基于金字塔Lucas-Kanande寻找的视频流运动检测
来源:互联网 发布:中国真实gdp数据 编辑:程序博客网 时间:2024/05/17 06:26
//-----------基于金字塔Lucas-Kanande寻找的视频流运动检测------------
#include "cv.h"
#include "highgui.h"
#include <time.h>
#include <math.h>
#include <ctype.h>
#include <stdio.h>
#include <string.h>
const int MAX_CORNERS = 250;
const int N = 3;
// ring image buffer 圈出图像缓冲
IplImage **buf = 0;//指针的指针
int last = 0;
// temporary images临时图像
IplImage *mhi = 0; // MHI: motion history image
CvPoint pt[4];
int win_size = 10;
// 参数:
// img – 输入视频帧
// dst – 检测结果
void update_mhi(IplImage* img, IplImage* dst, int diff_threshold)
{
CvSize size = cvSize(img->width, img->height);
// get current frame size,得到当前帧的尺寸
int i, idx1, idx2;
/*先进行数据的初始化*/
if (!mhi || mhi->width != size.width || mhi->height != size.height)
{
if (buf == 0) //若尚没有初始化则分配内存给他
{
buf = (IplImage**)malloc(N*sizeof(buf[0]));
memset(buf, 0, N*sizeof(buf[0]));
}
for (i = 0; i < N; i++)
{
cvReleaseImage(&buf[i]);
buf[i] = cvCreateImage(size, IPL_DEPTH_8U, 1);
cvZero(buf[i]);// clear Buffer Frame at the beginning
}
cvReleaseImage(&mhi);
mhi = cvCreateImage(size, IPL_DEPTH_32F, 1);
cvZero(mhi); // clear MHI at the beginning
} // end of if(mhi)
/*将当前要处理的帧转化为灰度放到buffer的最后一帧中*/
cvCvtColor(img, buf[last], CV_BGR2GRAY); // convert frame to grayscale
/*设定帧的序号*/
idx1 = last;
idx2 = (last + 1) % N; // index of (last - (N-1))th frame
last = idx2;
IplImage* eig_image = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* tmp_image = cvCreateImage(size, IPL_DEPTH_8U, 1);
int corner_count = MAX_CORNERS;
CvPoint2D32f* cornersA = new CvPoint2D32f[MAX_CORNERS];
cvGoodFeaturesToTrack(
buf[idx1],
eig_image,
tmp_image,
cornersA,
&corner_count,
0.01,
20,
0,
3,
0,
0.04
);
cvFindCornerSubPix(
buf[idx1],
cornersA,//输入角点的初始坐标,也存储精确的输出坐标。
corner_count,
cvSize(win_size, win_size),
cvSize(-1, -1),
cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03)
);
// Call the Lucas Kanade algorithm
//
char features_found[MAX_CORNERS];
float feature_errors[MAX_CORNERS];
CvSize pyr_sz = cvSize(buf[idx1]->width + 8, buf[idx1]->height / 3);
IplImage* pyrA = cvCreateImage(pyr_sz, IPL_DEPTH_32F, 1);
IplImage* pyrB = cvCreateImage(pyr_sz, IPL_DEPTH_32F, 1);
CvPoint2D32f* cornersB = new CvPoint2D32f[MAX_CORNERS];
cvCalcOpticalFlowPyrLK(
buf[idx1],
buf[idx2],
pyrA,
pyrB,
cornersA,
cornersB,
corner_count,
cvSize(win_size, win_size),
5,
features_found,
feature_errors,
cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3),
0
);
// Now make some image of what we are looking at:
//
for (int i = 0; i<corner_count; i++) {
if (features_found[i] == 0 || feature_errors[i]>100) {
// printf("Error is %f/n",feature_errors[i]);
continue;
}
// printf("Got it/n");
CvPoint p0 = cvPoint(
cvRound(cornersA[i].x),
cvRound(cornersA[i].y)
);
