> ## Documentation Index
> Fetch the complete documentation index at: https://dragonwingdocs.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Estimate image depth values with sample_depth_estimation

> This sample application allows you to input an RGB image named input_image.jpg or subscribe to the ROS topic /cam0_stream1 from qrb_ro_camera. It then uses QNN to perform model inference and publishes the result as the /depth_map ROS topic containing per-pixel depth values.

The `sample_depth_estimation` sample application takes an RGB image named `input_image.jpg` or the `/cam0_stream1` ROS topic from `qrb_ros_camera`. It runs model inference with the [Qualcomm AI Engine Direct SDK (QNN)](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) and publishes the result as the `/depth_map` ROS topic containing per-pixel depth values.

The model has its source from [Depth Anything V2](https://aihub.qualcomm.com/iot/models/depth_anything_v2?searchTerm=depth\&domain=Computer+Vision), a deep convolutional neural network model for depth estimation.

The following figure shows an example depth estimation result.

<Frame>
  <img src="https://mintcdn.com/qualcomm-prod/eHVY1H1lLEVhgq58/SDKs/QIR-SDK-Ubuntu/images/image37.gif?s=29542e84a56d294663095e7afae7ea6f" alt="Office scene on the left beside its colorized depth map on the right, where warmer colors mark surfaces closer to the camera." width="384" height="144" data-path="SDKs/QIR-SDK-Ubuntu/images/image37.gif" />
</Frame>

## `sample_depth_estimation` pipeline flow

The following figure shows the pipeline flow for `sample_depth_estimation`.

<Frame caption="sample_depth_estimation pipeline.">
  <img src="https://mintcdn.com/qualcomm-prod/eHVY1H1lLEVhgq58/SDKs/QIR-SDK-Ubuntu/images/image39.svg?fit=max&auto=format&n=eHVY1H1lLEVhgq58&q=85&s=e14ef3b08cf8d1a6b2e13b5ab3cf7e7d" alt="Depth estimation pipeline from camera or image publisher input through preprocessing, inference, and postprocessing to the depth map topic." width="572" height="402" data-path="SDKs/QIR-SDK-Ubuntu/images/image39.svg" />
</Frame>

## ROS nodes used in `sample_depth_estimation`

The following table lists the ROS nodes used in `sample_depth_estimation`.

| Node name                                                                        | Description                                                                                                                                 |
| -------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| [qrb ros camera](https://github.com/qualcomm-qrb-ros/qrb_ros_camera)             | Qualcomm ROS 2 package that captures images with parameters and publishes them to ROS topics.                                               |
| Image publisher                                                                  | Publishes image data to a ROS topic—can be camera frames, local files, or processed outputs.                                                |
| Sample depth estimation                                                          | Subscribes to input images for preprocessing, then performs postprocessing on the output tensor published by the qrb ros nn interface node. |
| [qrb ros nn interface](https://github.com/qualcomm-qrb-ros/qrb_ros_nn_inference) | Loads a trained AI model, receives preprocessed images, performs inference, and publishes results.                                          |

## ROS topics used in `sample_depth_estimation`

The following table lists the ROS topics used in `sample_depth_estimation`.

| ROS topic                      | Type                                        | Description                                 |
| ------------------------------ | ------------------------------------------- | ------------------------------------------- |
| `/image_raw`                   | `<sensor_msgs.msg.Image>`                   | Published image information.                |
| `/qrb_inference_input_tensor`  | `<qrb_ros_tensor_list_msgs.msg.TensorList>` | Preprocessed message.                       |
| `/qrb_inference_output_tensor` | `<qrb_ros_tensor_list_msgs.msg.TensorList>` | Neural network interface result with model. |
| `/depth_map`                   | `<sensor_msgs.msg.Image>`                   | Depth map result.                           |

## Prerequisites

You have set up the device, installed ROS2 Jazzy and Qualcomm Intelligent Robotics (QIR) SDK on the device according to [Install the QIR SDK](./install-the-qir-sdk).

## Build from the source of `sample_depth_estimation`

Build `sample_depth_estimation` from source when you need to change the sample code. The device steps install the dependencies, clone the source repository, and build the package.

### Device steps

<Steps>
  <Step title="Download the Depth-Anything-V2 model">
    ```bash Download the model theme={null}
    sudo mkdir -p /opt/model && cd /opt/model
    sudo wget https://huggingface.co/qualcomm/Depth-Anything-V2/resolve/19ce3645e11de17eed7e869eebcc07dd352834f3/Depth-Anything-V2.bin?download=true -O Depth-Anything-V2.bin
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash Install the dependencies theme={null}
    sudo apt install -y ros-dev-tools
    sudo apt install -y ros-jazzy-qrb-ros-camera
    ```
  </Step>

  <Step title="Download the source code from the qrb_ros_samples repository">
    ```bash Download the source code theme={null}
    mkdir -p ~/qrb_ros_ws/src && cd ~/qrb_ros_ws/src
    git clone -b jazzy-rel https://github.com/qualcomm-qrb-ros/qrb_ros_samples.git
    ```
  </Step>

  <Step title="Build the sample application from source code">
    ```bash Build the sample application theme={null}
    cd ~/qrb_ros_ws/src/qrb_ros_samples/ai_vision/sample_depth_estimation
    rosdep install -i --from-path ./ --rosdistro jazzy -y
    colcon build
    ```
  </Step>

  <Step title="Set up the environment with the source command and launch the sample application">
    ```bash Launch the sample application theme={null}
    source install/setup.bash
    ros2 launch sample_depth_estimation launch_with_image_publisher.py
    ```
  </Step>

  <Step title="You can replace this with a custom image file or model path">
    ```bash Launch with a custom image and model theme={null}
    source install/setup.bash
    ros2 launch sample_depth_estimation launch_with_image_publisher.py image_path:=<your local image path> model_path:=<your local model path>
    ```
  </Step>

  <Step title="You can also launch with qrb_ros_camera if you connect to the GMSL camera">
    ```bash Launch with qrb_ros_camera theme={null}
    source install/setup.bash
    ros2 launch sample_depth_estimation launch_with_qrb_ros_camera.py
    ```
  </Step>

  <Step title="When using this launch script, it uses the default parameters, which send the local input_image.jpg file with a publishing rate of 10 Hz">
    ```python theme={null}
    image_path_arg = DeclareLaunchArgument(
             'image_path',
             default_value=os.path.join(package_path, "resource", "input_image.jpg"),
             description='Path to the input image file'
        )
        # Node for image_publisher
        image_publisher_node = Node(
            package='image_publisher',
            executable='image_publisher_node',
            namespace=namespace,
            name='image_publisher_node',
            output='screen',
            parameters=[
                {'filename': image_path},
                {'rate': 10.0},  # Set the publishing rate to 10 Hz
            ]
        )
    ```
  </Step>

  <Step title="You can then check ROS topics with the topic name /depth_map in rqt">
    To install `rqt`, see the [ROS 2 Jazzy documentation](https://docs.ros.org/en/jazzy/Tutorials/Beginner-CLI-Tools/Introducing-Turtlesim/Introducing-Turtlesim.html).

    a. Run `rqt` and select the following buttons in sequence: `Plugins --> Visualization --> Image View`.

    b. Select `/sample_container/depth_map` to see the results.
  </Step>
</Steps>