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[pre-commit.ci] auto fixes from pre-commit.com hooks
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examples/devkit_tutorial.ipynb

Lines changed: 6 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -60,12 +60,10 @@
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"plt.rcParams[\"figure.figsize\"] = [20, 10]\n",
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"\n",
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"# import the ZOD DevKit\n",
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"from zod import ZodFrames\n",
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"from zod import ZodSequences\n",
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"\n",
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"# import default constants\n",
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"import zod.constants as constants\n",
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"from zod.constants import Camera, Lidar, Anonymization, AnnotationProject\n",
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"from zod import ZodFrames, ZodSequences\n",
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"from zod.constants import AnnotationProject, Anonymization, Camera, Lidar\n",
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"\n",
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"# import useful data classes\n",
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"from zod.data_classes import LidarData\n",
@@ -427,9 +425,7 @@
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"# but for this we also need the calibrations of the sensor\n",
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"calibrations = zod_frame.calibration\n",
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"assert annotation_3d is not None\n",
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"image = overlay_object_3d_box_on_image(\n",
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" image, annotation_3d, calibrations, color=(255, 0, 0), line_thickness=10\n",
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")\n",
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"image = overlay_object_3d_box_on_image(image, annotation_3d, calibrations, color=(255, 0, 0), line_thickness=10)\n",
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"\n",
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"plt.figure()\n",
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"plt.axis(\"off\")\n",
@@ -570,12 +566,8 @@
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" np.hstack((pcd.points, pcd.intensity[:, None])),\n",
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" (\n",
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" np.array([obj.name for obj in object_annotations if obj.box3d]),\n",
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" np.concatenate(\n",
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" [obj.box3d.center[None, :] for obj in object_annotations if obj.box3d], axis=0\n",
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" ),\n",
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" np.concatenate(\n",
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" [obj.box3d.size[None, :] for obj in object_annotations if obj.box3d], axis=0\n",
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" ),\n",
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" np.concatenate([obj.box3d.center[None, :] for obj in object_annotations if obj.box3d], axis=0),\n",
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" np.concatenate([obj.box3d.size[None, :] for obj in object_annotations if obj.box3d], axis=0),\n",
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" np.array([obj.box3d.orientation for obj in object_annotations if obj.box3d]),\n",
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" ),\n",
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")"
@@ -612,9 +604,7 @@
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"plt.show()\n",
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"\n",
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"# Plot aggregated Lidar point cloud\n",
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"aggregated_lidar = zod_frame.get_aggregated_lidar(\n",
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" num_before=10, num_after=0, timestamp=image_timestamp\n",
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")\n",
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"aggregated_lidar = zod_frame.get_aggregated_lidar(num_before=10, num_after=0, timestamp=image_timestamp)\n",
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"lid_image = visualize_lidar_on_image(\n",
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" aggregated_lidar,\n",
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" zod_frame.calibration,\n",

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