//连续自适应的MeanShift算法
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <ctype.h>
using namespace cv;
using namespace std;
//-----------------------------------【全局变量声明】-----------------------------------------
// 描述：声明全局变量
//-------------------------------------------------------------------------------------------------
Mat image;
bool backprojMode = false;
bool selectObject = false;
int trackObject = 0;
bool showHist = true;
Point origin;
Rect selection;
int vmin = 10, vmax = 256, smin = 30;
//--------------------------------【onMouse( )回调函数】------------------------------------
// 描述：鼠标操作回调
//-------------------------------------------------------------------------------------------------
static void onMouse( int event, int x, int y, int, void* ){
if( selectObject ){
selection.x = MIN(x, origin.x);
selection.y = MIN(y, origin.y);
selection.width = std::abs(x - origin.x);
selection.height = std::abs(y - origin.y);
selection &= Rect(0, 0, image.cols, image.rows);
}
switch( event ){
case EVENT_LBUTTONDOWN:
origin = Point(x,y);
selection = Rect(x,y,0,0);
selectObject = true;
break;
case EVENT_LBUTTONUP:
selectObject = false;
if( selection.width > 0 && selection.height > 0 )
trackObject = -1;
break;
}
}
//--------------------------------【help( )函数】----------------------------------------------
// 描述：输出帮助信息
//-------------------------------------------------------------------------------------------------
static void ShowHelpText(){
cout << "\n\n\t操作说明： \n"
"\t\t用鼠标框选对象来初始化跟踪\n"
"\t\tESC - 退出程序\n"
"\t\tc - 停止追踪\n"
"\t\tb - 开/关-投影视图\n"
"\t\th - 显示/隐藏-对象直方图\n"
"\t\tp - 暂停视频\n";
}
const char* keys ={ "{1| | 0 | camera number}"};
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main( int argc, const char** argv ){
ShowHelpText();
VideoCapture cap;
Rect trackWindow;
int hsize = 16;
float hranges[] = {0,180};
const float* phranges = hranges;
cap.open(0);
if(!cap.isOpened()){cout << "不能初始化摄像头\n";}
namedWindow( "Histogram", 0 );
namedWindow( "CamShift Demo", 0 );
setMouseCallback( "CamShift Demo", onMouse, 0 );
createTrackbar( "Vmin", "CamShift Demo", &vmin, 256, 0 );
createTrackbar( "Vmax", "CamShift Demo", &vmax, 256, 0 );
createTrackbar( "Smin", "CamShift Demo", &smin, 256, 0 );
Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj;
bool paused = false;
for(;;){
if(!paused ){
cap >> frame;
if( frame.empty() ) break;
}
frame.copyTo(image);
if( !paused ){
cvtColor(image, hsv, COLOR_BGR2HSV);
if( trackObject ){
int _vmin = vmin, _vmax = vmax;
inRange(hsv, Scalar(0, smin, MIN(_vmin,_vmax)),
Scalar(180, 256, MAX(_vmin, _vmax)), mask);
int ch[] = {0, 0};
hue.create(hsv.size(), hsv.depth());
mixChannels(&hsv, 1, &hue, 1, ch, 1);
if( trackObject < 0 ){
Mat roi(hue, selection), maskroi(mask, selection);
calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);
normalize(hist, hist, 0, 255, NORM_MINMAX);
trackWindow = selection;
trackObject = 1;
histimg = Scalar::all(0);
int binW = histimg.cols / hsize;
Mat buf(1, hsize, CV_8UC3);
for( int i = 0; i < hsize; i++ )
buf.at<Vec3b>(i) = Vec3b(saturate_cast<uchar>(i*180./hsize), 255, 255);
cvtColor(buf, buf, COLOR_HSV2BGR);
for( int i = 0; i < hsize; i++ ){
int val = saturate_cast<int>(hist.at<float>(i)*histimg.rows/255);
rectangle( histimg, Point(i*binW,histimg.rows),
Point((i+1)*binW,histimg.rows - val),
Scalar(buf.at<Vec3b>(i)), -1, 8 );
}
}
calcBackProject(&hue, 1, 0, hist, backproj, &phranges);
backproj &= mask;
RotatedRect trackBox = CamShift(backproj, trackWindow,
TermCriteria( TermCriteria::EPS | TermCriteria::COUNT, 10, 1 ));
if( trackWindow.area() <= 1 ){
int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5)/6;
trackWindow = Rect(trackWindow.x - r, trackWindow.y - r,
