The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

Last update: Dec 27, 2022

Overview

Deep Exemplar-based Video Colorization (Pytorch Implementation)

Paper | Pretrained Model | Youtube video 🔥 | Colab demo

Deep Exemplar-based Video Colorization, CVPR2019

Bo Zhang^1,3, Mingming He^1,5, Jing Liao², Pedro V. Sander¹, Lu Yuan⁴, Amine Bermak¹, Dong Chen³
¹Hong Kong University of Science and Technology,²City University of Hong Kong, ³Microsoft Research Asia, ⁴Microsoft Cloud&AI, ⁵USC Institute for Creative Technologies

Prerequisites

Python 3.6+
Nvidia GPU + CUDA, CuDNN

Installation

First use the following commands to prepare the environment:

conda create -n ColorVid python=3.6
source activate ColorVid
pip install -r requirements.txt

Then, download the pretrained models from this link, unzip the file and place the files into the corresponding folders:

video_moredata_l1 under the checkpoints folder
vgg19_conv.pth and vgg19_gray.pth under the data folder

Data Preparation

In order to colorize your own video, it requires to extract the video frames, and provide a reference image as an example.

Place your video frames into one folder, e.g., ./sample_videos/v32_180
Place your reference images into another folder, e.g., ./sample_videos/v32

If you want to automatically retrieve color images, you can try the retrieval algorithm from this link which will retrieve similar images from the ImageNet dataset. Or you can try this link on your own image database.

Test

python test.py --image-size [image-size] \
               --clip_path [path-to-video-frames] \
               --ref_path [path-to-reference] \
               --output_path [path-to-output]

We provide several sample video clips with corresponding references. For example, one can colorize one sample legacy video using:

python test.py --clip_path ./sample_videos/clips/v32 \
               --ref_path ./sample_videos/ref/v32 \
               --output_path ./sample_videos/output

Note that we use 216*384 images for training, which has aspect ratio of 1:2. During inference, we scale the input to this size and then rescale the output back to the original size.

Train

We also provide training code for reference. The training can be started by running:

python --data_root [root of video samples] \
       --data_root_imagenet [root of image samples] \
       --gpu_ids [gpu ids] \

We do not provide the full video dataset due to the copyright issue. For image samples, we retrieve semantically similar images from ImageNet using this repository. Still, one can refer to our code to understand the detailed procedure of augmenting the image dataset to mimic the video frames.

Comparison with State-of-the-Arts

More results

⭐ Please check our Youtube demo for results of video colorization.

Citation

If you use this code for your research, please cite our paper.

@inproceedings{zhang2019deep,
title={Deep exemplar-based video colorization},
author={Zhang, Bo and He, Mingming and Liao, Jing and Sander, Pedro V and Yuan, Lu and Bermak, Amine and Chen, Dong},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8052--8061},
year={2019}
}

Old Photo Restoration 🔥

If you are also interested in restoring the artifacts in the legacy photo, please check our recent work, bringing old photo back to life.

@inproceedings{wan2020bringing,
title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2747--2757},
year={2020}
}

License

This project is licensed under the MIT license.

Comments

I met "CUDA error: an illegal memory access was encountered" problem

When i tested your pre-trained model, i met the problem called "CUDA error: an illegal memory access was encountered", can you provide the version of your CUDA, cudnn and pytorch

opened by Zonobia-A 3
Could i apply this method to image colorization

Hi, could i apply this method to image colorization and remove the temporal consistency loss? BTW, how to get the pairs.txt/pairs_mid.txt/pairs_bad.txt used in videoloader_imagenet.py?

opened by buptlj 2
video size very low

video colorization very good and very impressive . but render image low size 768x432 . and video size also same. how to in increase the image size and video size. thank you..

opened by srinivas68 2
training command is wrong.

The original training command is python --data_root [root of video samples] \ --data_root_imagenet [root of image samples] \ --gpu_ids [gpu ids] \ Maybe it should be python train.py --data_root [root of video samples] \ --data_root_imagenet [root of image samples] \ --gpu_ids [gpu ids] \ ?

opened by Horizon2333 1
There seems a bug ofr feature centering with x_features - y_features.mean

There seems a bug ofr feature centering with x_features - y_features.mean which I think should be x_features - x_features.mean https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization/blob/37639748f12dfecbb0a3fe265b533887b5fe46ce/models/ContextualLoss.py#L100

opened by JerryLeolfl 1
the test code

Thanks for your great work! I have a question when i run test.py. Why don't you extract the feature of inference image out of the for loop. I haven't found any difference.

