This is a ROS2 wrapper for the image detection package, YOLOv5
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This is a ROS2 wrapper for the image detection package, YOLOv5. The main idea is for this container to act as a standalone interface and node, removing the necessity to integrate separate packages and solve numerous dependency issues.
docker pull shaderobotics/yolov5:${ROS2_DISTRO}git pull https://github.com/open-shade/yolov5_ros2.gitcd yolov5_ros2Dockerfile and change the default of ARG ROS_DISTRO=humble on line 1 and 4 to be equal to your desired distro.docker build . -t [name]. This will take a while.Run docker run --gpus all --net=host -t [name]. Your node should be running now. Then, by running ros2 topic list, you should see all the possible pub and sub routes. Careful, the --gpus all flag will only function if you have a CUDA compatible graphics card with CUDA drivers installed and the NVIDIA Container Toolkit installed as well. Here is a good guide to walk you through it if you need more assistance.
For more details explaining how to run Docker images, visit the official Docker documentation here. Also, additional information as to how ROS2 communicates between external environment or multiple docker containers, visit the official ROS2 docs here.
This wrapper utilizes 4 optional parameters to modify the data coming out of the published topics as well as the dataset YOLOv5 utilizes for comparison. Most parameters can be modified during runtime. However, if you wish to use your own dataset, you must pass that parameter in before runtime. If you are unsure how to pass or update parameters before or during runtime, visit the official ROS2 docs here.
The supported, optional parameters are...
| Name | Type | Default | Use |
|---|---|---|---|
| pub_image | Boolean | False | Enable or disable the pub of the processed image |
| pub_json | Boolean | False | Enable or disable the pub of the data in stringified json |
| pub_boxes | Boolean | True | Enable or disable the pub of vision_msgs.msgs DetectionBoxesArray |
| weights_url | String | yolov5m | Download URL for your YOLOv5 training set. Has to be in .pt format. |
You do not need to specify any parameters, unless you wish to modify the defaults.
| Name | IO | Type | Use |
|---|---|---|---|
| yolov5/image_raw | sub | sensor_msgs.msg.Image | Takes the raw camera output to be processed |
| yolov5/image | pub | sensor_msgs.msg.Image | Outputs the processed image with bounding boxes drawn on the image |
| yolov5/json | pub | std_msgs.msg.String | Outputs the detected objects in a frame in stringified json format |
| yolov5/detection_boxes | pub | vision_msgs.msg.Detection2DArray | Outputs the detected bounding box location in a unified format |
To test and ensure that this package is properly installed, replace the Dockerfile in the root of this repo with what exists in the demo folder. Installed in the demo image contains a camera stream emulator by klintan which directly pubs images to the YOLOv5 node and processes it for you to observe the outputs.
To run this, run docker build . -t [name], then docker run -t [name]. Observing the logs for this will show you what is occuring within the container. If you wish to enter the running container and preform other activities, run docker ps, find the id of the running container, then run docker exec -it [containerId] /bin/bash
Content type
Image
Digest
Size
6 GB
Last updated
about 4 years ago
docker pull shaderobotics/yolov5:foxy