formatting.

limap/line2d/SOLD2/model/model_util.py

@@ -180,7 +180,8 @@ def get_heatmap_decoder(self):
     def get_descriptor_decoder(self):
         """Get the descriptor decoder."""
         if (
-            self.cfg["descriptor_decoder"] not in self.supported_descriptor_decoder
+            self.cfg["descriptor_decoder"]
+            not in self.supported_descriptor_decoder
         ):
             raise ValueError(
                 "[Error] The descriptor decoder selection is not supported."

limap/line2d/base_detector.py

@@ -217,9 +217,7 @@ def detect_all_images(self, output_folder, imagecols, skip_exists=False):
             if self.visualize:
                 img = imagecols.read_image(img_id)
                 img = limapvis.draw_segments(img, segs, (0, 255, 0))
-                fname = os.path.join(
-                    vis_folder, f"img_{img_id}_det.png"
-                )
+                fname = os.path.join(vis_folder, f"img_{img_id}_det.png")
                 cv2.imwrite(fname, img)
         all_2d_segs = limapio.read_all_segments_from_folder(seg_folder)
         all_2d_segs = {id: all_2d_segs[id] for id in imagecols.get_img_ids()}
@@ -306,9 +304,7 @@ def detect_and_extract_all_images(
             if self.visualize:
                 img = imagecols.read_image(img_id)
                 img = limapvis.draw_segments(img, segs, (0, 255, 0))
-                fname = os.path.join(
-                    vis_folder, f"img_{img_id}_det.png"
-                )
+                fname = os.path.join(vis_folder, f"img_{img_id}_det.png")
                 cv2.imwrite(fname, img)
         all_2d_segs = limapio.read_all_segments_from_folder(seg_folder)
         all_2d_segs = {id: all_2d_segs[id] for id in imagecols.get_img_ids()}

limap/optimize/extract_heatmaps_sold2.py

@@ -1,4 +1,3 @@
-
 import limap.base as _base
 import limap.line2d
 import limap.util.io as limapio

limap/optimize/extract_track_patches_s2dnet.py

@@ -44,9 +44,7 @@ def extract_track_patches_s2dnet(
             camview = imagecols.camview(img_id)
             line2d_range = extractor.GetLine2DRange(track, img_id, camview)
             line2d_collections[img_id].append([line2d_range, track_id])
-        limapio.check_makedirs(
-            os.path.join(output_dir, f"track{track_id}")
-        )
+        limapio.check_makedirs(os.path.join(output_dir, f"track{track_id}"))
 
     # extract line patches for each image
     for img_id in tqdm(imagecols.get_img_ids()):

limap/pointsfm/colmap_sfm.py

@@ -174,9 +174,7 @@ def run_colmap_sfm(
     keypoints_in_order = []
     for idx, img_id in enumerate(imagecols.get_img_ids()):
         img = imagecols.read_image(img_id)
-        fname_to_save = os.path.join(
-            image_path, f"image{img_id:08d}.png"
-        )
+        fname_to_save = os.path.join(image_path, f"image{img_id:08d}.png")
         cv2.imwrite(fname_to_save, img)
         if keypoints is not None:
             keypoints_in_order.append(keypoints[img_id])
@@ -251,15 +249,11 @@ def run_colmap_sfm_with_known_poses(
     imagecols_tmp = copy.deepcopy(imagecols)
     for idx, img_id in enumerate(imagecols.get_img_ids()):
         img = imagecols.read_image(img_id)
-        fname_to_save = os.path.join(
-            image_path, f"image{img_id:08d}.png"
-        )
+        fname_to_save = os.path.join(image_path, f"image{img_id:08d}.png")
         cv2.imwrite(fname_to_save, img)
         if keypoints is not None:
             keypoints_in_order.append(keypoints[img_id])
-        imagecols_tmp.change_image_name(
-            img_id, f"image{img_id:08d}.png"
-        )
+        imagecols_tmp.change_image_name(img_id, f"image{img_id:08d}.png")
 
     # feature extraction and matching
     run_hloc_matches(

limap/pointsfm/functions.py

@@ -39,9 +39,7 @@ def ComputeNeighbors(
 
 def compute_metainfos(cfg, model, n_neighbors=20):
     # get neighbors
-    print(
-        f"Computing visual neighbors... (n_neighbors = {n_neighbors})"
-    )
+    print(f"Computing visual neighbors... (n_neighbors = {n_neighbors})")
     neighbors = ComputeNeighbors(
         model,
         n_neighbors,

limap/pointsfm/model_converter.py

@@ -51,9 +51,7 @@ def convert_colmap_to_visualsfm(colmap_model_path, output_nvm_file):
             qvec, tvec = colmap_image.qvec, colmap_image.tvec
             R = rotation_from_quaternion(qvec)
             center = -R.transpose() @ tvec
-            f.write(
-                f" {qvec[0]} {qvec[1]} {qvec[2]} {qvec[3]}"
-            )
+            f.write(f" {qvec[0]} {qvec[1]} {qvec[2]} {qvec[3]}")
             f.write(f" {center[0]} {center[1]} {center[2]}")
             f.write(f" {k1} 0\n")
         f.write("\n")

