isaac_ros_segment_anything

Source code on GitHub.

Quickstart

Set Up Development Environment

1. Set up your development environment by following the instructions in getting started.

2. Clone isaac_ros_common under ${ISAAC_ROS_WS}/src.

   cd ${ISAAC_ROS_WS}/src && \
      git clone -b release-3.2 https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common.git isaac_ros_common
   

3. (Optional) Install dependencies for any sensors you want to use by following the sensor-specific guides.

   Note

   We strongly recommend installing all sensor dependencies before starting any quickstarts. Some sensor dependencies require restarting the Isaac ROS Dev container during installation, which will interrupt the quickstart process.

Download Quickstart Assets

1. Download quickstart data from NGC:

   Make sure required libraries are installed.

   sudo apt-get install -y curl jq tar
   

   Then, run these commands to download the asset from NGC:

   NGC_ORG="nvidia"
   NGC_TEAM="isaac"
   PACKAGE_NAME="isaac_ros_segment_anything"
   NGC_RESOURCE="isaac_ros_segment_anything_assets"
   NGC_FILENAME="quickstart.tar.gz"
   MAJOR_VERSION=3
   MINOR_VERSION=2
   VERSION_REQ_URL="https://catalog.ngc.nvidia.com/api/resources/versions?orgName=$NGC_ORG&teamName=$NGC_TEAM&name=$NGC_RESOURCE&isPublic=true&pageNumber=0&pageSize=100&sortOrder=CREATED_DATE_DESC"
   AVAILABLE_VERSIONS=$(curl -s \
       -H "Accept: application/json" "$VERSION_REQ_URL")
   LATEST_VERSION_ID=$(echo $AVAILABLE_VERSIONS | jq -r "
       .recipeVersions[]
       | .versionId as \$v
       | \$v | select(test(\"^\\\\d+\\\\.\\\\d+\\\\.\\\\d+$\"))
       | split(\".\") | {major: .[0]|tonumber, minor: .[1]|tonumber, patch: .[2]|tonumber}
       | select(.major == $MAJOR_VERSION and .minor <= $MINOR_VERSION)
       | \$v
       " | sort -V | tail -n 1
   )
   if [ -z "$LATEST_VERSION_ID" ]; then
       echo "No corresponding version found for Isaac ROS $MAJOR_VERSION.$MINOR_VERSION"
       echo "Found versions:"
       echo $AVAILABLE_VERSIONS | jq -r '.recipeVersions[].versionId'
   else
       mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets && \
       FILE_REQ_URL="https://api.ngc.nvidia.com/v2/resources/$NGC_ORG/$NGC_TEAM/$NGC_RESOURCE/\
   versions/$LATEST_VERSION_ID/files/$NGC_FILENAME" && \
       curl -LO --request GET "${FILE_REQ_URL}" && \
       tar -xf ${NGC_FILENAME} -C ${ISAAC_ROS_WS}/isaac_ros_assets && \
       rm ${NGC_FILENAME}
   fi
   

Build isaac_ros_segment_anything

Binary PackageBuild from Source

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

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

2. Install the prebuilt Debian package:

   sudo apt-get update
   

   sudo apt-get install -y ros-humble-isaac-ros-segment-anything
   

Prepare Segment Anything ONNX Model

There are two Segment Anything models available to select from in the steps below: SAM and Mobile SAM. SAM provides the full accuracy of Segment Anything but can consume over 8GB of GPU memory and take more computation effort for inference. Mobile SAM, has been tuned to operate with a much smaller memory footprint and less computation overhead, but with some level of degradation in quality. Choose the model which will be the most effective for your use case.

1. Make a directory to place models (inside the Docker container):

   mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets/models/segment_anything/1/
   

2. Create the ONNX file for SAM or for Mobile SAM.

Note

SAM is only supported with Triton using the ONNX backend. The following step is to be performed only on x86_64. You must copy the generated ONNX file to a Jetson, in case you want to run the package on the device.

Note

It is recommended to run SAM with at least 12GB+ GPU memory. Mobile SAM smaller version of SAM which can be used if GPU VRAM is not sufficient to run SAM.

SAMMobileSAM

Steps to use SAM

To create ONNX file for SAM. Download the PyTorch weights from Segment Anything official repo. Then, copy the downloaded model weights into the container. The model is expected to be downloaded to ~/Downloads outside the Docker container.

Move this file to ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/ using a console outside of the container and then continue inside the container.

For example, if the model vit_b.pth was downloaded to ~/Downloads:

On x86_64:

# Outside container
mv ~/Downloads/</path/to/vit_b> ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/vit_b.pth

# Inside container
cd ${ISAAC_ROS_WS}
pip install git+https://github.com/facebookresearch/segment-anything.git

# Inside container
ros2 run isaac_ros_segment_anything torch_to_onnx.py \
--checkpoint ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/vit_b.pth \
--output ${ISAAC_ROS_WS}/isaac_ros_assets/models/segment_anything/1/model.onnx --model-type vit_b --sam-type SAM

3. Copy the config file:

   cp ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/sam_config_onnx.pbtxt ${ISAAC_ROS_WS}/isaac_ros_assets/models/segment_anything/config.pbtxt
   

Run Launch File

Segment Anything requires a prompt to indicate what in the image we want to segment. In this example, we will use YOLOv8 object detection to determine the image space bounding box of an object of interest to use as the prompt for Segment Anything to create an image segmentation mask for.

