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]
void cv::CascadeClassifier::detectMultiScale ( 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]
void cv::CascadeClassifier::detectMultiScale ( 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]
void cv::CascadeClassifier::detectMultiScale ( 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( "
This program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.
"
"You can use Haar or LBP features.
" );
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
"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade
"); return -1; };
//-- 2. Read the video stream //读取视频流
capture.open( 0 );
if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture
"); 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 eyes
eyes_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 got
imshow( window_name, frame );
}
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版权声明：本文为CSDN博主「马泽红」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/weixin_43940163/article/details/90692656