#Methodology

In the RAPP case, the human detection functionality is implemented in the form of a C++ developed ROS node, interfaced by a Web service. The Web service is invoked using the RAPP API and gets an RGB image as input, in which humans has to be checked. The second step is for the HOP service to locally save the input image. At the same time, the hazard_detection ROS node is executed in the background, waiting to server requests. The Web service calls the ROS service via the ROS Bridge, the ROS node make the necessary computations and a response is delivered.

#ROS Services

Human detection

Service URL: /rapp/rapp_hazard_detection/light_check

Service type: ```bash

Contains info about time and reference

Header header

The image's filename to perform light checking

string imageFilename

List of bounding box borders, where the humans were detected

geometry_msgs/PointStamped[] humans_up_left geometry_msgs/PointStamped[] humans_down_right

Possible error

string error ```

#Launchers

Standard launcher

Launches the human_detection node and can be launched using ``` roslaunch rapp_human_detection human_detection.launch ```

#Web services

Human detection

URL

localhost:9001/hop/human_detection

Input / Output

``` Input = { "file": “THE_ACTUAL_IMAGE_DATA” } Output = { "humans": [{ "up_left_point": {x: 0, y: 0}, "down_right_point": {x: 0, y: 0} }] } ```

The full documentation exists here