arsh09/rasberry_perception

General purpose ROS package for using deep learning/object detection frameworks on robots

rasberry_perception

The rasberry_perception package aims to interleave ROS and deep learning frameworks for perception. If using any of the models in research please contact Raymond Kirk to obtain the relevant citation and ensure no conflict of interest.

Quick start

roslaunch rasberry_perception detector.launch backend:="detectron2" password:="obtain_from_raymond" image_ns:="/your_camera/colour" depth_ns:="/your_camera/depth" score:="0.5"

Installation

Cuda 10.2 must be installed locally to run gpu based backends.

cd catkin_ws/src
git clone https://github.com/RaymondKirk/rasberry_perception
catkin build rasberry_perception

Detection Backends

Modular detection backends are available in rasberry_perception enabling users to utilise deep learning frameworks/non-ros methods to detect objects.

You can try to launch both the backend and detector with the command below:

# Run together (will download the backend from docker_hub if it exists)
roslaunch rasberry_perception detector.launch colour_ns:="" depth_ns:="" score:="" show_vis:="" backend:="" backend_arg1:=""

# Or run separately! (Will use a local installation of the backend if available)
rosrun rasberry_perception detection_server.py backend:="" backend_arg1:=""
roslaunch rasberry_perception detector.launch colour_ns:='' depth_ns:='' score:=''

Adding a new detection backend

Adding custom backends such as TensorFlow, PyTorch, Detectron, Onnx etc. to rasberry_perception is easy. See interfaces for examples.

A simple example given in four steps, register the name in the detection registry with the class decorator (1), inherit from the base (2), implement the service call logic (3) and finally add to the __all__ definition here (4).

import ros_numpy
from rasberry_perception.interfaces.default import BaseDetectionServer
from rasberry_perception.msg import Detections, ServiceStatus

@DETECTION_REGISTRY.register_detection_backend("CustomBackendName")  # (1)
class CustomVisionBackend(BaseDetectionServer):  # (2)
    # These args are passed from ros parameters when running the backend
    def __init__(self, custom_arg1, custom_arg2, default_arg1="hello"): 
        # Do your imports here i.e import image_to_results_function
        # Do initialisation code here
        self.busy = False 
        BaseDetectionServer.__init__(self)  # Spins the server and waits for requests!

    def get_detector_results(self, request):  # (3)
        if self.busy:  # Example of other status responses
            return GetDetectorResultsResponse(status=ServiceStatus(BUSY=True))
        # Populate a detections message
        detections = Detections()
        # i.e. detections = image_to_results_function(image=ros_numpy.numpify(request.image))
        return GetDetectorResultsResponse(status=ServiceStatus(OKAY=True), results=detections)

When launching the detection server via rosrun or roslaunch you can pass in arguments to your custom backend as you would usually. The node will fail if you do not pass any non-default arguments such as custom_arg1 and custom_arg2 in the example.

rosrun rasberry_perception detection_server.py  backend:="CustomBackendName" _custom_arg1:="a1" _custom_arg2:="a2" _default_arg1"="world"