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Code for HDR Video Reconstruction

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)
Guanying Chen, Chaofeng Chen, Shi Guo, Zhetong Liang, Kwan-Yee K. Wong, Lei Zhang

Table of Contents

Overview:

We provide testing and training codes. Details of the training and testing dataset can be found in DeepHDRVideo-Dataset. Datasets, the trained models, and the computed results can be download in BaiduYun.

Dependencies

This method is implemented in PyTorch and tested with Ubuntu (14.04 and 16.04) and Centos 7.

  • Python 3.7
  • PyTorch 1.10 and torchvision 0.30

You are highly recommended to use Anaconda and create a new environment to run this code. The following is an example procedure to install the dependencies.

# Create a new python3.7 environment named hdr
conda create -n hdr python=3.7

# Activate the created environment
source activate hdr

pip install -r requirements.txt

# Build deformable convolutional layer, tested with PyTorch 1.1, g++5.5, and Cuda 9.0
cd extensions/dcn/
python setup.py develop
# Please refer to https://github.com/xinntao/EDVR if you have difficulty in building this module

Testing

Please first go through DeepHDRVideo-Dataset to familiarize yourself with the testing dataset.

The trained models can be found in BaiduYun (Models/). Download and place it to data/models/.

Testing on the synthetic test dataset

The synthetic test dataset can be found in BaiduYun (/Synthetic_Dataset/HDR_Synthetic_Test_Dataset.tgz). Download and unzip it to data/. Note that we donot perform global motion alignment for this synthetic dataset.

# Test our method on two-exposure data. Results can be found in data/models/CoarseToFine_2Exp/
python run_model.py --gpu_ids 0 --model hdr2E_flow2s_model \
    --benchmark syn_test_dataset --bm_dir data/HDR_Synthetic_Test_Dataset \
    --mnet_name weight_net --mnet_checkp data/models/CoarseToFine_2Exp/weight_net.pth --fnet_checkp data/models/CoarseToFine_2Exp/flow_net.pth --mnet2_checkp data/models/CoarseToFine_2Exp/refine_net.pth

# Test our method on three-exposure data. The results can be found in data/models/CoarseToFine_3Exp/
python run_model.py --gpu_ids 0 --model hdr3E_flow2s_model \
    --benchmark syn_test_dataset --bm_dir data/HDR_Synthetic_Test_Dataset \
    --mnet_name weight_net --mnet_checkp data/models/CoarseToFine_3Exp/weight_net.pth --fnet_checkp data/models/CoarseToFine_3Exp/flow_net.pth --mnet2_checkp data/models/CoarseToFine_3Exp/refine_net.pth

Testing on the TOG13 dataset

Please download this dataset from TOG13_Dynamic_Dataset.tgz and unzip to data/. Normally when testing on a video, we have to first compute the similarity transformation matrices between neighboring frames using the following commands.

# However, this is optional as the downloaded dataset already contains the required transformation matrices for each scene in Affine_Trans_Matrices/.
python utils/compute_nbr_trans_for_video.py --in_dir data/TOG13_Dynamic_Dataset/ --crf data/TOG13_Dynamic_Dataset/BaslerCRF.mat --scene_list 2Exp_scenes.txt
python utils/compute_nbr_trans_for_video.py --in_dir data/TOG13_Dynamic_Dataset/ --crf data/TOG13_Dynamic_Dataset/BaslerCRF.mat --scene_list 3Exp_scenes.txt
# Test our method on two-exposure data. The results can be found in data/models/CoarseToFine_2Exp/
# Specify the testing scene with --test_scene. Available options are Ninja-2Exp-3Stop WavingHands-2Exp-3Stop Skateboarder2-3Exp-2Stop ThrowingTowel-2Exp-3Stop 
python run_model.py --gpu_ids 0 --model hdr2E_flow2s_model \
    --benchmark tog13_online_align_dataset --bm_dir data/TOG13_Dynamic_Dataset --test_scene ThrowingTowel-2Exp-3Stop --align \ --mnet_name weight_net --fnet_checkp data/models/CoarseToFine_2Exp/flow_net.pth --mnet_checkp data/models/CoarseToFine_2Exp/weight_net.pth --mnet2_checkp data/models/CoarseToFine_2Exp/refine_net.pth 
# To test on a specific scene, you can use the --test_scene argument, e.g., "--test_scene ThrowingTowel-2Exp-3Stop".

# Test our method on three-exposure data. The results can be found in data/models/CoarseToFine_3Exp/
# Specify the testing scene with --test_scene. Available options are Cleaning-3Exp-2Stop Dog-3Exp-2Stop CheckingEmail-3Exp-2Stop Fire-2Exp-3Stop
python run_model.py --gpu_ids 0 --model hdr3E_flow2s_model \
    --benchmark tog13_online_align_dataset --bm_dir data/TOG13_Dynamic_Dataset --test_scene Dog-3Exp-2Stop --align \
    --mnet_name weight_net --fnet_checkp data/models/CoarseToFine_3Exp/flow_net.pth --mnet_checkp data/models/CoarseToFine_3Exp/weight_net.pth --mnet2_checkp data/models/CoarseToFine_3Exp/refine_net.pth

Testing on the captured static dataset

The global motion augmented static dataset can be found in BaiduYun (/Real_Dataset/Static/).