limap/pointsfm/visualsfm_reader.py

@@ -11,9 +11,7 @@
 def ReadModelVisualSfM(vsfm_path, nvm_file="reconstruction.nvm"):
     input_file = os.path.join(vsfm_path, nvm_file)
     if not os.path.exists(input_file):
-        raise ValueError(
-            f"Error! Input file {input_file} does not exist."
-        )
+        raise ValueError(f"Error! Input file {input_file} does not exist.")
     with open(input_file) as f:
         txt_lines = f.readlines()
 

limap/runners/functions.py

@@ -69,9 +69,7 @@ def undistort_images(
     # start undistortion
     if n_jobs == -1:
         n_jobs = os.cpu_count()
-    print(
-        f"[LOG] Start undistorting images (n_images = {len(unload_ids)})..."
-    )
+    print(f"[LOG] Start undistorting images (n_images = {len(unload_ids)})...")
     import cv2
     import imagesize
     import joblib

limap/runners/functions_structures.py

@@ -22,9 +22,7 @@ def compute_2d_feature_points_sp(imagecols, output_path="tmp/featurepoints"):
     ### copy images to tmp folder
     for img_id in imagecols.get_img_ids():
         img = imagecols.read_image(img_id)
-        fname_to_save = os.path.join(
-            image_path, f"image{img_id:08d}.png"
-        )
+        fname_to_save = os.path.join(image_path, f"image{img_id:08d}.png")
         cv2.imwrite(fname_to_save, img)
 
     # run superpoint

limap/undistortion/undistort.py

@@ -1,4 +1,3 @@
-
 import cv2
 from _limap import _base, _undistortion
 

limap/util/evaluation.py

@@ -27,9 +27,7 @@ def eval_imagecols(
     imagecols, imagecols_gt, max_error=0.01, enable_logging=True
 ):
     if enable_logging:
-        print(
-            f"[LOG EVAL] imagecols.NumImages() = {imagecols.NumImages()}"
-        )
+        print(f"[LOG EVAL] imagecols.NumImages() = {imagecols.NumImages()}")
         print(
             f"[LOG EVAL] imagecols_gt.NumImages() = {imagecols_gt.NumImages()}"
         )
@@ -61,9 +59,7 @@ def eval_imagecols_relpose(
     )
     assert len(shared_img_ids) == imagecols.NumImages()
     if enable_logging:
-        print(
-            f"[LOG EVAL] imagecols.NumImages() = {imagecols.NumImages()}"
-        )
+        print(f"[LOG EVAL] imagecols.NumImages() = {imagecols.NumImages()}")
         print(
             f"[LOG EVAL] imagecols_gt.NumImages() = {imagecols_gt.NumImages()}"
         )

limap/util/io.py

@@ -251,9 +251,7 @@ def save_l3dpp(folder, imagecols, all_2d_segs):
             f.write(f"{n_segments}\n")
             for line_id in range(n_segments):
                 line = segs[line_id]
-                f.write(
-                    f"{line[0]} {line[1]} {line[2]} {line[3]}\n"
-                )
+                f.write(f"{line[0]} {line[1]} {line[2]} {line[3]}\n")
         print(f"Writing for L3DPP: {fname}")
 
 
@@ -397,9 +395,7 @@ def read_lines_from_input(input_file):
     General reader for lines
     """
     if not os.path.exists(input_file):
-        raise ValueError(
-            f"Error! Input file/directory {input_file} not found."
-        )
+        raise ValueError(f"Error! Input file/directory {input_file} not found.")
 
     # linetracks folder
     if not os.path.isfile(input_file):
@@ -443,9 +439,7 @@ def save_txt_segments(folder, img_id, segs):
         f.write(f"{n_segments}\n")
         for line_id in range(n_segments):
             line = segs[line_id]
-            f.write(
-                f"{line[0]} {line[1]} {line[2]} {line[3]}\n"
-            )
+            f.write(f"{line[0]} {line[1]} {line[2]} {line[3]}\n")
 
 
 def read_txt_segments(folder, img_id):

limap/visualize/vis_utils.py

@@ -429,7 +429,11 @@ def vis_vpresult(
         colors = [[255, 0, 0]]
     for line_id, line in enumerate(lines):
         c = [255, 255, 255]  # default color: white
-        if not vpres.HasVP(line_id) or vp_id >= 0 and vpres.labels[line_id] != vp_id:
+        if (
+            not vpres.HasVP(line_id)
+            or vp_id >= 0
+            and vpres.labels[line_id] != vp_id
+        ):
             if not show_original:
                 continue
         else:

runners/hypersim/Hypersim.py

@@ -118,9 +118,7 @@ def read_mpau(self, scene_dir):
         if key in param_dict:
             return float(param_dict[key])
         else:
-            raise ValueError(
-                f"Key {key} not exists in {fname_metascene}"
-            )
+            raise ValueError(f"Key {key} not exists in {fname_metascene}")
 
     def set_scene_id(self, scene_id):
         self.scene_dir = os.path.join(self.data_dir, scene_id)

runners/scannet/ScanNet.py

@@ -89,9 +89,7 @@ def loadinfos(self):
         index_list = np.arange(0, n_images, self.stride).tolist()
 
