Pytorch DataLoader for face video data of the AMIGOS dataset [1].
- Python 3.8
- ffmpeg version n4.3.1
- python packages in requirements.txt
Unzip files: python AMIGOS/preprocess/unzipfiles.py <folder_path> <destination_folder_path>
Transform External_Annotations.xlsx to json: python AMIGOS/preprocess/External_Annotations_xlsxtojson.py <file_path>
Split videos to frames: python AMIGOS/preprocess/mov_split_frames.py <origin_folder_path> <destination_folder_path>
from torch.utils import data
from torchvision import transforms
from AMIGOS import AMIGOS
if __name__ == '__main__':
amigos = AMIGOS(
root_path='Frames',
annotation_path='amigos.json',
spatial_transform=transforms.ToTensor(),
feature_type='RGB'
)
data_loader = data.DataLoader(amigos)
for i, data in enumerate(data_loader):
clip = data[0]
labels = data[1]Expected folder structure:
Root dir
└── Unzipped Folder
├── Video Folder 1
│ ├── 1 # segment
│ │ └── image_00001.jpg # frame
│ ├── 2
│ │ └── image_00001.jpg
│ └── 3
│ └── image_00001.jpg
└── Video Folder 1
├── 1
│ └── image_00001.jpg
├── 2
│ └── image_00001.jpg
├── 3
│ └── image_00001.jpg
└── 4
└── image_00001.jpg
To use features extracted from the RGB images, the folder structure needs to be the same as for the RGB and contain a single json file with features for the entire segment clip:
{
"features": {
"1": {"feat1": 0, "feat2": 0, "feat3": 0},
"2": {"feat1": 0, "feat2": 0, "feat3": 0}
},
"Other Metadata": {}
}- Handle features in json files
- Method to get user_id for leave one out training
[1] J. A. Miranda Correa, M. K. Abadi, N. Sebe, and I. Patras, ‘AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups’, IEEE Trans. Affective Comput., pp. 1–1, 2018, doi: 10.1109/TAFFC.2018.2884461.