# Test our method on two-exposure data. Download static_RGB_data_2exp_rand_motion_release.tgz and unzip to data/
# Results can be found in data/models/CoarseToFine_2Exp/
python run_model.py --gpu_ids 0 --model hdr2E_flow2s_model \
    --benchmark real_benchmark_dataset --bm_dir data/static_RGB_data_2exp_rand_motion_release --test_scene all \
    --mnet_name weight_net --mnet_checkp data/models/CoarseToFine_2Exp/weight_net.pth --fnet_checkp data/models/CoarseToFine_2Exp/flow_net.pth --mnet2_checkp data/models/CoarseToFine_2Exp/refine_net.pth

# Test our method on three-exposure data. Download static_RGB_data_3exp_rand_motion_release.tgz and unzip to data/
# The results can be found in data/models/CoarseToFine_3Exp/
python run_model.py --gpu_ids 0 --model hdr3E_flow2s_model \
    --benchmark real_benchmark_dataset --bm_dir data/static_RGB_data_3exp_rand_motion_release --test_scene all \
    --mnet_name weight_net --mnet_checkp data/models/CoarseToFine_3Exp/weight_net.pth --fnet_checkp data/models/CoarseToFine_3Exp/flow_net.pth --mnet2_checkp data/models/CoarseToFine_3Exp/refine_net.pth

Testing on the captured dynamic with GT dataset

The dynamic with GT dataset can be found in BaiduYun (/Real_Dataset/Dynamic/).

# Test our method on two-exposure data. Download dynamic_RGB_data_2exp_release.tgz and unzip to data/
python run_model.py --gpu_ids 0 --model hdr2E_flow2s_model \
    --benchmark real_benchmark_dataset --bm_dir data/dynamic_RGB_data_2exp_release --test_scene all \
    --mnet_name weight_net  --fnet_checkp data/models/CoarseToFine_2Exp/flow_net.pth --mnet_checkp data/models/CoarseToFine_2Exp/weight_net.pth --mnet2_checkp data/models/CoarseToFine_2Exp/refine_net.pth

# Test our method on three-exposure data. Download dynamic_RGB_data_3exp_release.tgz and unzip to data/
python run_model.py --gpu_ids 0 --model hdr3E_flow2s_model \
    --benchmark real_benchmark_dataset --bm_dir data/dynamic_RGB_data_3exp_release --test_scene all \
    --mnet_name weight_net  --fnet_checkp data/models/CoarseToFine_3Exp/flow_net.pth --mnet_checkp data/models/CoarseToFine_3Exp/weight_net.pth --mnet2_checkp data/models/CoarseToFine_3Exp/refine_net.pth

Testing on the captured dynamic without GT dataset

The dynamic with GT dataset can be found in BaiduYun (/Real_Dataset/Dynamic_noGT/).

# Test our method on two-exposure data. Download dynamic_data_noGT_2exp_RGB_JPG.tgz and unzip to data/
# Note that we provide the JPG dataset only for illustrating the testing process
# Results can be found in data/models/CoarseToFine_2Exp/
python run_model.py --gpu_ids 0 --model hdr2E_flow2s_model \
    --benchmark real_benchmark_dataset --bm_dir data/dynamic_data_noGT_2exp_RGB_JPG --test_scene all \
    --mnet_name weight_net --mnet_checkp data/models/CoarseToFine_2Exp/weight_net.pth --fnet_checkp data/models/CoarseToFine_2Exp/flow_net.pth --mnet2_checkp data/models/CoarseToFine_2Exp/refine_net.pth
# It is similar to test on three-exposure data

Testing on the customized dataset

You have two options to test our method on your dataset. In the first option, you have to implement a customized Dataset class to load your data, which should not be difficult. Please refer to datasets/tog13_online_align_dataset.py.

If you don't want to implement your own Dataset class, you may reuse datasets/tog13_online_align_dataset.py. However, you have to first arrange your dataset similar to the TOG13 dataset. Then you can run utils/compute_nbr_trans_for_video.py to compute the similarity transformation matrices between neighboring frames to enable global alignment.

# Use gamma curve if you do not know the camera response function
python utils/compute_nb_transformation_video.py --in_dir /path/to/your/dataset/ --crf gamma --scene_list your_scene_list

HDR evaluation metrics

We evaluate PSRN, HDR-VDP, HDR-VQM metrics using the Matlab code. Please first install HDR Toolbox to read HDR. Then set the paths of the ground-truth HDR and the estimated HDR in matlab/config_eval.m. Last, run main_eval.m in the Matlab console in the directory of matlab/.

main_eval(2, 'Ours')
main_eval(3, 'Ours')

Tonemapping

All visual results in the experiment are tonemapped using Reinhard et al.’s method. Please first install luminance-hdr-cli. In Ubuntu, you may use sudo apt-get install -y luminance-hdr to install it. Then you can use the following command to produce the tonemmapped results.

python utils/tonemapper.py -i /path/to/HDR/

Precomputed results

The precomputed results can be found in BaiduYun (/Results).

Training

The training process is described in docs/training.md.

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation

If you find this code useful in your research, please consider citing:

@article{chen2021hdr,
  title={{HDR} Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset},
  author={Chen, Guanying and Chen, Chaofeng and Guo, Shi and Liang, Zhetong and Wong, Kwan-Yee~K. and Zhang, Lei},
  journal=ICCV,
  year={2021}
}