diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..a89cd42
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,14 @@
+# Isaac ROS Contribution Rules
+
+Any contribution that you make to this repository will
+be under the Apache 2 License, as dictated by that
+[license](http://www.apache.org/licenses/LICENSE-2.0.html):
+
+> **5. Submission of Contributions.** Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+Contributors must sign-off each commit by adding a `Signed-off-by: ...`
+line to commit messages to certify that they have the right to submit
+the code they are contributing to the project according to the
+[Developer Certificate of Origin (DCO)](https://developercertificate.org/).
+
+[//]: # (202201002)
diff --git a/LICENSE b/LICENSE
index 88bc900..7a4a3ea 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,66 +1,202 @@
-NVIDIA ISAAC ROS SOFTWARE LICENSE
-This license is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs the use of the NVIDIA Isaac ROS software and materials provided hereunder (“SOFTWARE”).
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diff --git a/README.md b/README.md
index d6763f8..8d238ae 100644
--- a/README.md
+++ b/README.md
@@ -3,20 +3,25 @@
7.7ms | 72 fps
18ms | 450 fps
3.2ms |
> **Note:** These numbers are reported with defaults parameter values found in [params.yaml](./isaac_ros_detectnet/config/params.yaml).
+
+These data have been collected per the methodology described [here](https://github.com/NVIDIA-ISAAC-ROS/.github/blob/main/profile/performance-summary.md#methodology).
+
### ROS2 Graph Configuration
To run the DetectNet object detection inference, the following ROS2 nodes should be set up and running:
@@ -25,13 +30,14 @@ To run the DetectNet object detection inference, the following ROS2 nodes should
1. **Isaac ROS DNN Image encoder**: This will take an image message and convert it to a tensor ([`TensorList`](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/isaac_ros_tensor_list_interfaces/msg/TensorList.msg) that can be
processed by the network.
-2. **Isaac ROS DNN Inference - Triton**: This will execute the DetectNet network and take as input the tensor from the DNN Image Encoder.
+2. **Isaac ROS DNN Inference - Triton**: This will execute the DetectNet network and take as input the tensor from the DNN Image Encoder.
> **Note:** The [Isaac ROS TensorRT](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_dnn_inference/tree/main/isaac_ros_tensor_rt) package is not able to perform inference with DetectNet models at this time.
The output will be a TensorList message containing the encoded detections. Use the parameters `model_name` and `model_repository_paths` to point to the model folder and set the model name. The `.plan` file should be located at `$model_repository_path/$model_name/1/model.plan`
3. **Isaac ROS Detectnet Decoder**: This node will take the TensorList with encoded detections as input, and output `Detection2DArray` messages for each frame. See the following section for the parameters.
## Table of Contents
+
- [Isaac ROS Object Detection](#isaac-ros-object-detection)
- [Overview](#overview)
- [Isaac ROS NITROS Acceleration](#isaac-ros-nitros-acceleration)
@@ -44,7 +50,6 @@ To run the DetectNet object detection inference, the following ROS2 nodes should
- [Quickstart](#quickstart)
- [Next Steps](#next-steps)
- [Try More Examples](#try-more-examples)
- - [Use Different Models](#use-different-models)
- [Customize your Dev Environment](#customize-your-dev-environment)
- [Package Reference](#package-reference)
- [`isaac_ros_detectnet`](#isaac_ros_detectnet)
@@ -58,26 +63,28 @@ To run the DetectNet object detection inference, the following ROS2 nodes should
- [Updates](#updates)
## Latest Update
-Update 2022-08-31: Update to use [NVIDIA Isaac Transport for ROS (NITROS)](https://developer.nvidia.com/blog/improve-perception-performance-for-ros-2-applications-with-nvidia-isaac-transport-for-ros/) and to be compatible with JetPack 5.0.2
+
+Update 2022-10-19: Updated OSS licensing
## Supported Platforms
+
This package is designed and tested to be compatible with ROS2 Humble running on [Jetson](https://developer.nvidia.com/embedded-computing) or an x86_64 system with an NVIDIA GPU.
> **Note**: Versions of ROS2 earlier than Humble are **not** supported. This package depends on specific ROS2 implementation features that were only introduced beginning with the Humble release.
-
-| Platform | Hardware | Software | Notes |
-| -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Jetson | [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/)
[Jetson Xavier](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/) | [JetPack 5.0.2](https://developer.nvidia.com/embedded/jetpack) | For best performance, ensure that [power settings](https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/SD/PlatformPowerAndPerformance.html) are configured appropriately. |
-| x86_64 | NVIDIA GPU | [Ubuntu 20.04+](https://releases.ubuntu.com/20.04/)
[CUDA 11.6.1+](https://developer.nvidia.com/cuda-downloads) |
-
+| Platform | Hardware | Software | Notes |
+| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Jetson | [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/)
[Jetson Xavier](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/) | [JetPack 5.0.2](https://developer.nvidia.com/embedded/jetpack) | For best performance, ensure that [power settings](https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/SD/PlatformPowerAndPerformance.html) are configured appropriately. |
+| x86_64 | NVIDIA GPU | [Ubuntu 20.04+](https://releases.ubuntu.com/20.04/)
[CUDA 11.6.1+](https://developer.nvidia.com/cuda-downloads) |
### Docker
+
To simplify development, we strongly recommend leveraging the Isaac ROS Dev Docker images by following [these steps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/dev-env-setup.md). This will streamline your development environment setup with the correct versions of dependencies on both Jetson and x86_64 platforms.
