This is a ROS2 wrapper for Pre-Training BERT for Image Transformers with an image segmentation head, BEiT. We utilize huggingface and the transformers for the source of the algorithm. 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.
- Install Docker and ensure the Docker daemon is running in the background.
- Run
docker pull shaderobotics/beit-segmentation:${ROS2_DISTRO}-${MODEL_VERSION}we support all ROS2 distributions along with all model versions found in the model version section below. - Follow the run commands in the usage section below
- Install Docker and ensure the Docker daemon is running in the background.
- Clone this repo with
git pull https://github.com/open-shade/beit_segmentation.git - Enter the repo with
cd segformer_seg - To pick a specific model version, edit the
ALGO_VERSIONconstant in/segformer_seg/segformer_seg.py - Build the container with
docker build . -t [name]. This will take a while. We have also provided associatedcloudbuild.shscripts to build on GCP all of the associated versions. - Follow the run commands in the usage section below.
base-finetuned-ade-640-640large-finetuned-ade-640-640
More information about these versions can be found in the paper. Size of the model increases with the number.
docker pull shaderobotics/beit-segmentation:foxy-base-finetuned-ade-640-640This wrapper utilizes 4 optional parameters to modify the data coming out of the published topics as well as the dataset YOLOS 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 | True | Enable or disable the pub of the processed image (with bounding boxes) |
| pub_pixels | Boolean | True | Enable or disable the pub of the pixels with associated classification IDs (8-bit image stream) |
| pub_detections | Boolean | True | Enable or disable the publishing of detections (whether or not to send back a string with all detections found) |
| pub_masks | Boolean | True | Enable or disable the publishing of masks (whether or not to send back a string with all detections found) |
You do not need to specify any parameters, unless you wish to modify the defaults.
| Name | IO | Type | Use |
|---|---|---|---|
| beit/image_raw | sub | sensor_msgs.msg.Image | Takes the raw camera output to be processed |
| beit/image | pub | sensor_msgs.msg.Image | Outputs the processed image with segmentation on top of the image |
| beit/pixels | pub | sensor_msgs.msg.Image | Outputs each pixel classified with the associated class ID as an 8-bit stream |
| beit/detections | pub | std_msgs.msg.String | Outputs all detected classes in the image |
| beit/masks | pub | sensor_msgs.msg.Image | Outputs the masks all in one image colorized based on class |
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 BEiT node and processes it for you to observe the outputs.
To run this, run docker build . -t --net=host [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