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python 计算积分图和haar特征的实例代码

更新时间:2020-08-08 23:06 作者:startmvc
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

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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