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

Don't forget to create a weight folder in yolact_ros/yolact, and download yolact_base_54_800000.pth

Modifed version of original project.

Add pointcloud processing related things

Change the way the masks being packed and published. Not using np.packbits anymore. Now the mask will be published as binary mask with the same shape of original image. add image shape as msg.

Corresponding modified ros_msg: modified_yolact_ros_msgs

ROS wrapper for Yolact.

yolact_ros_msgs: Provides messages for publishing the detection results.

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"

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: