Here is a demo program. See also this repo.
pip install -r requirements.txt
bash scripts/download_mpiigaze_dataset.sh
python tools/preprocess_mpiigaze.py --dataset datasets/MPIIGaze -o datasets/
bash scripts/download_mpiifacegaze_dataset.sh
python tools/preprocess_mpiifacegaze.py --dataset datasets/MPIIFaceGaze_normalized -o datasets/
This repository uses YACS for
configuration management.
Default parameters are specified in
gaze_estimation/config/defaults.py
(which is not supposed to be modified directly).
You can overwrite those default parameters using a YAML file like
configs/mpiigaze/lenet_train.yaml
.
By running the following code, you can train a model using all the data except the person with ID 0, and run test on that person.
python train.py --config configs/mpiigaze/lenet_train.yaml
python evaluate.py --config configs/mpiigaze/lenet_eval.yaml
Using scripts/run_all_mpiigaze_lenet.sh
and
scripts/run_all_mpiigaze_resnet_preact.sh
,
you can run all training and evaluation for LeNet and ResNet-8 with
default parameters.
Model | Mean Test Angle Error [degree] | Training Time |
---|---|---|
LeNet | 6.52 | 3.5 s/epoch |
ResNet-preact-8 | 5.73 | 7 s/epoch |
The training time is the value when using GTX 1080Ti.
Model | Mean Test Angle Error [degree] | Training Time |
---|---|---|
AlexNet | 5.06 | 135 s/epoch |
ResNet-14 | 4.83 | 62 s/epoch |
The training time is the value when using GTX 1080Ti.
This demo program runs gaze estimation on the video from a webcam.
Download the dlib pretrained model for landmark detection.
bash scripts/download_dlib_model.sh
Calibrate the camera.
Save the calibration result in the same format as the sample
file data/calib/sample_params.yaml
.
Run demo.
Specify the model path and the path of the camera calibration results
in the configuration file as in
configs/demo_mpiigaze_resnet.yaml
.
python demo.py --config configs/demo_mpiigaze_resnet.yaml