facial_req.py

#! /usr/bin/python

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import imutils
import pickle
import time
import cv2
import time
import RPi.GPIO as GPIO

# setup raspberry pi GPIO input for PIR motion
# detector on GPIO 7 (pin 26)
GPIO.setmode(GPIO.BCM)
PIR_PIN = 23
GPIO.setup(PIR_PIN, GPIO.IN)
lastMotion = time.time()

#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"

# 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())

# initialize the video stream and allow the camera sensor to warm up
# Set the ser to the followng
# src = 0 : for the build in single web cam, could be your laptop webcam
# src = 2 : I had to set it to 2 inorder to use the USB webcam attached to my laptop
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:
	if GPIO.input(PIR_PIN) or time.time() < lastMotion + 15:
		# if motion input, start timer again.
		if GPIO.input(PIR_PIN):
			lastMotion = time.time()
		# 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)
		# Detect the fce boxes
		boxes = face_recognition.face_locations(frame)
		# compute the facial embeddings for each face bounding box
		encodings = face_recognition.face_encodings(frame, boxes)
		names = []

		# loop over the facial embeddings
		start = time.time()
    
		truecounter = 0
		SimilarityScore = 1
		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
			for j in matches:
				if j == True:
					truecounter = truecounter + 1
			mat = len(matches)
			SimilarityScore = int(float(truecounter) / mat * 100)
			SS = str(SimilarityScore) + "%"
		        
			# 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
				if currentname != name:
					currentname = name
					print("------   Name   ------")
					print(currentname)
					end = time.time()
					print("------   Similarity Score   ------")
					print(SS)
					print("------   Time taken   ------")
					print(end - start)

					landmarks = face_recognition.face_landmarks(frame)
					print("------   Landmarks   ------")
					print("Chin:", len(landmarks[0]["chin"]), "points")
					print("Left eyebrow:", len(landmarks[0]["left_eyebrow"]), "points")
					print("Right eyebrow:", len(landmarks[0]["right_eyebrow"]), "points")
					print("Nose bridge:", len(landmarks[0]["nose_bridge"]), "points")
					print("Nose tip:", len(landmarks[0]["nose_tip"]), "points")
					print("Left eye:", len(landmarks[0]["left_eye"]), "points")
					print("Right eye:", len(landmarks[0]["right_eye"]), "points")
					print("Top lip:", len(landmarks[0]["top_lip"]), " points")
					print("Bottom lip:", len(landmarks[0]["bottom_lip"]), "points")

			# 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, 225), 2)
			y = top - 15 if top - 15 > 15 else top + 15
			cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
				.8, (0, 255, 255), 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()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()