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ros-semantic-segmentation

A generalized semantic segmentation package for ROS that is agnostic to deep learning framework and model.

Three models are provided. All are extremely lightweight, fast models so they can be included inside the repo without asking you to download some zip file from DropBox. They are not the most accurate models. You can implement your own following the examples.

TensorFlow models

• mnv2_bdd100k_driveable_513 -- TensorFlow >= 1.11, Deeplab V3+ on a MobileNet v2 backbone, trained on BDD100K driveable area, 513x513 input size.
• mnv2_coco2017_driving -- TensorFlow >= 1.11, Deeplab V3+ on a subset of 13 COCO2017 classes related to driving
• mnv2_dm05_voc -- TensorFlow >= 1.11, Deeplab V3+ on VOC 2012 classes (pretrained Google model mnv2_dm05_coco_voc_trainaug).

PyTorch models

• espnetv2_bdd100k_driveable -- PyTorch, ESPNETv2 on BDD100K driveable area, 1024x512 input size, scale 1.0.

To implement another model, you can follow these examples. You need to create a new directory under models and have a class called Model inside init.py that implements infer().

Try it

rosrun semantic_segmentation segmentation_node __ns:=/camera

Parameters:

• model (string) -- name of the model to use. Defaults to "mnv2_bdd100k_driveable_513".
• rate (float) -- the maximum frame rate to run inferences. Default to 30.0. Note that if your system is too slow, it will run at the maximum speed possible while dropping frames.
• topic_image (string) -- topic to listen for images. Defaults to "image_raw".
• topic_semantic (string) -- topic to output semantic predictions. Defaults to "semantic". Outputs a mono8 image indicating semantic classes at each pixel.
• topic_semantic_color (string) -- topic to output a colored RGB version of the semantic predictions for visualization purposes. Defaults to "semantic_color". Outputs a rgb8 image.

When the node is initialized, it will set an additional ROS parameter semantic_categories as is defined in the chose model. This parameter can be read by other nodes to know which IDs correspond to which classes.

Subscribers:

• image_raw (sensor_msgs/Image)

Publishers:

• semantic (sensor_msgs/Image)
• semantic_color (sensor_msgs/Image)

Disclaimer

This is not intended to be used for production autonomous vehicles. This is provided "as-is" for educational purposes. I am not liable for any damage or injury that may result from the use of this software.