Unofficial PyTorch implementation of GLAMpoints: Greedily Learned Accurate Match points. The majority of code is based on the repository https://gitlab.com/retinai_sandro/glampoints
Please, use Python 3, install PyTorch 1.4, OpenCV and additional libraries from requirements.txt
In order to re-train network please use PS-Dataset, train/test split is already prepared in datasets/ps_dataset/
Training configurations and paths to datasets are stored in configs/glampoints_training.yml.
python train.py --path_ymlfile configs/glampoints_training.yml
Logs and checkpoints are stored in tensorboard format in the directory logs/experiment_name/
Validation on HPatches-sequences
Validation code is adapted from D2-Net evaluation on HPatches
To run validation on ported version of weights please use
python evaluate_hpatches.py --path_hpatches - path_to_hpatches_sequences --init_weights init ---path_ymlfile glampoints_eval_ported_weights.yml --name glampoints_retina
To run validation on trained on PS-dataset version please use
python evaluate_hpatches.py --path_hpatches - path_to_hpatches_sequences --init_weights modified ---path_ymlfile glampoints_eval.yml --name glampoints_retina**
To create plots, please downlod results of other methods from D2Net repo and use Add in methods,
python eval/generate_hpatches_plot.py --path_to_hpatches_sequences --path_to_cache_dir
Ported weights from Tensorflow implementation
Trained network on PS-Dataset
