Sending email from Raspberry Pi

from picamera.array import PiRGBArray # Generates a 3D RGB array
from picamera import PiCamera # Provides a Python interface for the RPi Camera Module
import time # Provides time-related functions
from time import sleep
import datetime
import cv2 # OpenCV library
import numpy as np # Import NumPy library
import datetime
import os
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders

def sendEmail():
    email_user = "xxxxxxxxxxxxxx@gmail.com"
    email_password = "xxxxxxxxxxxx"
    email_send = "xxxxxxxxxxx@yahoo.com"
    subject = "Test email from pi"

    msg = MIMEMultipart()
    msg["From"] = email_user
    msg["To"] = email_send
    msg["Subject"] = subject

    body = "Hi there, motion detected. Sending this email from Raspy!"
    msg.attach(MIMEText(body,"plain"))

    text = msg.as_string()
    server = smtplib.SMTP("smtp.gmail.com",587)
    server.starttls()
    server.login(email_user,email_password)
    server.sendmail(email_user,email_send,text)
    server.quit()
    
 
# Initialize the camera
camera = PiCamera()

camera.rotation = 180
 
# Set the camera resolution
camera.resolution = (640, 480)
#camera.resolution = (1280, 720)
#camera.resolution = (1640, 922)
#camera.resolution = (3280, 2464)
 
# Set the number of frames per second
#camera.framerate = 30
#camera.framerate = 60
camera.framerate = 30

# Generates a 3D RGB array and stores it in rawCapture
raw_capture = PiRGBArray(camera, size=(640, 480))
#raw_capture = PiRGBArray(camera, size=(1280, 720))
#raw_capture = PiRGBArray(camera, size=(1640, 922))
#raw_capture = PiRGBArray(camera, size=(3280, 2464))
 
# Create the background subtractor object
# Feel free to modify the history as you see fit.
#back_sub = cv2.createBackgroundSubtractorMOG2(history=150,
  #varThreshold=25, detectShadows=True)
back_sub = cv2.createBackgroundSubtractorMOG2(history=25,
  varThreshold=20, detectShadows=True)
 
# Wait a certain number of seconds to allow the camera time to warmup
time.sleep(0.1)
 
# Create kernel for morphological operation. You can tweak
# the dimensions of the kernel.
# e.g. instead of 20, 20, you can try 30, 30
kernel = np.ones((20,20),np.uint8)
 
# Capture frames continuously from the camera
for frame in camera.capture_continuous(raw_capture, format="bgr", use_video_port=True):
     
    # Grab the raw NumPy array representing the image
    image = frame.array
 
    # Convert to foreground mask
    fg_mask = back_sub.apply(image)
     
    # Close gaps using closing
    fg_mask = cv2.morphologyEx(fg_mask,cv2.MORPH_CLOSE,kernel)
       
    # Remove salt and pepper noise with a median filter
    fg_mask = cv2.medianBlur(fg_mask,5)
       
    # If a pixel is less than ##, it is considered black (background). 
    # Otherwise, it is white (foreground). 255 is upper limit.
    # Modify the number after fg_mask as you see fit.
    _, fg_mask = cv2.threshold(fg_mask, 127, 255, cv2.THRESH_BINARY)
 
    # Find the contours of the object inside the binary image
    contours, hierarchy = cv2.findContours(fg_mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[-2:]
    areas = [cv2.contourArea(c) for c in contours]
  
    # If there are no countours
    if len(areas) < 1:
  
      # Display the resulting frame
      cv2.imshow('Frame',image)
  
      # Wait for keyPress for 1 millisecond
      key = cv2.waitKey(1) & 0xFF
  
      # Clear the stream in preparation for the next frame
      raw_capture.truncate(0)
     
      # If "q" is pressed on the keyboard, 
      # exit this loop
      if key == ord("q"):
        break
     
      # Go to the top of the for loop
      continue
  
    else:
         
      # Find the largest moving object in the image
      max_index = np.argmax(areas)
       
    # Draw the bounding box
    cnt = contours[max_index]
    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
  
    # Draw circle in the center of the bounding box
    x2 = x + int(w/2)
    y2 = y + int(h/2)
    cv2.circle(image,(x2,y2),4,(0,255,0),-1)
  
    # Print the centroid coordinates (we'll use the center of the
    # bounding box) on the image
    text = "x: " + str(x2) + ", y: " + str(y2)
    cv2.putText(image, text, (x2 - 10, y2 - 10),
      cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
          
    # Display the resulting frameq
    cv2.imshow("Frame",image)
    
    #Record.....
    #while w > 500 and h > 500:    #doesn't work
    while h > 350:
        date = datetime.datetime.now().strftime("%d-%m-%Y_%H-%M-%S")
        camera.start_recording('/mnt/mydisk/'+date+'.h264')
        sleep(5)
        camera.stop_recording()
        
        sendEmail()
        
        break
        
    # Wait for keyPress for 1 millisecond
    key = cv2.waitKey(1) & 0xFF
  
    # Clear the stream in preparation for the next frame
    raw_capture.truncate(0)
     
    # If "q" is pressed on the keyboard, 
    # exit this loop
    if key == ord("q"):
      break
    
    ##Delete video files which are more than 7 days old
    path = '/mnt/mydisk/'
    today = datetime.datetime.today()#gets current time
    os.chdir(path) #changing path to current path(same as cd command)

    #Iterate thru files
    for root, directories, files in os.walk(path,topdown=False): 
        for name in files:
            #last modified time
            timeStamp = os.stat(os.path.join(root, name))[8] 
            filetime = datetime.datetime.fromtimestamp(timeStamp) - today

            #check if recirdings file is more than 7 days old, if yes then delete them
            if filetime.days <= -7:
                print(os.path.join(root, name), filetime.days)
                os.remove(os.path.join(root, name))
    
 
# Close down windows
cv2.destroyAllWindows()