10. Object Detection
This ti_vision_cnn node is a versatile deep-learning (DL) inference ROS node that is optimized on DL cores and hardware accelerator of TI Processors. The ti_vision_cnn node supports compute-intensive DL inference operations, including 2D object detection and semantic segmentation. Figure 1 shows the high-level block diagram of the applications around the ti_vision_cnn node, which consists of multiple processing blocks that are deployed on hardware accelerators and DSP processors for pre-processing and post-processing in an optimized manner.
For details of block diagram and parameters of ti_vision_cnn, please refer to README.md.
10.1. Object Detection Demo
Note
video_id and subdev_id for cameras: You can check the device_id and subdev_id for the camera
attached to the SK board by running /opt/edgeai-gst-apps/scripts/setup_cameras.sh on the target
host Linux. Accordingly please update the parameters or pass as launch arguments.
Note
IMX390 Camera: Before running, please refer to gscam/README_TI.md for generating required LUT files for tiovxldc.
10.1.1. Run the Application in ROS 1
[SK] To launch the object detection demo with playing back a ROSBAG file, run the following command inside the Docker container on the the target SK board:
roslaunch ti_vision_cnn bag_objdet_cnn.launch
To process the image stream from a ZED stereo camera:
roslaunch ti_vision_cnn zed_objdet_cnn.launch video_id:=x zed_sn:=SNxxxxx
To process the image stream from a USB mono camera:
roslaunch ti_vision_cnn gscam_objdet_cnn.launch video_id:=x
For IMX219 camera as input,
roslaunch ti_vision_cnn gscam_objdet_cnn_imx219.launch video_id:=x subdev_id=y
For IMX390 camera as input, depending on choice of resolution, run one from the following.
roslaunch ti_vision_cnn gscam_objdet_cnn_imx390.launch video_id:=x subdev_id=y width:=1920 height:=1080
roslaunch ti_vision_cnn gscam_objdet_cnn_imx390.launch video_id:=x subdev_id=y width:=1280 height:=720
roslaunch ti_vision_cnn gscam_objdet_cnn_imx390.launch video_id:=x subdev_id=y width:=960 height:=540
[Visualization on Ubuntu PC] For setting up environment of the remote PC, please follow Docker Setup for ROS 1
To launch visualization, depending on the resolution setting of the capture node run one from the following:
roslaunch ti_viz_nodes rviz_objdet_cnn.launch width:=1920 height:=1080
roslaunch ti_viz_nodes rviz_objdet_cnn.launch width:=1280 height:=720 # default for USB camera and IMX219
roslaunch ti_viz_nodes rviz_objdet_cnn.launch width:=960 height:=540
10.1.2. Run the Application in ROS 2
[SK] To launch the object detection demo with a ZED stereo camera, run the following command inside the Docker container on the target:
ros2 launch ti_vision_cnn zed_objdet_cnn_launch.py video_id:=x zed_sn:=SNxxxxx
To process the image stream from a USB mono camera:
roslaunch ti_vision_cnn gscam_objdet_cnn.launch video_id:=x
For IMX219 camera as input,
ros2 launch ti_vision_cnn gscam_objdet_cnn_imx219_launch.py video_id:=x subdev_id=y
For IMX390 camera as input, depending on choice of resolution, run one from the following commands.
ros launch ti_vision_cnn gscam_objdet_cnn_imx390_launch.py video_id:=x subdev_id=y width:=1920 height:=1080
ros launch ti_vision_cnn gscam_objdet_cnn_imx390_launch.py video_id:=x subdev_id=y width:=1280 height:=720
ros launch ti_vision_cnn gscam_objdet_cnn_imx390_launch.py video_id:=x subdev_id=y width:=960 height:=540
[Visualization on Ubuntu PC] For setting up environment of the remote PC, please follow Docker Setup for ROS 2
Depending on the resolution setting of the capture node, run one from the following:
ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py width:=1920 height:=1080
ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py width:=1280 height:=720 $ default for USB camera and IMX219
ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py width:=960 height:=540