Tutorial to Run NITROS-Accelerated Graph with Argus Camera
==========================================================

.. mermaid::

   graph LR;
       argus_node("ArgusMonoNode (Raw Image)") --> rectify_node("RectifyNode (Rectified Image)");
       rectify_node --> encoder_node("DnnImageEncoderNode (DNN Pre-Processed Tensors)");
       encoder_node --> triton_node("TritonNode (DNN Inference)");
       triton_node --> unet_decoder_node("UNetDecoderNode (Segmentation Image)");

If you have an :ir_repo:`Argus-compatible camera <isaac_ros_argus_camera>`,
you can also use the launch file provided in this package to start a
fully NITROS-accelerated image segmentation graph.

To start the graph:

1. Follow the :ref:`quickstart <repositories_and_packages/isaac_ros_image_segmentation/isaac_ros_unet/index:quickstart>` up to
   step 7.

2. Inside the container, install the ``isaac_ros_argus_camera`` package.

   :ir_apt:

   .. code:: bash

      sudo apt-get install -y ros-humble-isaac-ros-argus-camera

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

   .. code:: bash

      ros2 launch isaac_ros_unet isaac_ros_argus_unet_triton.launch.py model_name:=peoplesemsegnet_shuffleseg model_repository_paths:=['/tmp/models'] input_binding_names:=['input_2:0'] output_binding_names:=['argmax_1'] network_output_type:='argmax'

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

   .. code:: bash

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

   Then launch ``rqt_image_view``:

   .. code:: bash

      ros2 run rqt_image_view rqt_image_view

  To view a colorized segmentation
  mask, inside the ``rqt_image_view`` GUI, change the topic to
  ``/unet/colored_segmentation_mask``.

  You can also view the raw segmentation, which is published to
   ``/unet/raw_segmentation_mask``, where the raw pixels correspond to
   the class labels making it unsuitable for human visual inspection.