python 计算积分图和haar特征的实例代码

发布时间:2020-09-13 00:27:43 作者:阳光玻璃杯
来源:脚本之家 阅读:341

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)

以上这篇python 计算积分图和haar特征的实例代码就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持亿速云。

推荐阅读:
  1. 基于python图像处理API的案例
  2. 应用OpenCV和Python进行SIFT算法的实现详解

免责声明:本站发布的内容(图片、视频和文字)以原创、转载和分享为主,文章观点不代表本网站立场,如果涉及侵权请联系站长邮箱:is@yisu.com进行举报,并提供相关证据,一经查实,将立刻删除涉嫌侵权内容。

python 积分图 haar特征

上一篇:Python线程创建和终止实例代码

下一篇:docker容器启动后添加端口映射

相关阅读

您好,登录后才能下订单哦!

密码登录
登录注册
其他方式登录
点击 登录注册 即表示同意《亿速云用户服务条款》