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Installation & Quick start

Environment

The code is developed using python 3.6.12, pytorch-1.4.0, and CUDA 10.0 on Ubuntu 18.04. For our experiments, we used 2 NVIDIA 2080Ti GPUs.

Installation

1.Create a conda virtual environment and activate it

conda create -n DCPose python=3.6.12
source activate DCPose

2.Install dependencies through DCPose_requirements.txt

pip install -r DCPose_requirement.txt

3.Install DCN

cd thirdparty/deform_conv
python setup.py develop

4.Download our pretrained models and supplementary .Put it in the directory ${DCPose_SUPP_DIR}. (Note that part of the pre-trained models are available, and more pre-train models will be released soon later.)

Data preparation

First, create a folder ${DATASET_DIR} to store the data of PoseTrack17 and PoseTrack18.

The directory structure should look like this:

${DATASET_DIR}
	|--${POSETRACK17_DIR}  
	|--${POSETRACK18_DIR}
	
# For example, our directory structure is as follows.
# If you don't know much about configuration file(.yaml), please refer to our settings.
DataSet
	|--PoseTrack17
	|--PoseTrack18

For PoseTrack17 data, we use a slightly modified version of the PoseTrack dataset where we rename the frames to follow %08d format, with first frame indexed as 1 (i.e. 00000001.jpg). First, download the data from PoseTrack download page. Then, rename the frames for each video as described above using this script.

Like PoseWarper, We provide all the required JSON files, which have already been converted to COCO format. Evaluation is performed using the official PoseTrack evaluation code, poseval, which uses py-motmetrics internally. We also provide required MAT/JSON files that are required for the evaluation.

Your extracted PoseTrack17 directory should look like this:

|--${POSETRACK17_DIR}
	|--images
        |-- bonn
        `-- bonn_5sec
        `-- bonn_mpii_test_5sec
        `-- bonn_mpii_test_v2_5sec
        `-- bonn_mpii_train_5sec
        `-- bonn_mpii_train_v2_5sec
        `-- mpii
        `-- mpii_5sec
    |--images_renamed   # first frame indexed as 1  (i.e. 00000001.jpg)
     	|-- bonn
        `-- bonn_5sec
        `-- bonn_mpii_test_5sec
        `-- bonn_mpii_test_v2_5sec
        `-- bonn_mpii_train_5sec
        `-- bonn_mpii_train_v2_5sec
        `-- mpii
        `-- mpii_5sec

For PoseTrack18 data, please download the data from PoseTrack download page. Since the video frames are named properly, you only need to extract them into a directory of your choice (no need to rename the video frames). As with PoseTrack17, we provide all required JSON files for PoseTrack18 dataset as well.

Your extracted PoseTrack18 images directory should look like this:

${POSETRACK18_DIR}
    |--images
        |-- test
        `-- train
        `-- val

Create Symbolic link

ln -s  ${DCPose_SUPP_DIR}  ${DCPose_Project_Dir}  # For DCPose supplementary file
ln -s  ${DATASET_DIR}  ${DCPose_Project_Dir}      #  For Dataset


# For example
${DCPose_Project_Dir} = /your/project/path/Pose_Estimation_DCPose
${DCPose_SUPP_DIR}    = /your/supp/path/DcPose_supp_files
${DATASET_DIR}        = /your/dataset/path/DataSet

ln -s /your/supp/path/DcPose_supp_files  /your/project/path/Pose_Estimation_DCPose  # SUP File Symbolic link 
ln -s /your/dataset/path/DataSet         /your/project/path/Pose_Estimation_DCPose  # DATASET Symbolic link 2

Training from scratch

For PoseTrack17

cd tools
# train & val 
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN.yaml --train --val

The results are saved in ${DCPose_Project_Dir}/output/PE/DcPose/DCPose_Network_Model_RSN/PoseTrack17/{Network_structure _hyperparameters} by default

For PoseTrack18

cd tools
# train & val 
python run.py --cfg ../configs/posetimation/DcPose/posetrack18/model_RSN.yaml --train --val

The results are saved in ${DCPose_Project_Dir}/output/PE/DcPose/DCPose_Network_Model_RSN/PoseTrack18/{Network_structure _hyperparameters} by default

Validating/Testing from our pretrained models

# Evaluate on the PoseTrack17 validation set
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN_trained.yaml --val  
# Evaluate on the PoseTrack17 test set
python run.py --cfg ../configs/posetimation/DcPose/posetrack17/model_RSN_trained.yaml --test

Run on video

cd demo/                   
mkdir input/
# Put your video in the input directory
python video.py