下面的代码通过积分图计算一张图片的一种haar特征的所有可能的值。初步学习图像处理并尝试写代码，如有错误，欢迎指出。
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)
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