dloc - the deeplearning localization toolbox
This is dloc, a modular toolbox for state-of-the-art local feature extaction and matching.
Support
Reference
- d2net: extract keypoint from 1/8 feature map
- superpoint: could extract in corner points, pretrained with magicpoint
- superglue: excellent matching algorithm but pretrained model only support superpoint, we have implementation superglue with sift/superpoint in megadepth datasets.
- disk: add reinforcement for keypoints extraction
- aslfeat: build multiscale extraction network
- cotr: build transformer network for points matching
- loftr: dense extraction and matching with end-to-end network
- r2d2: add repeatability and reliability for keypoints extraction
- contextdesc: keypoints use sift, use full image context to enhence descriptor. expensive calculation.
- NGRANSAC
If you are interested in local feature, https://drive.weixin.qq.com/s?k=AJEAIQdfAAo0ta3HBsAKMA9AatACk is useful.
Installation
pip install superMatch
dloc could run on mirrors.tencent.com/xlab/colmap:v2.0 with python3.
Debugging and Visualization
Download https://drive.weixin.qq.com/s?k=AJEAIQdfAAob00fTSEAKMA9AatACk datasets to assets.
Weights could be download from https://drive.weixin.qq.com/s?k=AJEAIQdfAAo97Nnovq.
industrial visualization and debug
## initial model
model, config = dloc.build_model('superpoint_aachen', 'superglue_outdoor', 'SuperMatch/weights', landmark=False, direct=False)
## get matches or relative pose
results = dloc.get_matches('SuperMatch/assets/2543051003_7b970fc234_o.jpg', 'SuperMatch/assets/301490830_aded42bc67_o.jpg', model, config, landmarks=None)
or
results_pose = dloc.get_pose('SuperMatch/assets/2543051003_7b970fc234_o.jpg', 'SuperMatch/assets/301490830_aded42bc67_o.jpg', model, config, mode='H')
explanation
build_model(extractor, matcher, model_path='', landmark=False, direct=False)
get_matches(name0, name1, model, config, resize=[-1], with_desc=False, landmarks=None)
get_pose(img1, img2, model, config, resize=[-1], landmarks=None, mode='H')
- extractors: 'superpoint_aachen', 'superpoint_inloc', 'd2net-ss', 'r2d2-desc','context-desc', 'landmark', 'aslfeat-desc', 'disk-desc', 'swin-disk-desc'
- matchers: 'superglue_outdoor', 'superglue_disk', 'superglue_swin_disk', 'superglue_indoor', 'NN', 'disk', 'cotr', 'loftr'
Release files for superMatch 0.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| superMatch-0.0.5-py3-none-any.whl | Python 3 | none | any | Details |
Release files / superMatch-0.0.5-py3-none-any.whl
| Download URL | superMatch-0.0.5-py3-none-any.whl |
|---|---|
| Size | 196.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d711a0f53f61bcfbe6b6488c0b72996d2d187833335bd6d7154161ad7a8e5707
|
|
BLAKE2b-256 checksum How to use checksums |
33fefc1ea6d7eea800b8e473efc92246a0c1178df9a56d6315d5c1991ad503c1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/3.7.2 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.6.8
|