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Urgent !!! Python + OpenCV Object Tracking

I've gone through all the tutorials here and proceeded to try make my own  Home Security Program for my School Proj but my issue is that the program not only runs very very slowly but it at times doesn't seem to detect the face can someone please help me?

import sys
sys.path.append('/usr/local/lib/python3.4/site-packages')
import numpy as np
import cv2
import imutils
from imutils import contours
import datetime
import time
import dropbox

dbx = dropbox.Dropbox('-RNfRO6AtsAAAAAAAAAAyeOGokola1JfbvMElfNUHtpmxEqOlBdiOz3ibXtckPxQ')
dbx.users_get_current_account()

#cap = cv2.VideoCapture("/home/pi/Desktop/Proj/VideoTestSample.mp4")
cap = cv2.VideoCapture(1)

#Creating froeground and removing Background
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False)

#Set format
fourcc = cv2.VideoWriter_fourcc(*'XVID')
#Get Datetime
timestr = time.strftime("%Y_%m_%d_%H_%M_%S")
#Creating name of folder
timestr = timestr + '.avi'
#Setting Name, Format, FPS, FrameSize
out = cv2.VideoWriter(timestr,fourcc, 5.0, (640,480))

#setting casacade for use
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

#setting criteria for  termination
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )

#As long as the VideoCapture is open loop to show the frames
while (cap.isOpened()):
    #capture frame-by-frame
    (grabbed, frame) = cap.read()
    text = " "
    
    if not grabbed:
        break
    
    #Convert frame to Black white and gray
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    #placing Cascade detection
    faces = face_cascade.detectMultiScale(gray, 1.2)
    
    #Drawing around the detected "face"
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x -20,y-20), (x + w + 20, y + h + 20), (255,0,0), 2)
        roi_color = frame[y-20:y + h + 20, x -20:x + w + 20]
    
        #writing image of face as png in the file
        timestring = time.strftime("%Y_%m_%d_%H_%M_%S")
        face_timestr = 'face_' + timestring + '.png'
        cv2.imwrite(face_timestr, roi_color)
        
        #Opening for [r]eading as [b]inary
        FaceFile = open(face_timestr, mode = "rb")
        #Reads the number of bytes of the video
        data = FaceFile.read()
    
        #Setting the save location with file name
        SavetoLocation = '/FYP_Face_Save/'+ face_timestr
        SaveToLocation = str(SavetoLocation)
        
        dbx.files_upload(data, SaveToLocation)
        #Close for reading and binary
        FaceFile.close()
    
    #Apply the Background SubtractionMOG2
    fgmask = fgbg.apply(gray)
    #Erode away the boundaries of the foreground object
    thresh = cv2.erode(fgmask, None, iterations=2)
    
    #Set detect as none
    detect = None
    
    #Find outline of a new foreground object from thresh after the erossion
    (_,cnts,hierarchy) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    #Draw the DateTime on the bottom left hand corner
    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
                (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,(0,0,255), 1)    
    
    #detect is object found or not found
    detect = (_,cnts,hierarchy)
    
    #if object found is detected run these codes
    if detect == (_,cnts,hierarchy):
        
        #if area of object is lower than 300 ignore it 
        for (i,c) in enumerate(cnts):
            if cv2.contourArea(c) < 1100:
                print("ignore small contours", cv2.contourArea(c))
                continue
            
            '''
            #Draw Rectangle around found contour object
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)
            text = "REC"
            '''
            
            #Temporary code
            text = "Movement Detected ... Snapping"
            
            #Capture image
            timestring = time.strftime("%Y_%m_%d_%H_%M_%S")
            image_timestr = 'image_' + timestring + '.png'
            cv2.imwrite(image_timestr, frame)
            
            #Opening for [r]eading as [b]inary
            ImageFile = open(image_timestr, mode = "rb")
            #Reads the number of bytes of the video
            data = ImageFile.read()
            
            #Setting the save location with file name
            SavetoLocation = '/FYP_Image_Save/'+ image_timestr
            SaveToLocation = str(SavetoLocation)
            
            detect= None 
            
            if detect != (_,cnts,hierarchy):
                continue
            
    elif  detect != (_,cnts,hierarchy):
        print("Not Snaping")
        
    else:
        continue
    
    #Draw the text at top right hand corner
    cv2.putText(frame, "{}". format(text), (10,20),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    
    #Write which window into video in this case Frame
    out.write(frame) 
    #Display the following windows
    cv2.imshow('frame',frame)
    cv2.imshow('gray', gray)
    cv2.imshow('fgmask', fgmask)
    
    
    dbx.files_upload(data, SaveToLocation)
    #Close for reading and binary
    ImageFile.close()
        
    #if q is pressed break loop
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

#Stop recording 
out.release()
#Kill all windows
cap.release()
cv2.destroyAllWindows()

#Opening for [r]eading as [b]inary
VideoFile = open(timestr, mode = "rb")
#Reads the number of bytes of the video
data = VideoFile.read()

#Setting the save location with file name
SavetoLocation = '/FYP_Video_Save/'+timestr
SaveToLocation = str(SavetoLocation)

#Upload the file
print("Sending to Dropbox")
dbx.files_upload(data, SaveToLocation)
#Close for reading and binary
VideoFile.close()