+ return translated_polygon
+
+def Gaussian(x, z, sigma=0.5):
+ return np.exp((-(np.linalg.norm(x - z, axis=1) ** 2)) / (2 * sigma ** 2))
+
+def create_deformation(points, control_point, VECTOR):
+ deformed = Gaussian(points, control_point)[:, None] @ VECTOR[None, :] # + points
+ return deformed
+
+def random_deform_polygon(arr_contour_pts, std_deviation=1.0, scale=2.0):
+ """
+ Add random deformation to the polygon.
+ """
+ # num_points = polygon.shape[0]
+ # random_deformation = (np.random.rand(num_points, 2) * 2 - 1) * scale
+ # random_deformation = gaussian_filter(random_deformation, sigma=std_deviation, mode='wrap')
+ #
+ # deformed_polygon = polygon + random_deformation
+
+ control_points = np.array([[-2, ]])
+
+ nx, ny = (5, 5)
+ x = np.linspace(-2, 2, nx)
+ y = np.linspace(-2, 2, ny)
+ xv, yv = np.meshgrid(x, y)
+ arr_control_pts = np.concatenate((xv.ravel()[:, None], yv.ravel()[:, None]), axis=1)
+
+ arr_deformed = arr_contour_pts.copy()
+ for ith_con_pt in arr_control_pts:
+ #Gaussian(x, ith_con_pt)
+ arr_deformed += create_deformation(arr_contour_pts, ith_con_pt, np.random.rand(2) * 0.5)
+ return arr_deformed
+
+
+def deform_polygon(polygon, rotation_deg=130, translation=(10, 10), std_deviation=1.0, deformation_scale=2.0):
+ """
+ Combine the rotation, translation, and random deformation.
+ """
+ #rotated = rotate_polygon(polygon, rotation_deg)
+ #translated = translate_polygon(rotated, translation)
+ deformed = random_deform_polygon(polygon, std_deviation, deformation_scale)
+
+ return deformed
+
+def show_shapely_polygon(polygon):
+ fig, ax = plt.subplots()
+ ax.plot(*polygon.exterior.xy, 'b-', label='Polygon')
+ ax.set_aspect('equal', adjustable='datalim')
+ ax.legend()
+ plt.show()
+ return
+
+# Example usage
+polygon = np.array([
+ [0, 0],
+ [1, 0],
+ [1, 1],
+ [0, 1]
+])
+
+import pyvista as pv
+import numpy as np
+from shapely.geometry import Polygon, Point
+from shapely.geometry import Point, LineString, Polygon
+import matplotlib.pyplot as plt
+import pyvista as pv
+
+path_starman_vtk = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+path_control_points = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+# "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+mesh = pv.read(path_starman_vtk)
+arr_contour_pts = mesh.points[:, [0,1]]
+deformed_polygon = deform_polygon(arr_contour_pts )
+polygon = Polygon(deformed_polygon)
+show_shapely_polygon(polygon)
+
+print(deformed_polygon)
diff --git a/publicdata/demo_split_adni_dataset.py b/publicdata/demo_split_adni_dataset.py
new file mode 100644
index 0000000..af4ed27
--- /dev/null
+++ b/publicdata/demo_split_adni_dataset.py
@@ -0,0 +1,154 @@
+import os
+import numpy as np
+import pandas as pd
+import math
+import pyvista as pv
+import trimesh
+from utils_3d import *
+import skimage.measure as measure
+import datetime
+from sklearn.model_selection import train_test_split
+import yaml
+
+def transform_group_to_num(str_class):
+ if str_class == 'CN':
+ return 0
+ elif str_class == 'MCI':
+ return 1
+ elif str_class == 'AD':
+ return 2
+
+def extract_subj_list(root_dataset):
+ #root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" #"/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ list_all_subjects = os.listdir(root_dataset)
+ list_of_subjects = []
+ dict_subj_scans = {}
+ for ith_scan in list_all_subjects:
+ # current patient
+ if os.path.isdir(os.path.join(root_dataset, ith_scan)):
+ ith_subj = int(ith_scan.split('_')[-1])
+ # current folder
+ current_scan_folder = os.path.join(root_dataset, ith_scan, "Hippocampal_Mask")
+ # attach scans to subjects
+ dict_subj_scans[ith_scan] = os.listdir(current_scan_folder)
+ list_of_subjects.append(ith_scan)
+ return list_of_subjects, dict_subj_scans
+
+def extract_patient_convariates(root_adni_dataset):
+ #root_dataset = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data" # "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data"
+ list_subjects, dict_subj_scans = extract_subj_list(root_adni_dataset)
+ pd_demmog = pd.read_csv(ROOTPATH_DEMMOG)
+ pd_dataset_info = pd.read_csv(ROOTPATH_DATASET_INFO)
+ pd_dataset_info['Study Date'] = pd.to_datetime(pd_dataset_info['Study Date'], yearfirst=True)
+ list_data = []
+
+ for ith_subject in list_subjects:
+ # print("processing " + str(ith_subject))
+ RID = int(ith_subject.split('_')[-1])
+ df_current_subj = pd_demmog[pd_demmog['RID'] == RID]
+ list_edu_level = []
+ # get ages
+ for i in range(len(df_current_subj)):
+ # get education
+ list_edu_level.append(df_current_subj.iloc[i]["PTEDUCAT"])
+
+ edu_level = np.array(list_edu_level).max()
+
+ list_current_scans = dict_subj_scans[ith_subject]
+
+
+ current_datasetinfo = pd_dataset_info[pd_dataset_info['Subject ID']==ith_subject]
+ # id,PID,cov_1,cov_2,cov_3,cov_4,2dsdf,2dshape
+ for ith_scan in list_current_scans:
+ scandate = ith_scan.split("_")[0]
+ current_folder = os.path.join(root_adni_dataset, ith_subject, "Hippocampal_Mask", ith_scan)
+ current_scanfile_folder = os.path.join(current_folder, os.listdir(current_folder)[0])
+ current_scanfile = os.listdir(current_scanfile_folder)[0]
+ current_path = os.path.join(current_scanfile_folder, current_scanfile)
+
+ #date = str(int(scandate.split('-')[1])) + '/' + str(scandate.split('-')[2]) + '/' + str(scandate.split('-')[0])
+ #try:
+
+ #current_age = float(current_datasetinfo[current_datasetinfo['Study Date'] == scandate]['Age'].values[0])
+ time_diff = (current_datasetinfo['Study Date'] - pd.to_datetime(scandate)) / pd.Timedelta(1, 'D')
+ current_age = float(current_datasetinfo[time_diff.abs()<=1]['Age'].values[0])
+ #except:
+ # print('a')
+ #try:
+ current_group = current_datasetinfo[time_diff.abs()<=1]['Research Group'].values[0]
+ current_group = float(transform_group_to_num(current_group))
+ #except:
+ # print('a')
+
