Overview

This document provides a step by step guide to integrating the RTSP output from a Meraki MV camera, with a pretrained OpenCV 3.3 dnn module, running SSD Detection on the COCO dataset.

The script shall:

launch a local video stream, with a bounding box for detected objects and associated confidence score
print to cli the object and confidence score of detected objects
publish to an MQTT broker the object and confidence of objects detected, using the topic "python/test"

This was developed and tested using a Macbook Pro. The detection time for objects entering the frame to being reported on the video and MQTT stream is of the order 1.5-2.0 seconds.

Dependencies

Python

This script was develeoped using python version 3.7.3

Meraki Camera Requirements

Firmware version 4.2 or newer
2nd generation MV camera
For further details:
https://documentation.meraki.com/MV/Advanced_Configuration/External_RTSP

Steps

Installation

Create a Virtual Environment:

$virtualenv venv

Activate the virtual Environment:

$source venv/bin/activate

Clone the Repo:

$git clone https://github.com/andersm9/Meraki_OpenCV_RTSP.git
$cd Meraki_OpenCV_RTSP

Install the requirements:

$pip3 install -r requirements.txt

Dashboard configuration:

Cameras -> <your_camera> -> Setting -> External RTSP -> yes

Make a note of your RTSP stream URL

Create a file "credentials.ini" with the following contents:

[camera]

#RTSP source

host = **rtsp://<camera IP>/live**

#e.g.host = rtsp://192.168.127.29:9000/live

[mqtt]

broker = **mqtt broker location**

#e.g broker = broker.hivemq.com

port = **mqtt broker port**

#e.g. port = 1883

[ssd]

#set location of SSD model (preconfigured for this repo)

prototxt = MobileNetSSD_deploy.prototxt.txt

model = MobileNetSSD_deploy.caffemodel

#set confidence level for detection

conf = **0-1**

#e.g for 20% confidence or above for detection

#conf = 0.2

Launch the script:

$python3 MV_OpenCV.py

Meraki_OpenCV_RTSP