Orthopedic Rehabilitation System

Basis paper:
Everybody Dance Now
Caroline Chan, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros

Prerequisites

1. PyTorch
2. Python Library Dominate

pip install dominate

3. Clone this repository

git clone https://github.com/AnnikaAlonzo/DSP-1-2223-C2-Orthopedic-Rehabilitation-Exercise-System.git

Training

Global Stage

We follow similar stage training as in pix2pixHD. We first train a "global" stage model at 512x256 resolution

# train a model at 512x256 resolution
python train_fullts.py \
--name MY_MODEL_NAME_global \
--dataroot MY_TRAINING_DATASET \
--checkpoints_dir WHERE_TO_SAVE_CHECKPOINTS \
--loadSize 512 \
--no_instance \
--no_flip \
--tf_log \
--label_nc 6

Testing

The full checkpoint will be loaded from --checkpoints_dir/--name (i.e. if flags: "--name foo \ ... --checkpoints_dir bar "" are included, checkpoints will be loaded from foo/bar) Replace --howmany flag with an upper bound on how many test examples you have

Global Stage

# test model at 512x256 resolution
python test_fullts.py \
--name MY_MODEL_NAME_global \
--dataroot MY_TEST_DATASET \
--checkpoints_dir CHECKPOINT_FILE_LOCATION \
--results_dir WHERE_TO_SAVE_RESULTS \
--loadSize 512 \
--no_instance \
--how_many 10000 \
--label_nc 6

Dataset preparation

Our dataset preparation code is based on output formats from OpenPose and currently supports the COCO, BODY_23, and BODY_25 pose output format as well as hand and face keypoints. To install and run OpenPose please follow the directions at the OpenPose repository.

graph_train.py

will prepare a train dataset with subfolders

• train_label (contains 1024x512 inputs)
• train_img (contains 1024x512 targets)
• train_facetexts128 (contains face 128x128 bounding box coordinates in .txt files) No smoothing

python graph_train.py \
--keypoints_dir /data/scratch/caroline/keypoints/jason_keys \
--frames_dir /data/scratch/caroline/frames/jason_frames \
--save_dir /data/scratch/caroline/savefolder \
--spread 4000 25631 1 \
--facetexts

graph_avesmooth.py

will prepare a dataset with averaged smoothed keypoints with subfolders (usually for validation)

• test_label (contains 1024x512 inputs)
• test_img (contains 1024x512 targets)
• test_factexts128 (contains face 128x128 bounding box coordinates in .txt files)

python graph_avesmooth.py \
--keypoints_dir /data/scratch/caroline/keypoints/wholedance_keys \
--frames_dir /data/scratch/caroline/frames/wholedance \
--save_dir /data/scratch/caroline/savefolder \
--spread 500 29999 4 \
--facetexts

Acknowledgements

Model code adapted from pix2pixHD and pytorch-CycleGAN-and-pix2pix

Data Preparation code based on outputs from OpenPose