+ #datetime.strftime(format)
+ list_data.append({'ID': ith_subject,
+ 'age': current_age,
+ "edu": edu_level,
+ 'group': current_group,
+ "scan": current_scanfile,
+ "path": current_path
+ })
+
+ print("Done")
+ return list_subjects, dict_subj_scans, list_data
+
+
+if __name__ == "__main__":
+
+ path_adni_dataset = "/home/jyn/NAISR/examples/hippocampus/3dshape.csv"
+ root_adni_dataset = "/playpen-raid/jyn/NAISR/NAISR/examples/hippocampus/ADNI"
+ ROOTPATH_DEMMOG = "/home/jyn/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv" # "/home/jyn/NAISR/examples/hippocampus/PTDEMOG_18Aug2023.csv"
+ ROOTPATH_DATASET_INFO = "/home/jyn/NAISR/examples/hippocampus/DATASET_INFO.csv"
+
+ df_dataset = pd.read_csv(path_adni_dataset)
+ np.unique(list(df_dataset['PID'].values))
+ list_subjects, dict_subj_scans, list_data = extract_patient_convariates(root_adni_dataset)
+ train_split, test_split = train_test_split(list_subjects, train_size=0.8)
+
+
+ list_train = []
+ list_test = []
+ list_train_single = []
+ list_train_multiple = []
+ list_test_single = []
+ list_test_multiple = []
+
+ for i_train in train_split:
+ list_current_scans = list(df_dataset[df_dataset['PID']==i_train]['ID'].values) #dict_subj_scans[i_train]
+ list_train += list_current_scans
+ if len(list_current_scans) == 1:
+ list_train_single += list_current_scans
+ elif len(list_current_scans) > 1:
+ list_train_multiple.append({"name": i_train, "value": list_current_scans})
+
+ for i_test in test_split:
+ list_current_scans = list(df_dataset[df_dataset['PID']==i_test]['ID'].values) #dict_subj_scans[i_test]
+ list_test += list_current_scans
+ if len(list_current_scans) == 1:
+ list_test_single += list_current_scans
+ elif len(list_current_scans) > 1:
+ list_test_multiple.append({"name": i_test, "value":list_current_scans})
+
+ dict_split = {'train': list_train,
+ 'test': list_test,
+ 'train_single': list_train_single,
+ 'train_multiple': list_train_multiple,
+ 'test_single': list_test_single,
+ 'test_multiple': list_test_multiple
+ }
+
+ savepath = '/home/jyn/NAISR/examples/hippocampus/newsplit.yaml'
+ with open(savepath, 'w') as f:
+ yaml.dump(dict_split, f, default_flow_style=False, sort_keys=False)
+
+ dict_timeline = {}
+ for ith_case in dict_split['train_multiple'] + dict_split['test_multiple']:
+ dict_timeline[str(ith_case['name'])] = ith_case['value']
+
+ savepath = '/home/jyn/NAISR/examples/pediatric_airway/timeline_patients.yaml'
+ with open(savepath, 'w') as f:
+ yaml.dump(dict_timeline, f, default_flow_style=False, sort_keys=False)
+
+ print('finished')
+
+
diff --git a/publicdata/demo_starman.py b/publicdata/demo_starman.py
new file mode 100644
index 0000000..9a5120e
--- /dev/null
+++ b/publicdata/demo_starman.py
@@ -0,0 +1,172 @@
+import pyvista as pv
+import numpy as np
+from pyvista import examples
+import argparse
+
+from mesh_to_sdf import sample_sdf_near_surface
+
+import trimesh
+import pyrender
+import numpy as np
+import SimpleITK as sitk
+import trimesh
+import mesh_to_sdf
+
+def Gaussian(x,z,sigma=0.5):
+ return np.exp((-(np.linalg.norm(x-z, axis=1)**2))/(2*sigma**2))
+
+
+def create_deformation(points, control_point):
+ deformed = Gaussian(points, control_point)[:, None] @ UP_VECTOR[None, :] #+ points
+ return deformed
+
+def apply_deformation(points, deformation, covariate_value=1.):
+ return points + deformation * covariate_value
+
+
+def compute_normals(arr_contour):
+ arr_contour_pts = arr_contour.copy()
+ arr_contour_pts = arr_contour_pts[:, [0, 1]]
+ normals = []
+ for i in range(len(arr_contour_pts)):
+ x1, y1 = arr_contour_pts[i]
+ x2, y2 = arr_contour_pts[(i+1) % len(arr_contour_pts)] # wrap around for the last point
+ dx = x2 - x1
+ dy = y2 - y1
+ normals.append((-dy, dx))
+ arr_normals = np.array(normals) / np.linalg.norm(normals, axis=-1, keepdims=1)
+ arr_normals = np.concatenate((arr_normals, np.zeros_like(arr_normals[:, [0]])), axis=-1)
+ return arr_normals
+
+
+def generate_off_surface_points(number_of_points=250000):
+ x = (np.random.rand(number_of_points) - 0.5) * 6 * 60
+ y = (np.random.rand(number_of_points) - 0.5) * 6 * 60
+ z = (np.random.rand(number_of_points) - 0.5) * 6 * 60
+
+ coords = np.concatenate((x[:, None], y[:, None], z[:, None]), axis=-1)
+ return coords
+
+
+def apply_registration_to_surface(surf, R, T):
+ new_verts = T + np.matmul(R, np.array(surf.vertices).T)
+ new_surf = trimesh.Trimesh(vertices=new_verts.T, faces=surf.faces)
+ return new_surf
+
+def get_centroids(arr_bbx):
+ xmin, xmax, ymin, ymax, zmin, zmax = arr_bbx
+ arr_center = np.array([(xmin+xmax)/2, (ymin+ymax)/2, (zmin+zmax)/2])
+ return arr_center
+
+
+
+def calculate_sdf_from_mesh(surf_mesh):
+ import trimesh
+ # get centered mesh: mesh
+ mesh = surf_mesh
+ centroid = get_centroids(mesh.bounds)
+ vertices = mesh.points - centroid
+ distances = np.max(np.linalg.norm(vertices, axis=1))
+
+ # get sdf field, the mesh is normalized automatically
+ points, sdf = sample_sdf_near_surface(surf_mesh, number_of_points=250000, sign_method='depth')
+ # get off-surface points
+ off_surf_points = generate_off_surface_points(number_of_points=250000) + centroid
+ off_surf_sdf = mesh_to_sdf.mesh_to_sdf(mesh, off_surf_points, surface_point_method='scan', sign_method='depth', bounding_radius=None, scan_count=100, scan_resolution=1000, sample_point_count=10000000, normal_sample_count=11)
+ off_surf_points = off_surf_points[off_surf_sdf > 0 * 0.1]
+ off_surf_sdf = off_surf_sdf[off_surf_sdf > 0 * 0.1]
+ # denormalize
+ points = points * distances + centroid
+ sdf *= distances
+ #
+ points = np.concatenate((points, off_surf_points), axis=-2)
+ sdf = np.concatenate((sdf[:, None], off_surf_sdf[:, None]), axis=0)
+
+ npz_sdf = np.concatenate((points, sdf), axis=-1)
+ #np.save(savepath, npz_sdf)
+ return npz_sdf
+
+
+
+
+UP_VECTOR = np.array([0., 1., 0.])
