Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow

Introduction

This is a pytorch implementation for Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow.

For training the network, you need to download the perspective dataset Places2 or Coco. Then, move the downloaded images to

--data_prepare/picture

run

python data_prepare/get_dataset.py

to generate your fisheye dataset. The generated fisheye images and new GT will be placed in

--dataset/data/train 
--dataset/gt/train  
or 
--dataset/data/test
--dataset/gt/test

Before training, make sure that the fisheye image and corresponding GT have been placed in

--dataset/data/train
--dataset/gt/train

After that, generate your image lists

python dataset/flist.py

The updated file paths is in

--flist/dataset/train.flist 
--flist/dataset/train_gt.flist

Finally, training network by

python train.py

If you want to use our pre-train model, you can download Baidu(Extraction code: zv83) or Google Drive.

Put the pre-trained model in

--FISH-Net/release_model/pennet4_dataset_square256

Place test fisheye images and corresponding GT(not necessary, but can not be empty. You can placed the fisheye images to take up position.) in

--dataset/data/test
--dataset/gt/test

Update file paths

python dataset/flist.py

run

python test.py