(四)OpenCV级联分类器训练与使用_03_Haar与LBP级联分类器的比较

Hasana ·

更新时间:2024-09-20

· 937 次阅读

HAAR与LBP的区别(Haar的xml文件大)(LBP的xml文件小)
HAAR特征是浮点数计算
LBP特征是整数计算
LBP训练需要的样本数量要比HAAR大
同样的样本空间，HAAR训练出来的数据检测结果要比LBP准确
扩大LBP的样本数据，训练结果可以跟HAAR一样
LBP的速度一般可以比HAAR快几倍

#include 
#include 
using namespace std;
using namespace cv;
String Haar_fileName = "haarcascade_frontalface_alt.xml";//Haar
String LBP_fileName = "lbpcascade_frontalface.xml";//LBP
CascadeClassifier face_Haar_cascade;
CascadeClassifier face_LBP_cascade;
int main(int argc, char** argv)
{
	if (!face_Haar_cascade.load(Haar_fileName))
	{
		cout << "Haar_cascade error" << endl;
		return -1;
	};
	if (!face_LBP_cascade.load(LBP_fileName))
	{
		cout << "LBP_cascade error" << endl;
		return -2;
	};
	Mat src, gray;
	src = imread("C:/Users/good/Desktop/test14.jpg");
	if (src.empty())
	{
		cout << "could not load image1..." << endl;
		return -3;
	}
	//namedWindow("src", WINDOW_AUTOSIZE);
	//imshow("src", src);
	cvtColor(src, gray, COLOR_BGR2GRAY);//转换为灰度
	equalizeHist(gray, gray);//直方图均衡化
	vector faces;
	int st = getTickCount();//返回从操作系统启动所经过(elapsed)的毫秒数,它的返回值是DWORD
	face_Haar_cascade.detectMultiScale(gray, faces, 1.1, 3, 0, Size(30,30));
	int nd = (getTickCount() - st);
	cout << "Haar耗时:" << nd << endl;
	vector _faces;
	int _st = getTickCount();
	face_LBP_cascade.detectMultiScale(gray, _faces, 1.1, 3, 0, Size(30, 30));
	int _nd = (getTickCount() - _st);
	cout << "LBP耗时:" << _nd << endl;//LBP耗时更短,检测更快
	for (size_t i = 0; i < faces.size(); i++) 
	{
		//绘制矩形
		rectangle(src, faces[i], Scalar(255, 0, 0), 2, 8, 0);
	}
	imshow("显示", src);
	waitKey(0);
	return 0;
}

输出结果:
在这里插入图片描述