+
+
+a = np.array([0., 1., 0.])
+
+
+path_starman_vtk = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+path_control_points = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+ # "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+mesh = pv.read(path_starman_vtk)
+#mesh.points = mesh.points[:, [0,1]]
+
+control_points = np.loadtxt(path_control_points)
+arr_cp = np.concatenate((control_points, np.zeros_like(control_points[:, [0]])), axis=1)
+
+
+chart = pv.Chart2D()
+x = mesh.points[:, 0]
+y = mesh.points[:, 1]
+_ = chart.line(x, y, "y", 4)
+#chart.show()
+
+
+
+
+pl = pv.Plotter()
+pl.add_mesh(mesh)
+pl.add_points(arr_cp[[2]], color='r')
+
+# 1.
+arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[2]),
+ covariate_value=1.)
+deformed_mesh = mesh.copy()
+deformed_mesh.points = arr_deformed
+deformed_mesh.normals = compute_normals(deformed_mesh.points)
+import trimesh
+#a=calculate_sdf_from_mesh(deformed_mesh)
+
+#deformed_mesh.save('./demo.vtk')
+pl.add_mesh(deformed_mesh, color='y')
+
+# 0.1
+arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=0.1)
+deformed_mesh1 = mesh.copy()
+deformed_mesh1.points = arr_deformed
+pl.add_mesh(deformed_mesh1, color='g')
+
+
+# 0.5
+arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=0.5)
+deformed_mesh2 = mesh.copy()
+deformed_mesh2.points = arr_deformed
+pl.add_mesh(deformed_mesh2, color='b')
+
+# 0.1
+arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=-0.1)
+deformed_mesh3 = mesh.copy()
+deformed_mesh3.points = arr_deformed
+pl.add_mesh(deformed_mesh3, color='cyan')
+
+
+# 0.5
+arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=-0.5)
+deformed_mesh4 = mesh.copy()
+deformed_mesh4.points = arr_deformed
+pl.add_mesh(deformed_mesh4, color='purple')
+
+
+
+pl.view_xy()
+pl.show()
\ No newline at end of file
diff --git a/publicdata/demo_vis_starman.py b/publicdata/demo_vis_starman.py
new file mode 100644
index 0000000..2f114fa
--- /dev/null
+++ b/publicdata/demo_vis_starman.py
@@ -0,0 +1,37 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import measure
+
+
+# Construct some test data
+x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
+r = np.sin(np.exp(np.sin(x)**3 + np.cos(y)**2))
+
+# Find contours at a constant value of 0.8
+contours = measure.find_contours(r, 0.5)
+
+# Display the image and plot all contours found
+fig, ax = plt.subplots()
+ax.imshow(r, cmap=plt.cm.gray)
+
+for contour in contours:
+ ax.plot(contour[:, 1], contour[:, 0], linewidth=2)
+
+ax.axis('image')
+ax.set_xticks([])
+ax.set_yticks([])
+plt.show()
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+datapath = "/playpen-raid/jyn/NAISR/NAISR/examples/starman/train/2dsdf/0000_0_off.npy"
+np_sdf = np.load(datapath)
+
+
+plt.scatter(x=np_sdf[:, 0], y=np_sdf[:, 1], cmap='coolwarm', c=np_sdf[:, 2], s=1)
+plt.colorbar()
+plt.show()
+
diff --git a/publicdata/generate_shapes_and_sdfs.py b/publicdata/generate_shapes_and_sdfs.py
new file mode 100644
index 0000000..29d970c
--- /dev/null
+++ b/publicdata/generate_shapes_and_sdfs.py
@@ -0,0 +1,127 @@
+import os
+import numpy as np
+import pandas as pd
+import math
+import pyvista as pv
+import trimesh
+from utils_3d import *
+import skimage.measure as measure
+import datetime
+
+
+def read_origin_spacing(path):
+ img = sitk.ReadImage(path)
+ arr = sitk.GetArrayFromImage(img).transpose(2, 1, 0)
+ arr_rst = np.zeros_like(arr)
+ if (np.unique(arr) != np.array([0, 20, 21], dtype=np.int16)).sum():
+ arr = arr / 256
+ print('Inconsistent label value: ' + path)
+ arr_rst[arr == 20] = 20
+ Origin = np.array(img.GetOrigin())
+ Spacing = np.array(img.GetSpacing())
+ return arr_rst, Origin, Spacing
+
+
+def reverse_coords(arr):
+ rev = []
+ for x, y, z in arr:
+ rev.append([z, y, x])
+ return np.array(rev)
+
+
+def read_and_align_mesh(path_source, path_target):
+ mesh_target = surface_construction(path_target)
+ mesh_source = surface_construction(path_source)
+
+ _, aligned_vertices, _ = trimesh.registration.icp(mesh_source.vertices, mesh_target.vertices, scale=False)
+ mesh_aligned = mesh_source.copy()
+ mesh_aligned.vertices = aligned_vertices
+
+ # scene = pyrender.Scene()
+ # cloud = pyrender.Mesh.from_points(np.array(mesh_source.vertices), colors=(0,0,0.5))
+ # scene.add(cloud)
+ # cloud = pyrender.Mesh.from_points(mesh_target.vertices, colors= (0,0.5,0))
+ # scene.add(cloud)
+ # cloud = pyrender.Mesh.from_points(mesh_aligned.vertices, colors= (0.5,0,0))
+ # scene.add(cloud)
+ # viewer = pyrender.Viewer(scene, use_raymond_lighting=True, point_size=2)
+ # # viewer.save_gif(savepath)
+ # viewer.close()
+ return mesh_aligned, mesh_source
+
+
+
+
+def surface_construction(pathseg):
+ # unpacking
+ arr_airway, Origin, Spacing = read_origin_spacing(pathseg)
+ # polydata of airway surface segments
+ verts, faces, norm, val = \
+ measure.marching_cubes_lewiner(arr_airway,
+ spacing=Spacing,
+ allow_degenerate=True)
+ current_airway = pv.PolyData()
+ current_airway.points = verts #(verts + Origin) #* np.array([-1, -1, 1]) # + np.array([-35, 0, 0])
+ current_airway.faces = np.hstack((np.ones((faces.shape[0], 1)) * 3, faces)).ravel().astype('int')
+ airway_surface = current_airway#.extract_surface()#.smooth(n_iter=200)
+
+
+
+ mesh = trimesh.Trimesh(vertices=airway_surface.points, faces=faces, )
+ vertices = mesh.vertices - mesh.bounding_box.centroid
+ mesh.vertices = vertices
+ return mesh
+
+
+def generate_shape_and_sdf_for_a_seg(path_seg, root_dataset, path_target):
+
+
+ # get id
+ scan_id = path_seg.split('/')[-1][0:-4]
