python 计算积分图和haar特征

python 计算积分图和haar特征

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 = 0for 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-1heightLimit = height/2-1features = []for w in range(1,int(widthLimit)):for h in range(1,int(heightLimit)):wMoveLimit = width - whMoveLimit = height - 2*hfor 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 haarFeaturesimg = cv2.imread("faces/face00001.bmp",0)
integeralGraph = integral(img)
featureAreas = getHaarFeaturesArea(img.shape[0],img.shape[1])
haarFeatures = calHaarFeatures(integeralGraph,featureAreas)
print(haarFeatures)