卡爾曼濾波+opencv 實現跟蹤人臉 小demo

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/tracking.hpp"
#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

/** 函數聲明 */
void detectAndDisplay(Mat& frame);

/** 全局變量 */
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 = "Face detection with Kalman";
RNG rng(12345);
struct face{
    Point leftTop=0;
    int width=0;
    int height=0;
};
face preFace;
/** @主函數 */
int main()
{
    //kalman參數設置
    
    int stateNum = 4;
    int measureNum = 2;
    KalmanFilter KF(stateNum, measureNum, 0);
    //Mat processNoise(stateNum, 1, CV_32F);
    Mat measurement = Mat::zeros(measureNum, 1, CV_32F);
    KF.transitionMatrix = *(Mat_<float>(stateNum, stateNum) << 1, 0, 1, 0,//A 狀態轉移矩陣
        0, 1, 0, 1,
        0, 0, 1, 0,
        0, 0, 0, 1);
    //這里沒有設置控制矩陣B，默認為零
    setIdentity(KF.measurementMatrix);//H=[1,0,0,0;0,1,0,0] 測量矩陣
    setIdentity(KF.processNoiseCov, Scalar::all(1e-5));//Q高斯白噪聲，單位陣
    setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));//R高斯白噪聲，單位陣
    setIdentity(KF.errorCovPost, Scalar::all(1));//P后驗誤差估計協方差矩陣，初始化為單位陣
    randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));//初始化狀態為隨機值

    //讀入視頻
    
    if (!face_cascade.load(face_cascade_name)){ cout << "--(!)Error loading\n" << endl; };
    Mat frame, frame2;
    VideoCapture cap;
    cap.open("me1.mp4");
    //cap.open("me2.mp4");
    //cap.open("me3.mp4");
    while (true){
        for (int i = 0; i < 1; i++){
            cap >> frame;
        }
        if (!frame.empty())
        {
            resize(frame, frame2, Size(), 0.5, 0.5, INTER_LINEAR);
            Mat prediction = KF.predict();
            Point predict_pt = Point((int)prediction.at<float>(0), (int)prediction.at<float>(1));
            detectAndDisplay(frame2);
            measurement.at<float>(0) = (float)preFace.leftTop.x;
            measurement.at<float>(1) = (float)preFace.leftTop.y;
            KF.correct(measurement);
            //畫卡爾曼的效果
            Point center(predict_pt.x + preFace.width*0.5, predict_pt.y + preFace.height*0.5);
            ellipse(frame2, center, Size(preFace.width*0.3, preFace.height*0.3), 0, 0, 360, Scalar(0, 0, 255), 4, 8, 0);
            circle(frame2, center, 3, Scalar(0, 0, 255), -1);
            imshow(window_name, frame2);
            waitKey(1);
        }
        else
        {
            printf(" --(!) No frame -- Break!");
            break; 
        }
    }
    return 0;
}

/** @函數 detectAndDisplay */
void detectAndDisplay(Mat& frame)
{
    std::vector<Rect> faces;
    Mat frame_gray;
    int Max_area=0;
    int faceID=0;

    cvtColor(frame, frame_gray, CV_BGR2GRAY);
    equalizeHist(frame_gray, frame_gray);

    //-- 多尺寸檢測人臉
    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
    //找出最大的臉，可以去除不是臉的誤檢，這些誤檢一般比較小
    for (int i = 0; i < faces.size(); i++)
    {
        if ((int)(faces[i].width*faces[i].height) > Max_area){
            Max_area =(int) faces[i].width*faces[i].height;
            faceID=i;
        }    
    }

    if (faces.size() > 0)//必須是檢測到臉才繪制當前人臉圓圈，並且只能繪制最大的臉
    {
        preFace.leftTop.x = faces[faceID].x;
        preFace.leftTop.y = faces[faceID].y;
        preFace.height = faces[faceID].height;
        preFace.width = faces[faceID].width;
        Point center(faces[faceID].x + faces[faceID].width*0.5, faces[faceID].y + faces[faceID].height*0.5);
        ellipse(frame, center, Size(faces[faceID].width*0.5, faces[faceID].height*0.5), 0, 0, 360, Scalar(0, 255, 0), 1, 8, 0);
        circle(frame, center, 3, Scalar(0, 255,0), -1);
    }
    else{//沒檢測到人臉繪制之前的人臉
        Point center(preFace.leftTop.x + preFace.width*0.5, preFace.leftTop.y + preFace.height*0.5);
        ellipse(frame, center, Size(preFace.width*0.5, preFace.height*0.5), 0, 0, 360, Scalar(0, 255, 0), 1, 8, 0);
        circle(frame, center, 3, Scalar(0, 255, 0), -1);
    }
    
    
}