Awesome
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.
Requirements
- Linux or Windows
- Python 3
- Pytorch 1.5
Dataset
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
Training
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
Testing
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