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.