trackWindow.x + r, trackWindow.y + r) &
Rect(0, 0, cols, rows);
}
if( backprojMode )
cvtColor( backproj, image, COLOR_GRAY2BGR );
ellipse( image, trackBox, Scalar(0,0,255), 3, LINE_AA );
}
}
else if( trackObject < 0 )
paused = false;
if( selectObject && selection.width > 0 && selection.height > 0 ){
Mat roi(image, selection);
bitwise_not(roi, roi);
}
imshow( "CamShift Demo", image );
imshow( "Histogram", histimg );
char c = (char)waitKey(10);
if( c == 27 )break;
switch(c){
case 'b':
backprojMode = !backprojMode;
break;
case 'c':
trackObject = 0;
histimg = Scalar::all(0);
break;
case 'h':
showHist = !showHist;
if( !showHist )
destroyWindow( "Histogram" );
else
namedWindow( "Histogram", 1 );
break;
case 'p':
paused = !paused;
break;
default:
;
}
}
return 0;
}
光流
#include <opencv2/video/video.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <iostream>
#include <cstdio>
using namespace std;
using namespace cv;
//-----------------------------------【全局函数声明】-----------------------------------------
// 描述：声明全局函数
//-------------------------------------------------------------------------------------------------
void tracking(Mat &frame, Mat &output);
bool addNewPoints();
bool acceptTrackedPoint(int i);
//-----------------------------------【全局变量声明】-----------------------------------------
// 描述：声明全局变量
//-------------------------------------------------------------------------------------------------
string window_name = "optical flow tracking";
Mat gray; // 当前图片
Mat gray_prev; // 预测图片
vector<Point2f> points[2]; // point0为特征点的原来位置，point1为特征点的新位置
vector<Point2f> initial; // 初始化跟踪点的位置
vector<Point2f> features; // 检测的特征
int maxCount = 500; // 检测的最大特征数
double qLevel = 0.01; // 特征检测的等级
double minDist = 10.0; // 两特征点之间的最小距离
vector<uchar> status; // 跟踪特征的状态，特征的流发现为1，否则为0
vector<float> err;
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main(){
Mat frame,result;//定义每一帧
VideoCapture capture("1.avi");//开视频
if(capture.isOpened()){ // 摄像头读取文件开关
while(true){//死循环
capture >> frame;//读入
if(!frame.empty()){//非空
tracking(frame, result);//进行跟踪
}
else{//报错
printf(" --(!) No captured frame -- Break!");
break;
}
int c = waitKey(50);//等待50MS并读入期间按下的键
if( (char)c == 27 ){
break;
}
}
}
return 0;
}
//-------------------------------------------------------------------------------------------------
// function: tracking
// brief: 跟踪
// parameter: frame 输入的视频帧
// output 有跟踪结果的视频帧
// return: void
//-------------------------------------------------------------------------------------------------
void tracking(Mat &frame, Mat &output){//引用传入每一帧及输出图像
cvtColor(frame, gray, COLOR_BGR2GRAY);//读入帧变成灰度图
frame.copyTo(output);//先复制到输出帧
if (addNewPoints()) {//检测是否有新点应该被添加，应该说已有跟踪点少于10
goodFeaturesToTrack(gray, features, maxCount, qLevel, minDist);//得到好的跟踪点的话
points[0].insert(points[0].end(), features.begin(), features.end());//把FEATURES整体赋给POINT0
initial.insert(initial.end(), features.begin(), features.end());//初始数组也一样
}
if (gray_prev.empty()){//如果旧图为空（就是第一帧）
gray.copyTo(gray_prev);//把新图复制到旧图
}
calcOpticalFlowPyrLK(gray_prev, gray, points[0], points[1], status, err);// l-k光流法运动估计
int k = 0;// 去掉一些不好的特征点
for (size_t i=0; i<points[1].size(); i++){//逐点扫描
if (acceptTrackedPoint(i)){//判断是否可以接受
initial[k] = initial[i];//初如化跟踪点位置
points[1][k++] = points[1][i];//point1为特征点的新位置
}
}
points[1].resize(k);//去掉部分点后尺寸变小
initial.resize(k);//去掉部分点后尺寸变小
for (size_t i=0; i<points[1].size(); i++){// 显示特征点和运动轨迹
line(output, initial[i], points[1][i], Scalar(0, 0, 255));//由initial[i]到 points[1][i]画线
circle(output, points[1][i], 3, Scalar(0, 255, 0), -1);//以新点坐标为圆心画圆，突出表示新点
}
swap(points[1], points[0]); //把当前跟踪结果作为下一次跟踪的参考
swap(gray_prev, gray); // 把当前灰图作为下一图的原图参考
