The Making and Breaking of Camouflage

This repository provides the code for our paper: The Making and Breaking of Camouflage [ICCV'23]. Hala Lamdouar, Weidi Xie and Andrew Zisserman. paper PDF

Dataset Preparation Steps

1. Crop the images around the camouflaged animal so that only its immediate surrounding area is taken into account:

   For a still image dataset:

   python utils/crop.py --data_dir ../datasets/CHAMELEON --out_data_dir ../datasets/CHAMELEON_crop --type still
   

   For a video dataset:

   python utils/crop.py --data_dir ../datasets/MoCA_Video/TrainDataset_per_sq/ --out_data_dir ../datasets/Moca_Video_Train_crop --type video
   

2. To compute the boundary score and the combined score, generate global contours (for the cropped images) and ground truth contours (for the object of interest, using the mask) with an off-the-shelf contour detection method, e.g. [DexiNed](https://github.com/xavysp/DexiNed).
3. Compute camouflage scores

python eval.py --data_dir ../CHAMELEON_crop/ --type still --contour_path ../DexiNed/result/BIPED2CHAM/fused/ --contour_gt_path ../DexiNed/result/BIPED2CHAM-GT/fused/

Probabilistic scoring functions and more implementation will be added soon.

Acknowledgement

This research is supported by the UK EPSRC funded CDT in Autonomous Intelligent Machines and Systems (AIMS), the EPSRC Pro- gramme Grant VisualAI EP/T028572/1, a Schlumberger Studentship, and a Royal Society Research Professorship. WX is supported by the National Key R&D Program of China (No. 2022ZD0161400).

Citation

Please consider citing our paper:

@InProceedings{Lamdouar23,
  author       = "Hala Lamdouar and Weidi Xie and Andrew Zisserman",
  title        = "The Making and Breaking of Camouflage",
  booktitle    = "IEEE International Conference on Computer Vision",
  year         = "2023",
}