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https://github.com/dusty-nv/ros_deep_learning
Deep learning inference nodes for ROS / ROS2 with support for NVIDIA Jetson and TensorRT
https://github.com/dusty-nv/ros_deep_learning
Last synced: about 2 months ago
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Deep learning inference nodes for ROS / ROS2 with support for NVIDIA Jetson and TensorRT
- Host: GitHub
- URL: https://github.com/dusty-nv/ros_deep_learning
- Owner: dusty-nv
- Created: 2016-10-04T02:18:28.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-06-15T23:33:12.000Z (about 1 year ago)
- Last Synced: 2024-01-11T03:01:27.653Z (5 months ago)
- Language: C++
- Homepage:
- Size: 97.7 KB
- Stars: 804
- Watchers: 34
- Forks: 250
- Open Issues: 78
-
Metadata Files:
- Readme: README.md
Lists
- Awesome-Self-Driving - ros_deep_learning - Deep learning inference nodes for ROS with support for NVIDIA Jetson TX1/TX2/Xavier and TensorRT. (15. Other Tool / 8.8. Other)
- awesome-robotic-tooling - ros_deep_learning - Deep learning inference nodes for ROS with support for NVIDIA Jetson TX1/TX2/Xavier and TensorRT. (Sensor Processing / Image Processing)
- awesome-robotic-tooling - ros_deep_learning - Deep learning inference nodes for ROS with support for NVIDIA Jetson TX1/TX2/Xavier and TensorRT. (Sensor Processing / Image Processing)
README
# DNN Inference Nodes for ROS/ROS2
This package contains DNN inference nodes and camera/video streaming nodes for ROS/ROS2 with support for NVIDIA **[Jetson Nano / TX1 / TX2 / Xavier / Orin](https://developer.nvidia.com/embedded-computing)** devices and TensorRT.
The nodes use the image recognition, object detection, and semantic segmentation DNN's from the [`jetson-inference`](https://github.com/dusty-nv/jetson-inference) library and NVIDIA [Hello AI World](https://github.com/dusty-nv/jetson-inference#hello-ai-world) tutorial, which come with several built-in pretrained networks for classification, detection, and segmentation and the ability to load customized user-trained models.
The camera & video streaming nodes support the following [input/output interfaces](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md):
* MIPI CSI cameras
* V4L2 cameras
* RTP / RTSP streams
* WebRTC streams
* Videos & Images
* Image sequences
* OpenGL windows
Various distribution of ROS are supported either from source or through containers (including Melodic, Noetic, Foxy, Galactic, Humble, and Iron). The same branch supports both ROS1 and ROS2.
### Table of Contents
* [Installation](#installation)
* [Testing](#testing)
* [Video Viewer](#video-viewer)
* [imagenet Node](#imagenet-node)
* [detectnet Node](#detectnet-node)
* [segnet Node](#segnet-node)
* [Topics & Parameters](#topics-messages)
* [imagenet Node](#imagenet-node-1)
* [detectnet Node](#detectnet-node-1)
* [segnet Node](#segnet-node-1)
* [video_source Node](#video_source-node)
* [video_output Node](#video_output-node)
## Installation
The easiest way to get up and running is by cloning [jetson-inference](https://github.com/dusty-nv/jetson-inference) (which ros_deep_learning is a submodule of) and running the pre-built container, which automatically mounts the required model directories and devices:
``` bash
$ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ docker/run.sh --ros=humble # noetic, foxy, galactic, humble, iron
```
> **note**: the ros_deep_learning nodes rely on data from the jetson-inference tree for storing models, so clone and mount `jetson-inference/data` if you're using your own container or source installation method.
The `--ros` argument to the [`docker/run.sh`](https://github.com/dusty-nv/jetson-inference/blob/master/docker/run.sh) script selects the ROS distro to use. They in turn use the `ros:$ROS_DISTRO-pytorch` container images from [jetson-containers](https://github.com/dusty-nv/jetson-containers), which include jetson-inference and this.
