Vision Pipeline¶
Vision pipeline utilizes information obtained through camera sensors to select the appropriate action during the disassembly process.
Docker container that offers GPU support and a configured python environment for vision-pipeline and action-predictor.
Usage¶
Set the parameters you want in config.yaml
.
Run example:
python ros_pipeline.py
To enable the pipeline for realsense or basler use:
rosservice call /vision/realsense/enable True
or
rosservice call /vision/basler/enable True
Publishes:
Basler:
/vision/basler/colour
, Image/vision/basler/detections
, ROSDetections/vision/basler/markers
, MarkerArray/vision/basler/poses
, PoseArray
Realsense:
/vision/realsense/colour
, Image/vision/realsense/detections
, ROSDetections/vision/realsense/markers
, MarkerArray/vision/realsense/poses
, PoseArray/vision/realsense/gaps
, ROSGaps/vision/realsense/cluster
, Image/vision/realsense/mask
, Image/vision/realsense/depth
, Image/vision/realsense/lever
, PoseStamped
Services:
/vision/basler/enable
True/False/vision/realsense/enable
True/False/vision/vision_get_detection
VisionDetection.srv (from context_action_framework)
The /vision/vision_get_detection
service provides a single stable detection result from the requested camera.
For example, to get one Basler detection, run:
rosservice call /vision/vision_get_detection 0 False
To get a Realsense detection, run:
rosservice call /vision/vision_get_detection 1 True
where True provides the gaps as well.
** Camera Services:**
rosservice call /basler/set_sleeping
True/Falserosservice call /realsense/enable
True/False