feat: drs conversion

config/convert_comlops_rosbag2_to_non_annotated_t4_sample.yaml → config/convert_rosbag2_to_non_annotated_t4_drs.yaml

@@ -4,33 +4,40 @@ description:
 conversion:
   input_base: ./data/rosbag2
   output_base: ./data/non_annotated_t4_format
-  world_frame_id: "map"
+  world_frame_id: "base_link"
   start_timestamp_sec: 0 # Enter here if there is a timestamp for the start time. If not used, enter 0.
-  skip_timestamp: 2.0 # Do not load data for the first point cloud timestamp for skip_timestamp seconds.
+  skip_timestamp: 0.5 # Do not load data for the first point cloud timestamp for skip_timestamp seconds.
   num_load_frames: 0 # Maximum number of frames to save as t4 data. Set to 0 to automatically set it based on the number of lidar topics.
-  accept_frame_drop: False # If true, the conversion will continue even if the LiDAR frame is dropped.
+  accept_frame_drop: true # If true, the conversion will continue even if the LiDAR frame is dropped.
+  undistort_image: true # If true, the camera image will be undistorted.
   with_ins: true            # whether to use INS messages as a ego state instead of `/tf`
-  with_vehicle_status: true # whether to generate `vehicle_state.json`
+  with_vehicle_status: false # whether to generate `vehicle_state.json`
   # The following configuration is generally not modified unless there are changes to the vehicle sensor configuration.
   lidar_sensor:
     topic: /sensing/lidar/concatenated/pointcloud
     channel: LIDAR_CONCAT
   camera_sensors: #  Keep the same order as each camera exposure timing
-    - topic: /sensing/camera/camera3/image_raw/compressed
-      channel: CAM_BACK_LEFT
-      delay_msec: 48.33
-    - topic: /sensing/camera/camera2/image_raw/compressed
-      channel: CAM_FRONT_LEFT
-      delay_msec: 65.0
     - topic: /sensing/camera/camera0/image_raw/compressed
-      channel: CAM_FRONT
-      delay_msec: 81.67
-    - topic: /sensing/camera/camera4/image_raw/compressed
+      channel: CAM_FRONT_NARROW
+      delay_msec: 16.0
+    - topic: /sensing/camera/camera1/image_raw/compressed
+      channel: CAM_FRONT_WIDE
+      delay_msec: 16.0
+    - topic: /sensing/camera/camera2/image_raw/compressed
       channel: CAM_FRONT_RIGHT
-      delay_msec: 98.33
-    - topic: /sensing/camera/camera5/image_raw/compressed
+      delay_msec: -9.0
+    - topic: /sensing/camera/camera3/image_raw/compressed
       channel: CAM_BACK_RIGHT
-      delay_msec: 115.0
-    - topic: /sensing/camera/camera1/image_raw/compressed
-      channel: CAM_BACK
-      delay_msec: 131.67
+      delay_msec: -59.0
+    - topic: /sensing/camera/camera4/image_raw/compressed
+      channel: CAM_BACK_NARROW
+      delay_msec: -34.0
+    - topic: /sensing/camera/camera5/image_raw/compressed
+      channel: CAM_BACK_WIDE
+      delay_msec: -84.0
+    - topic: /sensing/camera/camera6/image_raw/compressed
+      channel: CAM_BACK_LEFT
+      delay_msec: -59.0
+    - topic: /sensing/camera/camera7/image_raw/compressed
+      channel: CAM_FRONT_LEFT
+      delay_msec: -59.0

perception_dataset/constants.py

@@ -43,6 +43,14 @@ class SENSOR_ENUM(Enum):
         "channel": "CAM_BACK",
         "modality": SENSOR_MODALITY_ENUM.CAMERA.value,
     }
+    CAM_BACK_NARROW = {
+        "channel": "CAM_BACK_NARROW",
+        "modality": SENSOR_MODALITY_ENUM.CAMERA.value,
+    }
+    CAM_BACK_WIDE = {
+        "channel": "CAM_BACK_WIDE",
+        "modality": SENSOR_MODALITY_ENUM.CAMERA.value,
+    }
     CAM_FRONT_LEFT = {
         "channel": "CAM_FRONT_LEFT",
         "modality": SENSOR_MODALITY_ENUM.CAMERA.value,

perception_dataset/rosbag2/rosbag2_to_non_annotated_t4_converter.py

@@ -5,6 +5,7 @@
 import os.path as osp
 import shutil
 import sys
+import time
 from typing import Dict, List, Optional, Tuple, Union
 import warnings
 
@@ -81,21 +82,31 @@ def convert(self):
 
         if not self._overwrite_mode:
             dir_exist: bool = False
-            for bag_dir in bag_dirs:
+            for bag_dir in bag_dirs[:]:  # copy to avoid modifying list while iterating
                 bag_name: str = osp.basename(bag_dir)
 
                 output_dir = osp.join(self._output_base, bag_name)
                 if osp.exists(output_dir):
                     logger.error(f"{output_dir} already exists.")
                     dir_exist = True
-
-            if dir_exist:
+                    bag_dirs.remove(bag_dir)
+            if dir_exist and len(bag_dirs) == 0:
+                logger.error(f"{output_dir} already exists.")
                 raise ValueError("If you want to overwrite files, use --overwrite option.")
 
