Detic ros - A simple ROS wrapper for Detic instance segmentation using pre-trained dataset

detic_ros

ROS package for Detic.

This package is still in under active-development. Here is the current TODO list.

Howto use

Building docker image

git clone https://github.com/HiroIshida/detic_ros.git
cd detic_ros
docker build -t detic_ros .

step1 (launch Detic-segmentor node)

Example for running node on pr1040 network:

docker run --rm --net=host -it --gpus 1 detic_ros:latest \
    /bin/bash -i -c \
    'source ~/.bashrc; \
    rossetip; rossetmaster pr1040; \
    roslaunch detic_ros sample.launch \
    out_debug_img:=true \
    out_debug_segimg:=false \
    compressed:=false \
    input_image:=/kinect_head/rgb/image_color'

Change the pr1040 part and /kinect_head/rgb/image_color in command above by your custom host name and an image topic. If compressed image (e.g. /kinect_head/rgb/image_color/compressed) corresponding to the specified input_image is also published, by setting compressed:=true, you can reduce the topic pub-sub latency.

step2 (Subscribe from node in step1 and do something)

Example for using the published topic from the node above is masked_image_publisher.py. By using subscribed segmentation image and segmentation info and, this node converts a subscribed rgb image into a masked rgb image.

ROS node information

• ~input_image (sensor_msgs/Image)
  • Input image
• ~debug_image (sensor_msgs/Image)
  • debug image
• ~segmentation_image (sensor_msgs/Image with 8UC1 encoding)
  • Segmentation image. Suppose detected class number is 14, image is filled with 0~14 uint8 values. Note that 0 means background label.
• ~debug_segmentation_image (sensor_msgs/Image with 8UC1 encoding)
  • Say detected class number is 14, ~segmentation_image in grayscale image is almost completely dark and not good for debugging. Therefore this topic scale the value to [0 ~ 255] so that grayscale image is human-friendly.
• ~segmentation_info (detic_ros/SegmentationInfo)
  • class name list and confidence score list corresponding to ~segmentation_image. Note that score of background class is always 1.0

As for rosparam, see node_cofig.py.