Johan Sandoval
Published

Automatic Cereal Dispenser With Facial Recognition

A device that automatically dispenses cereal when a face is recognized on a Raspberry Pi camera running OpenCV.

IntermediateFull instructions provided3 hours1,993
Automatic Cereal Dispenser With Facial Recognition

Things used in this project

Hardware components

Raspberry Pi 2 Model B
Raspberry Pi 2 Model B
×1
Camera Module
Raspberry Pi Camera Module
×1
5V DC Stepper Motor with ULN2003 Driver Board
×4
Jumper wires (generic)
Jumper wires (generic)
×24
GPIO Ribbon Cable with Breakout board
×1
Plastic Containers
×4
Foam Disk
×4
Wood or Plastic Dowels
×1

Software apps and online services

Raspbian
Raspberry Pi Raspbian
Thonny Python IDE

Hand tools and fabrication machines

Wire Stripper / Cutter, Steel
Wire Stripper / Cutter, Steel
Scissor, Electrician
Scissor, Electrician
Drill / Driver, Cordless
Drill / Driver, Cordless
Tape, Electrical
Tape, Electrical
Tape, Clear
Tape, Clear

Story

Read more

Schematics

Wiring Diagram

Raspberry Pi With Stepper Motors

Schematic

Detailed Schematic of Electronics

Diagram and Schematic File

Fritzing file for wiring diagram and schematic

Code

face_shot.py

Python
Pops open a window that will access the camera and allow the user to take photos by holding down the spacebar which will go to a folder with their respective name in data set that will be used to train an algorithm to correctly identify the users face.
#Import packages
import cv2

name = 'Unknown' #Replace with your name

cam = cv2.VideoCapture(0)

cv2.namedWindow("press space to take a photo", cv2.WINDOW_NORMAL)
cv2.resizeWindow("press space to take a photo", 500, 300)

img_counter = 0

while True:
    ret, frame = cam.read()
    if not ret:
        print("failed to grab frame")
        break
    cv2.imshow("press space to take a photo", frame)

    k = cv2.waitKey(1)
    if k%256 == 27:
        # ESC pressed
        print("Escape hit, closing")
        break
    elif k%256 == 32:
        # SPACE pressed
        img_name = "dataset/"+ name +"/image_{}.jpg".format(img_counter)
        cv2.imwrite(img_name, frame)
        print("{} written!".format(img_name))
        img_counter += 1

cam.release()

cv2.destroyAllWindows()

train_model.py

Python
An algorithm that accesses all photos in the dataset folder and trains a model to identify between them
#Import  packages
from imutils import paths
import face_recognition
import pickle
import cv2
import os

#Our images are located in the dataset folder
print("[INFO] start processing faces")
imagePaths = list(paths.list_images("dataset"))

#Initialize the list of known encodings and known names
knownEncodings = []
knownNames = []

#Loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
	#Extract the person name from the image path
	print("[INFO] processing image {}/{}".format(i + 1,
		len(imagePaths)))
	name = imagePath.split(os.path.sep)[-2]

	#Load the input image and convert it from RGB (OpenCV ordering) to dlib ordering (RGB)
	image = cv2.imread(imagePath)
	rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

	#Detect the (x, y)-coordinates of the bounding boxes corresponding to each face in the input image
	boxes = face_recognition.face_locations(rgb,
		model="hog")

	#Compute the facial embedding for the face
	encodings = face_recognition.face_encodings(rgb, boxes)

	#Loop over the encodings
	for encoding in encodings:
		#Add each encoding + name to our set of known names and encodings
		knownEncodings.append(encoding)
		knownNames.append(name)

#Dump the facial encodings + names to disk
print("[INFO] serializing encodings")
data = {"encodings": knownEncodings, "names": knownNames}
f = open("encodings.pickle", "wb")
f.write(pickle.dumps(data))
f.close()

haarcascade_frontalface_default.xml

XML
Open source OpenCV model that is used to train the model of the AI
<?xml version="1.0"?>
<!--
    Stump-based 24x24 discrete(?) adaboost frontal face detector.
    Created by Rainer Lienhart.