-        for bag_dir in bag_dirs:
+        for bag_dir in sorted(bag_dirs):
+            logger.info(f"Start converting {bag_dir} to T4 format.")
             self._params.input_bag_path = bag_dir
-            bag_converter = _Rosbag2ToNonAnnotatedT4Converter(self._params)
-            bag_converter.convert()
+            try:
+                bag_converter = _Rosbag2ToNonAnnotatedT4Converter(self._params)
+                bag_converter.convert()
+            except Exception as e:
+                logger.error(f"Error occurred during conversion: {e}")
+                continue
+            logger.info(f"Conversion of {bag_dir} is completed")
+            print(
+                "--------------------------------------------------------------------------------------------------------------------------"
+            )
 
 
 class _Rosbag2ToNonAnnotatedT4Converter:
@@ -157,7 +168,7 @@ def __init__(self, params: Rosbag2ConverterParams) -> None:
         self._output_data_dir = osp.join(
             self._output_scene_dir, T4_FORMAT_DIRECTORY_NAME.DATA.value
         )
-        self._msg_display_interval = 10
+        self._msg_display_interval = 100
 
         shutil.rmtree(self._output_scene_dir, ignore_errors=True)
         self._make_directories()
@@ -259,16 +270,23 @@ def _init_tables(self):
 
     def convert(self):
         self._convert()
+
         self._save_tables()
         self._save_config()
         if not self._without_compress:
             self._compress_directory()
 
     def _save_tables(self):
+        print(
+            "--------------------------------------------------------------------------------------------------------------------------"
+        )
         for cls_attr in self.__dict__.values():
             if isinstance(cls_attr, AbstractTable):
                 print(f"{cls_attr.FILENAME}: #rows {len(cls_attr)}")
                 cls_attr.save_json(self._output_anno_dir)
+        print(
+            "--------------------------------------------------------------------------------------------------------------------------"
+        )
 
     def _save_config(self):
         config_data = {
@@ -318,6 +336,7 @@ def _convert(self) -> None:
             - is_key_frame=True
             - fill in next/prev
         """
+        start = time.time()
         sensor_channel_to_sample_data_token_list: Dict[str, List[str]] = {}
 
         self._init_tables()
@@ -327,6 +346,7 @@ def _convert(self) -> None:
         self._connect_sample_in_scene()
         self._connect_sample_data_in_scene(sensor_channel_to_sample_data_token_list)
         self._add_scene_description(self._scene_description)
+        print(f"Total elapsed time: {time.time() - start:.2f} sec")
 
         if self._with_vehicle_status:
             self._convert_vehicle_state()
@@ -348,6 +368,7 @@ def _convert_sensor_data(
         logger.info(f"set start_timestamp to {start_timestamp}")
 
         if self._sensor_mode == SensorMode.DEFAULT:
+            start = time.time()
             lidar_sensor_channel = self._lidar_sensor["channel"]
             sensor_channel_to_sample_data_token_list[lidar_sensor_channel] = (
                 self._convert_pointcloud(
@@ -357,7 +378,7 @@ def _convert_sensor_data(
                     scene_token=scene_token,
                 )
             )
-
+            print(f"LiDAR conversion. total elapsed time: {time.time() - start:.2f} sec\n")
             for radar_sensor in self._radar_sensors:
                 radar_sensor_channel = radar_sensor["channel"]
                 sensor_channel_to_sample_data_token_list[radar_sensor_channel] = (
@@ -382,6 +403,7 @@ def _convert_sensor_data(
             )
 
         for camera_sensor in self._camera_sensors:
+            start = time.time()
             sensor_channel = camera_sensor["channel"]
             sensor_channel_to_sample_data_token_list[sensor_channel] = self._convert_image(
                 start_timestamp=camera_start_timestamp,
@@ -400,6 +422,9 @@ def _convert_sensor_data(
             camera_start_timestamp = misc_utils.nusc_timestamp_to_unix_timestamp(
                 first_sample_data_record.timestamp
             )
+            print(
+                f"camera {camera_sensor['channel']} conversion. total elapsed time: {time.time() - start:.2f} sec\n"
+            )
 
     def _convert_static_data(self):
         # Log, Map
@@ -968,6 +993,7 @@ def _generate_calibrated_sensor(
         self, sensor_channel: str, start_timestamp: builtin_interfaces.msg.Time, topic_name=""
     ) -> Union[str, Tuple[str, CameraInfo]]:
         calibrated_sensor_token = str()
+        camera_info = None
         for sensor_enum in self._sensor_enums:
             channel = sensor_enum.value["channel"]
             modality = sensor_enum.value["modality"]

perception_dataset/utils/rosbag2.py

@@ -169,12 +169,13 @@ def pointcloud_msg_to_numpy(pointcloud_msg: PointCloud2) -> NDArray:
     points = point_cloud2_to_array(pointcloud_msg)
 
     # Extract the x, y, z coordinates and additional fields if available
-    xyz = points["xyz"]
+    points_arr = points["xyz"]
     if "intensity" in points.keys():
         intensity = points["intensity"].astype(np.float32)
-        points_arr = np.hstack((xyz, intensity))
-    else:
-        points_arr = xyz
+        points_arr = np.hstack((points_arr, intensity))
+    if "lidar_index" in points.keys():
+        lidar_index = points["lidar_index"].astype(np.float32)
+        points_arr = np.hstack((points_arr, lidar_index))
 
     # Ensure the resulting array has exactly NUM_DIMENSIONS columns
     if points_arr.shape[1] > NUM_DIMENSIONS: