yolact_ros

ROS wrapper for Yolact.

Related packages

yolact_ros_msgs: Provides messages for publishing the detection results.

Installation

Yolact uses Python 3. If you use a ROS version built with Python 2, additional steps are necessary to run the node.

• Set up a Python 3 virtual environment.
• Install the packages required by Yolact. See the Readme on https://github.com/dbolya/yolact for details.
• Additionally, install the packages rospkg and empy in the virtual environment.
• You need to build the cv_bridge module of ROS with Python 3. I recommend using a workspace separate from other ROS packages. Clone the package to the workspace. You might need to adjust some of the following instructions depending on your Python installation.

  git clone -b melodic https://github.com/ros-perception/vision_opencv.git

• If you use catkin_make, compile with

  catkin_make -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so

• For catkin tools, use

  catkin config -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so
  catkin build

• add the following lines to the postactivate script of your virtual environment (Change the paths according to your workspace path, virtual environment and Python installation):

  source $HOME/ros_python3/devel/setup.bash
  export OLD_PYTHONPATH="$PYTHONPATH"
  export PYTHONPATH="$HOME/.virtualenvs/yolact/lib/python3.6/site-packages:$PYTHONPATH"

• add the following lines to the postdeactivate script of your virtual environment:

  export PYTHONPATH="$OLD_PYTHONPATH"

Usage

First, download (or train) a model to use. You can find pre-trained models here. The default model is yolact_base_54_800000.pth. If you want to use a Yolact++ model, you'll have to install DCNv2 (see Yolact installation instructions). Note that the DCN version shipped with Yolact does currently not work with the newest Pytorch release. An updated version can be found here.

You can run yolact using rosrun:

  rosrun yolact_ros yolact_ros

If you want to change the default parameters, e.g. the model or image topic, you can specify them:

  rosrun yolact_ros yolact_ros _model_path:="$(rospack find yolact_ros)/scripts/yolact/weights/yolact_base_54_800000.pth" _image_topic:="/camera/color/image_raw"

Alternatively, you can add the node to a launch file. An example can be found in the launch folder. You can run that launch file using:

  roslaunch yolact_ros yolact_ros.launch

All parameters except for the model path are dynamically reconfigurable at runtime. Either run "rqt" and select the dynamic reconfigure plugin (Plugins -> Configuration), or run rqt_reconfigure directly ("rosrun rqt_reconfigure rqt_reconfigure"). Then select "yolact_ros" from the sidebar to see the available parameters.

The following parameters are available:

Parameter	Description	Default
image_topic	Image topic used for subscribing	/camera/color/image_raw
use_compressed_image	Subscribe to compressed image topic	False
publish_visualization	Publish images with detections	True
publish_detections	Publish detections as message	True
display_visualization	Display window with detection image	False
display_masks	Whether or not to display masks over bounding boxes	True
display_bboxes	Whether or not to display bboxes around masks	True
display_text	Whether or not to display text (class [score])	True
display_scores	Whether or not to display scores in addition to classes	True
display_fps	When displaying video, draw the FPS on the frame	False
score_threshold	Detections with a score under this threshold will not be considered	0.0
crop_masks	If true, crop output masks with the predicted bounding box	True
top_k	Further restrict the number of predictions to parse	5