This repository is forked from TFeat.
Additionally C++ frontend / API example (for PyTorch) is placed here. If you want to use this, you should do the following.

1. Export a model using export_model.ipynb.
   Now you have tfeat_model.pt. This is loaded by a cpp-example.

2. Download and unzip libtorch. This is necessary if we use cpp-frontend of PyTroch.

   cd cpp_example && bash setup_libtorch.sh

3. Compile tfeat_demo.cpp using CMakeLists.txt.

   mkdir build && cd build
   cmake .. && make

4. Execute tfeat_demo in build!

   (e.g.) ./tfeat_demo ../../tfeat_model.pt ../../imgs/v_churchill/1.ppm ../../imgs/v_churchill/6.ppm

This figure is the result of the above.

As you know, C++ API of PyTorch is "beta" stability.
Now this implementation works on my environment(Ubuntu18, Pytorch1.0), but in the future this may not work.
By the way, the result of C++ API is slightly different from the result of Python. I'm investigating this issue.

Code for the BMVC 2016 paper Learning local feature descriptors with triplets and shallow convolutional neural networks

We provide the following pre-trained models:

To run TFeat on a tensor of patches:

tfeat = tfeat_model.TNet()
net_name = 'tfeat-liberty'
models_path = 'pretrained-models'
net_name = 'tfeat-liberty'
tfeat.load_state_dict(torch.load(os.path.join(models_path,net_name+".params")))
tfeat.cuda()
tfeat.eval()

x = torch.rand(10,1,32,32).cuda()
descrs = tfeat(x)
print(descrs.size())

#torch.Size([10, 128])

Note that no normalisation is needed for the input patches, it is done internally inside the network.

We provide an ipython notebook that shows how to load and use the pre-trained networks. We also provide the following examples:

• extracting descriptors from image patches
• matching two images using openCV
• matching two images using vlfeat

For the testing example code, check tfeat-test notebook

We provide an ipython notebook with examples on how to train TFeat. Training can either use the UBC datasets Liberty, Notredame, Yosemite, the HPatches dataset, and combinations of all the datasets.

For the training code, check tfeat-train notebook