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PWStableNet: Learning Pixel-wise Warping Maps for Video Stabilization

This is a PyTorch implementation of PWStableNet: Learning Pixel-wise Warping Maps for Video Stabilization.

Source code and models will be opened soon!

If you have any questions, please contact with me: [email protected]

Table of Contents

       

Prerequisites

  • Linux
  • Python 3
  • NVIDIA GPU (12G or 24G memory) + CUDA cuDNN
  • pytorch 0.4.0+
  • numpy
  • cv2
  • ...

Datasets

The dataset for is the DeepStab dataset (7.9GB) http://cg.cs.tsinghua.edu.cn/download/DeepStab.zip thanks to Miao Wang [1].

Training

  • The code will download the VGG-16 PyTorch base network weights at: https://download.pytorch.org/models/vgg16-397923af.pth automatically.

  • To train PWNet using the train script simply specify the parameters listed in ./lib/cfg.py as a flag or manually change them.

  • The default parameters are set for the use of two NVIDIA 1080Ti graphic cards with 24G memory.

CUDA_VISIBLE_DEVICES=0,1 python3 main.py
  • Note:
    • For training, an NVIDIA GPU is strongly recommended for speed.
    • Before training, you should ensure the location of preprocessed dataset, which will be supplied soon.

Performance

Original result before improvement:

we show an example videos to compare our PWNet with StabNet [1] image

More video result with improved PWStableNet

Note: If you have any problem to download these videos, you can visit another website: http://home.ustc.edu.cn/~zmd1992/PWStableNet.html

Demos

Use a pre-trained PWNet for video stabilization

Download a pre-trained network

  • We are trying to provide a pre-trained model.
  • Currently, we provide the following PyTorch models: model can be get from home.ustc.edu.cn/~zmd1992/PWStableNet/netG_model.pth
  • You can test your own unstable videos by changing the parameter "train" with False and adjust the path yourself in function "process()".

Authors

  • Minda Zhao

References

[1] M. Wang, G.-Y. Yang, J.-K. Lin, S.-H. Zhang, A. Shamir, S.-P. Lu, and S.-M. Hu, “Deep online video stabilization with multi-grid warp- ing transformation learning,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2283–2292, 2019

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Implementation of pixel-wise video stabilization

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