# USAGE
# python detect_mask_video.py

# import the necessary packages
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 serial # Serial port initialize 
from playsound import playsound
import RPi.GPIO as GPIO #Led blink

#Arduino to Respberry pi connection
ser=serial.Serial("/dev/ttyACM0",9600)  #change ACM number as found from ls /dev/tty/ACM*
ser.baudrate=9600


GPIO.setmode(GPIO.BCM)

#Led's
GPIO.setup(17, GPIO.OUT)#Green
GPIO.setup(18, GPIO.OUT)#Red

#Buzzur
GPIO.setup(22, GPIO.OUT)#Mask Indicate
GPIO.setup(23, GPIO.OUT)#Temperature Indicate


label_details = ["Mask"]
print(len(label_details))

def detect_and_predict_mask(frame, faceNet, maskNet):
	# grab the dimensions of the frame and then construct a blob
	# from it
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	faceNet.setInput(blob)
	detections = faceNet.forward()

	# initialize our list of faces, their corresponding locations,
	# and the list of predictions from our face mask network
	faces = []
	locs = []
	preds = []

	# loop over the detections
	for i in range(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with
		# the detection
		confidence = detections[0, 0, i, 2]

		# filter out weak detections by ensuring the confidence is
		# greater than the minimum confidence
		if confidence > args["confidence"]:
			# compute the (x, y)-coordinates of the bounding box for
			# the object
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			# ensure the bounding boxes fall within the dimensions of
			# the frame
			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

			# extract the face ROI, convert it from BGR to RGB channel
			# ordering, resize it to 224x224, and preprocess it
			face = frame[startY:endY, startX:endX]
			
			try:
				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)

				# add the face and bounding boxes to their respective
				# lists
				faces.append(face)
				locs.append((startX, startY, endX, endY))

			except:
				pass

	# only make a predictions if at least one face was detected
	if len(faces) > 0:
		# for faster inference we'll make batch predictions on *all*
		# faces at the same time rather than one-by-one predictions
		# in the above `for` loop
		preds = maskNet.predict(faces)

	# return a 2-tuple of the face locations and their corresponding
	# locations
	return (locs, preds)

# construct the argument parser and parse the arguments
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())

# load our serialized face detector model from disk
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)

# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

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


def play_audio(path_of_audio):
	playsound(path_of_audio)

# loop over the frames from the video stream
while True:
	# grab the frame from the threaded video stream and resize it
	# to have a maximum width of 400 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=1000)

	# detect faces in the frame and determine if they are wearing a
	# face mask or not
	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
	
	
	
	

	#print("Unique File")
	if len(label_details) >= 5:
		if label_details.count("Mask") == 5:
			#play_audio('mask.mp3')
			label_details = ["Mask"]
			#print("Mask Cell ma gaya ha")
			#print("\n Detect Mask \n")
		elif label_details.count("No Mask") == 5:
			#play_audio('no_mask.mp3')
			label_details = ["No Mask"]
			#print("No Mask waly cell ma gaya ha")
			#print("\n Detect No Mask \n")
		else:
			pass

	# loop over the detected face locations and their corresponding
	# locations
	
	"""
	#Arduino To respberry pi conncection
	read_ser=ser.readline()
	print(read_ser)"""
	
	## Code edit for arduino Wired communication
	read_ser=ser.readline()
	command = read_ser.decode('ASCII')#Convert byte to string
	#print(command)
	
	
	#Ultrasonic Sensor Connection
	if (command.strip()=="Come Closer"):
		print("Your Distance is >20 cm \n Please Come Closer")
	
	else:
		#Arduino To respberry pi conncection
		if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
			print(command)
			print("Temeperature > 40")
			#print(1)
			#Buzzur beep beep
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)

		else:
			print(command)
			print("Temeperature < 40")
			#print(2)
			#Buzzur
			GPIO.output(23, False)#Temperature Normal

	
	
	for (box, pred) in zip(locs, preds):
		# unpack the bounding box and predictions
		(startX, startY, endX, endY) = box
		(mask, withoutMask) = pred

