Skip to content

High Dynamic Range Image Synthesis via Attention Non-Local Network

License

Notifications You must be signed in to change notification settings

tuvovan/ANL-HDRI

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ANLHDR Imaging

High Dynamic Range Image Synthesis via Attention Non-Local Network teaser

Content

Getting Started

  • Clone the repository

Prerequisites

  • Tensorflow 2.2.0+
  • Tensorflow_addons
  • Python 3.6+
  • Keras 2.3.0
  • PIL
  • numpy
pip install -r requirements.txt

Running

Training

  • Preprocess

    python src/create_dataset.py
    
  • Train ANL-HDRI

    python main.py
    
  • Test ANL-HDRI

    • Download weights here
    python test.py --weight_test_path ANLNet.h5 --test_path Test/EXTRA/001/
    

Usage

Training

usage: main.py [-h] [--images_path IMAGES_PATH] [--test_path TEST_PATH]
               [--lr LR] [--gpu GPU] [--num_epochs NUM_EPOCHS] 
               [--train_batch_size TRAIN_BATCH_SIZE]
               [--display_ep DISPLAY_EP] [--checkpoint_ep CHECKPOINT_EP]
               [--checkpoints_folder CHECKPOINTS_FOLDER]
               [--load_pretrain LOAD_PRETRAIN] [--pretrain_dir PRETRAIN_DIR]
               [--filter FILTER] [--kernel KERNEL]
               [--encoder_kernel ENCODER_KERNEL]
               [--decoder_kernel DECODER_KERNEL]
               [--triple_pass_filter TRIPLE_PASS_FILTER]
optional arguments: -h, --help                show this help message and exit
                    --images_path             training path
                    --lr                      LR
                    --gpu                     GPU
                    --num_epochs              NUM of EPOCHS
                    --train_batch_size        training batch size
                    --display_ep              display result every "x" epoch
                    --checkpoint_ep           save weights every "x" epoch
                    --checkpoints_folder      folder to save weight
                    --load_pretrain           load pretrained model
                    --pretrain_dir            pretrained model folder
                    --filter                  default filter
                    --kernel                  default kernel
                    --encoder_kernel          encoder filter size
                    --decoder_kernel          decoder filter size
                    --triple_pass_filter      number of filter in triple pass

Testing

The weight file was deprecated. Will be updated soon.

usage: test.py [-h] [--test_path TEST_PATH] [--gpu GPU]
                    [--weight_test_path WEIGHT_TEST_PATH] [--filter FILTER]
                    [--kernel KERNEL] [--encoder_kernel ENCODER_KERNEL]
                    [--decoder_kernel DECODER_KERNEL]
                    [--triple_pass_filter TRIPLE_PASS_FILTER]
optional arguments: -h, --help                    show this help message and exit
                    --test_path                   test path
                    --weight_test_path            weight test path
                    --filter                      default filter
                    --kernel                      default kernel
                    --encoder_kernel              encoder filter size
                    --decoder_kernel              decoder filter size
                    --triple_pass_filter          number of filter in triple pass

Result

DEMO0 DEMO1 DEMO2

License

This project is licensed under the MIT License - see the LICENSE file for details

References

[1] Training and Testing dataset - link

Citation

    @ARTICLE{8989959,  author={Tu V.V},   
    title={High Dynamic Range Image Synthesis via Attention Non-Local Network},   
    year={2020},  
    url={github.com/tuvovan/ANL-HDRI},}

Acknowledgments

  • My work is done on my sparse time so I should have some mistake or further possible improvement.
  • Any ideas on updating or misunderstanding, please send me an email: [email protected]
  • If you find this repo helpful, kindly give me a star.

Releases

No releases published

Packages

No packages published

Languages