update: api

common/api.py

@@ -27,28 +27,6 @@ warnings.simplefilter("ignore")
 
 class ImageMatchingAPI(torch.nn.Module):
     default_conf = {
-        "dense": True,
-        "matcher": {
-            "model": {
-                "name": "topicfm",
-                "match_threshold": 0.2,
-            }
-        },
-        "feature": {
-            "model": {
-                "name": "xfeat",
-                "max_keypoints": 1024,
-                "keypoint_threshold": 0.015,
-            },
-            "preprocessing": {
-                "grayscale": False,
-                "resize_max": 1600,
-                "force_resize": True,
-                "width": 640,
-                "height": 480,
-                "dfactor": 8,
-            },
-        },
         "ransac": {
             "enable": True,
             "estimator": "poselib",
@@ -83,7 +61,7 @@ class ImageMatchingAPI(torch.nn.Module):
         """
         super().__init__()
         self.device = device
-        self.conf = self.parse_match_config(conf)
+        self.conf = {**self.default_conf, **conf}
         self._updata_config(detect_threshold, max_keypoints, match_threshold)
         self._init_models()
         if device == "cuda":
@@ -126,14 +104,20 @@ class ImageMatchingAPI(torch.nn.Module):
     ):
         self.dense = self.conf["dense"]
         if self.conf["dense"]:
-            self.conf["matcher"]["model"]["match_threshold"] = match_threshold
+            try:
+                self.conf["matcher"]["model"][
+                    "match_threshold"
+                ] = match_threshold
+            except TypeError as e:
+                breakpoint()
         else:
             self.conf["feature"]["model"]["max_keypoints"] = max_keypoints
             self.conf["feature"]["model"][
                 "keypoint_threshold"
             ] = detect_threshold
+            self.extract_conf = self.conf["feature"]
+
         self.match_conf = self.conf["matcher"]
-        self.extract_conf = self.conf["feature"]
 
     def _init_models(self):
         # initialize matcher

common/config.yaml

@@ -16,29 +16,29 @@ defaults:
   setting_geometry: Homography
 
 matcher_zoo:
-  omniglue:
-    enable: true
-    matcher: omniglue
-    dense: true
-    info:
-      name: OmniGlue
-      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
-  DUSt3R:
-    # TODO: duster is under development  
-    enable: false
-    matcher: duster
-    dense: true
-    info: 
-      name: DUSt3R #dispaly name
-      source: "CVPR 2024"
-      github: https://github.com/naver/dust3r
-      paper: https://arxiv.org/abs/2312.14132
-      project: https://dust3r.europe.naverlabs.com
-      display: true  
+  # omniglue:
+  #   enable: true
+  #   matcher: omniglue
+  #   dense: true
+  #   info:
+  #     name: OmniGlue
+  #     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
+  # DUSt3R:
+  #   # TODO: duster is under development  
+  #   enable: false
+  #   matcher: duster
+  #   dense: true
+  #   info: 
+  #     name: DUSt3R #dispaly name
+  #     source: "CVPR 2024"
+  #     github: https://github.com/naver/dust3r
+  #     paper: https://arxiv.org/abs/2312.14132
+  #     project: https://dust3r.europe.naverlabs.com
+  #     display: true  
   GIM(dkm):
     matcher: gim(dkm)
     dense: true

test_app_cli.py

@@ -41,29 +41,32 @@ def test_one():
     image1 = cv2.imread(str(img_path2))[:, :, ::-1]  # RGB
     # sparse
     conf = {
-        "dense": False,
-        "matcher": {
-            "model": {
-                "name": "NN-mutual",
-                "match_threshold": 0.2,
-            }
-        },
         "feature": {
+            "output": "feats-superpoint-n4096-rmax1600",
             "model": {
-                "name": "xfeat",
-                "max_keypoints": 1024,
-                "keypoint_threshold": 0.015,
-            }
+                "name": "superpoint",
+                "nms_radius": 3,
+                "max_keypoints": 4096,
+                "keypoint_threshold": 0.005,
+            },
+            "preprocessing": {
+                "grayscale": True,
+                "force_resize": True,
+                "resize_max": 1600,
+                "width": 640,
+                "height": 480,
+                "dfactor": 8,
+            },
         },
-        "ransac": {
-            "enable": True,
-            "estimator": "poselib",
-            "geometry": "homography",
-            "method": "RANSAC",
-            "reproj_threshold": 3,
-            "confidence": 0.9999,
-            "max_iter": 10000,
+        "matcher": {
+            "output": "matches-NN-mutual",
+            "model": {
+                "name": "nearest_neighbor",
+                "do_mutual_check": True,
+                "match_threshold": 0.2,
+            },
         },
+        "dense": False,
     }
     api = ImageMatchingAPI(conf=conf, device=device)
     api(image0, image1)
@@ -73,24 +76,28 @@ def test_one():
 
     # dense
     conf = {
-        "dense": True,
         "matcher": {
+            "output": "matches-loftr",
             "model": {
                 "name": "loftr",
+                "weights": "outdoor",
+                "max_keypoints": 2000,
                 "match_threshold": 0.2,
-            }
-        },
-        "feature": {},
-        "ransac": {
-            "enable": True,
-            "estimator": "poselib",
-            "geometry": "homography",
-            "method": "RANSAC",
-            "reproj_threshold": 3,
-            "confidence": 0.9999,
-            "max_iter": 10000,
+            },
+            "preprocessing": {
+                "grayscale": True,
+                "resize_max": 1024,
+                "dfactor": 8,
+                "width": 640,
+                "height": 480,
+                "force_resize": True,
+            },
+            "max_error": 1,
+            "cell_size": 1,
         },
+        "dense": True,
     }
+
     api = ImageMatchingAPI(conf=conf, device=device)
     api(image0, image1)
     log_path = ROOT / "experiments" / "one"