HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)

Related tags

Deep Learningvideohdrmulti-exposure-fusion
Overview

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 code. Details of the training and testing dataset can be found in DeepHDRVideo-Dataset. Datasets and the trained models can be download in Google Drive or BaiduYun (TODO).

Dependencies

This model 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 Google Drive (Models/). Download and place it to data/models/.

Testing on the synthetic test dataset

The synthetic test dataset can be found in Google Drive (/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 require 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 Google Drive (/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 Google Drive (/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 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 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 Google Drive (/Results) (TODO).

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}
}
Owner
Guanying Chen
PhD student in HKU
Guanying Chen
GitHub Repository https://guanyingc.github.io/DeepHDRVideo
a pytorch implementation of auto-punctuation learned character by character

Learning Auto-Punctuation by Reading Engadget Articles Link to Other of my work 🌟 Deep Learning Notes: A collection of my notes going from basic mult

Ge Yang 137 Nov 09, 2022
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

Edson-Niu 60 Nov 29, 2022
DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download

Bubbliiiing 31 Nov 25, 2022
The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

Wordle RL The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble I know there are more deterministic

Aditya Arora 3 Feb 22, 2022
VGG16 model-based classification project about brain tumor detection.

Brain-Tumor-Classification-with-MRI VGG16 model-based classification project about brain tumor detection. First, you can check what people are doing o

Atakan Erdoğan 2 Mar 21, 2022
Food recognition model using convolutional neural network & computer vision

Food recognition model using convolutional neural network & computer vision. The goal is to match or beat the DeepFood Research Paper

Hemanth Chandran 1 Jan 13, 2022
Fast Scattering Transform with CuPy/PyTorch

Announcement 11/18 This package is no longer supported. We have now released kymatio: http://www.kymat.io/ , https://github.com/kymatio/kymatio which

Edouard Oyallon 289 Dec 07, 2022
EigenGAN Tensorflow, EigenGAN: Layer-Wise Eigen-Learning for GANs

Gender Bangs Body Side Pose (Yaw) Lighting Smile Face Shape Lipstick Color Painting Style Pose (Yaw) Pose (Pitch) Zoom & Rotate Flush & Eye Color Mout

Zhenliang He 321 Dec 01, 2022
The repo for reproducing Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study

ECIR Reproducibility Paper: Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study This code corresponds to the reproducibility

ielab 3 Mar 31, 2022
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
Where2Act: From Pixels to Actions for Articulated 3D Objects

Where2Act: From Pixels to Actions for Articulated 3D Objects The Proposed Where2Act Task. Given as input an articulated 3D object, we learn to propose

Kaichun Mo 69 Nov 28, 2022
RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues FGBG (foreground-background) pytorch package for defining and training model

Klaas Kelchtermans 1 Jun 02, 2022
Encoding Causal Macrovariables

Encoding Causal Macrovariables Data Natural climate data ('El Nino') Self-generated data ('Simulated') Experiments Detecting macrovariables through th

Benedikt Höltgen 3 Jul 31, 2022
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 02, 2023
Code to accompany the paper "Finding Bipartite Components in Hypergraphs", which is published in NeurIPS'21.

Finding Bipartite Components in Hypergraphs This repository contains code to accompany the paper "Finding Bipartite Components in Hypergraphs", publis

Peter Macgregor 5 May 06, 2022
PyTorch implementation of our CVPR2021 (oral) paper "Prototype Augmentation and Self-Supervision for Incremental Learning"

PASS - Official PyTorch Implementation [CVPR2021 Oral] Prototype Augmentation and Self-Supervision for Incremental Learning Fei Zhu, Xu-Yao Zhang, Chu

67 Dec 27, 2022
Public implementation of "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression" from CoRL'21

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

19 Dec 12, 2022
Implementation for the paper 'YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs'

YOLO-ReT This is the original implementation of the paper: YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs. Prakhar Ganesh, Ya

69 Oct 19, 2022
Python library for tracking human heads with FLAME (a 3D morphable head model)

Video Head Tracker 3D tracking library for human heads based on FLAME (a 3D morphable head model). The tracking algorithm is inspired by face2face. It

61 Dec 25, 2022
TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons

TensorFlow-LiveLessons Note that the second edition of this video series is now available here. The second edition contains all of the content from th

Deep Learning Study Group 830 Jan 03, 2023