Using DOPE at Different Image Resolutions
===============================================

Overview
------------

The DOPE network architecture, as outlined in the `original
paper <https://arxiv.org/abs/1809.10790>`__, can receive input images of
arbitrary size and subsequently produce output belief maps of the
corresponding dimensions.

However, the ONNX format used to run this network on Triton or TensorRT
is not as flexible, and an ONNX-exported model **does NOT** support
arbitrary image sizes at inference time. Instead, the desired input
image dimensions must be explicitly specified when preparing the ONNX
file using the ``dope_converter.py`` script, as referenced in the
:doc:`quickstart </repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index>`.

Tutorial Walkthrough
--------------------

1. Follow steps 1-6 of the main DOPE
   :doc:`quickstart </repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index>`.

2. At step 7, run the ``dope_converter.py`` script with the two
   additional arguments ``row`` and ``col`` specifying the desired input
   image size:

   .. code:: bash

      python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py --format onnx --input /tmp/models/Ketchup.pth --row 1080 --col 1920

3. Proceed through steps 8-9.
4. At step 10, launch the ROS 2 launch file with two additional arguments
   ``network_image_height`` and ``network_image_width`` specifying the
   desired input image size:

   .. code:: bash

      ros2 launch isaac_ros_dope isaac_ros_dope_tensor_rt.launch.py model_file_path:=/tmp/models/Ketchup.onnx engine_file_path:=/tmp/models/Ketchup.plan network_image_height:=1080 network_image_width:=1920 input_image_height:=1080 input_image_width:=1920

5. Continue with the rest of the quickstart. You should now be able to
   detect poses in images of your desired size.