file

import cv2
import string, random


vc = cv2.VideoCapture(0)

if vc.isOpened(): # try to get the first frame
    rval, frame = vc.read()
else:
    rval = False

def write_bb_on_image(image):
	face_cascade_path = ''
	face_cascade = cv2.CascadeClassifier(face_cascade_path)
	scale_factor = 1.1
	min_neighbors = 3
	min_size = (30, 30)
	outfname = ""
	flags = cv2.CASCADE_SCALE_IMAGE
	faces = face_cascade.detectMultiScale(image, scaleFactor = scale_factor, minNeighbors = min_neighbors, \
		minSize = min_size, flags = flags)
	#( x, y, w, h ) in faces
	return faces


def grab_face(x,y,w,h,frame):
	#a is array of size 96x96x3
	#a = 0
	a = frame[y: y + h, x: x + w]
	#reshape to 96x96x3
	resized_image = cv2.resize(a, (96, 96))
	return a

def recognize_person(grabbed_faces):
	print len(grabbed_faces)
	#generate random names
	names = ''.join(random.sample(string.ascii_lowercase, len(bbs)))
	return names

#RECOGNIZE ONLY ONE PERSON

while rval:
	rgbImg = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
	bbs = write_bb_on_image(rgbImg)
	#limit number of faces to only 4
	bbs = bbs[:4]
	#send x,y,w,h and 96x96x3 to recognize
	grabbed_faces = [0]*len(bbs)
	for index in range(len(bbs)):
		(x, y, w, h )  = bbs[index]
		grabbed_faces[index] = grab_face(x,y,w,h,frame)
	returned_box = recognize_person(grabbed_faces)
	for index in range(len(returned_box)):
		(x, y, w, h )  = bbs[index]
		name_of_person = list(returned_box)[index]
		cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
		cv2.putText(frame,name_of_person, (int(x),int(y)), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 6)
		cv2.imshow('see this', frame)
	rval, frame = vc.read()
	key = cv2.waitKey(20)
	if key == 27: # exit on ESC
		break


cv2.destroyWindow("OpenCV")

https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml