python 计算积分图和haar特征的实例代码
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。
importcv2
importnumpyasnp
importmatplotlib.pyplotasplt
#
#计算积分图
#
defintegral(img):
integ_graph=np.zeros((img.shape[0],img.shape[1]),dtype=np.int32)
forxinrange(img.shape[0]):
sum_clo=0
foryinrange(img.shape[1]):
sum_clo=sum_clo+img[x][y]
integ_graph[x][y]=integ_graph[x-1][y]+sum_clo;
returninteg_graph
#TypesofHaar-likerectanglefeatures
#------
#|||
#|-|+|
#|||
#------
#
#就算所有需要计算haar特征的区域
#
defgetHaarFeaturesArea(width,height):
widthLimit=width-1
heightLimit=height/2-1
features=[]
forwinrange(1,int(widthLimit)):
forhinrange(1,int(heightLimit)):
wMoveLimit=width-w
hMoveLimit=height-2*h
forxinrange(0,wMoveLimit):
foryinrange(0,hMoveLimit):
features.append([x,y,w,h])
returnfeatures
#
#通过积分图特征区域计算haar特征
#
defcalHaarFeatures(integral_graph,features_graph):
haarFeatures=[]
fornuminrange(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)
returnhaarFeatures
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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