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CGS-Pytorch

This is an unofficial PyTorch implementation of Composing Good Shots by Exploiting Mutual Relations.

Results

GAICD

I set the probability of mixing graph as 0.3 druing training, and scale the elements of adjacency matrix by the number of crops to produce more stable score prediction.

HCDB

Datasets Preparation

• GAICD [link]
• HCDB (FLMS) [Download Images] [Download Annotation]

Download&Unzip these datasets, palce them like this:

DATASET_FOLDER
  ├── GAICD
  │   └── images
  │   │    ├── image1.jpg
  │   │    └── image2.jpg
  │   └── annotations 
  │           ├── image1.txt
  │           └── image2.txt
  └── FLMS
      └── image
      │    ├── image1.jpg
      │    └── image2.jpg
      └── 500_image_dataset.mat 
      

Requirements

• PyTorch>=1.0

You can install packages using pip according to requirements.txt:

pip install -r requirements.txt

Usage

  # clone this repository
  git clone https://github.com/bo-zhang-cs/CGS-Pytorch.git
  cd CGS-Pytorch

Change the default dataset folder in config.py and you can check the paths by running cropping_dataset.py.

Install RoIAlign and RoDAlign

The source code of RoI&RoDAlign is from [here] compatible with PyTorch 1.0 or later. If you use Pytorch 0.4.1, please refer to [official implementation].

1. Change the CUDA_HOME and -arch=sm_86 in roi_align/make.sh and rod_align/make.sh according to your enviroment, respectively.
2. If you run this code in linux envoriment, make sure these bash files (make_all.sh, roi_align/make.sh, rod_align/make.sh) are Unix text file format by runing :set ff=unix in VIM.
3. cd CGS-Pytorch && sudo bash make_all.sh to build and install the packages.

Test

Download pretrained models (~75MB, ZIP format file) from [Google Drive] and unzip to the folder CGS-Pytorch/pretrained_model.

python test.py

This will produce a folder results where you can find the predicted best crops.

Train

python train.py

Track training process:

tensorboard --logdir=./experiments --bind_all

The model performance for each epoch is also recorded in .csv file under the produced folder ./experiments.

Citation

@inproceedings{li2020composing,
  title={Composing good shots by exploiting mutual relations},
  author={Li, Debang and Zhang, Junge and Huang, Kaiqi and Yang, Ming-Hsuan},
  booktitle={CVPR},
  year={2020}
}
@inproceedings{zeng2019reliable,
  title={Reliable and efficient image cropping: A grid anchor based approach},
  author={Zeng, Hui and Li, Lida and Cao, Zisheng and Zhang, Lei},
  booktitle={CVPR},
  year={2019}
}

More references about image cropping

Awesome Image Aesthetic Assessment and Cropping

Acknowledgments

Thanks to [GAIC] and [GAIC-Pytorch1.0+].