Merge branch 'main' into feat/fastlabel_api

# Conflicts:
#	poetry.lock
#	pyproject.toml

.github/workflows/build-and-test.yaml

@@ -51,8 +51,8 @@ jobs:
         run: |
           apt update -yqq
           apt install -yqq curl python3-venv python-is-python3 \
-            libgl1-mesa-dev ros-${ROS_DISTRO}-sensor-msgs-py ros-${ROS_DISTRO}-rosbag2-storage-mcap \
-            unzip tree
+            libgl1-mesa-dev libboost-dev ros-${ROS_DISTRO}-sensor-msgs-py ros-${ROS_DISTRO}-rosbag2-storage-mcap \
+            ros-${ROS_DISTRO}-radar-msgs unzip tree
 
       - name: Download test data
         uses: actions/download-artifact@v3
@@ -72,16 +72,9 @@ jobs:
         uses: autowarefoundation/autoware-github-actions/colcon-build@v1
         with:
           rosdistro: ${{ matrix.rosdistro }}
-          target-packages: autoware_auto_perception_msgs autoware_perception_msgs tier4_perception_msgs
+          target-packages: autoware_auto_perception_msgs autoware_perception_msgs tier4_perception_msgs oxts_msgs vehicle_msgs
           build-depends-repos: build_depends.repos
 
-      - name: Install dependencies
-        run: |
-          apt update -yqq
-          apt install -yqq curl python3-venv python-is-python3 \
-            libgl1-mesa-dev ros-${ROS_DISTRO}-sensor-msgs-py \
-            ros-humble-radar-msgs
-
       - name: Check python version
         run: |
           python -V

README.md

@@ -5,13 +5,17 @@ These tools facilitate the preparation and transformation of perception data for
 
 ## Table of Contents
 
-1. [Dataset Overview](#dataset-overview)
-2. [Usage of T4 dataset](#usage-of-t4-format-dataset)
-3. [Usage of Conversion Tools](#usage-of-conversion-tools)
-   - [Conversion Tools Overview](#conversion-tools-overview)
-   - [Setup](#setup)
-   - [Test](#test)
-   - [Pre Commit](#pre-commit)
+- [TIER IV Perception Dataset Conversion Tools](#tier-iv-perception-dataset-conversion-tools)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Overview](#dataset-overview)
+  - [Usage of T4 format dataset](#usage-of-t4-format-dataset)
+  - [Usage of Conversion Tools](#usage-of-conversion-tools)
+    - [Conversion Tools Overview](#conversion-tools-overview)
+    - [Setup](#setup)
+    - [Test](#test)
+      - [Download test data](#download-test-data)
+      - [Run tests](#run-tests)
+    - [Pre commit](#pre-commit)
 
 ## Dataset Overview
 
@@ -45,7 +49,7 @@ source /opt/ros/${ROS_DISTRO}/setup.sh
 sudo apt install -y ros-${ROS_DISTRO}-sensor-msgs-py ros-${ROS_DISTRO}-rosbag2-storage-mcap ros-${ROS_DISTRO}-radar-msgs
 