CvPoint p1 = cvPoint(
cvRound(cornersB[i].x),
cvRound(cornersB[i].y)
);
cvLine(img, p0, p1, CV_RGB(255, 0, 0), 2);
}
}
int main(int argc, char** argv)
{
IplImage* motion = 0;
CvCapture* capture = 0;
if (argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
capture = cvCaptureFromCAM(argc == 2 ? argv[1][0] - '0' : 0);//摄像头为视频来源
else if (argc == 2)
capture = cvCaptureFromAVI(argv[1]);//AVI为视频来源
if (capture)
{
cvNamedWindow("Motion", 1);//建立窗口
for (;;)
{
IplImage* image;
if (!cvGrabFrame(capture))//捕捉一桢
break;
image = cvRetrieveFrame(capture);//取出这个帧
if (image)//若取到则判断motion是否为空
{
if (!motion)
{
motion = cvCreateImage(cvSize(image->width, image->height), 8, 1);
//创建motion帧,八位,一通道
cvZero(motion);
//零填充motion
motion->origin = image->origin;
//内存存储的顺序和取出的帧相同
}
}
update_mhi(image, motion, 60);//更新历史图像
cvShowImage("Motion", image);//显示处理过的图像
if (cvWaitKey(10) >= 0)//10ms中按任意键退出
break;
}
cvReleaseCapture(&capture);//释放设备
cvDestroyWindow("Motion");//销毁窗口
}
return 0;
}
#include "cv.h"
#include "highgui.h"
#include <time.h>
#include <math.h>
#include <ctype.h>
#include <stdio.h>
#include <string.h>
const int MAX_CORNERS = 250;
const int N = 3;
// ring image buffer 圈出图像缓冲
IplImage **buf = 0;//指针的指针
int last = 0;
// temporary images临时图像
IplImage *mhi = 0; // MHI: motion history image
CvPoint pt[4];
int win_size = 10;
// 参数:
// img – 输入视频帧
// dst – 检测结果
void update_mhi(IplImage* img, IplImage* dst, int diff_threshold)
{
CvSize size = cvSize(img->width, img->height);
// get current frame size,得到当前帧的尺寸
int i, idx1, idx2;
/*先进行数据的初始化*/
if (!mhi || mhi->width != size.width || mhi->height != size.height)
{
if (buf == 0) //若尚没有初始化则分配内存给他
{
buf = (IplImage**)malloc(N*sizeof(buf[0]));
memset(buf, 0, N*sizeof(buf[0]));
}
for (i = 0; i < N; i++)
{
cvReleaseImage(&buf[i]);
buf[i] = cvCreateImage(size, IPL_DEPTH_8U, 1);
cvZero(buf[i]);// clear Buffer Frame at the beginning
}
cvReleaseImage(&mhi);
mhi = cvCreateImage(size, IPL_DEPTH_32F, 1);
cvZero(mhi); // clear MHI at the beginning
} // end of if(mhi)
/*将当前要处理的帧转化为灰度放到buffer的最后一帧中*/
cvCvtColor(img, buf[last], CV_BGR2GRAY); // convert frame to grayscale
/*设定帧的序号*/
idx1 = last;
idx2 = (last + 1) % N; // index of (last - (N-1))th frame
last = idx2;
IplImage* eig_image = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* tmp_image = cvCreateImage(size, IPL_DEPTH_8U, 1);
int corner_count = MAX_CORNERS;
CvPoint2D32f* cornersA = new CvPoint2D32f[MAX_CORNERS];
cvGoodFeaturesToTrack(
buf[idx1],
eig_image,
tmp_image,
cornersA,
&corner_count,
0.01,
20,
0,
3,
0,
0.04
);
cvFindCornerSubPix(
buf[idx1],
cornersA,//输入角点的初始坐标,也存储精确的输出坐标。
corner_count,
cvSize(win_size, win_size),
cvSize(-1, -1),
cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03)
);
// Call the Lucas Kanade algorithm