RosbagRealSense CameraHawk CameraZED Camera

1. Continuing inside the Docker container, install the following dependencies:

   sudo apt-get update
   

   sudo apt-get install -y ros-humble-isaac-ros-examples
   

2. Run the following launch file to spin up a demo of this package:

   cd ${ISAAC_ROS_WS} && \
      ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=segment_anything interface_specs_file:=${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/quickstart_interface_specs.json sam_model_repository_paths:=[${ISAAC_ROS_WS}/isaac_ros_assets/models]
   

3. Then open another terminal, and enter the Docker container again:

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

4. Then, play the rosbag:

   ros2 bag play -l ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_segment_anything/segment_anything_sample_data/
   

Visualize Results

1. Open a new terminal inside the Docker container:

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

2. Run the Python script to generate the colored segmentation mask from raw mask.

   Note

   For Rosbag use case run following command to run the script:

   ros2 run isaac_ros_segment_anything visualize_mask.py --remap /yolov8_encoder/resize/image:=/image
   

   ros2 run isaac_ros_segment_anything visualize_mask.py
   

3. Visualize and validate the output of the package by launching rqt_image_view. In another terminal enter the Docker container:

   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 /segment_anything/colored_segmentation_mask
   

   

   Note

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

Note

Segment Anything is designed to perform image segmentation on previously unseen objects without model retraining. As a result, inference requires significant GPU compute for this task. Refer to performance of the model for your target platform to determine which model variants to use.

More powerful discrete GPUs can outperform all other platforms for this task and should be preferred if higher performance is required. Interleaving image segmentation with other tasks rather than running continuously can be a more effective solution as well. Finally, if runtime performance is critical and offline training resources are available, developers can train Unet for their own target objects using synthetic data generation and/or real-world data for faster image segmentation.

Try More Examples

To continue your exploration, check out the following suggested examples:

• Tutorial with Isaac Sim

Troubleshooting

Isaac ROS Troubleshooting

For solutions to problems with Isaac ROS, see here.

Deep Learning Troubleshooting

For solutions to problems with using DNN models, see here.

API

Usage

A single launch file is provided for this package. The launch file launches isaac_ros_triton with ONNX backend. Only the ONNX backend with Triton is supported for this model.

Note

For your specific application, these launch files may need to be modified. Please consult the available components to see the configurable parameters.

Launch File	Components Used
isaac_ros_segment_anything_triton.launch.py	ResizeNode, PadNode, ImageFormatConverterNode, ImageToTensorNode, ImageTensorNormalizeNode, InterleavedToPlanarNode, ReshapeNode, TritonNode, SegmentAnythingDecoderNode, SegmentAnythingDataEncoderNode, DummyMaskPublisher

SegmentAnythingDecoderNode

ROS Parameters

ROS Parameter	Type	Default	Description
mask_width	int16_t	960	The width of the segmentation mask.
mask_height	int16_t	544	The height of the segmentation mask.
max_batch_size	int16_t	20	Maximum number of prompt inputs for each frame.

Note

• If a frame does not have any input prompt then no mask is generated for that particular frame.

• If frame input prompts are larger than the max_batch_size, then first max_batch_size input prompts are considered.

ROS Topics Subscribed

ROS Topic	Interface	Description
tensor_sub	isaac_ros_tensor_list_interfaces/TensorList	List of Output Tensors from the SAM model.

Note

All input images are required to have height and width that are both an even number of pixels.

ROS Topics Published

ROS Topic	Interface	Description
/segment_anything/raw_segmentation_mask	isaac_ros_tensor_list_interfaces/TensorList	The raw segmentation mask, A Tensor with shape [batch_size, 1, orig_img_height, orig_img_width]. Where batch_size is number of prompt inputs for that frame.

SegmentAnythingDataEncoderNode

ROS Parameters

ROS Parameter	Type	Default	Description
prompt_input_type	string	bbox	Type of Prompt Input. Supported Types: bbox and point
has_input_mask	bool	false	Whether there is any input mask for SAM or not.
max_batch_size	int16_t	20	Maximum number of prompt inputs for each frame.
orig_img_dims	double list	[632,1200]	Dimensions of the Original Image in [H,W] format. Please note, expectation is each bbox/point is in the coordinate system of given dimension image.

ROS Topics Subscribed

ROS Topic	Interface	Description
/prompts	vision_msgs/Detection2DArray	Input Detection2DArray, If prompt type is point then center of bbox is considered as point of interest.
/tensor_pub	isaac_ros_tensor_list_interfaces/TensorList	The preprocessed & encoded image tensor.
/mask	isaac_ros_tensor_list_interfaces/TensorList	Input mask for SAM.

ROS Topics Published

ROS Topic	Interface	Description
/tensor	isaac_ros_tensor_list_interfaces/TensorList	Tensor List which has all the required tensors for SAM inference.

DummyMaskPublisher

This node can be used to publish dummy mask to SAM in case there is no mask input to use.

ROS Parameters

ROS Parameter	Type	Default	Description
tensor_name	string	input_mask	Name of the mask tensor.

ROS Topics Subscribed

ROS Topic	Interface	Description
/tensor_pub	isaac_ros_tensor_list_interfaces/TensorList	The data from this topic is used to timestamp the dummy mask.

ROS Topics Published

ROS Topic	Interface	Description
/mask	isaac_ros_tensor_list_interfaces/TensorList	Dummy mask is published on this topic. Which is supposed to be ingested by SegmentAnythingDataEncoderNode