 mkdir src -p && vcs import src < build_depends.repos
-colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release --packages-up-to autoware_auto_perception_msgs autoware_perception_msgs tier4_perception_msgs
+colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release --packages-up-to autoware_auto_perception_msgs autoware_perception_msgs tier4_perception_msgs oxts_msgs vehicle_msgs
 source ./install/setup.bash
 ```
 

build_depends.repos

@@ -17,3 +17,17 @@ repositories:
     type: git
     url: https://github.com/tier4/autoware_auto_msgs.git
     version: tier4/main
+
+  # for Co-MLOps features
+  oxts_ros2_driver:
+    type: git
+    url: https://github.com/tier4/oxts_ros2_driver.git
+    version: feat/data_recording_system
+  pacmod3_msgs:
+    type: git
+    url: https://github.com/astuff/pacmod3_msgs.git
+    version: 1.0.0
+  pacmod_interface:
+    type: git
+    url: https://github.com/tier4/pacmod_interface.git
+    version: feat/comlops_drs

config/convert_comlops_rosbag2_to_non_annotated_t4_sample.yaml

@@ -0,0 +1,36 @@
+task: convert_rosbag2_to_non_annotated_t4
+description:
+  scene: ""
+conversion:
+  input_base: ./data/rosbag2
+  output_base: ./data/non_annotated_t4_format
+  world_frame_id: "map"
+  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.
+  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.
+  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`
+  # 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_RIGHT
+      delay_msec: 98.33
+    - topic: /sensing/camera/camera5/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

config/convert_deepen_to_t4_segmentation_painting_sample.yaml

@@ -0,0 +1,31 @@
+task: convert_deepen_to_t4
+description:
+  visibility:
+    full: "No occlusion of the object."
+    most: "Object is occluded, but by less than 50%."
+    partial: "The object is occluded by more than 50% (but not completely)."
+    none: "The object is 90-100% occluded and no points/pixels are visible in the label."
+  camera_index:
+    CAM_BACK_RIGHT: 0
+    CAM_BACK: 1
+    CAM_BACK_LEFT: 2
+    CAM_FRONT: 3
+    CAM_FRONT_LEFT: 4
+    CAM_FRONT_NARROW: 5
+    CAM_FRONT_RIGHT: 6
+    CAM_FRONT_WIDE: 7
+  with_lidar: false
+  surface_categories: ./config/label/surface.yaml
+
+conversion:
+  label_info: # for 3D/2D box only annotations, it is OK to skip specifying this field
+    label_type: 2d_segmentation
+    label_format: painting
+  input_base: ./data/non_annotated_dataset
+  input_anno_file: ./data/deepen_format.zip
+  input_bag_base: ./data/rosbag2
+  output_base: ./data/t4_format
+  topic_list: ./config/topic_list_tlr.yaml
+  ignore_interpolate_label: True
+  dataset_corresponding:
+    Dataset_name: dataset_id_in_Deepen_AI

config/convert_deepen_to_t4_segmentation_polygon_sample.yaml

@@ -0,0 +1,31 @@
+task: convert_deepen_to_t4
+description:
+  visibility:
+    full: "No occlusion of the object."
+    most: "Object is occluded, but by less than 50%."
+    partial: "The object is occluded by more than 50% (but not completely)."
+    none: "The object is 90-100% occluded and no points/pixels are visible in the label."
+  camera_index:
+    CAM_BACK_RIGHT: 0
+    CAM_BACK: 1
+    CAM_BACK_LEFT: 2
+    CAM_FRONT: 3
+    CAM_FRONT_LEFT: 4
+    CAM_FRONT_NARROW: 5
+    CAM_FRONT_RIGHT: 6
+    CAM_FRONT_WIDE: 7
+  with_lidar: false
+  surface_categories: ./config/label/surface.yaml
+
+conversion:
+  label_info: # for 3D/2D box only annotations, it is OK to skip specifying this field
+    label_type: 2d_segmentation
+    label_format: polygon
+  input_base: ./data/non_annotated_t4_format
+  input_anno_file: ./data/deepen_format/lidar_annotations_accepted_deepen.json
+  input_bag_base: ./data/rosbag2
+  output_base: ./data/t4_format
+  topic_list: ./config/topic_list_sample.yaml
+  ignore_interpolate_label: True
+  dataset_corresponding:
+    Dataset_name: dataset_id_in_Deepen_AI

config/label/surface.yaml

@@ -0,0 +1,11 @@
+# Category list of surfaces. Each category does not have "instance ID".
+# Reference(TIER IV INTERNAL LINK): https://drive.google.com/file/d/132nKnXc7vq9Bjj0quoXDFGi9q5jtrXuY/view?usp=drive_link
+- road
+- sidewalk
+- building
+- wall_fence
+- pole
+- vegetation_terrain
+- sky
+- road_paint_lane_solid_white
+- road_paint_lane_dash_white

perception_dataset/convert.py

@@ -150,6 +150,7 @@ def main():
         )
 
     elif task == "convert_deepen_to_t4":
+        from perception_dataset.deepen.deepen_annotation import LabelInfo
         from perception_dataset.deepen.deepen_to_t4_converter import DeepenToT4Converter
 
         input_base = config_dict["conversion"]["input_base"]
@@ -164,6 +165,11 @@ def main():
             ignore_interpolate_label = config_dict["conversion"]["ignore_interpolate_label"]
         with open(topic_list_yaml_path) as f:
             topic_list_yaml = yaml.safe_load(f)
+        label_info = (
+            LabelInfo(**config_dict["conversion"]["label_info"])
+            if config_dict["conversion"].get("label_info")
+            else None
+        )
 
         converter = DeepenToT4Converter(
             input_base=input_base,
@@ -175,6 +181,7 @@ def main():
             input_bag_base=input_bag_base,
             topic_list=topic_list_yaml,
             ignore_interpolate_label=ignore_interpolate_label,
+            label_info=label_info,
         )
 
         logger.info(f"[BEGIN] Converting Deepen data ({input_base}) to T4 data ({output_base})")