import random
from PIL import Image
def averageColor(cv, image, frequency = 1):
	size = cv.GetSize(image)
	
	width  = size[0]
	height = size[1]
	count  = int(round((frequency * (width * height)) / 100))
	
	sumR = 0
	sumB = 0
	squareR = 0
	squareB = 0
	
	rgb = []
	
	i=0
	while i < count:
		x = random.randint(0, width  - 1)
		y = random.randint(0, height  - 1)
		
		color = image[y, x]
		
		sumR += color[0];
                sumB += color[2];
		
		averR = int((sumR / count) << 16)
		averB = int( sumB / count)
		
		i += 1
      return (averR, averB)

colors = average.averageColor(cv, image)
diff = colors[0] ^ colors[1]

c = 1
red = []
blue = []
while True:
	#тут находится лицо и производятся прочие операции..
	
	#передаем на обработку участок лба
	av = average.averageColor(cv, forehead)
	red.append(av[0])
	blue.append(av[1])
	
	#каждые десять кадров получаем массив различий пикселей
	if c == 10:
		red = set(red)
		blue = set(blue)
		
		diff = list(red ^ blue)
		
		print diff
		
		c = 1
		red = []
		blue = []
	else:
		c += 1

import numpy as np
import matplotlib.pyplot as plt
Fs = 100                        # sampling rate
Ts = 1.0/Fs                      # sampling interval
signal = [10,11,12,13,-5,-6,-7,8,10,11,12]
n = len(signal)                       # length of the signal
k = np.arange(n)
T = n/Fs
frq = k/T # two sides frequency range
freq = frq[range(n/2)]           # one side frequency range
print freq
print "\n"
Y = np.fft.fft(signal)/n              # fft computing and normalization
Y = abs(Y[range(n/2)])
print Y
plt.plot(freq, Y, 'r-')
plt.xlabel('freq (Hz)')
plt.ylabel('|Y(freq)|')
plt.show()