An R, PyTorch, and MATLAB implementation of "Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics"
Each folder named after the coding language contains related codes for data processing, modeling, post-processing, or performance evaluation.
This implementation requires python >= 3.7 and requires the following dependencies before the full installation. The version numbers have been tested and shown to work, other versions are likely, but not guaranteed, to work.
- Progressbar2
- vectorheadt
- Suitesparse (used for the Vector Heat Method)
Here we refer to HSN for the instruction on installing the above three dependencies
Clone this repository and its submodules
$ git clone --recurse-submodules https://github.com/rubenwiersma/hsn.git
Install the vectorheat python module, explained in the following subsection.
[Update April 2021] Nick Sharp has created his own bindings for the Geometry Central library, called Potpourri3d. This library includes computations of the vector heat method on meshes and point clouds. You can install it with pip:
$ pip install potpourri3d
The transforms used in this repository make use of our own binding, which can be installed as follows:
To perform computations on the mesh - e.g. Vector Heat Method, surface area computation, etc. - we use Geometry Central. We have created a small Python binding into a C++ function that computes these quantities globally for each vertex in a mesh. Run the following commands in your shell to install the extension:
$ pip install ./vectorheat
Having trouble building? First make sure that you have the latest version of CMake installed (> 3.10). Next, check that every dependency is present in this repo (pybind11, geometrycentral). If not, you might not have cloned submodules. To fix this:
$ git submodule update --recursive
Suitesparse? When processing shapes with the Vector Heat Method, you might run into a solver error. This is fixed when you build the vectorheat extension with suitesparse. Installation in Linux with:
$ apt-get install libsuitesparse-dev
After install the dependencies, use pip or conda to install dependencies based on the package list: "python/requirements.txt":
For pip installation:
pip3 install -r python/requirements.txtFor conda installation:
conda install --yes --file python/requirements.txtOnce the dependecies and the packages are installed, users can refer to "python/Cuboid Descriptor Learning.ipynb" for training and evaluating the morphVQ model.