SaniStation - Face mask detect sanitize station for Covid-19

from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2
import os


import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
r1=17
r2=18
GPIO.setup(r1, GPIO.OUT)
GPIO.setup(r2, GPIO.OUT)



def say(text_tts):
	os.system('pico2wave -l en-GB -w  say.wav "%s" ' % text_tts)
	os.system("sudo aplay say.wav")
	os.system("sudo rm say.wav")

def detect_mask():
	while True:
		frame = vs.read()
		frame = imutils.resize(frame, width=400)

		(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

		for (box, pred) in zip(locs, preds):
			(startX, startY, endX, endY) = box
			(mask, withoutMask) = pred

			label = "Mask" if mask > withoutMask else "No Mask"
			color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

			if label == "Mask":
				if (max(mask, withoutMask) * 100) > 98 :
					say("You can continue with the hand sanitizing station, follow the instructions.")
					time.sleep(0.5)
					say("Put your hand close and wait for the liquid to be applied.")
					time.sleep(1.2)

					#********** TURN ON HAND SANITIZER 
					GPIO.output(r1,GPIO.HIGH)
					time.sleep(1.2)
					GPIO.output(r1,GPIO.LOW)
					time.sleep(0.5)
					#********** TURN OFF HAND SANITIZER 

					say("Continue with the shoes sanitizing station and wait fot the liquid to be applied.")
					time.sleep(1.2)

					#********** TURN ON HAND SANITIZER 
					GPIO.output(r2,GPIO.HIGH)
					time.sleep(1)
					GPIO.output(r2,GPIO.LOW)
					time.sleep(0.5)
					#********** TURN OFF HAND SANITIZER 

					say("You have completed the protocol to continue, you can enter to the venue")
					time.sleep(0.5)
					say("Remember, it is everyone'sresponsability to mantain social distance.")
			else:
				say("No face mask detected, You cannot enter to the venue")
				say("Please put on a mask to enter")

			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

			cv2.putText(frame, label, (startX, startY - 10),
				cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
			cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

		# cv2.imshow("Frame", frame)
		key = cv2.waitKey(1) & 0xFF

		# if the `q` key was pressed, break from the loop
		if key == ord("q"):
			GPIO.cleanup()
			break


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

def detect_and_predict_mask(frame, faceNet, maskNet):
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	faceNet.setInput(blob)
	detections = faceNet.forward()

	faces = []
	locs = []
	preds = []

	for i in range(0, detections.shape[2]):
		confidence = detections[0, 0, i, 2]

		if confidence > args["confidence"]:
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

			face = frame[startY:endY, startX:endX]
			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
			face = cv2.resize(face, (224, 224))
			face = img_to_array(face)
			face = preprocess_input(face)
			face = np.expand_dims(face, axis=0)

			faces.append(face)
			locs.append((startX, startY, endX, endY))

	if len(faces) > 0:
		preds = maskNet.predict(faces)

	return (locs, preds)

ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", type=str,
	default="face_detector",
	help="path to face detector model directory")
ap.add_argument("-m", "--model", type=str,
	default="mask_detector.model",
	help="path to trained face mask detector model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
	"res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)


#  Read Ultrasonic proximity sensor
#  If value of sensor if less tha 50 cm call detect_mask
#  and if the value is lather than 2m, continue 
detect_mask()



# If mask detect is true, continue to hand alcohol gel dispeneser
# Read sensor, if the value es less than 15 cm, activate pump to dispense liquid
# and store value: hand = true
# Continue to foot sanitizer station
# Read sensor, if the value es less than 15 cm, activate pump to dispense sanitizer liquid
# and store value: foot = true
# If mask  = true & hand = true & foot = true --> Can coninue to enter and open door
# If any of those variables is false, repeat process from begining.