Object Analytics (OA) is ROS2 module for real time object tracking and 3D localization. These packages aim to provide real-time object analyses over RGB-D camera inputs, enabling ROS developer to easily create amazing robotics advanced features, like intelligent collision avoidance, people follow and semantic SLAM. It consumes sensor_msgs::Image and sensor_msgs::PointClould2 data delivered by RGB-D camera, subscribes topic on object detection, publishes topics on object tracking in 2D RGB image and object localization in 3D camera coordination system.
OA keeps integrating with various "state-of-the-art" algorithms.
We support Ubuntu Linux Bionic Beaver 18.04 on 64-bit. We not support Mac OS X and Windows.
- Intel NUC (CPU: Intel i7-6700HQ @2.60GHz, Mem:16G)
Install ROS2 desktop packages ros-dashing-desktop
sudo apt-get install ros-dashing-desktop
The ros-dashing-desktop will include below packages.
- ament_cmake
- std_msgs
- sensor_msgs
- geometry_msgs
- rclcpp
- rosidl_default_generators
- rosidl_interface_packages
- launch
- ros2run
- class_loader
- pcl_conversions
sudo apt-get install ros-dashing-cv-bridge ros-dashing-object-msgs ros-dashing-image-transport
sudo apt-get install ros-dashing-object-analytics-msgs ros-dashing-object-analytics-node ros-dashing-object-analytics-rviz
Notes: debian installed package does not support 2d tracking feature since the dependent opencv3.3 debian package is not available. For full feature, please build opencv3.3 and install object analytics from source.
- OpenCV3 & opencv-contrib 3.3 (OA depends on tracking feature from OpenCV Contrib 3.3. OpenCV 3.3 is not integrated in ROS2 dashing release, need to build and install Opencv3 with contrib from source to apply tracking feature)
# Build and Install OpenCV3 with opencv-contrib
mkdir ${HOME}/opencv
cd ${HOME}/opencv
git clone https://github.com/opencv/opencv.git -b 3.3.0
git clone https://github.com/opencv/opencv_contrib.git -b 3.3.0
mkdir opencv/build -p
cd opencv/build
cmake -DOPENCV_EXTRA_MODULES_PATH=${HOME}/opencv/opencv_contrib/modules \
-DCMAKE_INSTALL_PREFIX=/usr/local -DBUILD_opencv_cnn_3dobj=OFF ..
make -j8
sudo make install
sudo ldconfig
sudo apt-get install liblz4-dev
# get code
cd ~/ros2_ws/src
git clone https://github.com/intel/ros2_object_analytics.git -b devel (devel branch is the latest code with 2D tracking features, while master branch is stable for ros2 released distributions)
# build
cd ~/ros2_ws
source /opt/ros/dashing/setup.bash
colcon build --symlink-installObject Analytics Module consumes 2D image/Point cloud/Detection bounding box from outside, so you need config the sources according to your specific condition. We provided a sample launch file "object_analytics_sample.launch.py", you can customize the remapping topics to have your own launcher.
By default, object analytics will launch both tracking and localization features, but either tracking or localization or both can be dropped. Detailed please refer arguments embedded in launch file "object_analytics_sample.launch.py".
# Start OA demo to co-work with Realsense and OpenVINO
Step1: launch ROS2 Realsense and OpenVINO
a) Please refer below links to enable ROS2 realsense and openvino.
Realsense: https://github.com/intel/ros2_intel_realsense
OpenVino: https://github.com/intel/ros2_openvino_toolkit
b) Make sure below topics works well, or please config the remapping topics in "object_analytics_sample.launch.py":
1. /camera/color/image_raw
2. /camera/aligned_depth_to_color/color/points
3. /ros2_openvino_toolkit/detected_objects
Step2: if ros2_openvino_toolkit got from Robotics_SDK
ros2 launch object_analytics_node object_analytics_sample.launch.py
/object_analytics/rgb (sensor_msgs::msg::Image)
/object_analytics/detected_objects (object_msgs::msg::ObjectsInBoxes)
/object_analytics/pointcloud (sensor_msgs::msg::PointCloud2)
/object_analytics/localization (object_analytics_msgs::msg::ObjectsInBoxes3D)
/object_analytics/tracking (object_analytics_msgs::msg::TrackedObjects)
To ensure the algorithms in OA components to archive best performance in ROS2, we have below tools used to examine design/development performance/accuracy/precision..., more tools are in developing progress and will publish later.
The tools is used to feed tracking node with raw images from datasets within fixed time interval(33ms), also simulate detector send ground truth as detections to tracking node for rectification, then receive tracking results for precision and recall statistics. It support multiple algorithms(dynamic configure to tracking node when start).
# ros2 run object_analytics_node tracker_regression --options
options: [-a algorithm] [-p dataset_path] [-t dataset_type] [-n dataset_name] [-h];
-h : Print this help function.
-a algorithm_name : Specify the tracking algorithm in the tracker.
supported algorithms: KCF,TLD,BOOSTING,MEDIAN_FLOW,MIL,GOTURN.
-p dataset_path : Specify the tracking datasets location.
-t dataset_type : Specify the dataset type: video,image.
-n dataset_name : Specify the dataset name
Video dataset with tracking algorithm("MEDIAN_FLOW"):
# ros2 run object_analytics_node tracker_regression -p /your/video/datasets/root/path -t video -n dudek -a MEDIAN_FLOW
Image dataset with default algorithm("MEDIAN_FLOW"):
# ros2 run object_analytics_node tracker_regression -p /your/image/datasets/root/path -t image -n Biker -a MEDIAN_FLOW
Support both video and image dataset, but you may need to translate into below formats.
Video dataset: (Refer to opencv_extra tracking dataset)
track_vid/ (/your/video/datasets/root/path)
├── david
│ ├── data
│ │ └── david.webm
│ ├── david.yml
│ ├── gt.txt
│ └── initOmit
│ └── david.txt
├── dudek
│ ├── data
│ │ └── dudek.webm
│ ├── dudek.yml
│ ├── gt.txt
│ └── initOmit
│ └── dudek.txt
├── faceocc2
│ ├── data
│ │ └── faceocc2.webm
│ ├── faceocc2.yml
│ ├── gt.txt
│ └── initOmit
│ └── faceocc2.txt
├── list.txt (Note: this is manually added, list the dataset names which will be used)
└── README.md
Image dataset: (Refer to database from Computer Vision Lab@HYU)
track_img/ (/your/video/datasets/root/path)
├── Biker
├── Bird1
├── Bird2
├── list.txt (Note: this is manually added, list the dataset names which will be used)
├── Man
├── Matrix
└── Woman