+
+ # get savepath
+ savepath_on_npy = os.path.join(root_dataset, '3dshape', scan_id.split('.')[0] + '_on.npy')
+ savepath_off_npy = os.path.join(root_dataset, '3dsdf', scan_id.split('.')[0] + '_off.npy')
+ savepath_on_pv_aligned = os.path.join(root_dataset, '3dvis', scan_id.split('.')[0] + '_on_aligned.stl')
+ savepath_on_pv_source = os.path.join(root_dataset, '3dvis', scan_id.split('.')[0] + '_on_source.stl')
+ # if os.path.exists(savepath_on_pv) and os.path.exists(savepath_on_npy) and os.path.exists(savepath_off_npy):
+ # return
+ #path_examples = "/Users/jyn/jyn/research/projects/NAISR/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/3d/hippocampi/data/sub-ADNI002S0729_ses-M00.vtk"
+
+ # off surface points
+ #mesh = surface_construction(path_seg)
+ mesh, mesh_source = read_and_align_mesh(path_seg, path_target)
+ # on surface points
+ mesh.export(savepath_on_pv_aligned)
+ mesh_source.export(savepath_on_pv_source)
+
+ del mesh_source
+
+ # off surface points
+ arr_off = calculate_sdf_from_mesh(mesh)
+
+ np.save(savepath_on_npy, np.concatenate((np.array(mesh.vertices), np.array(mesh.vertex_normals)), axis=-1))
+ np.save(savepath_off_npy, arr_off)
+
+ #del arr_off, mesh, pv_data
+
+ return
+
+def generate_volume_for_a_seg(path_seg, path_target):
+ # off surface points
+ mesh = read_and_align_mesh(path_seg, path_target)
+
+ return
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ "Given some groups (can be scans for one subject), this script generates their airway min-max plot")
+ parser.add_argument("--path_seg", type=str, required=True)
+ parser.add_argument("--root_dataset", type=str, required=True)
+ parser.add_argument("--path_target", type=str, required=True)
+ args = parser.parse_args()
+
+ root_dataset = args.root_dataset
+ path_seg = args.path_seg
+ path_target = args.path_target
+ generate_shape_and_sdf_for_a_seg(path_seg, root_dataset, path_target)
\ No newline at end of file
diff --git a/publicdata/run_generation.py b/publicdata/run_generation.py
new file mode 100644
index 0000000..6ba688b
--- /dev/null
+++ b/publicdata/run_generation.py
@@ -0,0 +1,132 @@
+
+from utils_2d import *
+from deformation import *
+import pandas as pd
+from tqdm import trange
+if __name__ == "__main__":
+ np.random.seed(980)
+ '''
+ train
+ '''
+ # define the dataset
+ NUM_OF_PATIENTS = 1000
+
+ #
+ path_starman_vtk = "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+ path_control_points = "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+ # "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+ pv_template = pv.read(path_starman_vtk)
+ arr_control_pts = np.loadtxt(path_control_points)
+
+ arr_resampled_pts = resample_polygon(pv_template.points, n_points=1000)
+ arr_resampled_lines = np.concatenate((np.ones(len(arr_resampled_pts)-1)[:, None]*2,
+ np.arange(len(arr_resampled_pts)-1)[:, None],
+ np.arange(len(arr_resampled_pts)-1)[:, None]+1), axis=-1)
+ arr_resampled_lines = list(arr_resampled_lines)
+ arr_resampled_lines.append(np.array([2, 999, 0]))
+ arr_resampled_lines = np.array(arr_resampled_lines).ravel()
+
+
+ pv_template.points = np.concatenate((arr_resampled_pts, np.zeros_like(arr_resampled_pts[:, [0]])), axis=-1)
+ pv_template.lines = arr_resampled_lines.astype('int')
+ pv_template.save("/home/jyn/NAISR/examples/starman/template.vtk")
+ np.save("/home/jyn/NAISR/examples/starman/control_template.npy", arr_control_pts)
+
+
+ rootpath = '/playpen-raid/jyn/NAISR/NAISR/examples/starman/train/' #"/home/jyn/NAISR/publicdata/starman"
+ if not os.path.exists(rootpath):
+ os.mkdir(rootpath)
+
+ list_data = []
+ for ith_subject in trange(NUM_OF_PATIENTS):
+ # generate longitudinal templates
+ num_of_observations = np.random.randint(1, 10)
+
+ #
+ PID ='{0:04}'.format(ith_subject)
+ arr_deformed_contour, arr_deformed_controls = deform_polygon_randomly(pv_template.points[:, [0, 1]], arr_control_pts[:, [0, 1]])
+ save_personalized_template(pv_template, arr_deformed_contour, rootpath, PID)
+ save_personalized_template_contour_pts( arr_deformed_contour, rootpath, PID)
+ save_personalized_template_control_pts(arr_deformed_controls, rootpath, PID)
+
+ pv_deformed_contour = pv_template.copy()
+ arr_deformed_contour = np.concatenate((arr_deformed_contour, np.zeros_like(arr_deformed_contour[:, [0]])), axis=-1)
+ #arr_deformed_controls = np.concatenate((arr_deformed_controls, np.zeros_like(arr_deformed_controls[:, [0]])), axis=-1)
+
+ pv_deformed_contour.points = arr_deformed_contour
+ list_data_of_current_subj = generate_dataset_for_one_subject(PID,
+ pv_deformed_contour,
+ arr_deformed_controls,
+ num_of_shapes=num_of_observations,
+ num_of_covariates=2,
+ rootoath=rootpath)
+ list_data+=list_data_of_current_subj
+
+ # save the data set info to a csv
+ savepath_dataset = "/home/jyn/NAISR/examples/starman/2dshape_train.csv"
+ pd.DataFrame.from_records(list_data).to_csv(savepath_dataset)
+
+
+ '''
+ test
+ '''
+
+ # define the dataset
+ NUM_OF_PATIENTS = 1000
+
+ #
+ path_starman_vtk = "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+ path_control_points = "/home/jyn/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+ # "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+ pv_template = pv.read(path_starman_vtk)
+ arr_control_pts = np.loadtxt(path_control_points)
+
+ arr_resampled_pts = resample_polygon(pv_template.points, n_points=1000)
+ arr_resampled_lines = np.concatenate((np.ones(len(arr_resampled_pts)-1)[:, None]*2,
+ np.arange(len(arr_resampled_pts)-1)[:, None],
