顏色區塊取重心座標

不規則區域的矩，表示把一個歸一化的灰度級圖像函數理解為一個二維隨機變量的概率密度,這個隨機變量的屬性可以用統計特徵--矩（Moments）來描述。通過假設非零的像素值表示區域，矩可以用於二值或灰度級的區域描述

       M _pq = sigma(i)sigma(j) i ^p  j ^q  f(i,j)

       其中x,y,i,j是區域點的坐標（在數字圖像中的像素坐標）。

       令Xc,Yc表示區域重心的坐標，則：

              Xc = M ₁₀ /M ₀₀ ;

              Yc = M ₀₁ /M ₀₀ ;

import cv2
import numpy as np
def  track(frame):
    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    cv2.imshow('hsv',hsv)
    # define range of blue color in HSV
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_blue, upper_blue)
    moments = cv2.moments(mask)
    
    m00 = moments['m00']
    centroid_x, centroid_y = None, None
    if m00 != 0:
        centroid_x = int(moments['m10']/m00)#Take X coordinate
        centroid_y = int(moments['m01']/m00)#Take Y coordinate
 print 'x=',centroid_x,
 print 'y=',centroid_y

    ctr = (-1,-1) 
    if centroid_x != None and centroid_y !=None:
     ctr = (centroid_x, centroid_y)

     # Put black circle in at centroid in image
     cv2.circle(frame, ctr, 15, (0,0,255))
    # Bitwise-AND mask and original image
    res = cv2.bitwise_and(frame,frame, mask= mask)
    #cv2.imshow('mask',mask)
    #cv2.imshow('res',res)
    cv2.imshow('frame',frame)

    return ctr
#cv2.destroyAllWindows()

if __name__ == '__main__':
 cap = cv2.VideoCapture(0)

 while True:
  # Take each frame
      _, frame = cap.read()
      track(frame)
      if cv2.waitKey(1) & 0xFF == 27:
            break

參考資料： http://blog.csdn.net/fengbingchun/article/details/6938895