//
char features_found[MAX_CORNERS];
float feature_errors[MAX_CORNERS];
CvSize pyr_sz = cvSize(buf[idx1]->width + 8, buf[idx1]->height / 3);
IplImage* pyrA = cvCreateImage(pyr_sz, IPL_DEPTH_32F, 1);
IplImage* pyrB = cvCreateImage(pyr_sz, IPL_DEPTH_32F, 1);
CvPoint2D32f* cornersB = new CvPoint2D32f[MAX_CORNERS];
cvCalcOpticalFlowPyrLK(
buf[idx1],
buf[idx2],
pyrA,
pyrB,
cornersA,
cornersB,
corner_count,
cvSize(win_size, win_size),
5,
features_found,
feature_errors,
cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3),
0
);
// Now make some image of what we are looking at:
//
for (int i = 0; i<corner_count; i++) {
if (features_found[i] == 0 || feature_errors[i]>100) {
// printf("Error is %f/n",feature_errors[i]);
continue;
}
// printf("Got it/n");
CvPoint p0 = cvPoint(
cvRound(cornersA[i].x),
cvRound(cornersA[i].y)
);
CvPoint p1 = cvPoint(
cvRound(cornersB[i].x),
cvRound(cornersB[i].y)
);
cvLine(img, p0, p1, CV_RGB(255, 0, 0), 2);
}
}
int main(int argc, char** argv)
{
IplImage* motion = 0;
CvCapture* capture = 0;
if (argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
capture = cvCaptureFromCAM(argc == 2 ? argv[1][0] - '0' : 0);//摄像头为视频来源
else if (argc == 2)
capture = cvCaptureFromAVI(argv[1]);//AVI为视频来源
if (capture)
{
cvNamedWindow("Motion", 1);//建立窗口
for (;;)
{
IplImage* image;
if (!cvGrabFrame(capture))//捕捉一桢
break;
image = cvRetrieveFrame(capture);//取出这个帧
if (image)//若取到则判断motion是否为空
{
if (!motion)
{
motion = cvCreateImage(cvSize(image->width, image->height), 8, 1);
//创建motion帧,八位,一通道
cvZero(motion);
//零填充motion
motion->origin = image->origin;
//内存存储的顺序和取出的帧相同
}
}
update_mhi(image, motion, 60);//更新历史图像
cvShowImage("Motion", image);//显示处理过的图像
if (cvWaitKey(10) >= 0)//10ms中按任意键退出
break;
}
cvReleaseCapture(&capture);//释放设备
cvDestroyWindow("Motion");//销毁窗口
}
return 0;
}
1 0
- 基于金字塔Lucas-Kanande寻找的视频流运动检测
- 基于轮廓寻找的视频流运动检测
- opencv基于轮廓寻找的视频流运动检测
- OpenCV学习——基于轮廓寻找的视频流运动检测
- OpenCV学习——基于轮廓寻找的视频流运动检测
- [learning opencv]第十章 跟踪与运动:金字塔Lucas-kanade(cvCalcOpticalFlowPyrLK)检测光流
- OpenCV学习之寻找轮廓实现视频流的运动目标检测
- Lerning OpenCV金字塔Lucas-Kanade光流-------运动与跟踪
- opencv关于图像金字塔Lucas-Kanade光流检测的实现
- 【opencv学习】lucas金字塔光流算法的实现——基于opencv3.0+vs2013+windows10
- 基于帧间差法的运动检测
- 运动物体的检测--对视频检测
- MATLAB实现的视频运动检测方法
- 基于Vibe算法的运动目标检测
- 基于OpenCv的运动物体检测算法
- 基于opencv运动检测的一些方法
- 基于 PIR 的运动检测:传感器解决方案
- 基于帧间差分法的运动目标检测
- 运行fast-rcnn-windows遇到的问题
- Linux/Unix select函数 及select/poll与epoll的对比
- C语言堆栈入门——堆和栈的区别
- hdoj5053the Sum of Cube(数学,打表)
- windows安装composer及使用教程
- 基于金字塔Lucas-Kanande寻找的视频流运动检测
- 双线性插值(Bilinear Interpolation)
- 部署好静态网页,上传文件在gh-pages分支转不到master的解决办法
- dom4j解析xml文档
- 4.文字在div垂直居中的方式
- VB查询数据库用于Ext.NET的Store
- TCP选项之SO_RCVBUF和SO_SNDBUF
- application/x-www-form-urlencoded
- Robolectric测试环境移除stetho