		# determine the class label and color we'll use to draw
		# the bounding box and text
		#label = "Mask" if mask > withoutMask else "No Mask"
		if mask > withoutMask:
			label = "Mask"
			label_details.append(label)
			print("\n ***** Detect Mask ***** \n")
			# Led Declear
			GPIO.output(17, True)
			GPIO.output(18, False)
			#print(label_details)
			
			#Buzzur
			GPIO.output(22, False) #Mask Detect
			"""
			#Arduino To respberry pi conncection
			if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
				print("Temeperature > 40")
				#print(1)
				#Buzzur
				GPIO.output(23, True)#Temperature Excide
				
			else:
				print("Temeperature < 40")
				#print(2)
				#Buzzur
				GPIO.output(23, False)#Temperature Normal"""
		else:
			label = "No Mask"
			label_details.append(label)
			print("\n ***** Detect No Mask ***** \n")

			# Led Declear
			GPIO.output(17, False)
			GPIO.output(18, True)

			#Buzzur
			#GPIO.output(22, True)#Mask Not detect
			
			#Buzzur beep beep Mask Not detect
			
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)

				
			#print(label_details)
			"""
			#Arduino To respberry pi conncection
			if(command.strip()=="Temperature Excide"):
				print("Temeperature > 40")
				#print(3)
				#Buzzur
				GPIO.output(23, True)#Temperature Excide
			else:
				print("Temeperature < 40")
				#print(4)
				#Buzzur
				GPIO.output(23, False)#Temperature Normal"""
				
		"""		
		#Arduino To respberry pi conncection
		if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
			print("Temeperature > 40")
			#print(1)
			#Buzzur
			GPIO.output(23, True)#Temperature Excide
				
		else:
			print("Temeperature < 40")
			#print(2)
			#Buzzur
			GPIO.output(23, False)#Temperature Normal """
			
			
			
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

		#print(label_details)
		#print(len(label_details))


		# include the probability in the label
		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

		# display the label and bounding box rectangle on the output
		# frame
		cv2.putText(frame, label, (startX, startY - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

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

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



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

# USAGE
# python detect_mask_video.py

# import the necessary packages
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 serial # Serial port initialize 
from playsound import playsound
import RPi.GPIO as GPIO #Led blink

#Arduino to Respberry pi connection
ser=serial.Serial("/dev/ttyACM0",9600)  #change ACM number as found from ls /dev/tty/ACM*
ser.baudrate=9600


GPIO.setmode(GPIO.BCM)

#Led's
GPIO.setup(17, GPIO.OUT)#Green
GPIO.setup(18, GPIO.OUT)#Red

#Buzzur
GPIO.setup(22, GPIO.OUT)#Mask Indicate
GPIO.setup(23, GPIO.OUT)#Temperature Indicate


label_details = ["Mask"]
print(len(label_details))

def detect_and_predict_mask(frame, faceNet, maskNet):
	# grab the dimensions of the frame and then construct a blob
	# from it
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	faceNet.setInput(blob)
	detections = faceNet.forward()

	# initialize our list of faces, their corresponding locations,
	# and the list of predictions from our face mask network
	faces = []
	locs = []
	preds = []

	# loop over the detections
	for i in range(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with
		# the detection
		confidence = detections[0, 0, i, 2]

		# filter out weak detections by ensuring the confidence is
		# greater than the minimum confidence
		if confidence > args["confidence"]:
			# compute the (x, y)-coordinates of the bounding box for
			# the object
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			# ensure the bounding boxes fall within the dimensions of
			# the frame
			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

			# extract the face ROI, convert it from BGR to RGB channel
			# ordering, resize it to 224x224, and preprocess it
			face = frame[startY:endY, startX:endX]
			
			try:
				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)

				# add the face and bounding boxes to their respective
				# lists
				faces.append(face)
				locs.append((startX, startY, endX, endY))

			except:
				pass

	# only make a predictions if at least one face was detected
	if len(faces) > 0:
		# for faster inference we'll make batch predictions on *all*
		# faces at the same time rather than one-by-one predictions
		# in the above `for` loop
		preds = maskNet.predict(faces)

