Intelligent Door Lock

The boolean variable “lock” is still in effect for this code. The code uses those true and false values to determine which functions to run. It will either open the door or close it. If the button is pressed and the variable is true, the camera will take a picture and send it to me via a fake email account called [email protected]. It saves the picture as Chris.jpg and runs facematch.py on it using the facematch() function. If it matches a picture I have uploaded to the bucket, the lock will open. If not, it will stay locked. Once the door unlocks with the OpenDoor() function, the variable “lock” is set to true like in the last milestone. Pressing the button again will lock the door.

Finishing this project is truly incredible to me. I essentially designed a home security system using a Raspberry Pi, Python code, and a 3-D printed lock. There are countless possible modifications to be made such as adding a fingerprint scanner and a keypad. This project is, at it’s core, completed, but there’s always more to do.

I used 3D-printed parts from a design I found on Thingiverse.com. https://www.thingiverse.com/thing:465349

I used metal wire to attach the micro servo to the container part and then screwed the container to the base. I then but the gear onto the micro servo which fits comfortably. Then I did some research on the Raspberry Pi’s GPIO pins, specifically how to connect them with jumper cables to send a signal to the servo. Here’s the result of using the code “pinout” on the Linux Terminal.

This is how I was able to know which pins were the right ones. Here’s the jumper cable setup on my Raspberry Pi.

First a cable had to be connected from a GPIO pin (I chose GPIO 2). The cable runs to a breadboard pinhole and sends a signal to a button. Another cable runs from the button back to a Ground pin on the Raspberry Pi. Then, 3 cables are connected between the Raspberry Pi to the micro servo. There are 3 different colored wires on the micro servo: Brown, Red, and Orange. The Brown wire connects to another Ground pin on the Raspberry Pi. The Red wire connects to a 5V pin that sends power to the servo. The orange wire connects to another GPIO pin (I chose GPIO27) and sends a signal to the servo to open and close the lock. The micro servo works by moving its angle to a certain position when I want to open and close the lock. Here’s a description of the code I used.

This function allows me to change the angle of the servo’s gears. Here’s the OpenDoor() function:

It sets the angle of the servo to 0. Here’s the CloseDoor() function:

Similarly, to the open door function, it sets the angle to 90 to close the door. To get these functions to work at the right time, I created a global Boolean variable called “lock.” Boolean means that the value is either true or false. When it’s true and someone hits the button, the door is locked and the code goes through its process of taking the picture and choosing whether to unlock the door or keep it locked. At the end of the code, lock becomes false. When the button is pressed and lock is false, the Raspberry Pi just sends a signal to the servo to lock the door. Here is how the code runs:

When I press the button, the Linux Terminal prints the words “Unlocking” and then the lock unlocks or prints “Locking” and the lock locks.

Getting to this point is super cool for me. I’m really proud of all the work I’ve done so far. The biggest struggle of this milestone was definitely putting the lock together. Moving the big gear wouldn’t move the gears inside of the servo. It turns out the screws in the 3D-printed door-lock were just too tight. At this point I have used the camera and recognized my face and now I’ve opened and closed the lock. Now I have to combine it all into one code and I’ll have a functioning intelligent door lock.

First, I installed NOOBS to the Raspberry Pi’s Micro SD card. NOOBS is a zip file that includes software for setting up Raspberry Pi, including the Raspbian operating system. I plugged a monitor, keyboard, and mouse into the Raspberry Pi, creating a mini-computer with about 1 GB of data storage. Once I installed the Raspbian Operating System, I opened up the Terminal and installed Python with the code: “Sudo apt-get install python-serial”. Terminal is a window which allows users to input commands that directly manipulate their Raspberry Pi’s system. https://www.raspberrypi.org/documentation/usage/terminal/. Python is one of various coding languages that can interact with the Raspberry Pi. Most code for the camera and the Pi itself comes from Python files. Having the prefix “sudo” which stands for “super user do” gives the code certain elevated privileges to perform certain administrative tasks such as listing the objects in a file and shutting down the system. https://www.computerhope.com/unix/sudo.htm. I used the website codeacademy.com to learn the basics of Python. Using codeacademy.com, I learned how to print out words based on specific criteria and even how to write my own Mad Libs. I plugged in the Raspberry Pi’s camera module and researched how to set it up. I enabled the camera’s software inside of the system’s preferences. Then I opened up a new Python file and typed in this code:

This code calls the camera’s start_preview function and delays the camera using the sleep function for 10 seconds, and then calls the stop_preview function to close the window. The monitor displays everything the camera sees for those 10 seconds. “From picamera import PiCamera” is necessary code for every time the camera module is being used. It takes a class called PiCamera (capitalized) from a library called picamera (not capitalized). A library is a collection of code. A class is used to create an object and gives an object its variables and functions. https://www.learnpython.org/en/Classes_and_Objects. In this case, PiCamera() is a new object associated with the variable “camera” which allows you to open and close this preview on the monitor. Then I inputted the following Python code into a new Python file:

Like the last code, this one opens up the preview on the monitor. After 5 seconds, it takes a picture and then closes the preview. The picture is saved as “image.jpg” which is saved to the desktop folder inside of the Pi folder. Eventually, the camera module successfully takes pictures which will later be stored on Amazon and each face’s features will be recognized using the Amazon Rekognition software. 

Picture 1: Raspberry Pi computer with monitor, keyboard, and mouse

Picture 2: Installing Raspbian

Picture 3: Raspberry Pi Camera Module