Tutorial for People Segmentation with Segformer and TensorRT

Overview

This tutorial walks you through a graph for Image Segmentation for segmenting people in an image using TensorRT.

Tutorial Walkthrough

  1. Complete the quickstart up until model preparation step.

  2. Launch the Docker container using the run_dev.sh script:

    cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
  ./scripts/run_dev.sh

  3. Run the following launch files to start the inferencing:

    ros2 launch isaac_ros_segformer isaac_ros_people_sem_segformer_tensor_rt.launch.py engine_file_path:=${ISAAC_ROS_WS}/isaac_ros_assets/models/peoplesemsegformer/1/model.plan

  4. Open another terminal and play the rosbag:

    cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
    ./scripts/run_dev.sh

    ros2 bag play -l isaac_ros_assets/isaac_ros_segformer/segformer_sample_data --remap image_rect:=image camera_info_rect:=camera_info

  5. In another terminal, visualize and validate the output of the package by launching rqt_image_view:

    cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
   ./scripts/run_dev.sh

    Then launch rqt_image_view:

    ros2 run rqt_image_view rqt_image_view

    Then inside the rqt_image_view GUI, change the topic to /segformer/colored_segmentation_mask to view a colorized segmentation mask.

    https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_image_segmentation/isaac_ros_segformer/peoplesemsegformer_output_rqt.png/

    Note

    The raw segmentation is also published to /segformer/raw_segmentation_mask. However, the raw pixels correspond to the class labels and so the output is unsuitable for human visual inspection.