一.稠密光流法跟踪移动物体
例1
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
VideoCapture capture("mulballs.mp4");
Mat prevFrame, prevGray;
if (!capture.read(prevFrame))
{
cout << "请确认视频文件名称是否正确" << endl;
return -1;
}
//将彩色图像转换成灰度图像
cvtColor(prevFrame, prevGray, COLOR_BGR2GRAY);
while (true)
{
Mat nextFrame, nextGray;
//所有图像处理完成后推出程序
if (!capture.read(nextFrame))
{
break;
}
imshow("视频图像", nextFrame);
//计算稠密光流
cvtColor(nextFrame, nextGray, COLOR_BGR2GRAY);
Mat_<Point2f> flow; //两个方向的运动速度
calcOpticalFlowFarneback(prevGray, nextGray, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
Mat xV = Mat::zeros(prevFrame.size(), CV_32FC1); //x方向移动速度
Mat yV = Mat::zeros(prevFrame.size(), CV_32FC1); //y方向移动速度
//提取两个方向的速度
for (int row = 0; row < flow.rows; row++)
{
for (int col = 0; col < flow.cols; col++)
{
const Point2f& flow_xy = flow.at<Point2f>(row, col);
xV.at<float>(row, col) = flow_xy.x;
yV.at<float>(row, col) = flow_xy.y;
}
}
//计算向量角度和幅值
Mat magnitude, angle;
cartToPolar(xV, yV, magnitude, angle);//cartToPolar是OpenCV中的一个函数
//讲角度转换成角度制
angle = angle * 180.0 / CV_PI / 2.0;
//把幅值归一化到0-255区间便于显示结果
normalize(magnitude, magnitude, 0, 255, NORM_MINMAX);
//计算角度和幅值的绝对值
convertScaleAbs(magnitude, magnitude);
convertScaleAbs(angle, angle);
//讲运动的幅值和角度生成HSV颜色空间的图像
Mat HSV = Mat::zeros(prevFrame.size(), prevFrame.type());
vector<Mat> result;
split(HSV, result);
result[0] = angle; //决定颜色
result[1] = Scalar(255);
result[2] = magnitude; //决定形态
//将三个多通道图像合并成三通道图像
merge(result, HSV);
//讲HSV颜色空间图像转换到RGB颜色空间中
Mat rgbImg;
cvtColor(HSV, rgbImg, COLOR_HSV2BGR);
//显示检测结果
imshow("运动检测结果", rgbImg);
int ch = waitKey(5);
if (ch == 27)
{
break;
}
}
waitKey(0);
return 0;
}
运行结果：
二.稀疏光流法跟踪移动物体
通常情况下，我们使用的是托马斯角点
例2
*附例2代码：
nclude <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
void draw_lines(Mat &image, vector<Point2f> pt1, vector<Point2f> pt2);
vector<Scalar> color_lut; //颜色查找表
int main()
{
VideoCapture capture("mulballs.mp4");
Mat prevframe, prevImg;
if (!capture.read(prevframe))
{
cout << "请确认输入视频文件是否正确" << endl;
return -1;
}
cvtColor(prevframe, prevImg, COLOR_BGR2GRAY);
//角点检测相关参数设置
vector<Point2f> Points;
double qualityLevel = 0.01;
int minDistance = 10;
int blockSize = 3;
bool useHarrisDetector = false;
double k = 0.04;
int Corners = 5000;
//角点检测
goodFeaturesToTrack(prevImg, Points, Corners, qualityLevel, minDistance, Mat(),
blockSize, useHarrisDetector, k);
//稀疏光流检测相关参数设置
vector<Point2f> prevPts; //前一帧图像角点坐标
vector<Point2f> nextPts; //当前帧图像角点坐标
vector<uchar> status; //检点检测到的状态
vector<float> err;
TermCriteria criteria = TermCriteria(TermCriteria::COUNT
+ TermCriteria::EPS, 30, 0.01);
double derivlambda = 0.5;
int flags = 0;
//初始状态的角点
vector<Point2f> initPoints;
initPoints.insert(initPoints.end(), Points.begin(), Points.end());
//前一帧图像中的角点坐标
prevPts.insert(prevPts.end(), Points.begin(), Points.end());
while (true)
{
Mat nextframe, nextImg;
if (!capture.read(nextframe))
{
break;
}
imshow("nextframe", nextframe);
//光流跟踪
cvtColor(nextframe, nextImg, COLOR_BGR2GRAY);
calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err,
Size(31, 31), 3, criteria, derivlambda, flags);
//判断角点是否移动，如果不移动就删除
size_t i, k;
for (i = k = 0; i < nextPts.size(); i++)
{
// 距离与状态测量
double dist = abs(prevPts[i].x - nextPts[i].x) + abs(prevPts[i].y - nextPts[i].y);
if (status[i] && dist > 2)
{
prevPts[k] = prevPts[i];
initPoints[k] = initPoints[i];
nextPts[k++] = nextPts[i];
circle(nextframe, nextPts[i], 3, Scalar(0, 255, 0), -1, 8);
}
}
//更新移动角点数目
nextPts.resize(k);
prevPts.resize(k);
initPoints.resize(k);
// 绘制跟踪轨迹
draw_lines(nextframe, initPoints, nextPts);
imshow("result", nextframe);
char c = waitKey(50);
if (c == 27)
{
break;
}
//更新角点坐标和前一帧图像
std::swap(nextPts, prevPts);
nextImg.copyTo(prevImg);
//如果角点数目少于30，就重新检测角点
if (initPoints.size() < 30)
{
goodFeaturesToTrack(prevImg, Points, Corners, qualityLevel,
minDistance, Mat(), blockSize, useHarrisDetector, k);
initPoints.insert(initPoints.end(), Points.begin(), Points.end());
prevPts.insert(prevPts.end(), Points.begin(), Points.end());
printf("total feature points : %d\n", prevPts.size());
}
}
return 0;
}
void draw_lines(Mat &image, vector<Point2f> pt1, vector<Point2f> pt2)
{
RNG rng(5000);
if (color_lut.size() < pt1.size())
{
for (size_t t = 0; t < pt1.size(); t++)
{
color_lut.push_back(Scalar(rng.uniform(0, 255), rng.uniform(0, 255),
rng.uniform(0, 255)));
}
}
for (size_t t = 0; t < pt1.size(); t++) {
line(image, pt1[t], pt2[t], color_lut[t], 2, 8, 0);
}
}
运行结果：
