bash run.sh ${VIDEO_ID}
to finish all the steps in one run.ffmpeg -i data/raw/videos/${VIDEO_ID}.mp4 -vf fps=25,scale=w=512:h=512 -qmin 1 -q:v 1 data/raw/videos/${VIDEO_ID}_512.mp4
mv data/raw/videos/${VIDEO_ID}.mp4 data/raw/videos/${VIDEO_ID}_to_rm.mp4
mv data/raw/videos/${VIDEO_ID}_512.mp4 data/raw/videos/${VIDEO_ID}.mp4
export CUDA_VISIBLE_DEVICES=0
export VIDEO_ID=May
export PYTHONPATH=./
mkdir -p data/processed/videos/${VIDEO_ID}
ffmpeg -i data/raw/videos/${VIDEO_ID}.mp4 -f wav -ar 16000 data/processed/videos/${VIDEO_ID}/aud.wav
python data_gen/utils/process_audio/extract_hubert.py --video_id=${VIDEO_ID}
python data_gen/utils/process_audio/extract_mel_f0.py --video_id=${VIDEO_ID}
TIPS: add --force_single_process
if you discover that multiprocessing does not work well with Mediapipe
export PYTHONPATH=./
export VIDEO_ID=May
export CUDA_VISIBLE_DEVICES=0
mkdir -p data/processed/videos/${VIDEO_ID}/gt_imgs
ffmpeg -i data/raw/videos/${VIDEO_ID}.mp4 -vf fps=25,scale=w=512:h=512 -qmin 1 -q:v 1 -start_number 0 data/processed/videos/${VIDEO_ID}/gt_imgs/%08d.jpg
python data_gen/utils/process_video/extract_segment_imgs.py --ds_name=nerf --vid_dir=data/raw/videos/${VIDEO_ID}.mp4 # extract image, segmap, and background
export PYTHONPATH=./
export VIDEO_ID=May
python data_gen/utils/process_video/extract_lm2d.py --ds_name=nerf --vid_dir=data/raw/videos/${VIDEO_ID}.mp4
export VIDEO_ID=May
export PYTHONPATH=./
export CUDA_VISIBLE_DEVICES=0
python data_gen/utils/process_video/fit_3dmm_landmark.py --ds_name=nerf --vid_dir=data/raw/videos/${VIDEO_ID}.mp4 --reset --debug --id_mode=global
export PYTHONPATH=./
export VIDEO_ID=May
python data_gen/runs/binarizer_nerf.py --video_id=${VIDEO_ID}
You can check data/binary/videos/May
for generated dataset.