python 계산 포인트 그래프와haar 특징

다음 코드는 적분도를 통해 한 장의 그림의haar특징의 모든 가능한 값을 계산한다.이미지 처리를 초보적으로 배우고 코드를 써 보십시오. 오류가 있으면 지적해 주십시오.

import cv2
import numpy as np
import matplotlib.pyplot as plt
#
#     
#
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
#   --- ---
# |   |   |
# | - | + |
# |   |   |
# --- ---
#
#        haar     
#
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
#
#           haar  
#
def calHaarFeatures(integral_graph,features_graph):
    haarFeatures = []
    for num in range(len(features_graph)):
        #             
        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]]
        #             
        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]]
        #              
        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)