+ np.arange(len(arr_resampled_pts)-1)[:, None]+1), axis=-1)
+ arr_resampled_lines = list(arr_resampled_lines)
+ arr_resampled_lines.append(np.array([2, 999, 0]))
+ arr_resampled_lines = np.array(arr_resampled_lines).ravel()
+
+
+ pv_template.points = np.concatenate((arr_resampled_pts, np.zeros_like(arr_resampled_pts[:, [0]])), axis=-1)
+ pv_template.lines = arr_resampled_lines.astype('int')
+ pv_template.save("/home/jyn/NAISR/examples/starman/template.vtk")
+ np.save("/home/jyn/NAISR/examples/starman/control_template.npy", arr_control_pts)
+
+ rootpath = '/playpen-raid/jyn/NAISR/NAISR/examples/starman/test/' #"/home/jyn/NAISR/publicdata/starman"
+ if not os.path.exists(rootpath):
+ os.mkdir(rootpath)
+
+ list_data = []
+ for ith_subject in trange(NUM_OF_PATIENTS):
+ # generate longitudinal templates
+ num_of_observations = np.random.randint(1, 10)
+
+ #
+ PID ='{0:04}'.format(ith_subject)
+ arr_deformed_contour, arr_deformed_controls = deform_polygon_randomly(pv_template.points[:, [0, 1]], arr_control_pts[:, [0, 1]])
+ save_personalized_template(pv_template, arr_deformed_contour, rootpath, PID)
+ save_personalized_template_contour_pts(arr_deformed_contour, rootpath, PID)
+ save_personalized_template_control_pts(arr_deformed_controls, rootpath, PID)
+
+
+ pv_deformed_contour = pv_template.copy()
+ arr_deformed_contour = np.concatenate((arr_deformed_contour, np.zeros_like(arr_deformed_contour[:, [0]])), axis=-1)
+ #arr_deformed_controls = np.concatenate((arr_deformed_controls, np.zeros_like(arr_deformed_controls[:, [0]])), axis=-1)
+
+ pv_deformed_contour.points = arr_deformed_contour
+ list_data_of_current_subj = generate_dataset_for_one_subject(PID,
+ pv_deformed_contour,
+ arr_deformed_controls,
+ num_of_shapes=num_of_observations,
+ num_of_covariates=4,
+ rootoath=rootpath)
+ list_data+=list_data_of_current_subj
+
+ # save the data set info to a csv
+ savepath_dataset = "/home/jyn/NAISR/examples/starman/2dshape_test.csv"
+ pd.DataFrame.from_records(list_data).to_csv(savepath_dataset)
\ No newline at end of file
diff --git a/publicdata/test_2dsdf.py b/publicdata/test_2dsdf.py
new file mode 100644
index 0000000..68cdcca
--- /dev/null
+++ b/publicdata/test_2dsdf.py
@@ -0,0 +1,86 @@
+import numpy as np
+from shapely.geometry import Polygon, Point
+from shapely.geometry import Point, LineString, Polygon
+import matplotlib.pyplot as plt
+import pyvista as pv
+
+
+
+
+
+
+def show_shapely_polygon(polygo):
+ fig, ax = plt.subplots()
+ ax.plot(*polygon.exterior.xy, 'b-', label='Polygon')
+ ax.set_aspect('equal', adjustable='datalim')
+ ax.legend()
+ plt.show()
+ return
+
+def signed_distance_polygon(polygon, point):
+ """
+ Calculate the signed distance from a point to a polygon.
+
+ Parameters:
+ polygon (Polygon): Shapely Polygon object representing the polygon.
+ point (Point): Shapely Point object representing the point.
+
+ Returns:
+ float: Signed distance from the point to the polygon.
+ """
+ if polygon.contains(point):
+ return -point.distance(polygon.boundary)
+ else:
+ return point.distance(polygon.boundary)
+
+def extract_2d_sdf_from_contour(query_points, arr_contour_pts):
+ polygon = Polygon(arr_contour_pts)
+ #show_shapely_polygon(polygon)
+ # Define a point for which you want to calculate the signed distance
+ list_of_2dsdf = []
+ for ith_query in query_points:
+ query_point = Point(ith_query)
+ # Calculate the signed distance
+ signed_dist = signed_distance_polygon(polygon, query_point)
+ # get 2dsdf
+ list_of_2dsdf.append(signed_dist)
+ return np.array(list_of_2dsdf)[:, None]
+
+def generate_off_surface_points(number_of_points=250000):
+ # the range of the data is around [-2, 2]
+ x = (np.random.rand(number_of_points) - 0.5) * 4
+ y = (np.random.rand(number_of_points) - 0.5) * 4
+ coords = np.concatenate((x[:, None], y[:, None]), axis=-1)
+ return coords
+
+
+
+def calculate_2dsdf_from_polygon(arr_contour_pts, number_of_points=500000):
+ query_points = generate_off_surface_points(number_of_points=number_of_points)
+ arr_sdf = extract_2d_sdf_from_contour(query_points, arr_contour_pts)
+ npz_sdf = np.concatenate((query_points, arr_sdf), axis=-1)
+ return npz_sdf
+
+
+
+
+path_starman_vtk = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+path_control_points = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+ # "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+mesh = pv.read(path_starman_vtk)
+
+
+# Define the vertices of the polygon
+polygon_vertices = mesh.points #[(0, 0), (2, 0), (2, 2), (1, 3), (0, 2)]
+
+polygon = Polygon(polygon_vertices)
+show_shapely_polygon(polygon)
+# Define a point for which you want to calculate the signed distance
+query_point = Point(0, 0)
+query_point2 = Point(polygon_vertices[0])
+
+# Calculate the signed distance
+signed_dist = signed_distance_polygon(polygon, query_point)
+print("Signed distance:", signed_dist)
+
diff --git a/publicdata/utils_2d.py b/publicdata/utils_2d.py
new file mode 100644
index 0000000..488c725
--- /dev/null
+++ b/publicdata/utils_2d.py
@@ -0,0 +1,305 @@
+import os
+
+
+import argparse
+
+import numpy as np
+
+from shapely.geometry import Point, LineString, Polygon
+import matplotlib.pyplot as plt
+import pyvista as pv
+
+import utils
+UP_VECTOR = np.array([0., 1.])
+FORWARD_VECTOR = np.array([1., 0.]) #* 0.4
+
+def cond_mkdir(path):
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+def resample_polygon(xy: np.ndarray, n_points: int = 100) -> np.ndarray:
+ # Cumulative Euclidean distance between successive polygon points.
