style: pre-commit fixes

Author: pre-commit-ci[bot]

imcui/hloc/extract_features.py

@@ -181,7 +181,7 @@ def main(
     overwrite: bool = False,
 ) -> Path:
     logger.info(
-        "Extracting local features with configuration:" f"\n{pprint.pformat(conf)}"
+        f"Extracting local features with configuration:\n{pprint.pformat(conf)}"
     )
 
     dataset = ImageDataset(image_dir, conf["preprocessing"], image_list)

imcui/hloc/extractors/dog.py

@@ -40,7 +40,7 @@ def _init(self, conf):
         elif conf["descriptor"] == "hardnet":
             self.describe = kornia.feature.HardNet(pretrained=True)
         elif conf["descriptor"] not in ["sift", "rootsift"]:
-            raise ValueError(f'Unknown descriptor: {conf["descriptor"]}')
+            raise ValueError(f"Unknown descriptor: {conf['descriptor']}")
 
         self.sift = None  # lazily instantiated on the first image
         self.dummy_param = torch.nn.Parameter(torch.empty(0))
@@ -104,7 +104,7 @@ def _forward(self, data):
                         patches[start_idx:end_idx]
                     )
         else:
-            raise ValueError(f'Unknown descriptor: {self.conf["descriptor"]}')
+            raise ValueError(f"Unknown descriptor: {self.conf['descriptor']}")
 
         keypoints = torch.from_numpy(keypoints[:, :2])  # keep only x, y
         scales = torch.from_numpy(scales)

imcui/hloc/extractors/example.py

@@ -26,7 +26,7 @@ class Example(BaseModel):
 
     def _init(self, conf):
         # set checkpoints paths if needed
-        model_path = example_path / "checkpoints" / f'{conf["model_name"]}'
+        model_path = example_path / "checkpoints" / f"{conf['model_name']}"
         if not model_path.exists():
             logger.info(f"No model found at {model_path}")
 

imcui/hloc/extractors/sift.py

@@ -132,7 +132,7 @@ def _init(self, conf):
         else:
             backends = {"opencv", "pycolmap", "pycolmap_cpu", "pycolmap_cuda"}
             raise ValueError(
-                f"Unknown backend: {backend} not in " f"{{{','.join(backends)}}}."
+                f"Unknown backend: {backend} not in {{{','.join(backends)}}}."
             )
         logger.info("Load SIFT model done.")
 

imcui/hloc/match_dense.py

@@ -495,7 +495,7 @@ def match_and_assign(
 
     # Invalidate matches that are far from selected bin by reassignment
     if max_kps is not None:
-        logger.info(f'Reassign matches with max_error={conf["max_error"]}.')
+        logger.info(f"Reassign matches with max_error={conf['max_error']}.")
         assign_matches(pairs, match_path, cpdict, max_error=conf["max_error"])
 
 
@@ -737,7 +737,7 @@ def main(
     overwrite: bool = False,
 ) -> Path:
     logger.info(
-        "Extracting semi-dense features with configuration:" f"\n{pprint.pformat(conf)}"
+        f"Extracting semi-dense features with configuration:\n{pprint.pformat(conf)}"
     )
 
     if features is None:
@@ -747,17 +747,17 @@ def main(
         features_q = features
         if matches is None:
             raise ValueError(
-                "Either provide both features and matches as Path" " or both as names."
+                "Either provide both features and matches as Path or both as names."
             )
     else:
         if export_dir is None:
             raise ValueError(
                 "Provide an export_dir if features and matches"
                 f" are not file paths: {features}, {matches}."
             )
-        features_q = Path(export_dir, f'{features}{conf["output"]}.h5')
+        features_q = Path(export_dir, f"{features}{conf['output']}.h5")
         if matches is None:
-            matches = Path(export_dir, f'{conf["output"]}_{pairs.stem}.h5')
+            matches = Path(export_dir, f"{conf['output']}_{pairs.stem}.h5")
 
     if features_ref is None:
         features_ref = []

