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 imutils
import time
import cv2
import os
import easygui
import requests
import smtplib
from email.message import EmailMessage
from twilio.rest import Client
import winsound

account_sid = "ACe368ef538ae07c1644c0fad7ceac2928"
auth_token = "ca0581d49ec31ab308e0506d7b37cb08"
client = Client(account_sid, auth_token)

def email_alert(subject, body, to):
    msg = EmailMessage()
    msg.set_content(body)
    msg['subject'] = subject
    msg['to'] = to
    
    user = "mahalakshmithirumurthy@gmail.com"
    password = "qvlhwmdprzztfrpz"
    msg['from'] = user
    server = smtplib.SMTP("smtp.gmail.com", 587)
    server.starttls()
    server.login(user,password)
    server.send_message(msg)
    server.quit()

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, (224, 224),
		(104.0, 177.0, 123.0))

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

	# 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 > 0.5:
			# 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]
			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
			face = cv2.resize(face, (224, 224))
			face = img_to_array(face)
			face = preprocess_input(face)

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

	# 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
		faces = np.array(faces, dtype="float32")
		preds = maskNet.predict(faces, batch_size=32)

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

# load our serialized face detector model from disk
prototxtPath = r"face_detector\deploy.prototxt"
weightsPath = r"face_detector\res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

# load the face mask detector model from disk
maskNet = load_model("mask_detector.model")

# initialize the video stream
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
# loop over the frames from the video stream

while True:
    frame = vs.read()
    frame = imutils.resize(frame, width = 400)
    # 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)
    
    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 == "No Mask"):
            print("Inside if")
            winsound.PlaySound("beep-06", winsound.SND_FILENAME)
            easygui.msgbox("Access Denied. Kindly wear a face mask. Else you won't be permitted", title="WARNING!!!!!!!!")
            client.messages.create(from_ = "+12565768150", body = "A person has violated facial mask policy. Kindly check the camera to recognise the person", to="+91 98943 85585")
            email_alert("Alert!!", "A person has violated facial mask policy. Kindly check the camera to recognise the person", "maha.lakshmi140@gmail.com")
            break
        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)
        
        
    cv2.imshow("Frame",frame)
    key = cv2.waitKey(1) & 0xFF
    
    if key == ord("q"):
        break

cv2.destroyAllWindows()
vs.stop()
		
        