docker images method notes

• video demo has added, You need to do these things:

  • use the new Dockerfile to build image
    • python lib version all specified
  • run and within the container
    • docker run --gpus all -v DensePose/DensePoseData:/denseposedata -v DensePose/tools:/densepose/tools_kk -it densepose:c2-cuda9-cudnn7 bash
  • replace the local DensePoseData directory with the host one
    • mv /densepose/DensePoseData /densepose/DensePoseDataLocal
    • ln -s /denseposedata DensePoseData
  • copy tools_kk/vis_kk.py to detectron/utils(you can also do it in infer_vid.py when running demo)
  • install ffmpeg(you can also do it in dockerfile when building the image)
  • save container to new image
    • docker commit $(docker ps --last 1 -q) densepose:c2-cuda9-cudnn7-kk

• video demo command:

  • docker run --rm --gpus all -v /home/kevin/aj/DensePose/DensePoseData:/denseposedata -v /home/kevin/aj/DensePose/tools:/densepose/tools_kk -it densepose:c2-cuda9-cudnn7-kk python2 tools_kk/infer_vid.py --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml --output-dir DensePoseData/infer_out/ --wts ./DensePoseData/DensePose_ResNet101_FPN_s1x-e2e.pkl --input-file tools_kk/video.mp4

• image demo command:

  • docker run --rm --gpus all -v /home/kevin/aj/DensePose/DensePoseData:/denseposedata -v /home/kevin/aj/DensePose/tools:/densepose/tools_kk -it densepose:c2-cuda9-cudnn7-kk python2 tools_kk/infer_simple.py --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml --output-dir DensePoseData/infer_out/ --image-ext jpg --wts ./DensePoseData/DensePose_ResNet101_FPN_s1x-e2e.pkl DensePoseData/demo_data/grc.jpg

• reference

  • https://github.com/trrahul/densepose-video

DensePose:

Dense Human Pose Estimation In The Wild

Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos

[densepose.org] [arXiv] [BibTeX]

Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2.

In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.

Installation

Please find installation instructions for Caffe2 and DensePose in INSTALL.md, a document based on the Detectron installation instructions.

Inference-Training-Testing

After installation, please see GETTING_STARTED.md for examples of inference and training and testing.

Notebooks

Visualization of DensePose-COCO annotations:

See notebooks/DensePose-COCO-Visualize.ipynb to visualize the DensePose-COCO annotations on the images:

---

DensePose-COCO in 3D:

See notebooks/DensePose-COCO-on-SMPL.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:

---

Visualize DensePose-RCNN Results:

See notebooks/DensePose-RCNN-Visualize-Results.ipynb to visualize the inferred DensePose-RCNN Results.

---

DensePose-RCNN Texture Transfer:

See notebooks/DensePose-RCNN-Texture-Transfer.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:

License

This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.

Citing DensePose

If you use Densepose, please use the following BibTeX entry.

  @InProceedings{Guler2018DensePose,
  title={DensePose: Dense Human Pose Estimation In The Wild},
  author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
  }