python 计算积分图和haar特征的实例代码

Python  /  管理员 发布于 7年前   177

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 cv2import numpy as npimport 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 haarFeaturesimg = 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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