         # load intrinsic
-        fname_meta = os.path.join(
-            self.scene_dir, f"{self.scene_id}.txt"
-        )
+        fname_meta = os.path.join(self.scene_dir, f"{self.scene_id}.txt")
         K_orig, img_hw_orig = self.read_intrinsics(fname_meta)
         h_orig, w_orig = img_hw_orig[0], img_hw_orig[1]
         # reshape w.r.t max_image_dim
@@ -116,14 +114,10 @@ def loadinfos(self):
         # get imname_list and cameras
         self.imname_list, self.Rs, self.Ts = [], [], []
         for index in index_list:
-            imname = os.path.join(
-                self.scene_dir, "color", f"{index}.jpg"
-            )
+            imname = os.path.join(self.scene_dir, "color", f"{index}.jpg")
             self.imname_list.append(imname)
 
-            pose_txt = os.path.join(
-                self.scene_dir, "pose", f"{index}.txt"
-            )
+            pose_txt = os.path.join(self.scene_dir, "pose", f"{index}.txt")
             R, T = self.read_pose(pose_txt)
             self.Rs.append(R)
             self.Ts.append(T)

scripts/aachen_undistort.py

@@ -20,7 +20,7 @@
 
 
 def load_list_file(fname):
-    with open(fname, "r") as f:
+    with open(fname) as f:
         lines = f.readlines()
     imname_list, cameras = [], []
     for line in lines:
@@ -35,7 +35,7 @@ def load_list_file(fname):
         k1 = float(k[7])
         K = np.array([[f, 0, cx], [0, f, cy], [0, 0, 1.0]])
         camera = _base.Camera(
-            K, np.eye(3), np.zeros((3)), np.array([k1, 0, 0, 0, 0])
+            K, np.eye(3), np.zeros(3), np.array([k1, 0, 0, 0, 0])
         )
         imname_list.append(imname)
         cameras.append(camera)
@@ -61,9 +61,7 @@ def process(image_list, cameras):
             fx = camera_undistorted.K[0, 0]
             cx, cy = camera_undistorted.K[0, 2], camera_undistorted.K[1, 2]
             f.write(
-                "{0} SIMPLE_PINHOLE {1} {2} {3} {4} {5}\n".format(
-                    imname_undist, w, h, fx, cx, cy
-                )
+                f"{imname_undist} SIMPLE_PINHOLE {w} {h} {fx} {cx} {cy}\n"
             )
 
 

scripts/eval_hypersim.py

@@ -62,9 +62,7 @@ def report_error_to_GT(evaluator, lines, vis_err_th=None):
     print("R: recall, P: precision")
     for idx, threshold in enumerate(thresholds):
         print(
-            "R / P at {0}mm: {1:.2f} / {2:.2f}".format(
-                int(threshold * 1000), list_recall[idx], list_precision[idx]
-            )
+            f"R / P at {int(threshold * 1000)}mm: {list_recall[idx]:.2f} / {list_precision[idx]:.2f}"
         )
     return evaluator
 
@@ -77,7 +75,7 @@ def read_ply(fname):
     y = np.asarray(plydata.elements[0].data["y"])
     z = np.asarray(plydata.elements[0].data["z"])
     points = np.stack([x, y, z], axis=1)
-    print("number of points: {0}".format(points.shape[0]))
+    print(f"number of points: {points.shape[0]}")
     return points
 
 
@@ -150,14 +148,14 @@ def eval_hypersim(
                 [line.as_array() for line in inlier_lines]
             )
             limapio.save_obj(
-                "tmp/inliers_th_{0:.4f}.obj".format(threshold), inlier_lines_np
+                f"tmp/inliers_th_{threshold:.4f}.obj", inlier_lines_np
             )
             outlier_lines = evaluator.ComputeOutlierSegs(lines, threshold)
             outlier_lines_np = np.array(
                 [line.as_array() for line in outlier_lines]
             )
             limap.save_obj(
-                "tmp/outliers_th_{0:.4f}.obj".format(threshold),
+                f"tmp/outliers_th_{threshold:.4f}.obj",
                 outlier_lines_np,
             )
 
@@ -286,9 +284,7 @@ def main():
             [track.count_lines() for track in linetracks]
         )
         print(
-            "supporting images / lines: ({0:.2f} / {1:.2f})".format(
-                sup_image_counts.mean(), sup_line_counts.mean()
-            )
+            f"supporting images / lines: ({sup_image_counts.mean():.2f} / {sup_line_counts.mean():.2f})"
         )