imshow(window_name, output);//输出图片
}
//-------------------------------------------------------------------------------------------------
// function: addNewPoints
// brief: 检测新点是否应该被添加
// parameter:
// return: 是否被添加标志
//-------------------------------------------------------------------------------------------------
bool addNewPoints(){//检测新点是否应该被添加
return points[0].size() <= 10;//point0为特征点的原来位置，10个以下表示可以继续添加
}
//-------------------------------------------------------------------------------------------------
// function: acceptTrackedPoint
// brief: 决定哪些跟踪点被接受
// parameter:
// return:
//-------------------------------------------------------------------------------------------------
bool acceptTrackedPoint(int i){//就是说与原来的点的差别不能太小
return status[i] && ((abs(points[0][i].x - points[1][i].x) + abs(points[0][i].y - points[1][i].y)) > 2);
//status有流发现，然后新旧点的曼哈顿距离和必须大于2
}
点追踪
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <ctype.h>
using namespace cv;
using namespace std;
Point2f point;
bool addRemovePt = false;
static void help(){
cout << "\n\n\t该Demo演示了 Lukas-Kanade基于光流的lkdemo\n";
cout << "\n\t程序默认从摄像头读入视频，可以按需改为从视频文件读入图像\n";
cout << "\n\t操作说明: \n"
"\t\t通过点击在图像中添加/删除特征点\n"
"\t\tESC - 退出程序\n"
"\t\tr -自动进行追踪\n"
"\t\tc - 删除所有点\n"
"\t\tn - 开/光-夜晚模式\n"<< endl;
}
//--------------------------------【onMouse( )回调函数】------------------------------------
// 描述：鼠标操作回调
//-------------------------------------------------------------------------------------------------
static void onMouse( int event, int x, int y, int /*flags*/, void* /*param*/ ){//有鼠标操作就跳入
if( event == EVENT_LBUTTONDOWN ){//如果是鼠标左键点击事件
point = Point2f((float)x, (float)y);//读出鼠标的坐标
addRemovePt = true;//增加移动点标记置1
}
}
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main( int argc, char** argv ){
help();
VideoCapture cap;
TermCriteria termcrit(TermCriteria::MAX_ITER|TermCriteria::EPS, 20, 0.03);
Size subPixWinSize(10,10), winSize(31,31);
const int MAX_COUNT = 500;
bool needToInit = false;
bool nightMode = false;
cap.open(0);
if( !cap.isOpened() ) {
cout << "Could not initialize capturing...\n";
return 0;
}
namedWindow( "LK Demo", 1 );//开新窗口
setMouseCallback( "LK Demo", onMouse, 0 );//设置鼠标事件
Mat gray, prevGray, image;//定义变量
vector<Point2f> points[2];//定义点向量，即特征点
for(;;) {//死环
Mat frame;
cap >> frame;
if( frame.empty() ) break;
frame.copyTo(image);//入图复制到出图
cvtColor(image, gray, COLOR_BGR2GRAY);//转灰度图
if( nightMode ) image = Scalar::all(0);//
if( needToInit ){// 自动初始化，就是说不个人选
goodFeaturesToTrack(gray, points[1], MAX_COUNT, 0.01, 10, Mat(), 3, 0, 0.04);//寻找好的追踪点
cornerSubPix(gray, points[1], subPixWinSize, Size(-1,-1), termcrit);//亚像素级角点检测
addRemovePt = false;//用户点击点标志清0
}
else if( !points[0].empty() ){//否则就是要用户选择追踪点
vector<uchar> status;
vector<float> err;
if(prevGray.empty())//空
gray.copyTo(prevGray);//复制
calcOpticalFlowPyrLK(prevGray, gray, points[0], points[1], status, err, winSize,3, termcrit, 0, 0.001);
size_t i, k;
for( i = k = 0; i < points[1].size(); i++ ){
if( addRemovePt ){
if( norm(point - points[1][i]) <= 5 ){
addRemovePt = false;
continue;
}
}
if( !status[i] )continue;
points[1][k++] = points[1][i];
circle( image, points[1][i], 3, Scalar(0,255,0), -1, 8);
}
points[1].resize(k);
}
if( addRemovePt && points[1].size() < (size_t)MAX_COUNT ){
vector<Point2f> tmp;//开向量
tmp.push_back(point);//压入点
cornerSubPix( gray, tmp, winSize, Size(-1,-1), termcrit);//亚像素级角点检测
points[1].push_back(tmp[0]);//把临时点压进新点向量中
addRemovePt = false;//标记清0
}
needToInit = false;//清空初始化标志
imshow("LK Demo", image);//输出
char c = (char)waitKey(10);//等待鼠标事件
if( c == 27 )break;
switch( c ){
case 'r'://自动
needToInit = true;
break;
case 'c'://清空
points[0].clear();
points[1].clear();
break;