////////////////////////////////////////////////////////////////////////////////////////

  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.

  By downloading, copying, installing or using the software you agree to this license.
  If you do not agree to this license, do not download, install,
  copy or use the software.


                        Intel License Agreement
                For Open Source Computer Vision Library

 Copyright (C) 2000, Intel Corporation, all rights reserved.
 Third party copyrights are property of their respective owners.

 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:

   * Redistribution's of source code must retain the above copyright notice,
     this list of conditions and the following disclaimer.

   * Redistribution's in binary form must reproduce the above copyright notice,
     this list of conditions and the following disclaimer in the documentation
     and/or other materials provided with the distribution.

   * The name of Intel Corporation may not be used to endorse or promote products
     derived from this software without specific prior written permission.

 This software is provided by the copyright holders and contributors "as is" and
 any express or implied warranties, including, but not limited to, the implied
 warranties of merchantability and fitness for a particular purpose are disclaimed.
 In no event shall the Intel Corporation or contributors be liable for any direct,
 indirect, incidental, special, exemplary, or consequential damages
 (including, but not limited to, procurement of substitute goods or services;
 loss of use, data, or profits; or business interruption) however caused
 and on any theory of liability, whether in contract, strict liability,
 or tort (including negligence or otherwise) arising in any way out of
 the use of this software, even if advised of the possibility of such damage.
-->
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This file has been truncated, please download it to see its full contents.

step.py

Python
A function to control the motors that is passed a name from face_rec.py which allows the correct motor to open in order for the correct cereal to be dropped
def steppers(name):
    #Import  packages
    import RPi.GPIO as GPIO
    import time
    
    #Set the board to refernce GPIO numbers rather than pins
    GPIO.setmode(GPIO.BCM)
    
    #Initialize GPIO pins of forawrd and backwards sequences to open and close doors
    ControlPin1 = [12, 16, 19, 21]
    ControlPin10 = [21, 19, 16, 12]
    ControlPin2 = [18, 23, 24, 25]
    ControlPin20 = [25, 24, 23, 18]
    ControlPin3 = [4, 17, 27, 22]
    ControlPin30 = [22, 27, 17, 4]
    ControlPin4 = [5, 6, 13, 26]
    ControlPin40 = [26, 13, 6, 5]
    
    #Set the arrays to a list to make it easier to initialize them
    pins = [ControlPin1, ControlPin2, ControlPin3, ControlPin4]
    #Disable warnings due to Pi using a breadboard to wire and not directly wired
    GPIO.setwarnings(False)

    #Initialize pins and initial values
    for controls in pins:
        for pin in controls:
            GPIO.setup(pin, GPIO.OUT)
            GPIO.output(pin, 0)

    #Sequence for the stepper motor array
    seq = [ [1, 0, 0, 0],
            [1, 1, 0, 0],
            [0, 1, 0, 0],
            [0, 1, 1, 0],
            [0, 0, 1, 0],
            [0, 0, 1, 1],
            [0, 0, 0, 1],
            [1, 0, 0, 1] ]
    
    def openMotor1():
        #Opens motor by looping through the GPIO pins as well as the stepper motor sequence
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin4[pin], seq[halfstep][pin])
                #Speed of the action
                time.sleep(0.001)
                
        #Time to allow the door to stay open        
        time.sleep(0.005)  
        
        #Closes motor by looping through the reverse GPIO pins as well as the stepper motor sequence
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin40[pin], seq[halfstep][pin])
                time.sleep(0.001)
    
    def openMotor2():
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin3[pin], seq[halfstep][pin])
                time.sleep(0.001)
                
        time.sleep(0.01)  
            
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin30[pin], seq[halfstep][pin])
                time.sleep(0.001)
                
    def openMotor3():
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin2[pin], seq[halfstep][pin])
                time.sleep(0.0009)
                
        time.sleep(0.005)  
            
        for i in range(60):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin20[pin], seq[halfstep][pin])
                time.sleep(0.0009)
                
    def openMotor4():
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin1[pin], seq[halfstep][pin])
                time.sleep(0.001)
                
        time.sleep(0.005)  
            
        for i in range(40):
            for halfstep in range(8):
                for pin in range(4):
                    GPIO.output(ControlPin10[pin], seq[halfstep][pin])
                time.sleep(0.001)
               