+ # This will be the "x" for interpolation
+ d = np.cumsum(np.r_[0, np.sqrt((np.diff(xy, axis=0) ** 2).sum(axis=1))])
+
+ # get linearly spaced points along the cumulative Euclidean distance
+ d_sampled = np.linspace(0, d.max(), n_points)
+
+ # interpolate x and y coordinates
+ xy_interp = np.c_[
+ np.interp(d_sampled, d, xy[:, 0]),
+ np.interp(d_sampled, d, xy[:, 1]),
+ ]
+
+ return xy_interp
+
+
+def show_shapely_polygon(polygon):
+ fig, ax = plt.subplots()
+ ax.plot(*polygon.exterior.xy, 'b-', label='Polygon')
+ ax.set_aspect('equal', adjustable='datalim')
+ ax.legend()
+ plt.show()
+ return
+
+
+def signed_distance_polygon(polygon, point):
+ """
+ Calculate the signed distance from a point to a polygon.
+
+ Parameters:
+ polygon (Polygon): Shapely Polygon object representing the polygon.
+ point (Point): Shapely Point object representing the point.
+
+ Returns:
+ float: Signed distance from the point to the polygon.
+ """
+ if polygon.contains(point):
+ return -point.distance(polygon.boundary)
+ else:
+ return point.distance(polygon.boundary)
+
+
+def extract_2d_sdf_from_contour(query_points, arr_contour_pts):
+ polygon = Polygon(arr_contour_pts)
+ # show_shapely_polygon(polygon)
+ # Define a point for which you want to calculate the signed distance
+ list_of_2dsdf = []
+ for ith_query in query_points:
+ query_point = Point(ith_query)
+ # Calculate the signed distance
+ signed_dist = signed_distance_polygon(polygon, query_point)
+ # get 2dsdf
+ list_of_2dsdf.append(signed_dist)
+ return np.array(list_of_2dsdf)[:, None]
+
+
+def generate_off_surface_points(number_of_points=250000):
+ # the range of the data is around [-2, 2]
+ x = (np.random.rand(number_of_points) - 0.5) * 6
+ y = (np.random.rand(number_of_points) - 0.5) * 6
+ coords = np.concatenate((x[:, None], y[:, None]), axis=-1)
+ return coords
+
+
+def calculate_2dsdf_from_polygon(arr_contour_pts, number_of_points=500000):
+ query_points = generate_off_surface_points(number_of_points=number_of_points)
+ arr_sdf = extract_2d_sdf_from_contour(query_points, arr_contour_pts)
+ npz_sdf = np.concatenate((query_points, arr_sdf), axis=-1)
+ return npz_sdf
+
+
+
+
+def Gaussian(x, z, sigma=0.5):
+ return np.exp((-(np.linalg.norm(x - z, axis=1) ** 2)) / (2 * sigma ** 2))
+
+
+def create_deformation(points, control_point, VECTOR):
+ deformed = Gaussian(points, control_point)[:, None] @ VECTOR[None, :] # + points
+ return deformed
+
+
+def apply_deformation(points, deformation, covariate_value=1.):
+ return points + deformation * covariate_value
+
+
+def compute_normals(arr_contour):
+ arr_contour_pts = arr_contour.copy()
+ arr_contour_pts = arr_contour_pts[:, [0, 1]]
+ normals = []
+ for i in range(len(arr_contour_pts)):
+ x1, y1 = arr_contour_pts[i]
+ x2, y2 = arr_contour_pts[(i + 1) % len(arr_contour_pts)] # wrap around for the last point
+ dx = x2 - x1
+ dy = y2 - y1
+ normals.append((-dy, dx))
+ arr_normals = np.array(normals) / np.linalg.norm(normals, axis=-1, keepdims=1)
+ #arr_normals = np.concatenate((arr_normals, np.zeros_like(arr_normals[:, [0]])), axis=-1)
+ return arr_normals
+
+
+def sampling_the_covariate_space(num_of_shapes, num_of_covariates=4):
+
+ list_of_covariates = []
+ for i in range(num_of_shapes):
+ list_of_covariates.append(np.random.rand(num_of_covariates))
+ arr_covariates = 2 * (np.array(list_of_covariates) - 0.5)
+ return arr_covariates
+
+
+def generate_shape_conditioned_on_covariates(arr_points, covariates, arr_control_pts):
+ covariates = [covariates[0], covariates[0], covariates[1], covariates[1]]
+ arr_deformed = arr_points.copy()
+ #arr_deformed = polydata.points.copy()
+ list_of_VECTORS = [UP_VECTOR, UP_VECTOR, FORWARD_VECTOR * (-1), FORWARD_VECTOR]
+ for ith_cov in range(len(covariates)):
+ if ith_cov < 2:
+ ith_control_pts = arr_control_pts[ith_cov]
+ arr_deformed = apply_deformation(points=arr_deformed,
+ deformation= create_deformation(arr_points, control_point=ith_control_pts, VECTOR=list_of_VECTORS[ith_cov]),
+ covariate_value=covariates[ith_cov])
+ else:
+ ith_control_pts = arr_control_pts[ith_cov]
+ arr_deformed = apply_deformation(points=arr_deformed,
+ deformation= create_deformation(arr_points, control_point=ith_control_pts, VECTOR=list_of_VECTORS[ith_cov]),
+ covariate_value=covariates[ith_cov] + 1)
+ return arr_deformed
+
+
+
+def generate_dataset_for_one_subject(PID,
+ pv_contour,
+ arr_control_pts,
+ num_of_shapes,
+ num_of_covariates=2,
+ rootoath='/playpen-raid/jyn/NAISR/NAISR/examples/starmen/'):
+
+ arr_contour_pts = pv_contour.points[:, [0,1]]
+
+ # get path
+ path_2d_shape = os.path.join(rootoath, '2dshape')
+ path_2d_sdf = os.path.join(rootoath, '2dsdf')
+ cond_mkdir(path_2d_shape)
+ cond_mkdir(path_2d_sdf)
+
+
+ # sample covariates uniformly
+ arr_covariates = sampling_the_covariate_space(num_of_shapes, num_of_covariates)
+
+ list_current_data = []
+ for i in range(len(arr_covariates)):
+ # 2d shape, which are the on-surface points, with normal vectors
+ arr_deformed_points = generate_shape_conditioned_on_covariates(arr_contour_pts, arr_covariates[i], arr_control_pts)
+ arr_deformed_normals = compute_normals(arr_deformed_points)
+
+ arr_on = np.concatenate((arr_deformed_points, arr_deformed_normals), axis=-1)
+
+ # 2d sdf, which are points with sdf
+ arr_off = calculate_2dsdf_from_polygon(arr_deformed_points, number_of_points=5000)
+
+ current_id = str(PID) + '_' + str(i) #str(arr_covariates[i][0]) + '_' + str(arr_covariates[i][1]) + '_' + str(arr_covariates[i][2]) + '_' + str(arr_covariates[i][3])
+ # get savepath
+ filename_on_npy = current_id + '_on.npy'
+ filename_off_npy = current_id + '_off.npy'
