Before starting any of the tutorials below, create a workspace and clone this repository so you can inspect and manipulate the code:
$ mkdir -p ~/image_transport_tutorials_ws/src
$ cd ~/image_transport_tutorials_ws/src
$ git clone https://github.com/ros-perception/image_transport_tutorials.git
Install needed dependencies:
$ cd ~/image_transport_tutorials_ws/
$ source /opt/ros/iron/setup.bash
$ rosdep install -i --from-path src --rosdistro iron -y
$ colcon build
Make sure to include the correct setup file (in the above example it is for Iron on Ubuntu and for bash).
Description: This tutorial shows how to create a publisher node that will continually publish an image.
Tutorial Level: Beginner
Take a look at my_publisher.cpp.
Now, let's break down the code piece by piece. For lines not explained here, review Writing a Simple Publisher and Subscriber (C++).
#include "cv_bridge/cv_bridge.h"
#include "image_transport/image_transport.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "rclcpp/rclcpp.hpp"
These headers will allow us to load an image using OpenCV, convert it to the ROS message format, and publish it.
rclcpp::Node::SharedPtr node = rclcpp::Node::make_shared("image_publisher", options);
image_transport::ImageTransport it(node);
We create an ImageTransport instance, initializing it with our node.
We use methods of ImageTransport to create image publishers and subscribers, much as we use methods of Node to create generic ROS publishers and subscribers.
image_transport::Publisher pub = it.advertise("camera/image", 1);
Advertise that we are going to be publishing images on the base topic camera/image.
Depending on whether more plugins are built, additional (per-plugin) topics derived from the base topic may also be advertised.
The second argument is the size of our publishing queue.
advertise() returns an image_transport::Publisher object, which serves two purposes:
- It contains a
publish()method that lets you publish images onto the base topic it was created with - When it goes out of scope, it will automatically unadvertise
cv::Mat image = cv::imread(argv[1], cv::IMREAD_COLOR);
std_msgs::msg::Header hdr;
sensor_msgs::msg::Image::SharedPtr msg;
msg = cv_bridge::CvImage(hdr, "bgr8", image).toImageMsg();
We load a user-specified (on the command line) color image from disk using OpenCV, then convert it to the ROS type sensor_msgs/msg/Image.
rclcpp::WallRate loop_rate(5);
while (rclcpp::ok()) {
pub.publish(msg);
rclcpp::spin_some(node);
loop_rate.sleep();
}
We broadcast the image to anyone connected to one of our topics, exactly as we would have using an rclcpp::Publisher.
The example above requires a path to an image file to be added as a command line parameter. This image will be converted and sent as a message to an image subscriber. In most cases, however, this is not a very practical example as you are often required to handle streaming data. (For example: multiple webcams mounted on a robot record the scene around it and you have to pass the image data to some other node for further analysis).
The publisher example can be modified quite easily to make it work with a video device supported by cv::VideoCapture (in case it is not, you have to handle it accordingly).
Take a look at publisher_from_video.cpp to see how a video device can be passed in as a command line argument and used as the image source.
If you have a single device, you do not need to do the whole routine with passing a command line argument.
In this case, you can hard-code the index/address of the device and directly pass it to the video capturing structure in OpenCV (example: cv::VideoCapture(0) if /dev/video0 is used).
Multiple checks are also included here to make sure that the publisher does not break if the camera is shut down.
If the retrieved frame from the video device is not empty, it will then be converted to a ROS message which will be published by the publisher.
Description: This tutorial shows how to create a subscriber node that will display an image on the screen.
By using the image_transport subscriber to subscribe to images, any image transport can be used at runtime.
To learn how to actually use a specific image transport, see the next tutorial.
Tutorial Level: Beginner
Take a look at my_subscriber.cpp.
Now, let's break down the code piece by piece.
#include "cv_bridge/cv_bridge.h"
#include "image_transport/image_transport.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "rclcpp/logging.hpp"
#include "rclcpp/rclcpp.hpp"
These headers will allow us to subscribe to image messages, display images using OpenCV's simple GUI capabilities, and log errors.
void imageCallback(const sensor_msgs::msg::Image::ConstSharedPtr & msg)
This is the callback function that will be called when a new image has arrived on the camera/image topic.
Although the image may have been sent in some arbitrary transport-specific message type, notice that the callback need only handle the normal sensor_msgs/msg/Image type.
