[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.7
+    rev: v0.9.1
     hooks:
       # Run the linter.
       - id: ruff

examples/devkit_tutorial.ipynb

@@ -60,12 +60,10 @@
     "plt.rcParams[\"figure.figsize\"] = [20, 10]\n",
     "\n",
     "# import the ZOD DevKit\n",
-    "from zod import ZodFrames\n",
-    "from zod import ZodSequences\n",
-    "\n",
     "# import default constants\n",
     "import zod.constants as constants\n",
-    "from zod.constants import Camera, Lidar, Anonymization, AnnotationProject\n",
+    "from zod import ZodFrames, ZodSequences\n",
+    "from zod.constants import AnnotationProject, Anonymization, Camera, Lidar\n",
     "\n",
     "# import useful data classes\n",
     "from zod.data_classes import LidarData\n",
@@ -427,9 +425,7 @@
     "# but for this we also need the calibrations of the sensor\n",
     "calibrations = zod_frame.calibration\n",
     "assert annotation_3d is not None\n",
-    "image = overlay_object_3d_box_on_image(\n",
-    "    image, annotation_3d, calibrations, color=(255, 0, 0), line_thickness=10\n",
-    ")\n",
+    "image = overlay_object_3d_box_on_image(image, annotation_3d, calibrations, color=(255, 0, 0), line_thickness=10)\n",
     "\n",
     "plt.figure()\n",
     "plt.axis(\"off\")\n",
@@ -570,12 +566,8 @@
     "    np.hstack((pcd.points, pcd.intensity[:, None])),\n",
     "    (\n",
     "        np.array([obj.name for obj in object_annotations if obj.box3d]),\n",
-    "        np.concatenate(\n",
-    "            [obj.box3d.center[None, :] for obj in object_annotations if obj.box3d], axis=0\n",
-    "        ),\n",
-    "        np.concatenate(\n",
-    "            [obj.box3d.size[None, :] for obj in object_annotations if obj.box3d], axis=0\n",
-    "        ),\n",
+    "        np.concatenate([obj.box3d.center[None, :] for obj in object_annotations if obj.box3d], axis=0),\n",
+    "        np.concatenate([obj.box3d.size[None, :] for obj in object_annotations if obj.box3d], axis=0),\n",
     "        np.array([obj.box3d.orientation for obj in object_annotations if obj.box3d]),\n",
     "    ),\n",
     ")"
@@ -612,9 +604,7 @@
     "plt.show()\n",
     "\n",
     "# Plot aggregated Lidar point cloud\n",
-    "aggregated_lidar = zod_frame.get_aggregated_lidar(\n",
-    "    num_before=10, num_after=0, timestamp=image_timestamp\n",
-    ")\n",
+    "aggregated_lidar = zod_frame.get_aggregated_lidar(num_before=10, num_after=0, timestamp=image_timestamp)\n",
     "lid_image = visualize_lidar_on_image(\n",
     "    aggregated_lidar,\n",
     "    zod_frame.calibration,\n",

zod/cli/download.py

@@ -138,7 +138,7 @@ def _download(download_path: str, dbx: ResumableDropbox, info: DownloadExtractIn
     )
     if pbar.n > info.size:
         tqdm.write(
-            f"Error! File {download_path} already exists and is larger than expected. " "Please delete and try again."
+            f"Error! File {download_path} already exists and is larger than expected. Please delete and try again."
         )
     if pbar.n > 0:
         # this means we are retrying or resuming a previously interrupted download

zod/cli/extract_tsr_patches.py

@@ -78,9 +78,9 @@ def extract_tsr_patches(
     print("Will save dataset to", output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
-    assert not (
-        padding_factor is not None and (padding_px_x is not None or padding_px_y is not None)
-    ), "Cannot specify both padding and padding_factor"
+    assert not (padding_factor is not None and (padding_px_x is not None or padding_px_y is not None)), (
+        "Cannot specify both padding and padding_factor"
+    )
 
     padding = (padding_px_x, padding_px_y) if (padding_px_x is not None and padding_px_y is not None) else None
 

zod/cli/generate_coco_json.py

@@ -135,9 +135,7 @@ def convert_to_coco(
         if cls not in OBJECT_CLASSES:
             typer.echo(f"ERROR: Invalid class: {cls}.")
             raise typer.Exit(1)
-    typer.echo(
-        "Converting ZOD to COCO format. " f"Version: {version}, anonymization: {anonymization}, classes: {classes}"
-    )
+    typer.echo(f"Converting ZOD to COCO format. Version: {version}, anonymization: {anonymization}, classes: {classes}")
 
     zod_frames = ZodFrames(str(dataset_root), version.value)
 

zod/data_classes/box.py

@@ -244,9 +244,9 @@ def crop_from_image(
             tuple: padding in all four directions | (left,  top,  right, bottom)
         """
 
-        assert not (
-            padding is not None and padding_factor is not None
-        ), "Cannot specify both padding and padding_factor"
+        assert not (padding is not None and padding_factor is not None), (
+            "Cannot specify both padding and padding_factor"
+        )
 
         if padding is not None:
             padding_x, padding_y = padding

zod/data_classes/ego_motion.py

@@ -33,9 +33,9 @@ def get_poses(self, target_ts: Union[np.ndarray, float]) -> np.ndarray:
             [N, 4, 4] array of interpolated poses for each timestamp.
         """
         selfmin, selfmax = np.min(self.timestamps), np.max(self.timestamps)
-        assert (selfmin <= np.min(target_ts)) and (
-            selfmax >= np.max(target_ts)
-        ), f"targets not between pose timestamps, must be [{selfmin}, {selfmax}]"
+        assert (selfmin <= np.min(target_ts)) and (selfmax >= np.max(target_ts)), (
+            f"targets not between pose timestamps, must be [{selfmin}, {selfmax}]"
+        )
 
         if np.isin(target_ts, self.timestamps).all():
             return self.poses[self.timestamps.searchsorted(target_ts)]

zod/data_classes/info.py

@@ -67,9 +67,9 @@ def get_camera_lidar_map(
             A tuple of the camera frame and the closest lidar frame.
         """
         camera_name = f"{camera.value}_{anonymization.value}"
-        assert (
-            camera_name in self.camera_frames
-        ), f"Camera {camera_name} not found. Available cameras: {self.camera_frames.keys()}"
+        assert camera_name in self.camera_frames, (
+            f"Camera {camera_name} not found. Available cameras: {self.camera_frames.keys()}"
+        )
         assert lidar in self.lidar_frames, f"Lidar {lidar} not found. Available lidars: {self.lidar_frames.keys()}"
 
         for camera_frame in self.camera_frames[camera_name]:

zod/eval/detection/_experimental/matching.py

@@ -285,12 +285,12 @@ def match_one_frame(
         A list of tuples. Each tuple contains the ground truth object and the prediction.
     """
     # check that all objects are in the evaluation frame
-    assert all(
-        gt.box3d.frame == EVALUATION_FRAME for gt in ground_truth if gt.box3d is not None
-    ), "All ground truth objects must be in the evaluation frame."
-    assert all(
-        pred.box3d.frame == EVALUATION_FRAME for pred in predictions
-    ), "All predictions must be in the evaluation frame."
+    assert all(gt.box3d.frame == EVALUATION_FRAME for gt in ground_truth if gt.box3d is not None), (
+        "All ground truth objects must be in the evaluation frame."
+    )
+    assert all(pred.box3d.frame == EVALUATION_FRAME for pred in predictions), (
+        "All predictions must be in the evaluation frame."
+    )
 
     if method == "greedy":
         return greedy_match(