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README.md

Image_Harmonization_Datasets

Image Harmonization is to harmonize a composite image by adjusting its foreground appearances consistent with the background region. A real composite image is generated by a foreground region of one image combined with the background of another image. Though it's easy to create real composite images, the harmonized outputs are too time-consuming and skill-demanding to generate. So there is no high-quality publicly available dataset for image harmonization.

Our dataset is a synthesized dataset for Image Harmonization. It contains 4 sub-datasets: HCOCO, HAdobe5k, HFlickr, and Hday2night, each of which contains synthesized composite images, foreground masks of composite images and corresponding real images. The whole dataset is provided in Baidu Cloud

HCOCO HAdobe5k HFlickr Hday2night
Traning set 38545 19437 7449 311
Test set 4283 2160 828 133
  • HCOCO

HCOCO, containing 42k synthesized composite images, is generated based on Microsoft COCO dataset. The foreground region is corresponding object segmentation mask provided from COCO. Within the foreground region, the appearance of COCO image is edited using various color transfer methods. The sub-dataset and training/testing split are provided in Baidu Cloud

  • HAdobe5k

HAdobe5k is generated based on MIT-Adobe FiveK dataset. Provided with 6 editions of the same image, we manually segment the foreground region and exchange foregrounds between 2 versions. The sub-dataset and training/testing split are provided in Baidu Cloud

  • HFlickr

We collected 4833 images from Flickr. After manually segmenting the foreground region, we use the same method as HCOCO to generate HFlickr sub-dataset. The sub-dataset and training/testing split are provided in Baidu Cloud

  • Hday2night

Hday2night is generated based on day2night dataset. We manually segment the foreground region, which is cropped and overlaid on another image captured on a different time. The sub-dataset and training/testing split are provided in Baidu Cloud

Baselines

Lalonde

J.-F. Lalonde et al. provides their implementation of ICCV 2007 paper: Using color compatibility for assessing image realism in their GitHub.

And we have arranged the code to a "click-and-run" way. demo.m is available in /lalonde/colorStatistics/mycode/demo/. Don't forget to specify the path of the code and results in your computer in getPathName.m, and run setPath.m before run demo.mto get everything ready.

Xue

This is Xue's implementation of their paper in 2012 ACM Transactions on Graphics: Understanding and improving the realism of image composites

demo.m is available in /xue/demo/.

Notice to add the path of all dependent files using addpath(genpath('../dependency')).

Zhu

Jun-Yan Zhu released the code of their ICCV 2015 paper: Learning a discriminative model for the perception of realism in composite images in their GitHub.

Notice that it requires matcaffe interface. We make some changes corresponds to our dataset including how to preprocess data and how to save the harmonized results. Don't forget to specify DATA_DIR,MODEL_DIR and RST_DIR before running demo.m.

DIH

This is a Tensorflow implementation based on the caffe network released by the original authors in their GitHub.

Besides, we also discard one inner-most convolutional layer and one inner-most deconvolutional layer to make it suitable for input of 256*256 size.

To train DIH,

python train.py --data_dir <Your Path to Dataset> --init_lr 0.0001 --batch_size 32

Don't forget to specify the directory of Image Harmonization Dataset after data_dir.

U-net

This code of CVPR 2017: Image-to-Image Translation with Conditional Adversarial Networks, is released by Jun-Yan Zhu in their GitHub

Since our dataset is not organized like a normally aligned dataset, we have to implement image loading and processing part according to our dataset. For more details, you could refer to data/ihd_dataset.py. We implement the U-net backbone based on Zhu's implementation of unet_256, and train it alone instead of in an adversarial manner. Refer to unet_model.py for more details.

To train U-net:

python train_g.py --dataroot ./datasets/ihd/ --name unet --model unet --gpu_ids 1 --dataset_mode ihd --is_train 1 --no_flip --preprocess none --norm instance

Bibtex

When using images from our dataset, please cite this dataset using the following BibTeX:

@misc{
title={Deep Image Harmonization via Domain Verification},
author={Wenyan Cong and Jianfu Zhang and Li Niu and Liu Liu and Zhixin Ling and Weiyuan Li and Liqing Zhang},
year={2019},
eprint={1911.13239},
archivePrefix={arXiv},
primaryClass={cs.CV}}
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