All image encoding/decoding is handled automatically for you.
try {
cv::imshow("view", cv_bridge::toCvShare(msg, "bgr8")->image);
cv::waitKey(10);
} catch (cv_bridge::Exception & e) {
auto logger = rclcpp::get_logger("my_subscriber");
RCLCPP_ERROR(logger, "Could not convert from '%s' to 'bgr8'.", msg->encoding.c_str());
The body of the callback. We convert the ROS image message into an OpenCV image with BGR pixel encoding, then show it in a display window.
rclcpp::Node::SharedPtr node = rclcpp::Node::make_shared("image_listener", options);
image_transport::ImageTransport it(node);
We create an ImageTransport instance, initializing it with our node.
image_transport::Subscriber sub = it.subscribe("camera/image", 1, imageCallback);
Subscribe to the camera/image base topic.
The actual ROS topic subscribed to depends on which transport is used.
In the default case, "raw" transport, the topic is camera/image with type sensor_msgs/msg/Image.
ROS will call the imageCallback function whenever a new image arrives.
The 2nd argument is the queue size.
subscribe() returns an image_transport::Subscriber object that you must hold on to until you want to unsubscribe.
When the Subscriber object is destructed, it will automatically unsubscribe from the camera/image base topic.
In just a few lines of code, we have written a ROS image viewer that can handle images in both raw and a variety of compressed forms.
Description: This tutorial discusses running the simple image publisher and subscriber using multiple transports.
Tutorial Level: Beginner
In a previous tutorial we made a publisher node called my_publisher.
Now run the node with an image file as the command-line argument:
$ ros2 run image_transport_tutorials my_publisher path/to/some/image.jpg
To check that your node is running properly, list the topics being published:
$ ros2 topic list
You should see /camera/image in the output.
You can also get more information about the topic:
$ ros2 topic info /camera/image
The output should be:
Type: sensor_msgs/msg/Image
Publisher count: 1
Subscription count: 0
In the last tutorial, we made a subscriber node called my_subscriber. Now run it:
$ ros2 run image_transport_tutorials my_subscriber
You should see a window pop up with the image you gave to the publisher.
image_transport searches your ROS installation for transport plugins at runtime and dynamically loads all that are built.
This affords you great flexibility in adding additional transports, but makes it unclear which are available on your system.
image_transport provides a list_transports executable for this purpose:
$ ros2 run image_transport list_transports
Which should show:
Declared transports:
image_transport/raw
Details:
----------
"image_transport/raw"
- Provided by package: image_transport
- Publisher:
This is the default publisher. It publishes the Image as-is on the base topic.
- Subscriber:
This is the default pass-through subscriber for topics of type sensor_msgs/Image.
This the expected output for an otherwise new ROS installation after completing the previous tutorials. Depending on your setup, you may already have "theora" or other transports available.
Our nodes are currently communicating raw sensor_msgs/msg/Image messages, so we are not gaining anything over using rclcpp::Publisher and rclcpp::Subscriber.
Let's change that by introducing a new transport.
The compressed_image_transport package provides plugins for the "compressed" transport, which sends images over the wire in either JPEG- or PNG-compressed form.
Notice that compressed_image_transport is not a dependency of your package; image_transport will automatically discover all transport plugins built in your ROS system.
The easiest way to add the "compressed" transport is to install the package:
$ sudo apt-get install ros-iron-compressed-image-transport
Or install all the transport plugins at once:
$ sudo apt-get install ros-iron-image-transport-plugins
But you can also build from source.
Now let's start up a new subscriber, this one using compressed transport.
The key is that image_transport subscribers check the parameter _image_transport for the name of a transport to use in place of "raw".
Let's set this parameter and start a subscriber node with name "compressed_listener":
$ ros2 run image_transport_tutorials my_subscriber --ros-args --remap __name:=compressed_listener -p image_transport:=compressed
You should see an identical image window pop up.
compressed_listener is listening to a separate topic carrying JPEG-compressed versions of the same images published on /camera/image.
For a particular transport, we may want to tweak settings such as compression level, bit rate, etc.
Transport plugins can expose such settings through ROS parameters.
For example, /camera/image/compressed allows you to change the compression format and quality on the fly; see the package documentation for full details.
For now let's adjust the JPEG quality.
By default, the "compressed" transport uses JPEG compression at 80% quality.
Let's change it to 15%.
We can use the GUI, rqt_reconfigure, to change the quality:
$ ros2 run rqt_reconfigure rqt_reconfigure
Now pick /image_publisher in the drop-down menu and move the jpeg_quality slider down to 15%.
Do you see the compression artifacts in your second view window?
The rqt_reconfigure GUI has updated the ROS parameter /image_publisher/jpeg_quality.
You can verify this by running:
$ ros2 param get /image_publisher jpeg_quality
This should display 15.