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<p align="center">
  <h1 align="center"><ins>DeDoDe</ins> 馃幎<br>Detect, Don't Describe, Describe, Don't Detect, <br> for Local Feature Matching</h1>
  <p align="center">
    <a href="https://scholar.google.com/citations?user=Ul-vMR0AAAAJ">Johan Edstedt</a>
    路
    <a href="https://scholar.google.com/citations?user=FUE3Wd0AAAAJ">Georg B枚kman</a>
    路
    <a href="https://scholar.google.com/citations?user=6WRQpCQAAAAJ">M氓rten Wadenb盲ck</a>
    路
    <a href="https://scholar.google.com/citations?user=lkWfR08AAAAJ">Michael Felsberg</a>
    路
  </p>
  <h2 align="center"><p>
    <a href="TODO" align="center">Paper (TODO)</a> | 
    <a href="TODO" align="center">Project Page (TODO)</a>
  </p></h2>
  <div align="center"></div>
</p>
<p align="center">
    <img src="assets/matches.jpg" alt="example" width=80%>
    <br>
    <em>The DeDoDe detector learns to detect 3D consistent repeatable keypoints, which the DeDoDe descriptor learns to match. The result is a powerful decoupled local feature matcher.</em>
    <br>
        <img src="assets/teaser.png" alt="example" width=40%>
        <img src="assets/dedode_roma.png" alt="example" width=40%>
    <br>
    <em>
    We experimentally find that DeDoDe significantly closes the performance gap between detector + descriptor models and fully-fledged matchers. The potential of DeDoDe is not limited to local feature matching, in fact we find that we can improve state-of-the-art matchers by incorporating DeDoDe keypoints.
    </em>
</p>

## How to Use DeDoDe?
Below we show how DeDoDe can be run, you can also check out the [demos](demo)
```python
from DeDoDe import dedode_detector_L, dedode_descriptor_B
from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher

detector = dedode_detector_L(weights = torch.load("dedode_detector_L.pth"))
descriptor = dedode_descriptor_B(weights = torch.load("dedode_descriptor_B.pth"))
matcher = DualSoftMaxMatcher()

im_A_path = "assets/im_A.jpg"
im_B_path = "assets/im_B.jpg"
im_A = Image.open(im_A_path)
im_B = Image.open(im_B_path)
W_A, H_A = im_A.size
W_B, H_B = im_B.size


detections_A = detector.detect_from_path(im_A_path, num_keypoints = 10_000)
keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]

detections_B = detector.detect_from_path(im_B_path, num_keypoints = 10_000)
keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]

description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)["descriptions"]
description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)["descriptions"]

matches_A, matches_B, batch_ids = matcher.match(keypoints_A, description_A,
    keypoints_B, description_B,
    P_A = P_A, P_B = P_B,
    normalize = True, inv_temp=20, threshold = 0.1)#Increasing threshold -> fewer matches, fewer outliers

matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)

```
## Pretrained Models

Right now you can find them here: https://github.com/Parskatt/DeDoDe/releases/tag/dedode_pretrained_models
Probably we'll add some autoloading in the near future.

## BibTeX

Coming Soon ;)