The implementation of the 3D face reconstruction through Resnet and BFM model, for the course project.
use Singularity on linux(with GPU) /scratch/work/public/singularity/cuda10.2-cudnn8-devel-ubuntu18.04.sif Need python=3.7.0, pytorch=1.1.0, torchvision=0.3.0, cudatoolkit=10.0 In singularity container, you may need
Singularity>git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
Singularity>pip install h5py
Download and unzip the CACD original face from
Original face images (detected and croped by openCV face detector) can be downloaded here (3.5G)
Modify the corrsponding path of the dataset input image_list = glob.glob("../carc/CACD2000/*.jpg") and dataset out put
save_path=./data/CACD2000_{}.hdf5in the Dataprepocessing.py. Modify the SHORT_LEN to get a smaller subdataset.
Run python Dataprepocessing.py
Check the path in dataloader=("./data/CACD2000_train.hdf5")and you will get the trained model in ./model_trained/, with the visualized output images in ./result, and the loss curve in train.png.
Default NUM_EPOCH=25, BATCH_SIZE=8
Run python trainnet.py
Download our trained model form GoogleDrive , put it in the MODEL_LOAD_PATH ="./modelload/demo.pth"
Check the path in dataloader=("./data/CACD2000_test.hdf5")and you need to put the model into MODEL_LOAD_PATH ="./modelload/demo.pth", and you will get all of the visualized output images in ./test
Run python testnet.py
There are some output of our training and testing in the output folder, including the trainging curve, some loss in the training epoches, the contrast of ori/reconstructed images of the testing, the reconstructed mesh.