fix: use image matcher defaults

UCSD-E4E · Sep 15, 2024 · 594e714 · 594e714
1 parent e3eda9e
commit 594e714
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Showing 2 changed files with 13 additions and 10 deletions.
diff --git a/pyfishsensedev/library/homography/image_matcher.py b/pyfishsensedev/library/homography/image_matcher.py
@@ -4,6 +4,8 @@
 Due to licensing issues, make sure you are using the correct version of SuperPoint.
 """
 
+from typing import Tuple
+
 import torch
 
 from pyfishsensedev.library.homography.models.lightglue import LightGlue
@@ -29,7 +31,7 @@ def __init__(self, template: torch.Tensor, com_license=True, processing_conf={})
             processing_conf["extractor"] if "extractor" in processing_conf else {}
         )
 
-    def __call__(self, image1: torch.Tensor):
+    def __call__(self, image1: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         """Given a calibration image, return its matches with the template.
         Input:
             image1: torch.Tensor

diff --git a/pyfishsensedev/plane_detector/slate_detector.py b/pyfishsensedev/plane_detector/slate_detector.py
@@ -11,17 +11,18 @@ def __init__(self, image: np.ndarray[np.uint8], pdf: Pdf) -> None:
         super().__init__(image)
 
         self._pdf = pdf
-        self._image_matcher = ImageMatcher(
-            numpy_image_to_torch(self._pdf.image),
-            processing_conf={
-                "preprocess": {"gamma": 2.0, "sharpness": None, "scale": 1.6},
-                "extractor": {"max_num_keypoints": None},
-                "matcher": {"filter_threshold": 0.5},
-            },
-        )
+        self._image_matcher = ImageMatcher(numpy_image_to_torch(self._pdf.image))
+        #     processing_conf={
+        #         "preprocess": {"gamma": 2.0, "sharpness": None, "scale": 1.6},
+        #         "extractor": {"max_num_keypoints": None},
+        #         "matcher": {"filter_threshold": 0.5},
+        #     },
+        # )
 
     def _get_template_matches(self):
-        feats0_matches, feats1_matches = self._image_matcher(numpy_image_to_torch(self.image))
+        feats0_matches, feats1_matches = self._image_matcher(
+            numpy_image_to_torch(self.image)
+        )
 
         print("Done")