The following packages were used to evaluate the model.
- python==3.8.8
- pytorch==1.7.1
- torchvision==0.8.2
- cudatoolkit==10.1.243
- opencv-python==4.5.1.48
- numpy==1.19.2
- pillow==8.1.2
- cupy==9.0.0
from flolpips import calc_flolpips
ref_video = '<path to the reference>.mp4'
dis_video = '<path to the distorted>.mp4'
res = calc_flolpips(dis_video, ref_video)from flolpips import Flolpips
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
eval_metric = Flolpips().to(device)
batch = 8
I0 = torch.rand(8, 3, 256, 448).to(device) # first frame of the triplet
I1 = torch.rand(8, 3, 256, 448).to(device) # third frame of the triplet
frame_dis = torch.rand(8, 3, 256, 448).to(device) # prediction of the intermediate frame
frame_ref = torch.rand(8, 3, 256, 448).to(device) # ground-truth of the intermediate frame
flolpips = eval_metric.forward(I0, I1, frame_dis, frame_ref)@article{danier2022flolpips,
title={FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation},
author={Danier, Duolikun and Zhang, Fan and Bull, David},
journal={arXiv preprint arXiv:2207.08119},
year={2022}
}
Lots of code in this repository are adapted/taken from the following repositories:
We would like to thank the authors for sharing their code.