+
+ savepath_on_npy = os.path.join(path_2d_shape, filename_on_npy)
+ savepath_off_npy = os.path.join(path_2d_sdf, filename_off_npy)
+
+ np.save(savepath_on_npy, arr_on)
+ np.save(savepath_off_npy, arr_off)
+
+ # save visualize
+ filename_on_vtk = current_id + '_on.vtk'
+
+ savepath_on_vis = os.path.join(path_2d_shape, filename_on_vtk)
+ pv_deformed = pv_contour.copy()
+ pv_deformed.points = np.concatenate((arr_on[:, [0,1]], np.zeros_like(arr_on[:, [0]])), axis=-1)
+ pv_deformed.save(savepath_on_vis)
+
+ #
+ list_current_data.append({"id": current_id,
+ "PID": PID,
+ "cov_1": arr_covariates[i][0],
+ "cov_2": arr_covariates[i][1],
+ "2dsdf":savepath_off_npy,
+ "2dshape": savepath_on_npy})
+
+ return list_current_data
+
+
+def save_personalized_template(pv_template, new_verts, rootpath, PID):
+ pv_new_template = pv_template.copy()
+ pv_new_template.points = np.concatenate((new_verts, np.zeros_like(new_verts[:, [0]])), axis=-1)
+ dir_template = os.path.join(rootpath, 'template')
+ cond_mkdir(dir_template)
+ savepath = os.path.join(dir_template, str(PID) + "_template.vtk")
+ pv_new_template.save(savepath)
+ return
+
+def save_personalized_template_contour_pts(new_verts, rootpath, PID):
+ #savepath = os.path.join(rootpath, 'template', str(PID) + "_template_contour_pts.npy")
+ dir_template = os.path.join(rootpath, 'template')
+ cond_mkdir(dir_template)
+ savepath = os.path.join(dir_template, str(PID) + "_template_contour_pts.npy")
+ np.save(savepath, new_verts)
+ return
+
+def save_personalized_template_control_pts(new_verts, rootpath, PID):
+
+ #pv_new_template = pv_template.copy()
+ #pv_new_template.points = np.concatenate((new_verts, np.zeros_like(new_verts[:, [0]])), axis=-1)
+ dir_template = os.path.join(rootpath, 'template')
+ cond_mkdir(dir_template)
+ savepath = os.path.join(dir_template, str(PID) + "_template_control_pts.npy")
+ np.save(savepath, new_verts)
+ return
+
+if __name__ == "__main__":
+ path_starman_vtk = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__Template__GroundTruth.vtk"
+ path_control_points = "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen_for_simulation/data_ground_truth/ForSimulation__ControlPoints__GoundTruth.txt"
+ # "/Users/jyn/jyn/research/projects/NAISR/publicdata/deformetrica/examples/longitudinal_atlas/landmark/2d/starmen/data/ForInitialization__ControlPoints__FromLongitudinalAtlas.txt"
+
+ mesh = pv.read(path_starman_vtk)
+ # mesh.points = mesh.points[:, [0,1]]
+ control_points = np.loadtxt(path_control_points)
+ arr_cp = np.concatenate((control_points, np.zeros_like(control_points[:, [0]])), axis=1)
+
+ chart = pv.Chart2D()
+ x = mesh.points[:, 0]
+ y = mesh.points[:, 1]
+ _ = chart.line(x, y, "y", 4)
+ # chart.show()
+
+
+ pl = pv.Plotter()
+ pl.add_mesh(mesh)
+ pl.add_points(arr_cp[[0]], color='r')
+
+ # 1.
+ arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=1.)
+ deformed_mesh = mesh.copy()
+ deformed_mesh.points = arr_deformed
+ deformed_mesh.normals = compute_normals(deformed_mesh.points)
+ deformed_mesh.save('./demo.vtk')
+ pl.add_mesh(deformed_mesh, color='y')
+ pl.add_arrows(deformed_mesh.points, deformed_mesh.normals )
+
+ # 0.1
+ arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=0.1)
+ deformed_mesh1 = mesh.copy()
+ deformed_mesh1.points = arr_deformed
+ pl.add_mesh(deformed_mesh1, color='g')
+
+ # 0.5
+ arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=0.5)
+ deformed_mesh2 = mesh.copy()
+ deformed_mesh2.points = arr_deformed
+ pl.add_mesh(deformed_mesh2, color='b')
+
+ # 0.1
+ arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=-0.1)
+ deformed_mesh3 = mesh.copy()
+ deformed_mesh3.points = arr_deformed
+ pl.add_mesh(deformed_mesh3, color='cyan')
+
+ # 0.5
+ arr_deformed = apply_deformation(points=mesh.points,
+ deformation=create_deformation(mesh.points, control_point=arr_cp[0]),
+ covariate_value=-0.5)
+ deformed_mesh4 = mesh.copy()
+ deformed_mesh4.points = arr_deformed
+ pl.add_mesh(deformed_mesh4, color='purple')
+
+ pl.view_xy()
+ pl.show()
+
+
diff --git a/publicdata/utils_3d.py b/publicdata/utils_3d.py
new file mode 100644
index 0000000..e67b31e
--- /dev/null
+++ b/publicdata/utils_3d.py
@@ -0,0 +1,62 @@
+import argparse
+
+from mesh_to_sdf import sample_sdf_near_surface
+
+import trimesh
+import pyrender
+import numpy as np
+import SimpleITK as sitk
+import trimesh
+import mesh_to_sdf
+
+def calculate_sdf_from_mesh(surf_mesh):
+ # get centered mesh: mesh
+ mesh = surf_mesh.copy()
+ centroid = surf_mesh.bounding_box.centroid
+ vertices = surf_mesh.vertices - surf_mesh.bounding_box.centroid
+ distances = np.max(np.linalg.norm(vertices, axis=1))
+
+ # get sdf field, the mesh is normalized automatically
+ points, sdf = sample_sdf_near_surface(surf_mesh, number_of_points=250000, sign_method='depth')
+ # get off-surface points
+ off_surf_points = generate_off_surface_points(number_of_points=250000) + centroid
+ off_surf_sdf = mesh_to_sdf.mesh_to_sdf(mesh, off_surf_points, surface_point_method='scan', sign_method='depth', bounding_radius=None, scan_count=100, scan_resolution=1000, sample_point_count=10000000, normal_sample_count=11)
+ off_surf_points = off_surf_points[off_surf_sdf > 0 * 0.1]
+ off_surf_sdf = off_surf_sdf[off_surf_sdf > 0 * 0.1]
+ # denormalize
+ points = points * distances + centroid
+ sdf *= distances
+ #
+ points = np.concatenate((points, off_surf_points), axis=-2)
+ sdf = np.concatenate((sdf[:, None], off_surf_sdf[:, None]), axis=0)
+
+
+ '''
+ colors = np.zeros(points.shape)
+ colors[sdf[:, 0] < 0, 2] = 1
+ colors[sdf[:, 0] > 6, 1] = 1
+
+ cloud = pyrender.Mesh.from_points(points, colors=colors)
