update: ui

app.py

@@ -17,14 +17,12 @@ from common.utils import (
 )
 
 DESCRIPTION = """
-# Image Matching WebUI
-This Space demonstrates [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui) by vincent qin. Feel free to play with it, or duplicate to run image matching without a queue!
-
-🔎 For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
-
-🚀 All algorithms run on CPU for inference on HF, causing slow speeds and high latency. For faster inference, please download the [source code](https://github.com/Vincentqyw/image-matching-webui) for local deployment.
-
-🐛 Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
+# GIM Image Matching
+<p align="center">
+  <span style="color: gray; font-size: smaller;">
+    Thanks <a href="https://github.com/Vincentqyw/image-matching-webui" style="color: gray;">Vincentqyw/image-matching-webui</a> for the UI framework.
+  </span>
+</p>
 """
 
 
@@ -97,12 +95,12 @@ def run(config):
                 with gr.Row():
                     matcher_list = gr.Dropdown(
                         choices=list(matcher_zoo.keys()),
-                        value="disk+lightglue",
+                        value="gim",
                         label="Matching Model",
                         interactive=True,
                     )
                     match_image_src = gr.Radio(
-                        ["upload", "webcam", "canvas"],
+                        ["upload", "webcam"],
                         label="Image Source",
                         value="upload",
                     )
@@ -248,22 +246,21 @@ def run(config):
                 output_matches_ransac = gr.Image(
                     label="Ransac Matches", type="numpy"
                 )
+                output_wrapped = gr.Image(
+                    label="Wrapped Pair", type="numpy"
+                )
                 with gr.Accordion(
                     "Open for More: Matches Statistics", open=False
                 ):
                     matches_result_info = gr.JSON(label="Matches Statistics")
                     matcher_info = gr.JSON(label="Match info")
 
-                with gr.Accordion("Open for More: Warped Image", open=False):
-                    output_wrapped = gr.Image(
-                        label="Wrapped Pair", type="numpy"
+                with gr.Accordion(
+                    "Open for More: Geometry info", open=False
+                ):
+                    geometry_result = gr.JSON(
+                        label="Reconstructed Geometry"
                     )
-                    with gr.Accordion(
-                        "Open for More: Geometry info", open=False
-                    ):
-                        geometry_result = gr.JSON(
-                            label="Reconstructed Geometry"
-                        )
 
             # callbacks
             match_image_src.change(

common/utils.py

@@ -41,14 +41,15 @@ def get_feature_model(conf):
 def gen_examples():
     random.seed(1)
     example_matchers = [
+        "gim",
+        "gim",
+        "gim",
         "disk+lightglue",
         "loftr",
         "disk",
         "d2net",
-        "topicfm",
         "superpoint+superglue",
         "disk+dualsoftmax",
-        "lanet",
     ]
 
     def gen_images_pairs(path: str, count: int = 5):
@@ -64,6 +65,10 @@ def gen_examples():
     # image pair path
     path = "datasets/sacre_coeur/mapping"
     pairs = gen_images_pairs(path, len(example_matchers))
+    gim_pairs = [('datasets/gim/0a.png', 'datasets/gim/0b.png'),
+                 ('datasets/gim/1a.png', 'datasets/gim/1b.png'),
+                 ('datasets/gim/2a.png', 'datasets/gim/2b.png')]
+    pairs = gim_pairs + pairs
     match_setting_threshold = DEFAULT_SETTING_THRESHOLD
     match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES
     detect_keypoints_threshold = DEFAULT_DEFAULT_KEYPOINT_THRESHOLD

hloc/matchers/gim.py

@@ -38,12 +38,12 @@ class GIM(BaseModel):
             cmd = ["wget", link, "-O", str(model_path)]
             logger.info(f"Downloading the DKMv3 model with `{cmd}`.")
             subprocess.run(cmd, check=True)
-        logger.info(f"Loading DKMv3 model...")
+        logger.info(f"Loading GIM model...")
         # self.net = DKMv3(path_to_weights=str(model_path), device=device)
 
         model = DKMv3(None, 672, 896, upsample_preds=True)
 
-        checkpoints_path = join('checkpoints', conf['weights'])
+        checkpoints_path = str(model_path)
         state_dict = torch.load(checkpoints_path, map_location='cpu')
         if 'state_dict' in state_dict.keys(): state_dict = state_dict['state_dict']
         for k in list(state_dict.keys()):