python 计算积分图和haar特征的实例代码

import cv2
import numpy as np
import matplotlib.pyplot as plt
#
#计算积分图
#
def integral(img):
  integ_graph = np.zeros((img.shape[0],img.shape[1]),dtype = np.int32)
  for x in range(img.shape[0]):
    sum_clo = 0
    for y in range(img.shape[1]):
      sum_clo = sum_clo + img[x][y]
      integ_graph[x][y] = integ_graph[x-1][y] + sum_clo;
  return integ_graph

# Types of Haar-like rectangle features
#  --- ---
# |  |  |
# | - | + |
# |  |  |
# --- ---
#
#就算所有需要计算haar特征的区域
#
def getHaarFeaturesArea(width,height):
  widthLimit = width-1
  heightLimit = height/2-1
  features = []
  for w in range(1,int(widthLimit)):
    for h in range(1,int(heightLimit)):
      wMoveLimit = width - w
      hMoveLimit = height - 2*h
      for x in range(0, wMoveLimit):
        for y in range(0, hMoveLimit):
          features.append([x, y, w, h])
  return features
#
#通过积分图特征区域计算haar特征
#
def calHaarFeatures(integral_graph,features_graph):
  haarFeatures = []
  for num in range(len(features_graph)):
    #计算左面的矩形区局的像素和
    haar1 = integral_graph[features_graph[num][0]][features_graph[num][1]]-\
    integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]] -\
    integral_graph[features_graph[num][0]][features_graph[num][1]+features_graph[num][3]] +\
    integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+features_graph[num][3]]
    #计算右面的矩形区域的像素和
    haar2 = integral_graph[features_graph[num][0]][features_graph[num][1]+features_graph[num][3]]-\
    integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+features_graph[num][3]] -\
    integral_graph[features_graph[num][0]][features_graph[num][1]+2*features_graph[num][3]] +\
    integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+2*features_graph[num][3]]
    #右面的像素和减去左面的像素和
    haarFeatures.append(haar2-haar1)
  return haarFeatures


img = cv2.imread("faces/face00001.bmp",0)
integeralGraph = integral(img)
featureAreas = getHaarFeaturesArea(img.shape[0],img.shape[1])
haarFeatures = calHaarFeatures(integeralGraph,featureAreas)
print(haarFeatures)