Intrinsic Image Harmonization

Last update: Dec 21, 2022

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Deep Learning IntrinsicHarmony

Overview

Intrinsic Image Harmonization [Paper]

Zonghui Guo, Haiyong Zheng, Yufeng Jiang, Zhaorui Gu, Bing Zheng

Here we provide PyTorch implementation and the trained model of our framework.

Prerequisites

Linux
Python 3
CPU or NVIDIA GPU + CUDA CuDNN

Train/Test

Download iHarmony4 dataset, and our HVIDIT dataset Google Drive or BaiduCloud (access code: akbi).
Train a model:

CUDA_VISIBLE_DEVICES=0 python train.py --model retinexltifpm  --name retinexltifpm_allihd  --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

Test the model

CUDA_VISIBLE_DEVICES=0 python test.py --model retinexltifpm  --name retinexltifpm_allihd  --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

Apply a pre-trained model

Download the pretrained model from Google Drive or BaiduCloud (access code: 20m6), and put net_G.pth in the directory checkpoints/experiment. Run:

CUDA_VISIBLE_DEVICES=0 python test.py --model retinexltifpm  --name experiment  --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

Evaluation

We provide the code in ih_evaluation.py. Run:

CUDA_VISIBLE_DEVICES=0 python evaluation/ih_evaluation.py --dataroot <dataset_dir> --result_root  results/experiment/test_latest/images/ --evaluation_type our --dataset_name ALL

Quantitative Result

Dataset	Metrics	Composite	Ours (iHarmony4)	Ours (iHarmony4+HVIDIT)
HCOCO	PSNR MSE fMSE	33.99 69.37 996.59	37.61 23.25 386.39	37.77 21.84 367.38
HAdobe5k	PSNR MSE fMSE	28.52 345.54 2051.61	36.20 42.21 296.76	36.49 39.53 266.49
HFlickr	PSNR MSE fMSE	28.43 264.35 1574.37	31.74 100.86 676.71	32.08 96.87 635.60
Hday2night	PSNR MSE fMSE	34.36 109.65 1409.98	36.48 50.64 755.88	36.60 50.37 763.33
HVIDIT	PSNR MSE fMSE	38.72 53.12 1604.41	- - -	41.83 22.49 691.06
ALL	PSNR MSE fMSE	32.07 167.39 1386.12	36.53 37.95 399.34	36.96 35.33 388.50

Bibtex

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

@InProceedings{Guo_2021_CVPR,
    author    = {Guo, Zonghui and Zheng, Haiyong and Jiang, Yufeng and Gu, Zhaorui and Zheng, Bing},
    title     = {Intrinsic Image Harmonization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16367-16376}
}

Acknowledgement

For some of the data modules and model functions used in this source code, we need to acknowledge the repo of DoveNet and CycleGAN.

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Comments

Model Inference

Hello, is there a way to infer the model by reading an image and passing the image and its mask to the model and getting the harmonized output? Without the need to store the image's path in a text file and reading it from the text file then loading the image?

opened by AhmedHashish123 2
visdom interface is blank

first，thanks for your excellent work！ When I execute the training code, the visdom interface does not display the result picture and the training loss. it works when I execute the code of dovenet. could you tell me how to solve this problem? thanks again

opened by Ligouhi 0

Releases(v1.0)

v1.0(Feb 9, 2022)

Code version of our CVPR work [Paper].
Source code(tar.gz)
Source code(zip)

Intrinsic Image Harmonization

Related tags

Overview

Intrinsic Image Harmonization [Paper]

Prerequisites

Train/Test

Apply a pre-trained model

Evaluation

Quantitative Result

Bibtex

Acknowledgement

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Comments

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visdom interface is blank

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