CNN-based affine shape estimator.

AffNet model implementation in PyTorch for ECCV2018 paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"

The master branch is the one, which produced ECCV-paper results, python 2.7 and pytorch 0.4.0

Here is the one, which successfully runs on python 3.7, pytorch 1.4.0

AffNet generates up to twice more correspondeces compared to Baumberg iterations

To download datasets and start learning affnet:

git clone https://github.com/ducha-aiki/affnet
./run_me.sh

To reproduce Figure 1 in paper, run notebook

To reproduce Figure 2-3 in paper, run notebooks here

git clone https://github.com/ducha-aiki/affnet
./run_me.sh

Pre-trained models can be found in folder pretrained: AffNet.pth

We provide two examples, how to estimate affine shape with AffNet. First, on patch-column file, in HPatches format, i.e. grayscale image with w = patchSize and h = nPatches * patchSize

cd examples/just_shape
python detect_affine_shape.py imgs/face.png out.txt

Out file format is upright affine frame a11 0 a21 a22

Second, AffNet inside pytorch implementation of Hessian-Affine

2000 is number of regions to detect.

cd examples/hesaffnet
python hesaffnet.py img/cat.png ells-affnet.txt 2000
python hesaffBaum.py img/cat.png ells-Baumberg.txt 2000

output ells-affnet.txt is Oxford affine format

1.0
128
x y a b c 

Example is in [notebook](examples/hesaffnet/WBS demo.ipynb)

Please cite us if you use this code:

@inproceedings{AffNet2017,
 author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
    title = "{Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability}",
    year = 2018,
    month = sep,
    booktitle = {Proceedings of ECCV}
    }