deepduke/yolact-ros-pro

yolact-ros-pro

ROS wrapper for Instance and Semantic Segmentation algorithm- Yolact.

Related packages

This pakcage is modified from yolact_ros and mask_rcnn_ros.

Installation

Environment

Since Yolact uses Python 3, we highly recommand to use a docker container of Ubuntu 20.04 to run this ROS package. Then install all python packages need by Yolact in that container.

We run yolact_ros in Ubuntu 20.04 docker container while our other ros code runs in a Ubuntu 18.04 docker container. Docker provides communication mechanism for ros nodes between different docker containers.

Build

Git clone this repo into your ROS workspace.

cd ~/catkin_ws/src
[email protected]:DeepDuke/yolact-ros-pro.git

Then build,

catkin_make

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.

We also provide our trained model, you can download from this link: clink here to download model

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

ROS Topics

Topic Name	Topic Messsage Type	Description
/mask_rcnn/result	yolact_ros_msgs/Result	Instance segmantation results information, its message structure is same as in mask_rccn_ros package
/yolact_ros/detections	yolact_ros_msgs/Detections	Instance segmantation results information, the masks are represented by uint8 array not sensors_msgs/Image in mask_rcnn_ros
/yolact_ros/visualization	sensor_msgs/Image	visualization of segmented image