itspa1 / shop-analytics-pi

Raspberry-pi script to capture in-store people data and send them to a server

Shop-Analytics-Pi

This can run multiple types of collection of people data

• By MAC address identification i.e Capturing probe-requests nearby
• By Camera using people detection

How Each of them works

• Capturing probe-requests
  • This is done by either putting the wifi-card of the device on monitor mode and running a tcpdump with appropriate flags.
  • If the native wifi-card does not support monitor-mode, you could plug an esp8266 microcontroller to run as a wifi-sniffer.
• People Detection
  • Several models can be run using the tf module present in this, you just need to swap out the appropriate tensorflow models by downloading from their Github repo.
  • YOLO is also supported to run on this by using the native CV2 library.

Steps to Run this on a raspberry pi

• Ensure the pi's wifi card supports monitor mode (if using Native Sniffer module)

• Clone this repo onto the raspberry pi

• add gitignored files

  • env.json

  {
  "main": {
    "MQTT_USERNAME": "peekay",
    "MQTT_PASSWORD": "peekay",
    "MQTT_HOST": "localhost",
    "MQTT_PORT": 1883,
    "MQTT_TOPICS": ["remote_access"],
    "BUGSNAG_KEY": "abc",
    "PUBLISH_TOPIC": "frame_topic" //vary for different modules
  },
  "MODULE": "camera",
  "SUBMODULE": "yolo",
  "camera": {
    "MODEL_PATH": "./models/yolov3-tiny",
    "NMS_SUPPRESSION_PROBABILITY": 0.1,
    "MINIMUM_THRESHOLD": 0.6,
    "VIDEO_SOURCE": 0,
    "WIDTH": 416,
    "HEIGHT": 416,
    "THRESHOLD": 0.7
  },
  "wifi": {
    "REFRESH_INTERVAL": 5,
    "SERIAL_PATH": "/dev/ttyUSB0",
    "BAUD_RATE": 115200
  }
  }
  
  

• run pip3 install -r requirements.txt to install all the required modules for this repo

• P.S requirements.txt were generated using pipreqs, check the requirements once to ensure everything is setup properly

• while deploying add this to the crontab of the raspberry pi @reboot sleep 10 && /bin/bash /home/pi/pi-sniffer/startup.sh