Installation¶

Below, you’ll find instructions on how to install this module/package on your local machine. Please note that these instructions may be subject to modifications, and they are provided without any guarantees.

The latest version have been successfully tested with a Python environment running version 3.11 and PyTorch 2.0.

Warning

⚠️ Windows OS Not Supported

This package does not currently support Windows, you might be able to run it but it is not tested, and per detectron2’s installation guidelines , Windows is not officially supported.

Follow these steps to install and use the pupil-labs-dense-pose module:

Open a terminal window.
Run the following commands (change the python version to the one you have installed):

MacOS (Python 3.11)¶

In MacOS we can only use the CPU version of detectron2, when installing it from Meta’s repository there are some issues that you may find. So we will use @johnnynunez’s version of detectron2 that works with the latest pytorch version.

# Optional, but recommended, run it on a virtual environment
python3.11 -m venv venv
source venv/bin/activate
pip install -U pip setuptools wheel

# Install torch and torchvision
pip install torch==2.0.1 torchvision==0.15.2

# Now, we install detectron2, Meta hasn't updated it to run with the latest Pytorch version, but thanks to @johnnynunez
# we have a version that works with the latest. Grab the wheels for your version at https://github.com/johnnynunez/detectron2/actions/workflows/build-wheels.yml
# select the latest run for your system and matching Pytorch.
# Install them with pip, you will need to point to the wheel you downloaded, e.g.:

pip install detectron2-0.7-cp311-cp311-macosx_10_9_universal2.whl

# Installing `wheel` will avoid issues with poetry from Python, giving you errors with the torch module not being found even though it is installed.
# Now, we install densepose

export FORCE_CUDA="0" # as we don't have CUDA
pip install git+https://github.com/johnnynunez/detectron2@main#subdirectory=projects/DensePose

# Now we install the module
pip install git+https://github.com/pupil-labs/densepose-module
# And that's it!

Linux (Python 3.11)¶

On Linux we can either run inference on the CPU or the GPU (if we have CUDA installed). If you want to run it on the CPU, follow these steps:

CPU:¶

# Optional, but recommended, run it on a virtual environment
python3.11 -m venv venv
source venv/bin/activate
pip install -U pip setuptools wheel

# Install torch and torchvision
pip install torch==2.0.1 torchvision==0.15.2

# Now, we install detectron2, Meta hasn't update it to run with the latest Pytorch version, but thanks to @johnnynunez
# we have a version that works with the latest. Grab the wheels for your version at https://github.com/johnnynunez/detectron2/actions/workflows/build-wheels.yml
# and install them with pip, you will need to point to the wheel you downloaded, e.g.:

pip install detectron2-3.11-pytorch2.0.1-ubuntu-latest-wheel.whl

export FORCE_CUDA="0" # as we don't have CUDA
pip install git+https://github.com/johnnynunez/detectron2@main#subdirectory=projects/DensePose

# Now we install the module
pip install git+https://github.com/pupil-labs/densepose-module.git
# And that's it!

GPU:¶

# Optional, but recommended, run it on a virtual environment
python3.11 -m venv venv
source venv/bin/activate
pip install -U pip setuptools wheel

# Install torch and torchvision
pip3 install torch+cu torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Now, we install detectron2, Meta hasn't update to run with the latest Pytorch version, but thanks to @johnnynunez
# we have a version that works with the latest. There are wheels for cuda 11.8 and pytorch 2.01 at
#(https://app.circleci.com/pipelines/github/facebookresearch/detectron2/2924/workflows/9f85ee27-173e-494c-b699-8ceb110a3398/jobs/14336/artifacts)
# if you use a different version you will need to build it yourself.

pip install detectron2-0.7-cp311-cp311-linux_x86_64.whl
#or to try building your own wheels:
pip install git+https://github.com/johnnynunez/detectron2.git

export FORCE_CUDA="1" # as we want to use CUDA
# We might also need to specify the CUDA home directory
# like export CUDA_HOME="/usr/local/cuda-11.8"

pip install git+https://github.com/johnnynunez/detectron2@main#subdirectory=projects/DensePose

# Now we install the module
python -m pip install 'git+https://github.com/pupil-labs/densepose-module.git'
# And that's it!

After is installed properly, you should see a new package pupil_labs.dense_pose.