For previous information about building the ros_deep_learning package for an uncontainerized ROS installation, expand the section below (the parts about installing ROS may require adapting for the particular version of ROS/ROS2 that you want to install)
Legacy Install Instructions
### jetson-inference
These ROS nodes use the DNN objects from the [`jetson-inference`](https://github.com/dusty-nv/jetson-inference) project (aka Hello AI World). To build and install jetson-inference, see [this page](https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md) or run the commands below:
```bash
$ cd ~
$ sudo apt-get install git cmake
$ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ mkdir build
$ cd build
$ cmake ../
$ make -j$(nproc)
$ sudo make install
$ sudo ldconfig
```
Before proceeding, it's worthwhile to test that `jetson-inference` is working properly on your system by following this step of the Hello AI World tutorial:
* [Classifying Images with ImageNet](https://github.com/dusty-nv/jetson-inference/blob/master/docs/imagenet-console-2.md)
### ROS/ROS2
Install the `ros-melodic-ros-base` or `ros-eloquent-ros-base` package on your Jetson following these directions:
* ROS Melodic - [ROS Install Instructions](http://wiki.ros.org/melodic/Installation/Ubuntu)
* ROS2 Eloquent - [ROS2 Install Instructions](https://index.ros.org/doc/ros2/Installation/Eloquent/Linux-Install-Debians/)
Depending on which version of ROS you're using, install some additional dependencies and create a workspace:
#### ROS Melodic
```bash
$ sudo apt-get install ros-melodic-image-transport ros-melodic-vision-msgs
```
For ROS Melodic, create a Catkin workspace (`~/ros_workspace`) using these steps:
http://wiki.ros.org/ROS/Tutorials/InstallingandConfiguringROSEnvironment#Create_a_ROS_Workspace
#### ROS Eloquent
```bash
$ sudo apt-get install ros-eloquent-vision-msgs \
ros-eloquent-launch-xml \
ros-eloquent-launch-yaml \
python3-colcon-common-extensions
```
For ROS Eloquent, create a workspace (`~/ros_workspace`) to use:
```bash
$ mkdir -p ~/ros2_example_ws/src
```
### ros_deep_learning
Next, navigate into your ROS workspace's `src` directory and clone `ros_deep_learning`:
```bash
$ cd ~/ros_workspace/src
$ git clone https://github.com/dusty-nv/ros_deep_learning
```
Then build it - if you are using ROS Melodic, use `catkin_make`. If you are using ROS2 Eloquent, use `colcon build`:
```bash
$ cd ~/ros_workspace/
# ROS Melodic
$ catkin_make
$ source devel/setup.bash
# ROS2 Eloquent
$ colcon build
$ source install/local_setup.bash
```
The nodes should now be built and ready to use. Remember to source the overlay as shown above so that ROS can find the nodes.
## Testing
Before proceeding, if you're using ROS Melodic make sure that `roscore` is running first:
```bash
$ roscore
```
If you're using ROS2, running the core service is no longer required.
### Video Viewer
First, it's recommended to test that you can stream a video feed using the [`video_source`](#video-source-node) and [`video_output`](#video-output-node) nodes. See [Camera Streaming & Multimedia](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md) for valid input/output streams, and substitute your desired `input` and `output` argument below. For example, you can use video files for the input or output, or use V4L2 cameras instead of MIPI CSI cameras. You can also use RTP/RTSP streams over the network.
```bash
# ROS
$ roslaunch ros_deep_learning video_viewer.ros1.launch input:=csi://0 output:=display://0
# ROS2
$ ros2 launch ros_deep_learning video_viewer.ros2.launch input:=csi://0 output:=display://0
```
### imagenet Node
You can launch a classification demo with the following commands - substitute your desired camera or video path to the `input` argument below (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md) for valid input/output streams).
Note that the `imagenet` node also publishes classification metadata on the `imagenet/classification` topic in a [`vision_msgs/Detection2DArray`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/Detection2DArray.html) message -- see the [Topics & Parameters](#imagenet-node-1) section below for more info.
