Project to put the Unite the People project in a Docker image. The project contains 2 docker images because the pose and segmentation scripts require different environments.
- up_caffe: run the pose prediction with the p91 model
- up_deeplab: run the segmentation script with the s31 model
When you run the containers you need to mount a local folder which contains
the source images that you want to use for pose predictions and segmentations.
$ docker pull lukin0110/up_caffe
$ docker pull lukin0110/up_deeplab
$ docker run -it -v "$(pwd)"/input:/input lukin0110/up_caffe pose input/debruyne1.jpg
$ docker run -it -v "$(pwd)"/input:/input lukin0110/up_caffe bodyfit input/debruyne1.jpg
It's required to use NVIDIA Docker to run the segmentation since CUDA is being used.
$ nvidia-docker run -it -v "$(pwd)"/input:/input lukin0110/up_deeplab segmentation input/debruyne1.jpg
Before you execute the script: download SMPL_python_v.1.0.0.zip from http://smpl.is.tue.mpg.de/ and put it in the ./models folder. You need an account to download the package.
./prepare.sh
Build image:
$ docker-compose build
Generate pose prediction:
$ docker-compose run caffe pose input/debruyne1.jpg
This will generate .npz and .png files in the input folder.
Generate 3D body:
$ docker-compose run caffe bodyfit input/debruyne1.jpg
Generate segmentation:
$ nvidia-docker-compose run deeplab segmentation input/debruyne1.jpg
Install nvidia docker compose if it is not present yet: pip install nvidia-docker-compose.
$ docker tag demo2d3d_caffe:latest lukin0110/up_caffe:latest
$ docker tag demo2d3d_deeplab:latest lukin0110/up_deeplab:latest
$ docker push lukin0110/up_caffe:latest
$ docker push lukin0110/up_deeplab:latest