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January 16, 2020 21:40
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Snippet of code used for DevFest London 2017 to count faces in audience and send to Google Analytics (see https://mashe.hawksey.info/?p=17787)
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import io | |
import picamera | |
import cv2 | |
import numpy | |
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 | |
#Based on Face detection with Raspberry Pi | |
#For org. + setup http://rpihome.blogspot.co.uk/2015/03/face-detection-with-raspberry-pi.html | |
#Modified by mhawksey | |
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) | |
print ("Found " + str(facesInt) + " face(s)") | |
#Send faces counted to GA | |
hitGA(str(len(faces)) | |
#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) | |
#Show the result image | |
imS = cv2.resize(image, (640, 360)) | |
cv2.imshow('frame', imS) | |
k = cv2.waitKey(1000) |
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