For this setup, to be able to give adequate support if needed, I assume that the reader is somehow familiar with Python and is able to install required Python packages using PIP
I also assume that you are able to install OpenCV on the computer running the analyzer. I did follow this guide:
Furthermore I foresee that we have a common computer available with "decent" performance where the DNN analyze takes place. I have tried running it on a RPi3 and it works but the performance was not satisfying so instead I use an older Lenovo Intel Core i5 laptop now with Debian, 12GB RAM, SSD disk, in all, pretty good performance
1) Sending pictures via MQTT
The Python script, "pic_send_via_mqtt.py", reads a picture file and sends that to a camera specific topic on a MQTT broker. The script must be called with some parameters
- the path to and name of the picture file to be sent
- a camera id to tell the DNN analyzer from which camera the picture was taken
Typically you call the script like this:
python pic_send_via_mqtt.py -p /home/pi/Pictures/picname.jpg -c 32
In my setup I use the great Motion software so this call is simply executed when Motion detects motion...if you use some other software, you have to arrange the call to be made in a way that is supported by that software
Just some words about the camera id; I use numbers for my cameras that are defined in the DNN analyzer script but this can of course be modified. Only thing, you need to modify on both ends if you decide to do it.
This script shall be installed in all entities that will send pictures for analyze. In my case I have a number of distributed RPi3's handling 2 cameras each. So the script is installed in each of them.
The Python script do have some dependencies that are required as well, see the import section of the script
2) DNN analyze
The current functionality of the Python script "centralized_dnn_analyzer.py" (yes, the name can be discussed, change them if you like) subscribes to all camera specific topics on the MQTT broker.
When a picture arrives, the script starts analyzing, looking for objects using MobileNetSSD caffe model. If one or more persons are detected with a required confidence level, a text message and a picture with those persons framed is created and sent to a mobile phone using Telegram (if you plan to use Telegram you need to create an account to get chat id and a token).
To avoid filling up the phone only two pictures (configurable) are sent per event, assuming all and many more pictures taken by the cameras are saved locally for later investigations. Furthermore, pictures sent to the phone are compressed to make them lightweight, still good enough to give a good overview
The script also has some more additional features:
Heartbeat; that is used to check that the script is running. I let NR send the keyword "Heartbeat" every five minute and if the script is running, it responds with an answer that is analyzed. If no answer is given, NR can be configured to do necessary actions
Terminate; to stop the script, I let NR send the key phrase "Stop-DNN-Analyzer" that will terminate the script instead of just killing it
To install it is simple but you will have to install the required Python packages first before the script will run, see the import section of the script. I also recommend you to install and use the MQTT broker on this computer to get best possible performance.
To start the script (it should run all the time) there are several ways. I personally run it as a service using systemd. See this link "how to": https://www.raspberrypi-spy.co.uk/2015/10/how-to-autorun-a-python-script-on-boot-using-systemd/
Or why not install Node-RED in the computer and let it start the script using the Exec node?
The content of the zip file can be extracted to /home/pi, that is how I did it. Then you can copy the files "pic_send_via_mqtt.py" and "mosquitto.py" to each entity that shall send pictures.
(I had to provide an external link since uploading zip-files here is not allowed)
We will see if this works out well or if I have missed mention something...