python 計算積分圖和haar特徵

下面的代碼通過積分圖計算一張圖片的一種hear特徵的所有可能的值。初步學習圖像處理並嘗試寫代碼，如有錯誤，歡迎指出。

import cv2
import numpy as np
import matplotlib.pyplot as plt
#
#計算積分圖
#
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
#   --- ---
# |   |   |
# | - | + |
# |   |   |
# --- ---
#
#就算所有需要計算hear特徵的區域
#
def getHearFeaturesArea(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
#
#通過積分圖特徵區域計算hear特徵
#
def calHearFeatures(integral_graph,features_graph):
    hearFeatures = []
    for num in range(len(features_graph)):
        #計算左面的矩形區局的像素和
        hear1 = 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]]
        #計算右面的矩形區域的像素和
        hear2 = 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]]
        #右面的像素和減去左面的像素和
        hearFeatures.append(hear2-hear1)
    return hearFeatures


img = cv2.imread("faces/face00001.bmp",0)
integeralGraph = integral(img)
featureAreas = getHearFeaturesArea(img.shape[0],img.shape[1])
hearFeatures = calHearFeatures(integeralGraph,featureAreas)
print(hearFeatures)