    #If statement for given names in file to open specific motors depending on the person detected           
    if name == "Unknown":
        openMotor1()

    GPIO.cleanup()

face_rec.py

Python
The main program that will be used when testing and using the device. This programs opens a video stream of the users camera and will run the step.py program once a match is identified to one of the people in the dataset folder.
#Import packages
from imutils.video import VideoStream
from imutils.video import FPS
from step import steppers
import face_recognition
import imutils
import pickle
import time
import cv2

#Initialize 'currentname' to trigger only when a new person is identified.
currentname = "unknown"
#Determine faces from encodings.pickle file model created from train_model.py
encodingsP = "encodings.pickle"
cascade = "haarcascade_frontalface_default.xml"

#load the known faces and embeddings along with OpenCV's Haar cascade for face detection
print("[INFO] loading encodings + face detector")
data = pickle.loads(open(encodingsP, "rb").read())
detector = cv2.CascadeClassifier(cascade)

#Initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream")
#vs = VideoStream(src=0).start()
vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)

#Start the FPS counter
fps = FPS().start()

#Loop over frames from the video file stream
while True:
	#Grab the frame from the threaded video stream and resize it to 500px (to speedup processing)
	frame = vs.read()
	frame = imutils.resize(frame, width=500)
	
	#Convert the input frame from (1) BGR to grayscale (for face
	#Detection) and (2) from BGR to RGB (for face recognition)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	#Detect faces in the grayscale frame
	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
		minNeighbors=5, minSize=(30, 30),
		flags=cv2.CASCADE_SCALE_IMAGE)

	#OpenCV returns bounding box coordinates in (x, y, w, h) order but we need them in (top, right, bottom, left) order, so we need to do a bit of reordering
	boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]

	#Compute the facial embeddings for each face bounding box
	encodings = face_recognition.face_encodings(rgb, boxes)
	names = []

	#Loop over the facial embeddings
	for encoding in encodings:
		#Attempt to match each face in the input image to our known encodings
		matches = face_recognition.compare_faces(data["encodings"], encoding)
		name = "Unknown" #if face is not recognized, then print Unknown

		#Check to see if we have found a match
		if True in matches:
			#Find the indexes of all matched faces then initialize a dictionary to count the total number of times each face was matched
			matchedIdxs = [i for (i, b) in enumerate(matches) if b]
			counts = {}

			#Loop over the matched indexes and maintain a count for each recognized face face
			for i in matchedIdxs:
				name = data["names"][i]
				counts[name] = counts.get(name, 0) + 1

			#Determine the recognized face with the largest number of votes (note: in the event of an unlikely tie Python will select first entry in the dictionary)
			name = max(counts, key=counts.get)
			
			#If someone in your dataset is identified, print their name on the screen and send the name to step.py to carry out specific order
			if currentname != name:
				currentname = name
				print(currentname)
				steppers(currentname)
		#Update the list of names
		names.append(name)

	#Loop over the recognized faces
	for ((top, right, bottom, left), name) in zip(boxes, names):
		#Draw the predicted face name on the image  color is in BGR
		cv2.rectangle(frame, (left, top), (right, bottom),
			(0, 255, 0), 2)
		y = top - 15 if top - 15 > 15 else top + 15
		cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
			.8, (255, 0, 0), 2)

	#Display the image to our screen
	cv2.imshow("Facial Recognition is Running", frame)
	key = cv2.waitKey(1) & 0xFF

	#Quit when 'q' key is pressed
	if key == ord("q"):
		break

	#Update the FPS counter
	fps.update()

#Stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

cv2.destroyAllWindows()
vs.stop()

Credits

Johan Sandoval

Johan Sandoval

3 projects β€’ 4 followers
Thanks to Suad Anwar.

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