This repository contains code for annotation generation, and affordance correspondence described in the paper "One-Shot Transfer of Affordance Regions? AffCorrs!"

One-shot transfer example outputs from our model shown in pairs of refernce image and annotated areas (left) and the target image with estimated correspondences (right) without any training on these classes.

If you use the AffCorrs model or UMD_i dataset please consider citing our work:

@article{Hadjivelichkov2022,
    title = {{One-Shot Transfer of Affordance Regions? AffCorrs!}},
    year = {2022},
    journal = {{Proceedings of the 6th Conference on Robot Learning (CoRL)}},
    author = {Hadjivelichkov, Denis and Zwane, Sicelukwanda and Deisenroth, Marc and Agapito, Lourdes and Kanoulas, Dimitrios}
}

and its ancestors:

@article{Shir2021,
author = {Amir, Shir and Gandelsman, Yossi and Bagon, Shai and Dekel, Tali},
year = {2021},
month = {12},
title = {{Deep ViT Features as Dense Visual Descriptors}}
}

@inproceedings{Myers2015icra,
  author    = {Austin Myers and Ching L. Teo and Cornelia Ferm\"uller and Yiannis Aloimonos},
  title     = {Affordance Detection of Tool Parts from Geometric Features},
  booktitle = {ICRA},
  year      = {2015}
}

The code has been tested with the following packages:

pydensecrf=1.0
torch=1.10+cu113
faiss=1.5
pytorch_metric_learning=0.9.99
fast_pytorch_kmeans=0.1.6
timm=0.6.7
cv2=4.6
scikit-image=0.17.2

However, other versions of these packages will likely be sufficient as well.

pip3 install pydensecrf torch torchvision timm cv2 scikit-image\
faiss pytorch_metric_learning fast_pytorch_kmeans

The dataset is added as a .zip file. Unzip and refer to its README instruction.

To run a demo of the model cd demos; python3 show_part_annotation_correspondence.py.

To try on your own images, (i) change SUPPORT_DIR to point to a directory containing the affordance.npy and prototype.png and (ii) change TARGET_IMAGE_PATH to point toward an image file which contains the target scene.

Annotation can be generated via annotate_parts.py. Four named windows will appear. Drawing on the source image with the mouse will create the affordance mask. Middle-click will save the current affordance and proceed to the next affordance mask. Pressing s will save all the affordance masks generated for this source image into a folder called affordance_database/temp. Pressing the n-key will change the source image to the next in the folder. The target image and similarity windows are there just for reference while annotating, showing the best point correspondences.