```bash
# ROS
$ roslaunch ros_deep_learning imagenet.ros1.launch input:=csi://0 output:=display://0
# ROS2
$ ros2 launch ros_deep_learning imagenet.ros2.launch input:=csi://0 output:=display://0
```
### detectnet Node
To launch an object detection demo, substitute your desired camera or video path to the `input` argument below (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md) for valid input/output streams). Note that the `detectnet` node also publishes the metadata in a `vision_msgs/Detection2DArray` message -- see the [Topics & Parameters](#detectnet-node-1) section below for more info.
####
```bash
# ROS
$ roslaunch ros_deep_learning detectnet.ros1.launch input:=csi://0 output:=display://0
# ROS2
$ ros2 launch ros_deep_learning detectnet.ros2.launch input:=csi://0 output:=display://0
```
### segnet Node
To launch a semantic segmentation demo, substitute your desired camera or video path to the `input` argument below (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md) for valid input/output streams). Note that the `segnet` node also publishes raw segmentation results to the `segnet/class_mask` topic -- see the [Topics & Parameters](#segnet-node-1) section below for more info.
```bash
# ROS
$ roslaunch ros_deep_learning segnet.ros1.launch input:=csi://0 output:=display://0
# ROS2
$ ros2 launch ros_deep_learning segnet.ros2.launch input:=csi://0 output:=display://0
```
## Topics & Parameters
Below are the message topics and parameters that each node implements.
### imagenet Node
| Topic Name | I/O | Message Type | Description |
|----------------|:------:|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|
| image_in | Input | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Raw input image |
| classification | Output | [`vision_msgs/Classification2D`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/Classification2D.html) | Classification results (class ID + confidence) |
| vision_info | Output | [`vision_msgs/VisionInfo`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/VisionInfo.html) | Vision metadata (class labels parameter list name) |
| overlay | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Input image overlayed with the classification results |
| Parameter Name | Type | Default | Description |
|-------------------|:----------------:|:-------------:|--------------------------------------------------------------------------------------------------------------------|
| model_name | `string` | `"googlenet"` | Built-in model name (see [here](https://github.com/dusty-nv/jetson-inference#image-recognition) for valid values) |
| model_path | `string` | `""` | Path to custom caffe or ONNX model |
| prototxt_path | `string` | `""` | Path to custom caffe prototxt file |
| input_blob | `string` | `"data"` | Name of DNN input layer |
| output_blob | `string` | `"prob"` | Name of DNN output layer |
| class_labels_path | `string` | `""` | Path to custom class labels file |
| class_labels_HASH | `vector` | class names | List of class labels, where HASH is model-specific (actual name of parameter is found via the `vision_info` topic) |
### detectnet Node
| Topic Name | I/O | Message Type | Description |
|-------------|:------:|--------------------------------------------------------------------------------------------------------------|------------------------------------------------------------|
| image_in | Input | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Raw input image |
| detections | Output | [`vision_msgs/Detection2DArray`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/Detection2DArray.html) | Detection results (bounding boxes, class IDs, confidences) |
| vision_info | Output | [`vision_msgs/VisionInfo`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/VisionInfo.html) | Vision metadata (class labels parameter list name) |
| overlay | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Input image overlayed with the detection results |
| Parameter Name | Type | Default | Description |
|-------------------|:----------------:|:--------------------:|--------------------------------------------------------------------------------------------------------------------|
| model_name | `string` | `"ssd-mobilenet-v2"` | Built-in model name (see [here](https://github.com/dusty-nv/jetson-inference#object-detection) for valid values) |
| model_path | `string` | `""` | Path to custom caffe or ONNX model |
| prototxt_path | `string` | `""` | Path to custom caffe prototxt file |
| input_blob | `string` | `"data"` | Name of DNN input layer |
| output_cvg | `string` | `"coverage"` | Name of DNN output layer (coverage/scores) |
| output_bbox | `string` | `"bboxes"` | Name of DNN output layer (bounding boxes) |
| class_labels_path | `string` | `""` | Path to custom class labels file |
