/SuperGlue-pytorch

SuperGlue-pytorch training

Primary LanguagePythonOtherNOASSERTION

SuperGlue-pytorch training

modified from: https://github.com/HeatherJiaZG/SuperGlue-pytorch

1、based on official superpoint implementation

2、enable training batchsize > 1

3、loss forward speed 10x accelerated

4、enable training-set non-linear warping

5、negative pairs in training-set

6、enable offline data generation

offline building training-set (recommend):

python -m dataset.data_builder --debug 1

training:

python train.py --train_path {train_path} --superpoint_weight ./models/weights/superpoint_v1.pth --feature_dim 256 --dataset_offline_rebuild 1 --pretrained "" --batch_size 32 --debug 0

SuperGlue PyTorch Implementation

Dependencies

  • Python 3
  • PyTorch >= 1.1
  • OpenCV >= 3.4 (4.1.2.30 recommended for best GUI keyboard interaction, see this note)
  • Matplotlib >= 3.1
  • NumPy >= 1.18
  • tensorboardX >= 2.1

Contents

There are two main top-level scripts in this repo:

  1. train.py : trains the superglue model.
  2. load_data.py: reads images from files and creates pairs. It generates keypoints, descriptors and ground truth matches which will be used in training.

Additional useful command line parameters

  • Use --epoch to set the number of epochs (default: 20).
  • Use --train_path to set the path to the directory of training images.
  • Use --eval_output_dir to set the path to the directory in which the visualizations is written (default: dump_match_pairs/).
  • Use --show_keypoints to visualize the detected keypoints (default: False).
  • Use --viz_extension to set the visualization file extension (default: png). Use pdf for highest-quality.

Visualization Demo

The matches are colored by their predicted confidence in a jet colormap (Red: more confident, Blue: less confident).