case 'n':
nightMode = !nightMode;//黑夜模式是说背景是黑色
break;
}
std::swap(points[1], points[0]);//交换点集
cv::swap(prevGray, gray);//交换灰度图
}
return 0;
}
人脸识别
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;
void detectAndDisplay( Mat frame );
//--------------------------------【全局变量声明】----------------------------------------------
// 描述：声明全局变量
//-------------------------------------------------------------------------------------------------
//注意，需要把"haarcascade_frontalface_alt.xml"和"haarcascade_eye_tree_eyeglasses.xml"这两个文件复制到工程路径下
String face_cascade_name = "haarcascade_frontalface_alt.xml";
String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main( void ){
VideoCapture capture;
Mat frame;
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };//-- 1. 加载级联（cascades）
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };//-- 1. 加载级联（cascades）
capture.open(0);//-- 2. 读取视频
if( capture.isOpened() ){
for(;;){
capture >> frame;
if( !frame.empty() )
{ detectAndDisplay( frame ); }//-- 3. 对当前帧使用分类器（Apply the classifier to the frame）
else
{ printf(" --(!) No captured frame -- Break!"); break; }
int c = waitKey(10);
if( (char)c == 'c' ) { break; }
}
}
return 0;
}
void detectAndDisplay( Mat frame ){
std::vector<Rect> faces;//设置面矩形向量，因为脸可能不仅一个，所以开向量
Mat frame_gray;//灰度图
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );//灰度图
equalizeHist( frame_gray, frame_gray );//直方图均衡化，用于提高图像的质量
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );//-- 人脸检测
for( size_t i = 0; i < faces.size(); i++ ){//遍历
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );//读心
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 );//定半径
Mat faceROI = frame_gray( faces[i] );//画圆
std::vector<Rect> eyes;//开眼睛向量
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );//-- 在脸中检测眼睛
for( size_t j = 0; j < eyes.size(); j++ ){//遍历
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );//读心
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );//定半径
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 );//画圆
}
}
imshow( window_name, frame );//-- 显示最终效果图
}
支持向量机（划分点）
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
using namespace cv;
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
using namespace cv::ml;
//-----------------------------------【main( )函数】--------------------------------------------
// 描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main(){
// 视觉表达数据的设置（Data for visual representation）
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
//建立训练数据（ Set up training data）
int labels[4] = {1, -1, -1, -1};
Mat labelsMat(4, 1, CV_32SC1, labels);
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
//设置支持向量机的参数（Set up SVM's parameters）
SVM::Params params;
params.svmType = SVM::C_SVC;
params.kernelType = SVM::LINEAR;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6);
// 训练支持向量机（Train the SVM）
Ptr<SVM> svm = StatModel::train<SVM>(trainingDataMat, ROW_SAMPLE, labelsMat, params);
Vec3b green(0,255,0), blue (255,0,0);
//显示由SVM给出的决定区域 （Show the decision regions given by the SVM）
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j){
Mat sampleMat = (Mat_<float>(1,2) << j,i);
float response = svm->predict(sampleMat);
if (response == 1)
image.at<Vec3b>(i,j) = green;
else if (response == -1)
image.at<Vec3b>(i,j) = blue;
}
//显示训练数据 （Show the training data）
int thickness = -1;
int lineType = 8;
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
//显示支持向量 （Show support vectors）
thickness = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i){
const float* v = sv.ptr<float>(i);
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}
imwrite("result.png", image); // 保存图像
imshow("SVM Simple Example", image); // 显示图像
waitKey(0);
}