| class_labels_HASH | `vector` | class names | List of class labels, where HASH is model-specific (actual name of parameter is found via the `vision_info` topic) |
| overlay_flags | `string` | `"box,labels,conf"` | Flags used to generate the overlay (some combination of `none,box,labels,conf`) |
| mean_pixel_value | `float` | 0.0 | Mean pixel subtraction value to be applied to input (normally 0) |
| threshold | `float` | 0.5 | Minimum confidence value for positive detections (0.0 - 1.0) |
### segnet Node
| Topic Name | I/O | Message Type | Description |
|-------------|:------:|--------------------------------------------------------------------------------------------------|----------------------------------------------------------|
| image_in | Input | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Raw input image |
| vision_info | Output | [`vision_msgs/VisionInfo`](http://docs.ros.org/melodic/api/vision_msgs/html/msg/VisionInfo.html) | Vision metadata (class labels parameter list name) |
| overlay | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Input image overlayed with the classification results |
| color_mask | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Colorized segmentation class mask out |
| class_mask | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | 8-bit single-channel image where each pixel is a classID |
| Parameter Name | Type | Default | Description |
|-------------------|:----------------:|:------------------------------------:|-----------------------------------------------------------------------------------------------------------------------|
| model_name | `string` | `"fcn-resnet18-cityscapes-1024x512"` | Built-in model name (see [here](https://github.com/dusty-nv/jetson-inference#semantic-segmentation) for valid values) |
| model_path | `string` | `""` | Path to custom caffe or ONNX model |
| prototxt_path | `string` | `""` | Path to custom caffe prototxt file |
| input_blob | `string` | `"data"` | Name of DNN input layer |
| output_blob | `string` | `"score_fr_21classes"` | Name of DNN output layer |
| class_colors_path | `string` | `""` | Path to custom class colors file |
| class_labels_path | `string` | `""` | Path to custom class labels file |
| class_labels_HASH | `vector` | class names | List of class labels, where HASH is model-specific (actual name of parameter is found via the `vision_info` topic) |
| mask_filter | `string` | `"linear"` | Filtering to apply to color_mask topic (`linear` or `point`) |
| overlay_filter | `string` | `"linear"` | Filtering to apply to overlay topic (`linear` or `point`) |
| overlay_alpha | `float` | `180.0` | Alpha blending value used by overlay topic (0.0 - 255.0) |
### video_source Node
| Topic Name | I/O | Message Type | Description |
|------------|:------:|----------------------------------------------------------------------------------------|-------------------------|
| raw | Output | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Raw output image (BGR8) |
| Parameter | Type | Default | Description |
|----------------|:--------:|:-----------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| resource | `string` | `"csi://0"` | Input stream URI (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md#input-streams) for valid protocols) |
| codec | `string` | `""` | Manually specify codec for compressed streams (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md#input-options) for valid values) |
| width | `int` | 0 | Manually specify desired width of stream (0 = stream default) |
| height | `int` | 0 | Manually specify desired height of stream (0 = stream default) |
| framerate | `int` | 0 | Manually specify desired framerate of stream (0 = stream default) |
| loop | `int` | 0 | For video files: `0` = don't loop, `>0` = # of loops, `-1` = loop forever |
| flip | `string` | `""` | Set the flip method for MIPI CSI cameras (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md#input-options) for valid values) |
### video_output Node
| Topic Name | I/O | Message Type | Description |
|------------|:-----:|----------------------------------------------------------------------------------------|-----------------|
| image_in | Input | [`sensor_msgs/Image`](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html) | Raw input image |
| Parameter | Type | Default | Description |
|----------------|:--------:|:---------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| resource | `string` | `"display://0"` | Output stream URI (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md#output-streams) for valid protocols) |
| codec | `string` | `"h264"` | Codec used for compressed streams (see [here](https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md#input-options) for valid values) |
| bitrate | `int` | 4000000 | Target VBR bitrate of encoded streams (in bits per second) |