+ scene = pyrender.Scene()
+ scene.add(cloud)
+ viewer = pyrender.Viewer(scene, use_raymond_lighting=True, point_size=2)
+ #viewer.save_gif(savepath)
+ viewer.close()
+ '''
+ npz_sdf = np.concatenate((points, sdf), axis=-1)
+ #np.save(savepath, npz_sdf)
+ del points, sdf, off_surf_points, off_surf_sdf
+ return npz_sdf
+
+
+
+def generate_off_surface_points(number_of_points=250000):
+
+ # numbers set according to the hippocampus dataset
+ x = (np.random.rand(number_of_points) - 0.5) * 60
+ y = (np.random.rand(number_of_points) - 0.5) * 60
+ z = (np.random.rand(number_of_points) - 0.5) * 60
+
+ coords = np.concatenate((x[:, None], y[:, None], z[:, None]), axis=-1)
+ return coords
+
From fd010d624f7960d2c66427d9b9a6dea844f437b3 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 16:31:15 -0400
Subject: [PATCH 23/37] a
---
.idea/workspace.xml | 1 -
naisr_meshing.py | 22 +++++++++++-----------
2 files changed, 11 insertions(+), 12 deletions(-)
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 97a3c2a..a6f839d 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -6,7 +6,6 @@
-
diff --git a/naisr_meshing.py b/naisr_meshing.py
index 0611406..48dd0e5 100755
--- a/naisr_meshing.py
+++ b/naisr_meshing.py
@@ -1147,18 +1147,18 @@ def convert_3d_sdf_samples_to_ply(
##try:
print(numpy_3d_sdf_tensor.max())
print(numpy_3d_sdf_tensor.min())
- try:
- verts, faces, normals, values = measure.marching_cubes_lewiner(
- numpy_3d_sdf_tensor, level=0., spacing=[voxel_size] * 3
- )
+ #try:
+ verts, faces, normals, values = measure.marching_cubes_lewiner(
+ numpy_3d_sdf_tensor, level=0., spacing=[voxel_size] * 3
+ )
- except:
- path_aligned_surface = os.path.join(savedir, 'surface.stl')
- ply_filename_out = os.path.join(savedir, 'surface.ply')
- surface = pv.PolyData(verts)
- surface.save(path_aligned_surface)
- surface.save(ply_filename_out)
- return verts, faces
+ # except:
+ # path_aligned_surface = os.path.join(savedir, 'surface.stl')
+ # ply_filename_out = os.path.join(savedir, 'surface.ply')
+ # surface = pv.PolyData(verts)
+ # surface.save(path_aligned_surface)
+ # surface.save(ply_filename_out)
+ # return verts, faces
# transform from voxel coordinates to camera coordinates
# note x and y are flipped in the output of marching_cubes
From 97e4dd5731d7518d4132f6b423df9c0cf1f24af2 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 16:39:24 -0400
Subject: [PATCH 24/37] a
---
.idea/workspace.xml | 1 +
naisr_meshing.py | 2 +-
2 files changed, 2 insertions(+), 1 deletion(-)
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index a6f839d..406f4ec 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -6,6 +6,7 @@
+
diff --git a/naisr_meshing.py b/naisr_meshing.py
index 48dd0e5..e261f6d 100755
--- a/naisr_meshing.py
+++ b/naisr_meshing.py
@@ -344,7 +344,7 @@ def create_mesh_3d_reconstruction(
head = 0
num_samples = N ** 3
- max_batch = 32 ** dim
+ max_batch = 64 ** dim
# NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
voxel_origin = np.array([-1., -1., -1.]) * 3
From 90fc1032529606aaa6abc5fa3a1edb5d6307698f Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 16:54:25 -0400
Subject: [PATCH 25/37] a
---
naisr_meshing.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/naisr_meshing.py b/naisr_meshing.py
index e261f6d..48dd0e5 100755
--- a/naisr_meshing.py
+++ b/naisr_meshing.py
@@ -344,7 +344,7 @@ def create_mesh_3d_reconstruction(
head = 0
num_samples = N ** 3
- max_batch = 64 ** dim
+ max_batch = 32 ** dim
# NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
voxel_origin = np.array([-1., -1., -1.]) * 3
From 99f4c95186cb36e4142a66b5e188ba17fc3825f2 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 17:19:39 -0400
Subject: [PATCH 26/37] a
---
naisr_meshing.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/naisr_meshing.py b/naisr_meshing.py
index 48dd0e5..e433d7c 100755
--- a/naisr_meshing.py
+++ b/naisr_meshing.py
@@ -1148,7 +1148,7 @@ def convert_3d_sdf_samples_to_ply(
print(numpy_3d_sdf_tensor.max())
print(numpy_3d_sdf_tensor.min())
#try:
- verts, faces, normals, values = measure.marching_cubes_lewiner(
+ verts, faces, normals, values = measure.marching_cubes(
numpy_3d_sdf_tensor, level=0., spacing=[voxel_size] * 3
)
From 0e6a27b845d7062e986d26de1e57c60ee9a1fd84 Mon Sep 17 00:00:00 2001
From: Yining Jiao <44992829+jiaoyining@users.noreply.github.com>
Date: Wed, 24 Apr 2024 20:50:44 -0400
Subject: [PATCH 27/37] Update README.md
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 8ea1aed..85af4cf 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
# NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Pytorch implementation for our `NAISR` paper
-[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024 Spotlight.
+[NAISR: A 3D Neural Additive Model for Interpretable Shape Representation](https://arxiv.org/abs/2303.09234), ICLR 2024 **Spotlight**.
Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer.
UNC-Chapel Hill
From 47a55d445fb39cbca91c2b1a7dc5aff52f0c86b3 Mon Sep 17 00:00:00 2001
From: Yining Jiao
Date: Wed, 24 Apr 2024 20:55:37 -0400
Subject: [PATCH 28/37] COLAB
---
.idea/workspace.xml | 1 +
docs/index.html | 10 ++++++++++
naisr_meshing.py | 23 +++++++++++------------
3 files changed, 22 insertions(+), 12 deletions(-)
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 406f4ec..cc9a6b7 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -6,6 +6,7 @@
+
diff --git a/docs/index.html b/docs/index.html
index 45dd3c3..2072c43 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -104,6 +104,16 @@
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Pytorch implementation for our `ICLR 2024 Spotlight` paper 