> **Note:** All Isaac ROS Quickstarts, tutorials, and examples have been designed with the Isaac ROS Docker images as a prerequisite.
## Quickstart
+
1. Set up your development environment by following the instructions [here](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/dev-env-setup.md).
2. Clone this repository and its dependencies under `~/workspaces/isaac_ros-dev/src`.
@@ -102,47 +109,59 @@ To simplify development, we strongly recommend leveraging the Isaac ROS Dev Dock
```
3. Pull down a ROS Bag of sample data:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_object_detection/isaac_ros_detectnet && \
git lfs pull -X "" -I "resources/rosbags"
```
+
4. Launch the Docker container using the `run_dev.sh` script:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
5. Inside the container, build and source the workspace:
+
```bash
cd /workspaces/isaac_ros-dev && \
colcon build --symlink-install && \
source install/setup.bash
```
+
6. (Optional) Run tests to verify complete and correct installation:
+
```bash
colcon test --executor sequential
```
+
7. Run the quickstart setup script which will download the [PeopleNet Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet) from NVIDIA GPU Cloud(NGC)
+
```bash
cd /workspaces/isaac_ros-dev/src/isaac_ros_object_detection/isaac_ros_detectnet && \
./scripts/setup_model.sh --height 632 --width 1200 --config-file resources/quickstart_config.pbtxt
```
8. Run the following launch file to spin up a demo of this package:
+
```bash
cd /workspaces/isaac_ros-dev && \
ros2 launch isaac_ros_detectnet isaac_ros_detectnet_quickstart.launch.py
```
-9. Visualize and validate the output of the package in the `rqt_image_view` window. After about a minute, your output should look like this:
+
+9. Visualize and validate the output of the package in the `rqt_image_view` window. After about a minute, your output should look like this:
## Next Steps
+
### Try More Examples
+
To continue your exploration, check out the following suggested examples:
-- [Tutorial with Isaac Sim](docs/tutorial-isaac-sim.md)
-### Use Different Models
-Click [here](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_dnn_inference/blob/main/docs/model-preparation.md) for more information about how to use NGC models.
+- [Tutorial with Isaac Sim](docs/tutorial-isaac-sim.md)
+- [Tutorial with Custom Model](docs/tutorial-custom-model.md) For more info click [here](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_dnn_inference/blob/main/docs/model-preparation.md)
This package only supports models based on the `Detectnet_v2` architecture. Some of the [supported DetectNet models](https://catalog.ngc.nvidia.com/?filters=&orderBy=scoreDESC&query=DetectNet) from NGC:
@@ -153,12 +172,14 @@ This package only supports models based on the `Detectnet_v2` architecture. Some
| [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet) | Identify objects from a moving object |
| [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir) | Detect faces in a dark environment with IR camera |
-
### Customize your Dev Environment
+
To customize your development environment, reference [this guide](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/modify-dockerfile.md).
## Package Reference
+
### `isaac_ros_detectnet`
+
#### Usage
```bash
@@ -182,26 +203,32 @@ ros2 launch isaac_ros_detectnet isaac_ros_detectnet.launch.py label_list:=
+
+## Overview
+
+This tutorial walks you through a pipeline for object(people) detection using [DetectNet](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_object_detection) consuming images from Isaac Sim.
+
+## Tutorial Walkthrough
1. Complete the [Quickstart section](../README.md#quickstart) in the main README.
2. Launch the Docker container using the `run_dev.sh` script:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
3. Inside the container, build and source the workspace:
+
```bash
cd /workspaces/isaac_ros-dev && \
colcon build --symlink-install && \
source install/setup.bash
```
+
4. Run the setup script to download the [PeopleNet Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet) from NVIDIA GPU Cloud(NGC) and convert it to a .etlt file
- ```bash
+
+ ```bash
cd /workspaces/isaac_ros-dev/src/isaac_ros_object_detection/isaac_ros_detectnet && \
./scripts/setup_model.sh --height 720 --width 1280 --config-file resources/isaac_sim_config.pbtxt
```
-5. Launch the pre-composed pipeline launchfile:
+
+5. Launch the pre-composed pipeline launchfile:
+
```bash
cd /workspaces/isaac_ros-dev && \
ros2 launch isaac_ros_detectnet isaac_ros_detectnet_isaac_sim.launch.py
```
+
6. Install and launch Isaac Sim following the steps in the [Isaac ROS Isaac Sim Setup Guide](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/isaac-sim-sil-setup.md)
7. Open up the Isaac ROS Common USD scene (using the "content" window) located at:
-
+
`omniverse://localhost/NVIDIA/Assets/Isaac/2022.1/Isaac/Samples/ROS2/Scenario/carter_warehouse_apriltags_worker.usd`
-
+
Wait for it to load completely.
> **Note:** To use a different server, replace `localhost` with ``
-
-8. Change the Translate values for the Transform box inside the Property section of the Carter_ROS object to
+8. Change the Translate values for the Transform box inside the Property section of the Carter_ROS object to
`X=0.0 , Y=0.0`