opened by buptlj 1
CUDA OOM

Hello, I am running a 4gb nvidia GPU. Is that enough for inference? I try to run on ubuntu 18.04 as well as windows but always get a Out of memory error eventually. Sometimes happen after 2nd image and sometimes after 5th. This is 1080p video.

opened by quocthaitang 1
illustrative training data

Could you please release a tiny illustrative training dataset, such that the preparation of a custom training data can be easily followed. Currently, it is not easy to prepare a custom training data by reading the train.py. or could you please give a further explanation of the following fields? ( image1_name, image2_name, reference_video_name, reference_video_name1, reference_name1, reference_name2, reference_name3, reference_name4, reference_name5, reference_gt1, reference_gt2, reference_gt3, ) Thank you very much.

opened by davyfeng 1
Runtime error

Getting a runtime error when running the test cells at the 'first we visualize the input video'. I'm not good with code, but this is the first time I've experienced this issue with this wonderful program. No cells execute after this error. I've attached a screenshot.

opened by StevieMaccc 0
Test result problem

At present, after training, it is found that the generated test image is effective, but the color saturation is very low. Is it because of the colored model or other reasons? I'm looking forward to your reply！！！

opened by songyn95 0
Training has little effect

Hello, I read in the paper that "we train the network for 10 epichs with a batch size of 40 pairs of video frames. " Is it effective after only 10 iterations? Is your data 768 videos, 25 frames per video? I only train one video at present, epoch=40, but I find that it has little effect. What may be the reason?

opened by songyn95 0
Error 404 - Important files missing

I was working with the Colab program and there appears to be important models / files missing. As a result the program has ceased to function. I've brough to the designers attention so hopefully will be resolved.

opened by StevieMaccc 1
CUDA device error "module 'torch._C' has no attribute '_cuda_setDevice'" when running test.py

Hi !

Trying out test.py results in the following error:

Traceback (most recent call last): File "test.py", line 26, in <module> torch.cuda.set_device(0) File "C:\Users\natha\anaconda3\envs\ColorVid\lib\site-packages\torch\cuda\__init__.py", line 311, in set_device torch._C._cuda_setDevice(device) AttributeError: module 'torch._C' has no attribute '_cuda_setDevice'

I tried installing pytorch manually using their tool https://pytorch.org/get-started/locally/ (with CUDA 11.6) but that doesn't resolve the issue.

Can someone help me understand what is going on ? Thanks !!

opened by FoxTrotte 4
Questions about the test phase
Thanks for your outstanding work! I have some questions when I read it.

What are the settings when you test this video model on image colorization which used for comparing with other image colorization methods?

Could you please give me a url about your video testset (116 video clips collected from Videvo)? Thanks again for your attention.
opened by JerryLeolfl 0
It seems not correct of the code in TestTransforms.py line 341

https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization/blob/37639748f12dfecbb0a3fe265b533887b5fe46ce/lib/TestTransforms.py#L341 it seems a repeated define of call

opened by JerryLeolfl 0
Wrong output resolution

Processing 4x3 video 912x720 outputs cropped and downscaled 16x9 768x432. Playing around "python test.py --image-size [image-size] " doesn't help My be I don't properly specify an arguments? So, what the the proper use of --image-size [image-size] in order to get 912x720? Greatly appreciate for suggesting.

opened by semel1 5

Releases(v1.0)

v1.0(Dec 1, 2020)

Source code(tar.gz)
Source code(zip)
colorization_checkpoint.zip(770.16 MB)

Owner

Bo Zhang

Researcher

GitHub Repository https://arxiv.org/abs/1906.09909

Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Think Bayes 2 by Allen B. Downey The HTML version of this book is here. Think Bayes is an introduction to Bayesian statistics using computational meth

1.5k Jan 08, 2023

CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

83 Dec 14, 2022

Code for intrusion detection system (IDS) development using CNN models and transfer learning

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

38 Dec 12, 2022

Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

27 Nov 30, 2022

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

101 Dec 29, 2022

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

32 Oct 26, 2022

PyTorch implementation of paper "StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement" (ICCV 2021 Oral)

StarEnhancer StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement (ICCV 2021 Oral) Abstract: Image enhancement is a subjective process w

133 Dec 28, 2022

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

9 Jul 30, 2022

Raindrop strategy for Irregular time series

Graph-Guided Network For Irregularly Sampled Multivariate Time Series Overview This repository contains processed datasets and implementation code for

74 Jan 03, 2023

SWA Object Detection

SWA Object Detection This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA

237 Nov 28, 2022

Meta-learning for NLP

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks Code for training the meta-learning models and fine-tuning on downstr

43 Nov 08, 2022

Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Lin

1 Nov 22, 2022

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is

997 Dec 30, 2022