add: xfeat+lightglue

hloc/match_dense.py

@@ -205,6 +205,21 @@ confs = {
             "dfactor": 16,
         },
     },
+    "xfeat_lightglue": {
+        "output": "matches-xfeat_lightglue",
+        "model": {
+            "name": "xfeat_lightglue",
+            "max_keypoints": 8000,
+        },
+        "preprocessing": {
+            "grayscale": False,
+            "force_resize": False,
+            "resize_max": 1024,
+            "width": 640,
+            "height": 480,
+            "dfactor": 8,
+        },
+    },
     "xfeat_dense": {
         "output": "matches-xfeat_dense",
         "model": {

hloc/matchers/xfeat_lightglue.py

@@ -0,0 +1,48 @@
+import torch
+
+from hloc import logger
+
+from ..utils.base_model import BaseModel
+
+
+class XFeatLightGlue(BaseModel):
+    default_conf = {
+        "keypoint_threshold": 0.005,
+        "max_keypoints": 8000,
+    }
+    required_inputs = [
+        "image0",
+        "image1",
+    ]
+
+    def _init(self, conf):
+        self.net = torch.hub.load(
+            "verlab/accelerated_features",
+            "XFeat",
+            pretrained=True,
+            top_k=self.conf["max_keypoints"],
+        )
+        logger.info("Load XFeat(dense) model done.")
+
+    def _forward(self, data):
+        # we use results from one batch
+        im0 = data["image0"]
+        im1 = data["image1"]
+        # Compute coarse feats
+        out0 = self.net.detectAndCompute(im0, top_k=self.conf["max_keypoints"])[
+            0
+        ]
+        out1 = self.net.detectAndCompute(im1, top_k=self.conf["max_keypoints"])[
+            0
+        ]
+        out0.update({"image_size": (im0.shape[-1], im0.shape[-2])})  # W H
+        out1.update({"image_size": (im1.shape[-1], im1.shape[-2])})  # W H
+        mkpts_0, mkpts_1 = self.net.match_lighterglue(out0, out1)
+        mkpts_0 = torch.from_numpy(mkpts_0)  # n x 2
+        mkpts_1 = torch.from_numpy(mkpts_1)  # n x 2
+        pred = {
+            "keypoints0": mkpts_0,
+            "keypoints1": mkpts_1,
+            "mconf": torch.ones_like(mkpts_0[:, 0]),
+        }
+        return pred

requirements.txt

@@ -6,7 +6,7 @@ gradio_client==0.16.0
 h5py==3.9.0
 imageio==2.31.1
 Jinja2==3.1.2
-kornia==0.6.12
+kornia
 loguru==0.7.0
 matplotlib==3.7.1
 numpy==1.23.5

ui/config.yaml

@@ -133,6 +133,17 @@ matcher_zoo:
       paper: https://arxiv.org/abs/2208.14201
       project: null
       display: true
+  xfeat+lightglue:
+    enable: true
+    matcher: xfeat_lightglue
+    dense: true
+    info:
+      name: xfeat+lightglue
+      source: "CVPR 2024"
+      github: https://github.com/Vincentqyw/omniglue-onnx
+      paper: https://arxiv.org/abs/2405.12979
+      project: https://hwjiang1510.github.io/OmniGlue
+      display: true
   xfeat(sparse):
     matcher: NN-mutual
     feature: xfeat