imcui/hloc/match_features.py

@@ -96,16 +96,16 @@ def main(
         features_q = features
         if matches is None:
             raise ValueError(
-                "Either provide both features and matches as Path" " or both as names."
+                "Either provide both features and matches as Path or both as names."
             )
     else:
         if export_dir is None:
             raise ValueError(
-                "Provide an export_dir if features is not" f" a file path: {features}."
+                f"Provide an export_dir if features is not a file path: {features}."
             )
         features_q = Path(export_dir, features + ".h5")
         if matches is None:
-            matches = Path(export_dir, f'{features}_{conf["output"]}_{pairs.stem}.h5')
+            matches = Path(export_dir, f"{features}_{conf['output']}_{pairs.stem}.h5")
 
     if features_ref is None:
         features_ref = features_q
@@ -146,9 +146,7 @@ def match_from_paths(
     feature_path_ref: Path,
     overwrite: bool = False,
 ) -> Path:
-    logger.info(
-        "Matching local features with configuration:" f"\n{pprint.pformat(conf)}"
-    )
+    logger.info(f"Matching local features with configuration:\n{pprint.pformat(conf)}")
 
     if not feature_path_q.exists():
         raise FileNotFoundError(f"Query feature file {feature_path_q}.")

imcui/hloc/pipelines/4Seasons/utils.py

@@ -227,5 +227,5 @@ def evaluate_submission(submission_dir, relocs, ths=[0.1, 0.2, 0.5]):
         recall = [np.mean(error <= th) for th in ths]
         s = f"Relocalization evaluation {submission_dir.name}/{reloc.name}\n"
         s += " / ".join([f"{th:>7}m" for th in ths]) + "\n"
-        s += " / ".join([f"{100*r:>7.3f}%" for r in recall])
+        s += " / ".join([f"{100 * r:>7.3f}%" for r in recall])
         logger.info(s)

imcui/hloc/pipelines/7Scenes/create_gt_sfm.py

@@ -111,9 +111,9 @@ def correct_sfm_with_gt_depth(sfm_path, depth_folder_path, output_path):
         new_p3D_ids[new_p3D_ids != -1] = sub_p3D_ids
         img = img._replace(point3D_ids=new_p3D_ids)
 
-        assert len(img.point3D_ids[img.point3D_ids != -1]) == len(
-            scs
-        ), f"{len(scs)}, {len(img.point3D_ids[img.point3D_ids != -1])}"
+        assert len(img.point3D_ids[img.point3D_ids != -1]) == len(scs), (
+            f"{len(scs)}, {len(img.point3D_ids[img.point3D_ids != -1])}"
+        )
         for i, p3did in enumerate(img.point3D_ids[img.point3D_ids != -1]):
             points3D[p3did] = points3D[p3did]._replace(xyz=scs[i])
         images[imgid] = img

imcui/hloc/pipelines/Cambridge/utils.py

@@ -141,5 +141,5 @@ def evaluate(model, results, list_file=None, ext=".bin", only_localized=False):
     threshs_R = [1.0, 2.0, 3.0, 5.0, 2.0, 5.0, 10.0]
     for th_t, th_R in zip(threshs_t, threshs_R):
         ratio = np.mean((errors_t < th_t) & (errors_R < th_R))
-        out += f"\n\t{th_t*100:.0f}cm, {th_R:.0f}deg : {ratio*100:.2f}%"
+        out += f"\n\t{th_t * 100:.0f}cm, {th_R:.0f}deg : {ratio * 100:.2f}%"
     logger.info(out)

imcui/hloc/reconstruction.py

@@ -92,9 +92,7 @@ def run_reconstruction(
             largest_index = index
             largest_num_images = num_images
     assert largest_index is not None
-    logger.info(
-        f"Largest model is #{largest_index} " f"with {largest_num_images} images."
-    )
+    logger.info(f"Largest model is #{largest_index} with {largest_num_images} images.")
 
     for filename in ["images.bin", "cameras.bin", "points3D.bin"]:
         if (sfm_dir / filename).exists():