C++ USES opencv for face detection
- 2020-06-01 10:43:35
- OfStack
All posts on this site are based on the unbuntu system, of course, a little modification of the same trial in windows, after this site's brain, the just posted that python face and eye detection procedures with C++ implementation, of course, also referred to a lot of great god's blog, let's take a look at the following:
Install opencv on the Linux system and I'll say it again just in case someone doesn't install or debug the program that sprays this site is a trap,
sudo apt-get install libcv-dev
sudo apt-get install libopencv-dev
See your usr/share/opencv/haarcascades directory have appeared a few training set. XML file, then I take play under 1 face and eye detection as a instance, procedure is as follows:
Many people can't compile opencv, so let me write a few more sentences to solve the problem of many beginners
When you have finished your copy code, save it as xiaorun.cpp. Please compile and try g++ -o xiaorun. / xiaorun.cpp-lopencv_imgproc-lopencv_core_lopencv_objdetect
Can be realized
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>
using namespace cv;
using namespace std;
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,
double scale, bool tryflip );
int main()
{
CascadeClassifier cascade, nestedCascade;
bool stop = false;
cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
nestedCascade.load("/usr/share/opencv/haarcascades/haarcascade_eye.xml");
// frame = imread("renlian.jpg");
VideoCapture cap(0); // Turn on the default camera
if(!cap.isOpened())
{
return -1;
}
Mat frame;
Mat edges;
while(!stop)
{
cap>>frame;
detectAndDraw( frame, cascade, nestedCascade,2,0 );
if(waitKey(30) >=0)
stop = true;
imshow("cam",frame);
}
//CascadeClassifier cascade, nestedCascade;
// bool stop = false;
// The name of the trained file is placed in the same directory as the executable
// cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
// nestedCascade.load("/usr/share/opencv/haarcascades/aarcascade_eye.xml");
// frame = imread("renlian.jpg");
// detectAndDraw( frame, cascade, nestedCascade,2,0 );
// waitKey();
//while(!stop)
//{
// cap>>frame;
// detectAndDraw( frame, cascade, nestedCascade,2,0 );
if(waitKey(30) >=0)
stop = true;
//}
return 0;
}
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,
double scale, bool tryflip )
{
int i = 0;
double t = 0;
// Create a vector container for your face
vector<Rect> faces, faces2;
// define 1 Some colors are used to indicate different faces
const static Scalar colors[] = {
CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
// Create a smaller image to speed up detection
//nt cvRound (double value) right 1 a double Number of type 4 Give up 5 Enter and return 1 Integer number!
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
// Convert to a grayscale image, Harr Features are based on grayscale
cvtColor( img, gray, CV_BGR2GRAY );
// imshow(" gray ",gray);
// Change the image size and use the bilinear difference
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
// imshow(" Smaller size ",smallImg);
// The transformed image was averaged by histogram
equalizeHist( smallImg, smallImg );
//imshow(" Histogram mean value processing ",smallImg);
// Program start and end insert this function to get the time, the algorithm is calculated to get the execution time
t = (double)cvGetTickCount();
// To detect human faces
//detectMultiScale In the function smallImg Represents the input image to be detected as smallImg . faces Represents the detected face target sequence, 1.1 said
// The ratio of each image size reduction is 1.1 . 2 each 1 At least two targets must be detected 3 Second is the real goal ( Because the pixels around and the different Windows are big
// Small can detect faces ),CV_HAAR_SCALE_IMAGE Means that instead of scaling the classifier to detect, it scales the image, Size(30, 30) As the goal of
// Minimum and maximum size
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,Size(30, 30));
// If enabled, the image is flipped to continue detection
if( tryflip )
{
flip(smallImg, smallImg, 1);
// imshow(" Inverted image ",smallImg);
cascade.detectMultiScale( smallImg, faces2,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,Size(30, 30) );
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
{
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
}
}
t = (double)cvGetTickCount() - t;
// qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i%8];
int radius;
double aspect_ratio = (double)r->width/r->height;
if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
{
// The face is marked on the image before it is shrunk, so it is scaled back here
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
else
rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
color, 3, 8, 0);
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
// The same method is used to detect human eyes
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
,Size(30, 30) );
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
{
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
}
// imshow( " Identify the results ", img );
}