test.py

import io
import picamera
import cv2
import numpy
import setSegment
from time import sleep

for num in range(1,10):
	#Create a memory stream so photos doesn't need to be saved in a file
	stream = io.BytesIO()

	#Get the picture (low resolution, so it should be quite fast)
	#Here you can also specify other parameters (e.g.:rotate the image)
	with picamera.PiCamera() as camera:
    		camera.resolution = (320, 240)
    		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(len(faces))+" face(s)"

	#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
	cv2.imwrite('result.jpg',image)

	setSegment.setDisplay(len(faces))
	sleep(10)