Deep Pose

I think more tuning is possible. If you have some advice, please tell me!

Features

• Chainer implementation
• Subsequent stages
• Image viewer on web browsers. (Flask and flask-socketio are needed)

Supported Datasets

• FLIC
• LPS

Testing Environments

Ubuntu 14.04

• Python 2.7
• Chainer 1.9.1
• OpenCV 2.4.8
• Flask 0.11.1
• Flask_SocketIO 2.4

Arch Linux

• Python 3.5
• Chainer 1.9.1
• OpenCV 3.1.0
• Flask 0.10.1
• Flask_SocketIO 2.2

Windows 7 (not recommend)

• Python 2.7
• Chainer 1.9.1
• OpenCV 3.1.0
• Flask 0.11.1
• Flask_SocketIO 2.5

Training

First, download FLIC FULL and FLIC PLUS to some directory, and set the path to settings.json. And also fix CASCADE_PATHS to detect faces for your environment.

To start training, please execute the following command.

./scripts/train.py --stage 0

For subsequent stage training, --joint_idx argument is needed.

./scripts/train.py --stage 1 --joint_idx 0
./scripts/train.py --stage 1 --joint_idx 1
./scripts/train.py --stage 2 --joint_idx 0  # and so on

--resume argument is also supported.

To check current training state, please open http://localhost:8889/ (port number can be changed by settings.json). The error rate graph and visualized images can be seen.

If you want to use GPU, please set GPU parameter in settings.json to a positive number.

Use trained models

Execute the following command, and open http://localhost:8889/.

./scripts/use_model.py

Settings is common with training (settings.json).

Results

First stage

Subsequent stages are training now.

Async Mode Setting

This project uses Python threading or multiprocessing package and it can be configured by ASYNC_MODE in settings.json. On Linux process mode is better due to the speed, but on Windows only thread mode is valid.

TODO

• Tune training parameters (learning rate, bounding box sigma and so on).
• Replace multiprocess.Queue and Event to threading's ones on the thread mode.