	# return a 2-tuple of the face locations and their corresponding
	# locations
	return (locs, preds)

# construct the argument parser and parse the arguments
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())

# load our serialized face detector model from disk
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)

# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

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


def play_audio(path_of_audio):
	playsound(path_of_audio)

# loop over the frames from the video stream
while True:
	# grab the frame from the threaded video stream and resize it
	# to have a maximum width of 400 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=1000)

	# detect faces in the frame and determine if they are wearing a
	# face mask or not
	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
	
	
	
	

	#print("Unique File")
	if len(label_details) >= 5:
		if label_details.count("Mask") == 5:
			#play_audio('mask.mp3')
			label_details = ["Mask"]
			#print("Mask Cell ma gaya ha")
			#print("\n Detect Mask \n")
		elif label_details.count("No Mask") == 5:
			#play_audio('no_mask.mp3')
			label_details = ["No Mask"]
			#print("No Mask waly cell ma gaya ha")
			#print("\n Detect No Mask \n")
		else:
			pass

	# loop over the detected face locations and their corresponding
	# locations
	
	"""
	#Arduino To respberry pi conncection
	read_ser=ser.readline()
	print(read_ser)"""
	
	## Code edit for arduino Wired communication
	read_ser=ser.readline()
	command = read_ser.decode('ASCII')#Convert byte to string
	#print(command)
	
	
	#Ultrasonic Sensor Connection
	if (command.strip()=="Come Closer"):
		print("Your Distance is >20 cm \n Please Come Closer")
	
	else:
		#Arduino To respberry pi conncection
		if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
			print(command)
			print("Temeperature > 40")
			#print(1)
			#Buzzur beep beep
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, True)#Temperature Excide
			time.sleep(.1)
			GPIO.output(23, False)#Temperature Excide
			time.sleep(.1)

		else:
			print(command)
			print("Temeperature < 40")
			#print(2)
			#Buzzur
			GPIO.output(23, False)#Temperature Normal

	
	
	for (box, pred) in zip(locs, preds):
		# unpack the bounding box and predictions
		(startX, startY, endX, endY) = box
		(mask, withoutMask) = pred

		# determine the class label and color we'll use to draw
		# the bounding box and text
		#label = "Mask" if mask > withoutMask else "No Mask"
		if mask > withoutMask:
			label = "Mask"
			label_details.append(label)
			print("\n ***** Detect Mask ***** \n")
			# Led Declear
			GPIO.output(17, True)
			GPIO.output(18, False)
			#print(label_details)
			
			#Buzzur
			GPIO.output(22, False) #Mask Detect
			"""
			#Arduino To respberry pi conncection
			if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
				print("Temeperature > 40")
				#print(1)
				#Buzzur
				GPIO.output(23, True)#Temperature Excide
				
			else:
				print("Temeperature < 40")
				#print(2)
				#Buzzur
				GPIO.output(23, False)#Temperature Normal"""
		else:
			label = "No Mask"
			label_details.append(label)
			print("\n ***** Detect No Mask ***** \n")

			# Led Declear
			GPIO.output(17, False)
			GPIO.output(18, True)

			#Buzzur
			#GPIO.output(22, True)#Mask Not detect
			
			#Buzzur beep beep Mask Not detect
			
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)
			GPIO.output(22, True)
			time.sleep(.1)
			GPIO.output(22, False)
			time.sleep(.1)

				
			#print(label_details)
			"""
			#Arduino To respberry pi conncection
			if(command.strip()=="Temperature Excide"):
				print("Temeperature > 40")
				#print(3)
				#Buzzur
				GPIO.output(23, True)#Temperature Excide
			else:
				print("Temeperature < 40")
				#print(4)
				#Buzzur
				GPIO.output(23, False)#Temperature Normal"""
				
		"""		
		#Arduino To respberry pi conncection
		if(command.strip()=="Temperature Excide"):  # strip method to remove termination character
			print("Temeperature > 40")
			#print(1)
			#Buzzur
			GPIO.output(23, True)#Temperature Excide
				
		else:
			print("Temeperature < 40")
			#print(2)
			#Buzzur
			GPIO.output(23, False)#Temperature Normal """
			
