DeepPose
NOTE: This is not official implementation. Original paper is DeepPose: Human Pose Estimation via Deep Neural Networks.
Requirements
- Chainer 1.5+ (Neural network framework)
- numpy 1.9+
- scipy 0.16+
- scikit-learn 0.15+
- OpenCV 2.4+
Data preparation
bash shells/download.sh
python scripts/flic_dataset.py
python scripts/lsp_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.
MPII Dataset
- MPII Human Pose Dataset
-
of training images: 18079, # of test images: 6908
- test images don't have any annotations
- so we split trining imges into training/test joint set
- each joint set has
-
of training joint set: 17928, # of test joint set: 1991
Start training
For FLIC Dataset
Just run:
nohup python scripts/train.py > AlexNet_flic.log 2>&1 < /dev/null &
For speed:
CHAINER_TYPE_CHECK=0 nohup python scripts/train.py > AlexNet_flic.log 2>&1 < /dev/null &
It is same as:
nohup python scripts/train.py \
--model models/AlexNet_flic.py \
--gpu 0 \
--epoch 1000 \
--batchsize 32 \
--prefix AlexNet_LCN_AdaGrad_lr-0.0005 \
--snapshot 10 \
--datadir data/FLIC-full \
--channel 3 \
--flip 1 \
--size 220 \
--crop_pad_inf 1.5 \
--crop_pad_sup 2.0 \
--shift 5 \
--lcn 1 \
--joint_num 7 \
> AlexNet_LCN_AdaGrad_lr-0.0005.log 2>&1 &
--flip 1 means it performs LR flip augmentation, and --flip 0 does nothing. --lcn 1 means local(should be said "global"?) contrast normalization will be applied.
See the help messages with --help option for details.
GPU memory requirement
- batchsize: 128 -> about 2870 MiB
- batchsize: 64 -> about 1890 MiB
- batchsize: 32 (default) -> 1374 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