search

xiehousheng / SemLA

Information Fusion 2023 | Semantics lead all: Towards unified image registration and fusion from a semantic perspective.
36 stars 2 forks source link

readme

SemLA

H. Xie, Y. Zhang, J. Qiu, X. Zhai, X. Liu, Y. Yang, S. Zhao, Y. Luo, and J. Zhong, “Semantics lead all: Towards unified image registration and fusion from a semantic perspective,” Information Fusion, p. 101835, 2023. Paper

Note

We have updated the existing bugs in the original code. Please download the current project and weights again for testing and training.【07/10】

Data preparation

  1. Download the COCO dataset to .\datasets\COCO\ (path2COCO)
  2. Download the IVS dataset to .\datasets\IVS\ (path2IVS)
  3. Download the label of IVS dataset to .\datasets\IVS_Label\ (path2IVS_Label)
  4. Generate pseudo-infrared images for each image in the COCO dataset using CPSTN and store the results in .\datasets\COCO_CPSTN\ (path2COCO_CPSTN)
  5. Generate pseudo-infrared images for each image in the IVS dataset using CPSTN and store the results in .\datasets\IVS_CPSTN\ ((path2IVS_CPSTN))

Installation

The code is implemented in python=3.6, as well as pytorch=1.9 and opencv-python=4.6.0.66. Please follow the instructions here to install both PyTorch dependencies. Installing PyTorch with CUDA support is strongly recommended.

Training

  1. Train stage1: Registration and semantic feature extraction. cd train_stage1 and configuring dataset paths, then run python train_stage1.py

  2. Train stage2: Training CSC and SSR modules. cd train_stage2 and configuring dataset paths, then run python train_stage2.py

  3. Train stage3: Training fusion module. cd train_stage3 and configuring dataset paths, then run python train_stage3.py

Test

Download pre-trained models on Google Drive or Baidu Yun and configure the path reg_weight_path, fusion_weight_path. We provide two matching modes, one is semantic object-oriented matching, setting matchmode = "semantic", and the other is global image oriented matching, setting matchmode = "scene".

On a dataset

Configuring dataset paths, then run python test.py

On a pair of images

Configuring images path, then run python inference_one_pair_images.py

Citation

If this code is useful for your research, please cite our paper.

@article{xie2023semantics,
  title={Semantics lead all: Towards unified image registration and fusion from a semantic perspective},
  author={Xie, Housheng and Zhang, Yukuan and Qiu, Junhui and Zhai, Xiangshuai and Liu, Xuedong and Yang, Yang and Zhao, Shan and Luo, Yongfang and Zhong, Jianbo},
  journal={Information Fusion},
  pages={101835},
  year={2023},
  publisher={Elsevier}
}