Pytorch Implementation of rpautrat/SuperPoint

Overview

SuperPoint-Pytorch (A Pure Pytorch Implementation)

SuperPoint: Self-Supervised Interest Point Detection and Description

Thanks

This work is based on:

New update (20211016)

  • Train your MagicPoint and SuperPoint

New update (20210904)

Usage

  • 1 Prepare your data. Make directories data and export. The data directory should look like,
    data
    |-- coco
    |  |-- train2017
    |  |     |-- a.jpg
    |  |     |-- ...
    |  `-- test2017
    |        |-- b.jpg
    |        |-- ...
    |-- hpatches
    |   |-- i_ajuntament
    |   |   |--1.ppm
    |   |   |--...
    |   |   |--H_1_2
    |   |-- ...
    
    You can create soft links if you already have coco, hpatches data sets, the command is,
    cd data
    ln -s dir_to_coco ./coco
    
  • 2 The training steps are much similar to rpautrat/Superpoint
    • 2.1 Train MagicPoint: python train.py ./config/magic_point_train.yaml
    • 2.2 Export coco labels: python export detections.py
    • 2.3 Train MagicPoint on coco labels data set (export by step 2.2)
      python train.py ./config/superpoint_train.py
    • 2.4 Train SuperPoint.py: python train.py ./config/superpoint_train.py
    • others. Validate detection repeatability:
    python export detections_repeatability.py   
    python compute_repeatability.py
    
    (NOTE: You have to edit *.yaml files to run corresponding tasks, especially for the following items
    model
        name: superpoint # magicpoint
     ...
    data:
        name: coco #synthetic
        image_train_path: ['./data/mp_coco_v2/images/train2017',] #several data sets can be list here
        label_train_path: ['./data/mp_coco_v2/labels/train2017/',]
        image_test_path: './data/mp_coco_v2/images/test2017/'
        label_test_path: './data/mp_coco_v2/labels/test2017/'
    
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