Cascade Classifier

一.基础概念

1.Haar和LBP特征

参考博客：

https://blog.csdn.net/liudongdong19/article/details/81008160

2.主要函数

§ CascadeClassifier() [1/2]

cv::CascadeClassifier::CascadeClassifier	(		)

Python:
		=	cv.CascadeClassifier(		)
		=	cv.CascadeClassifier(	filename	)

§ CascadeClassifier() [2/2]

cv::CascadeClassifier::CascadeClassifier	(	const String &	filename	)

Python:
		=	cv.CascadeClassifier(		)
		=	cv.CascadeClassifier(	filename	)

Loads a classifier from a file.

Parameters

filenameName of the file from which the classifier is loaded.

§ load()

bool cv::CascadeClassifier::load	(	const String &	filename	)

Python:
	retval	=	cv.CascadeClassifier.load(	filename	)

§ detectMultiScale() [1/3]

(	InputArray	image,
	std::vector< Rect > &	objects,
	double	scaleFactor = `1.1`,
	int	minNeighbors = `3`,
	int	flags = `0`,
	Size	minSize = `Size()`,
	Size	maxSize = `Size()`
)

image 输入图像

objects 检测出的物体的矩形轮廓

scaleFactor 这个是每次缩小图像的比例，默认是1.1

minNeighbors 匹配成功所需要的周围矩形框的数目，每一个特征匹配到的区域都是一个矩形框，只有多个矩形框同时存在的时候，才认为是匹配成功，比如人脸，这个默认值是3。

flags

可以取如下这些值：
CASCADE_DO_CANNY_PRUNING=1, 利用canny边缘检测来排除一些边缘很少或者很多的图像区域
CASCADE_SCALE_IMAGE=2, 正常比例检测
CASCADE_FIND_BIGGEST_OBJECT=4, 只检测最大的物体

minObjectSize maxObjectSize：匹配物体的大小范围

§ detectMultiScale() [2/3]

(	InputArray	image,
	std::vector< Rect > &	objects,
	std::vector< int > &	numDetections,
	double	scaleFactor = `1.1`,
	int	minNeighbors = `3`,
	int	flags = `0`,
	Size	minSize = `Size()`,
	Size	maxSize = `Size()`
)

§ detectMultiScale() [3/3]

(	InputArray	image,
	std::vector< Rect > &	objects,
	std::vector< int > &	rejectLevels,
	std::vector< double > &	levelWeights,
	double	scaleFactor = `1.1`,
	int	minNeighbors = `3`,
	int	flags = `0`,
	Size	minSize = `Size()`,
	Size	maxSize = `Size()`,
	bool	outputRejectLevels = `false`
)

二.代码实现

#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"#include <stdio.h>using namespace std;
using namespace cv;/** Function Headers */
void detectAndDisplay( Mat frame );/** Global variables */
String face_cascade_name, eyes_cascade_name;
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
String window_name = "Capture - Face detection";/** @function main */
int main( int argc, const char** argv )
{CommandLineParser parser(argc, argv,"{help h||}""{face_cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}""{eyes_cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}");parser.about( "\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n""You can use Haar or LBP features.\n\n" );parser.printMessage();face_cascade_name = parser.get<String>("face_cascade");eyes_cascade_name = parser.get<String>("eyes_cascade");VideoCapture capture;Mat frame;//-- 1. Load the cascades //加载级联分类器文件if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };//-- 2. Read the video stream //读取视频流capture.open( 0 );if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }while ( capture.read(frame) ){if( frame.empty() ){printf(" --(!) No captured frame -- Break!");break;}//-- 3. Apply the classifier to the frame//用级联分类器来检测目标图片detectAndDisplay( frame );if( waitKey(10) == 27 ) { break; } // escape}return 0;
}/** @function detectAndDisplay */
void detectAndDisplay( Mat frame )
{std::vector<Rect> faces;Mat frame_gray;cvtColor( frame, frame_gray, COLOR_BGR2GRAY );//颜色空间转换，由于haar和LBP均是对灰度进行处理，所以必须事先转换成灰度equalizeHist( frame_gray, frame_gray );//直方图均衡化//-- Detect faces //检测脸face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(60, 60) );//在目标图像中检测出脸的矩形轮廓for ( size_t i = 0; i < faces.size(); i++ ){Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );//画出包围脸部的椭圆Mat faceROI = frame_gray( faces[i] );//确定脸部所在的矩形区域为感兴趣区域，然后进行后续的眼睛检测std::vector<Rect> eyes;//矩形向量//-- In each face, detect eyeseyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(50, 50) );//检测眼部for ( size_t j = 0; j < eyes.size(); j++ ){Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );//画出眼部所在的圆圈}}//-- Show what you gotimshow( window_name, frame );
}

![](/home/mazh/Pictures/Screenshot from 2019-05-28 16-53-58.png)

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Cascade Classifier

Cascade Classifier

一.基础概念

1.Haar和LBP特征

2.主要函数

§ CascadeClassifier() [1/2]

§ CascadeClassifier() [2/2]

§ load()

§ detectMultiScale() [1/3]

§ detectMultiScale() [2/3]

§ detectMultiScale() [3/3]

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