DeepCV / deeppose

DeepPose implementation in Chainer

DeepPose

NOTE: This is not official implementation. Original paper is DeepPose: Human Pose Estimation via Deep Neural Networks.

Requirements

• Python 2.7.11+

  • Chainer 1.5+ (Neural network framework)
  • numpy 1.9+
  • scipy 0.16+
  • scikit-learn 0.15+
  • OpenCV 2.4+

Installation of dependencies

pip install chainer
pip install numpy
pip install scipy
pip install scikit-learn
# for python3
conda install -c https://conda.binstar.org/menpo opencv3
# for python2
conda install opencv

Data preparation

bash shells/download.sh
python scripts/flic_dataset.py
python scripts/lsp_dataset.py
python scripts/mpii_dataset.py

This script downloads FLIC-full dataset (http://vision.grasp.upenn.edu/cgi-bin/index.php?n=VideoLearning.FLIC) and perform cropping regions of human and save poses as numpy files into FLIC-full directory. Same processes are performed for LSP, MPII datasets.

MPII Dataset

• MPII Human Pose Dataset

• training images: 18079, test images: 6908

  • test images don't have any annotations
  • so we split trining imges into training/test joint set
  • each joint set has

• training joint set: 17928, test joint set: 1991

Start training

Starting with the prepared shells is the easiest way. If you want to run train.py with your own settings, please check the options first by python scripts/train.py --help and modify one of the following shells to customize training settings.

For FLIC Dataset

bash shells/train_flic.sh

For LSP Dataset

bash shells/train_lsp.sh

For MPII Dataset

bash shells/train_mpii.sh

GPU memory requirement

• AlexNet

  • batchsize: 128 -> about 2870 MiB
  • batchsize: 64 -> about 1890 MiB
  • batchsize: 32 (default) -> 1374 MiB

• ResNet50

  • batchsize: 32 -> 6877 MiB

Visualize Filters of 1st conv layer

• Go to result dir of a model
• python ../../scripts/draw_filters.py

Visualize Prediction

Example

Prediction and visualize them and calc mean errors

python scripts/evaluate_flic.py \
--model results/AlexNet_2015/AlexNet.py \
--param results/AlexNet_2015/AlexNet_epoch_400.chainermodel \
--datadir data/FLIC-full
--gpu 0 \
--batchsize 128 \
--mode test

Tile some randomly selected result images

python scripts/evaluate_flic.py \
--model results/AlexNet_2015/AlexNet_flic.py \
--param results/AlexNet_2015/AlexNet_epoch_450.chainermodel \
--mode tile \
--n_imgs 25

Create animated GIF to intuitively compare predictions and labels

cd results/AlexNet_2015
bash ../../scripts/create_anime.sh test_450_tiled_pred.jpg test_450_tiled_label.jpg test_450.gif