update: return matching score

hloc/match_dense.py

@@ -353,6 +353,8 @@ def match_images(model, image_0, image_1, conf, device="cpu"):
         }
         if "mconf" in pred.keys():
             ret["mconf"] = pred["mconf"].cpu().numpy()
+        elif "scores" in pred.keys(): #adapting loftr
+            ret["mconf"] = pred["scores"].cpu().numpy()
         else:
             ret["mconf"] = np.ones_like(kpts0.cpu().numpy()[:, 0])
     if "lines0" in pred.keys() and "lines1" in pred.keys():

hloc/matchers/aspanformer.py

@@ -93,8 +93,9 @@ class ASpanFormer(BaseModel):
             "image1": data["image1"],
         }
         self.net(data_, online_resize=True)
-        corr0 = data_["mkpts0_f"]
-        corr1 = data_["mkpts1_f"]
-        pred = {}
-        pred["keypoints0"], pred["keypoints1"] = corr0, corr1
+        pred = {
+            "keypoints0": data_["mkpts0_f"],
+            "keypoints1": data_["mkpts1_f"],
+            "mconf": data_["mconf"],
+        }
         return pred

hloc/matchers/loftr.py

@@ -2,7 +2,7 @@ import torch
 import warnings
 from kornia.feature.loftr.loftr import default_cfg
 from kornia.feature import LoFTR as LoFTR_
-
+from hloc import logger
 from ..utils.base_model import BaseModel
 
 
@@ -18,7 +18,7 @@ class LoFTR(BaseModel):
         cfg = default_cfg
         cfg["match_coarse"]["thr"] = conf["match_threshold"]
         self.net = LoFTR_(pretrained=conf["weights"], config=cfg)
-
+        logger.info(f"Loaded LoFTR with weights {conf['weights']}")
     def _forward(self, data):
         # For consistency with hloc pairs, we refine kpts in image0!
         rename = {

hloc/matchers/roma.py

@@ -85,7 +85,10 @@ class Roma(BaseModel):
         kpts1, kpts2 = self.net.to_pixel_coordinates(
             matches, H_A, W_A, H_B, W_B
         )
-        pred = {}
-        pred["keypoints0"], pred["keypoints1"] = kpts1, kpts2
-        pred["mconf"] = certainty
+        pred = {
+            "keypoints0": kpts1,
+            "keypoints1": kpts2,
+            "mconf": certainty,
+        }
+
         return pred

hloc/matchers/topicfm.py

@@ -34,12 +34,9 @@ class TopicFM(BaseModel):
             "image1": data["image1"],
         }
         self.net(data_)
-        mkpts0 = data_["mkpts0_f"]
-        mkpts1 = data_["mkpts1_f"]
-        mconf = data_["mconf"]
-        total_n_matches = len(data_["mkpts0_f"])
-
-        pred = {}
-        pred["keypoints0"], pred["keypoints1"] = mkpts0, mkpts1
-        pred["mconf"] = mconf
+        pred = {
+            "keypoints0": data_["mkpts0_f"],
+            "keypoints1": data_["mkpts1_f"],
+            "mconf": data_["mconf"],
+        }
         return pred