Raspberry Pi Smart Camera
This project is an ongoing work under Anusua Trivedi, Data Scientist Sr. at Microsoft. Anusua is a mentor to Isha Chakraborty & Neelagreev Griddalur, who are the contributors to this project.
The Raspberry Pi Smart Camera is a platform that allows one to perform object recognition using a Raspberry Pi. This software implements the Microsoft Computer Vision API and the Microsoft Bing Speech API.
For more information on Microsoft Computer Vision APIs and the Microsoft Bing Speech APIs, see the Microsoft Computer Vision API Documentation and Microsoft Bing Speech APIs on docs.microsoft.com.
Inspiration for this project came from Charles Channon’s article at https://www.hackster.io/cchannon/raspberry-pi-smart-camera-a8c786.
Clone the sources: git clone https://github.com/Pi-Smart-Camera
This is a Python application to demonstrate the uses and integrations of the Computer Vision APIs and Bing Speech APIs.
This repository contains all of the necessary files and dependencies excluding Python needed to build this project and use it yourself. A drone is needed for recreating this project, a Parrot AR Drone 2.0 Power Edition Quadricopter is recommended for best compatibility.
Run “pip install git+https://github.com/westparkcom/Python-Bing-TTS.git”
Running the Project
When testing out the LCD libraries, enter the following in your terminal window.
sudo apt-get update
sudo apt-get install build-essential python-dev python-smbus python-pip git
sudo pip install RPi.GPIO
git clone https://github.com/adafruit/Adafruit_Python_CharLCD.git
sudo python setup.py install
sudo python char_lcd_plate.py
Enabling the i2c bus and the camera can be done from the control panel on the screen on the pi, and checking to see whether it has been enabled can be done by entering "lsmod | grep i2c_" into the Linux terminal and looking for “i2c 6780” at the bottom of the text generated.
Also, enter “sudo crontab -e” in the terminal window and then “@reboot sleep 30 ; sudo python /home/pi/ComputerVision.py &” after all of the commented lines. This allowed the automatic enabling of the app to execute when starting up
Run the command, “./start.sh” to run the shell file and follow the instructions present on your terminal window by entering the credentials of your desired network and reconnect to it after completion.
Open a second terminal window In the window, run “./object_detect.sh” to take the picture. This will run the ComputerVision.py file to identify the object.