fix: cached model

common/app_class.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Dict, Any, Optional, Tuple, List, Union
 from common.utils import (
     ransac_zoo,
-    change_estimate_geom,
+    generate_warp_images,
     load_config,
     get_matcher_zoo,
     run_matching,
@@ -290,7 +290,7 @@ class ImageMatchingApp:
 
                 # estimate geo
                 choice_geometry_type.change(
-                    fn=change_estimate_geom,
+                    fn=generate_warp_images,
                     inputs=[
                         input_image0,
                         input_image1,

common/config.yaml

@@ -16,26 +16,26 @@ defaults:
   setting_geometry: Homography
 
 matcher_zoo:
-  roma:
-    matcher: roma
-    dense: true
-    info: 
-      name: RoMa #dispaly name
-      source: "CVPR 2024"
-      github: https://github.com/Parskatt/RoMa
-      paper: https://arxiv.org/abs/2305.15404
-      project: https://parskatt.github.io/RoMa
-      display: true
-  dkm:
-    matcher: dkm
-    dense: true
-    info: 
-      name: DKM #dispaly name
-      source: "CVPR 2023"
-      github: https://github.com/Parskatt/DKM
-      paper: https://arxiv.org/abs/2202.00667
-      project: https://parskatt.github.io/DKM
-      display: true
+  # roma:
+  #   matcher: roma
+  #   dense: true
+  #   info: 
+  #     name: RoMa #dispaly name
+  #     source: "CVPR 2024"
+  #     github: https://github.com/Parskatt/RoMa
+  #     paper: https://arxiv.org/abs/2305.15404
+  #     project: https://parskatt.github.io/RoMa
+  #     display: true
+  # dkm:
+  #   matcher: dkm
+  #   dense: true
+  #   info: 
+  #     name: DKM #dispaly name
+  #     source: "CVPR 2023"
+  #     github: https://github.com/Parskatt/DKM
+  #     paper: https://arxiv.org/abs/2202.00667
+  #     project: https://parskatt.github.io/DKM
+  #     display: true
   loftr:
     matcher: loftr
     dense: true

common/utils.py

@@ -12,7 +12,6 @@ from hloc.utils.base_model import dynamic_load
 from hloc import match_dense, match_features, extract_features
 from hloc.utils.viz import add_text, plot_keypoints
 from .viz import (
-    draw_matches,
     fig2im,
     plot_images,
     display_matches,
@@ -242,7 +241,7 @@ def filter_matches(
     return pred
 
 
-def compute_geom(
+def compute_geometry(
     pred: Dict[str, Any],
     ransac_method: str = DEFAULT_RANSAC_METHOD,
     ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD,
@@ -373,7 +372,7 @@ def wrap_images(
         return None, None
 
 
-def change_estimate_geom(
+def generate_warp_images(
     input_image0: np.ndarray,
     input_image1: np.ndarray,
     matches_info: Dict[str, Any],
@@ -475,7 +474,7 @@ def run_matching(
     match_conf["model"]["match_threshold"] = match_threshold
     match_conf["model"]["max_keypoints"] = extract_max_keypoints
     t0 = time.time()
-    cache_key = match_conf["model"]["name"]
+    cache_key = "{}_{}".format(key, match_conf["model"]["name"])
     if cache_key in models_already_loaded:
         matcher = models_already_loaded[cache_key]
         matcher.conf["max_keypoints"] = extract_max_keypoints
@@ -499,7 +498,7 @@ def run_matching(
         # update extract config
         extract_conf["model"]["max_keypoints"] = extract_max_keypoints
         extract_conf["model"]["keypoint_threshold"] = keypoint_threshold
-        cache_key = extract_conf["model"]["name"]
+        cache_key = "{}_{}".format(key, extract_conf["model"]["name"])
         if cache_key in models_already_loaded:
             extractor = models_already_loaded[cache_key]
             extractor.conf["max_keypoints"] = extract_max_keypoints
@@ -567,8 +566,8 @@ def run_matching(
 
     t1 = time.time()
     # plot wrapped images
-    geom_info = compute_geom(pred)
-    output_wrapped, _ = change_estimate_geom(
+    geom_info = compute_geometry(pred)
+    output_wrapped, _ = generate_warp_images(
         pred["image0_orig"],
         pred["image1_orig"],
         {"geom_info": geom_info},

common/viz.py

@@ -247,7 +247,7 @@ def fig2im(fig: matplotlib.figure.Figure) -> np.ndarray:
     return buf_ndarray.reshape(height, width, 3)
 
 
-def draw_matches(
+def draw_matches_core(
     mkpts0: List[np.ndarray],
     mkpts1: List[np.ndarray],
     img0: np.ndarray,
@@ -293,7 +293,7 @@ def draw_matches(
                 mkpts1,
                 color,
                 titles=titles,
-                text=text,
+                # text=texts,
                 path=path,
                 dpi=dpi,
                 pad=pad,
@@ -308,7 +308,7 @@ def draw_matches(
                 mkpts1,
                 color,
                 titles=titles,
-                text=text,
+                # text=texts,
                 pad=pad,
                 dpi=dpi,
             )
@@ -406,7 +406,7 @@ def display_matches(
             mconf = pred["mconf"]
         else:
             mconf = np.ones(len(mkpts0))
-        fig_mkpts = draw_matches(
+        fig_mkpts = draw_matches_core(
             mkpts0,
             mkpts1,
             img0,
@@ -445,7 +445,9 @@ def display_matches(
                 mconf = pred["mconf"]
             else:
                 mconf = np.ones(len(mkpts0))
-            fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300)
+            fig_mkpts = draw_matches_core(
+                mkpts0, mkpts1, img0, img1, mconf, dpi=300
+            )
             fig_lines = cv2.resize(
                 fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0])
             )