Snippet of code used for DevFest London 2017 to count faces in audience and send to Google Analytics and update image in Google Slides (see https://mashe.hawksey.info/?p=17787)

gistfile1.py

import io
import picamera
import cv2
import numpy
import requests
import base64

def hitGA(faces):
    print("Sending to GA")
    requests.get("http://www.google-analytics.com/collect?v=1" \
                    + "&tid=YOUR_UA_TRACKING_ID_HERE" \
                    + "&cid=1111" \
                    + "&t=event" \
                    + "&ec=FaceDetection" \
                    + "&ea=faces" \
                    + "&el=DevFest17"
                    + "&ev=" + faces).close

maxFaces = -1

#Setup posting result to Slides
url = 'PUBLISHED_WEB_APP_URL_FROM_GOOGLE_APPS_SCRIPT'    
# prepare headers for http request
content_type = 'image/jpeg'
headers = {'content-type': content_type}

while True:
    #Create a memory stream so photos doesn't need to be saved in a file
    stream = io.BytesIO()

    #Here you can also specify other parameters (e.g.:rotate the image)
    with picamera.PiCamera() as camera:
        camera.resolution = (2592, 1944)
        camera.iso = 800
        camera.capture(stream, format='jpeg')

    #Convert the picture into a numpy array
    buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)

    #Now creates an OpenCV image
    image = cv2.imdecode(buff, 1)

    #Load a cascade file for detecting faces
    face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml')

    #Convert to grayscale
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

    #Look for faces in the image using the loaded cascade file
    faces = face_cascade.detectMultiScale(gray, 1.1, 5)
    facesInt = len(faces)

    print ("Found " + str(facesInt) + " face(s)")
    #Send faces counted to GA
    hitGA(str(facesInt))

    #Draw a rectangle around every found face
    for (x,y,w,h) in faces:
        cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,0),2)

    #Save the result image if new maximum
    if facesInt > maxFaces:
        retval, buffer = cv2.imencode('.jpg', image)
        img_encoded = base64.b64encode(buffer)
        response = requests.post(url, data=img_encoded, headers=headers)
        maxFaces = facesInt
        print (response.text)

    #Show the result image
    imS = cv2.resize(image, (640, 360))
    cv2.imshow('frame', imS)
    k = cv2.waitKey(1000)