Ouverture de la maison (Servomoteur)⚓

Si la "bonne" personne est détecté, on commande l'ouverture de sa maison.

On va se servir d'un servomoteur pour notre maquette, représentant le déverrouillage de la maison si la personne est reconnue devant sa maison.

Schéma de connexion

Le Servo moteur possède 3 fils :

  • Le fil rouge : +5V;

  • Le fil noir : GND, 0V;

  • Le fil blanc/jaune : fil de data, où les ordres circulent du Raspberry vers le Servo.

Pour alimenter le Servo, il est préférable d'utiliser une alimentation externe de 5V, surtout si l'alimentation de votre Raspberry délivre peu de courant.

Voici la description des broches du Raspberry Pi 3.

Exemple de code.

Tester ce code :

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import RPi.GPIO as GPIO
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import time
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GPIO.setmode(GPIO.BCM)
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GPIO.setup(17, GPIO.OUT)
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GPIO.setwarnings(False)
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ajoutAngle = 5
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print("\n+----------/ ServoMoteur  Controlleur /----------+")
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print("|                                                |")
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print("| Le Servo doit etre branche au pin 11 / GPIO 17 |")
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print("|                                                |")
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print("+------------------------------------------------+\n")
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print("Comment controler le Servo ?")
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choix = int(input("1. Choisir un angle\n2. Faire tourner de 0 a 180\n"))
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if (choix == 2) :
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    nbrTour = int(input("Entrez le nombre d'aller-retour que fera le Servo :\n"))
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    pwm=GPIO.PWM(17,100)
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    pwm.start(5)
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    angle1 = 0
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    duty1 = float(angle1)/10 + ajoutAngle
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    angle2=180
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    duty2= float(angle2)/10 + ajoutAngle
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    i = 0
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    while i <= nbrTour:
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         pwm.ChangeDutyCycle(duty1)
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         time.sleep(0.8)
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         pwm.ChangeDutyCycle(duty2)
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         time.sleep(0.8)
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         i = i+1
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    GPIO.cleanup()
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if (choix == 1) :
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    angle = float(input("Entrez l'angle souhaite :\n"))
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    duree = int(input("Entrez la duree durant laquelle le Servo devra tenir sa position : ( en secondes )\n"))
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    pwm=GPIO.PWM(17,100)
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    pwm.start(5)
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    angleChoisi = angle/10 + ajoutAngle
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    pwm.ChangeDutyCycle(angleChoisi)
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    time.sleep(duree)
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    GPIO.cleanup()
Question⚓
Réaliser le montage c-dessus.
Question⚓
Combiner les 2 codes (reconnaissance faciale et servomoteur pour remplir l'objectif fixée.
Code de base de la reconnaissance faciale
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# USAGE
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# python pi_face_recognition.py --cascade haarcascade_frontalface_default.xml --encodings encodings.pickle
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# import the necessary packages
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from imutils.video import VideoStream
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from imutils.video import FPS
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import face_recognition
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import argparse
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import imutils
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import pickle
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import time
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import cv2
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-c", "--cascade", required=True,
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	help = "path to where the face cascade resides")
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ap.add_argument("-e", "--encodings", required=True,
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	help="path to serialized db of facial encodings")
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args = vars(ap.parse_args())
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# load the known faces and embeddings along with OpenCV's Haar
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# cascade for face detection
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print("[INFO] loading encodings + face detector...")
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data = pickle.loads(open(args["encodings"], "rb").read())
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detector = cv2.CascadeClassifier(args["cascade"])
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# initialize the video stream and allow the camera sensor to warm up
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print("[INFO] starting video stream...")
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vs = VideoStream(src=0).start()
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# vs = VideoStream(usePiCamera=True).start()
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time.sleep(2.0)
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# start the FPS counter
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fps = FPS().start()
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# loop over frames from the video file stream
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while True:
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	# grab the frame from the threaded video stream and resize it
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	# to 500px (to speedup processing)
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	frame = vs.read()
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	frame = imutils.resize(frame, width=500)
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	# convert the input frame from (1) BGR to grayscale (for face
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	# detection) and (2) from BGR to RGB (for face recognition)
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	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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	# detect faces in the grayscale frame
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	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
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		minNeighbors=5, minSize=(30, 30),
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		flags=cv2.CASCADE_SCALE_IMAGE)
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	# OpenCV returns bounding box coordinates in (x, y, w, h) order
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	# but we need them in (top, right, bottom, left) order, so we
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	# need to do a bit of reordering
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	boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]
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	# compute the facial embeddings for each face bounding box
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	encodings = face_recognition.face_encodings(rgb, boxes)
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	names = []
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	# loop over the facial embeddings
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	for encoding in encodings:
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		# attempt to match each face in the input image to our known
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		# encodings
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		matches = face_recognition.compare_faces(data["encodings"],
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			encoding)
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		name = "Unknown"
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		# check to see if we have found a match
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		if True in matches:
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			# find the indexes of all matched faces then initialize a
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			# dictionary to count the total number of times each face
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			# was matched
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			matchedIdxs = [i for (i, b) in enumerate(matches) if b]
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			counts = {}
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			# loop over the matched indexes and maintain a count for
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			# each recognized face face
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			for i in matchedIdxs:
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				name = data["names"][i]
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				counts[name] = counts.get(name, 0) + 1
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			# determine the recognized face with the largest number
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			# of votes (note: in the event of an unlikely tie Python
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			# will select first entry in the dictionary)
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			name = max(counts, key=counts.get)
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		# update the list of names
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		names.append(name)
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	# loop over the recognized faces
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	for ((top, right, bottom, left), name) in zip(boxes, names):
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		# draw the predicted face name on the image
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		cv2.rectangle(frame, (left, top), (right, bottom),
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			(0, 255, 0), 2)
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		y = top - 15 if top - 15 > 15 else top + 15
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		cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
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			0.75, (0, 255, 0), 2)
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	# display the image to our screen
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	cv2.imshow("Frame", frame)
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	key = cv2.waitKey(1) & 0xFF
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	# if the `q` key was pressed, break from the loop
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	if key == ord("q"):
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		break
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	# update the FPS counter
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	fps.update()
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# stop the timer and display FPS information
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fps.stop()
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print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
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print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
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# do a bit of cleanup
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cv2.destroyAllWindows()
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vs.stop()