			
			
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

		#print(label_details)
		#print(len(label_details))


		# include the probability in the label
		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

		# display the label and bounding box rectangle on the output
		# frame
		cv2.putText(frame, label, (startX, startY - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

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

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



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

# USAGE # python detect_mask_video.py # import the necessary packages # Serial port initialize #Led blink #Arduino to Respberry pi connection #change ACM number as found from ls /dev/tty/ACM* #Led's #Green #Red #Buzzur #Mask Indicate #Temperature Indicate # grab the dimensions of the frame and then construct a blob # from it # pass the blob through the network and obtain the face detections # initialize our list of faces, their corresponding locations, # and the list of predictions from our face mask network # loop over the detections # extract the confidence (i.e., probability) associated with # the detection # filter out weak detections by ensuring the confidence is # greater than the minimum confidence # compute the (x, y)-coordinates of the bounding box for # the object # ensure the bounding boxes fall within the dimensions of # the frame # extract the face ROI, convert it from BGR to RGB channel # ordering, resize it to 224x224, and preprocess it # add the face and bounding boxes to their respective # lists # only make a predictions if at least one face was detected # for faster inference we'll make batch predictions on *all* # faces at the same time rather than one-by-one predictions # in the above `for` loop # return a 2-tuple of the face locations and their corresponding # locations # construct the argument parser and parse the arguments # load our serialized face detector model from disk # load the face mask detector model from disk # initialize the video stream and allow the camera sensor to warm up # loop over the frames from the video stream # grab the frame from the threaded video stream and resize it # to have a maximum width of 400 pixels # detect faces in the frame and determine if they are wearing a # face mask or not #print("Unique File") #play_audio('mask.mp3') #print("Mask Cell ma gaya ha") #print("\n Detect Mask \n") #play_audio('no_mask.mp3') #print("No Mask waly cell ma gaya ha") #print("\n Detect No Mask \n") # loop over the detected face locations and their corresponding # locations #Arduino To respberry pi conncection 	read_ser=ser.readline() 	print(read_ser) ## Code edit for arduino Wired communication #Convert byte to string #print(command) #Ultrasonic Sensor Connection #Arduino To respberry pi conncection # strip method to remove termination character #print(1) #Buzzur beep beep #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #Temperature Excide #print(2) #Buzzur #Temperature Normal # unpack the bounding box and predictions # determine the class label and color we'll use to draw # the bounding box and text #label = "Mask" if mask > withoutMask else "No Mask" # Led Declear #print(label_details) #Buzzur #Mask Detect #Arduino To respberry pi conncection 			if(command.strip()=="Temperature Excide"):  # strip method to remove termination character 				print("Temeperature > 40") 				#print(1) 				#Buzzur 				GPIO.output(23, True)#Temperature Excide 				 			else: 				print("Temeperature < 40") 				#print(2) 				#Buzzur 				GPIO.output(23, False)#Temperature Normal # Led Declear #Buzzur #GPIO.output(22, True)#Mask Not detect #Buzzur beep beep Mask Not detect #print(label_details) #Arduino To respberry pi conncection 			if(command.strip()=="Temperature Excide"): 				print("Temeperature > 40") 				#print(3) 				#Buzzur 				GPIO.output(23, True)#Temperature Excide 			else: 				print("Temeperature < 40") 				#print(4) 				#Buzzur 				GPIO.output(23, False)#Temperature Normal #Arduino To respberry pi conncection 		if(command.strip()=="Temperature Excide"):  # strip method to remove termination character 			print("Temeperature > 40") 			#print(1) 			#Buzzur 			GPIO.output(23, True)#Temperature Excide 				 		else: 			print("Temeperature < 40") 			#print(2) 			#Buzzur 			GPIO.output(23, False)#Temperature Normal #print(label_details) #print(len(label_details)) # include the probability in the label # display the label and bounding box rectangle on the output # frame # show the output frame # if the `q` key was pressed, break from the loop # do a bit of cleanup