求opencv视频中行人检测和追踪的c++代码

整个项目的结构图：

编写DetectFaceDemo.java,代码如下：

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package com.njupt.zhb.test

import org.opencv.core.Core

import org.opencv.core.Mat

import org.opencv.core.MatOfRect

import org.opencv.core.Point

import org.opencv.core.Rect

import org.opencv.core.Scalar

import org.opencv.highgui.Highgui

import org.opencv.objdetect.CascadeClassifier

//

//燃竖 Detects faces in an image, draws boxes around them, and writes the results

// to "faceDetection.png".

//

public class DetectFaceDemo {

public void run() {

System.out.println("森段并\nRunning DetectFaceDemo")

System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath())

// Create a face detector from the cascade file in the resources

//此迹 directory.

//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath())

//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath())

//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误

/*

* Detected 0 faces Writing faceDetection.png libpng warning: Image

* width is zero in IHDR libpng warning: Image height is zero in IHDR

* libpng error: Invalid IHDR data

*/

//因此，我们将第一个字符去掉

String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1)

CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath)

Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1))

// Detect faces in the image.

// MatOfRect is a special container class for Rect.

MatOfRect faceDetections = new MatOfRect()

faceDetector.detectMultiScale(image, faceDetections)

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length))

// Draw a bounding box around each face.

for (Rect rect : faceDetections.toArray()) {

Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0))

}

// Save the visualized detection.

String filename = "faceDetection.png"

System.out.println(String.format("Writing %s", filename))

Highgui.imwrite(filename, image)

}

}

package com.njupt.zhb.test

import org.opencv.core.Core

import org.opencv.core.Mat

import org.opencv.core.MatOfRect

import org.opencv.core.Point

import org.opencv.core.Rect

import org.opencv.core.Scalar

import org.opencv.highgui.Highgui

import org.opencv.objdetect.CascadeClassifier

//

// Detects faces in an image, draws boxes around them, and writes the results

// to "faceDetection.png".

//

public class DetectFaceDemo {

public void run() {

System.out.println("\nRunning DetectFaceDemo")

System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath())

// Create a face detector from the cascade file in the resources

// directory.

//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath())

//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath())

//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误

/*

* Detected 0 faces Writing faceDetection.png libpng warning: Image

* width is zero in IHDR libpng warning: Image height is zero in IHDR

* libpng error: Invalid IHDR data

*/

//因此，我们将第一个字符去掉

String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1)

CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath)

Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1))

// Detect faces in the image.

// MatOfRect is a special container class for Rect.

MatOfRect faceDetections = new MatOfRect()

faceDetector.detectMultiScale(image, faceDetections)

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length))

// Draw a bounding box around each face.

for (Rect rect : faceDetections.toArray()) {

Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0))

}

// Save the visualized detection.

String filename = "faceDetection.png"

System.out.println(String.format("Writing %s", filename))

Highgui.imwrite(filename, image)

}

}

3.编写测试类：

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package com.njupt.zhb.test

public class TestMain {

public static void main(String[] args) {

System.out.println("Hello, OpenCV")

// Load the native library.

System.loadLibrary("opencv_java246")

new DetectFaceDemo().run()

}

}

//运行结果:

//Hello, OpenCV

//

//Running DetectFaceDemo

///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml

//Detected 8 faces

//Writing faceDetection.png

package com.njupt.zhb.test

public class TestMain {

public static void main(String[] args) {

System.out.println("Hello, OpenCV")

// Load the native library.

System.loadLibrary("opencv_java246")

new DetectFaceDemo().run()

}

}

//运行结果:

//Hello, OpenCV

//

//Running DetectFaceDemo

///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml

//Detected 8 faces

//Writing faceDetection.png

记得给我分，急需

#include "cv.h"

#include <cxcore.h>

#include "highgui.h"

#include <time.h>

#include <math.h>

#include <ctype.h>

#include <stdio.h>

#include <string.h>

// various tracking parameters (in seconds)

const double MHI_DURATION = 0.5

const double MAX_TIME_DELTA = 0.5

const double MIN_TIME_DELTA = 0.05

const int N = 3

//

const int CONTOUR_MAX_AERA = 16

// ring image buffer

IplImage **buf = 0

int last = 0

// temporary images

IplImage *mhi = 0

/锋缓袜/ MHI: motion history image

int filter = CV_GAUSSIAN_5x5

CvConnectedComp *cur_comp, min_comp

CvConnectedComp comp

CvMemStorage *storageCvPoint pt[4]

// 参数：

// img – 输入视频帧

// dst – 检测结果

void update_mhi( IplImage* img, IplImage* dst, int diff_threshold )

{

double timestamp = clock()/100.

/银激/ get current time in seconds

CvSize size = cvSize(img->width,img->哪嫌height)

// get current frame size

int i, j, idx1, idx2

IplImage* silh

uchar val

float temp

IplImage* pyr = cvCreateImage( cvSize((size.width &-2)/2, (size.height &-2)/2), 8, 1 )

CvMemStorage *stor

CvSeq *cont, *result, *squares

CvSeqReader reader

if( !mhi || mhi->width != size.width || mhi->height != size.height )

{

if( buf == 0 )

{

buf = (IplImage**)malloc(N*sizeof(buf[0]))

memset( buf, 0, N*sizeof(buf[0]))

}

for( i = 0i <Ni++ )

{

cvReleaseImage( &buf[i] )

buf[i] = cvCreateImage( size, IPL_DEPTH_8U, 1 )

cvZero( buf[i] )

}

cvReleaseImage( &mhi )

mhi = cvCreateImage( size, IPL_DEPTH_32F, 1 )

cvZero( mhi )

// clear MHI at the beginning

}

// end of if(mhi)

cvCvtColor( img, buf[last], CV_BGR2GRAY )

// convert frame to grayscale

idx1 = last

idx2 = (last + 1) % N

// index of (last - (N-1))th frame

last = idx2

// 做帧差

silh = buf[idx2]

cvAbsDiff( buf[idx1], buf[idx2], silh )

// get difference between frames

// 对差图像做二值化

cvThreshold( silh, silh, 30, 255, CV_THRESH_BINARY )

// and threshold it

cvUpdateMotionHistory( silh, mhi, timestamp, MHI_DURATION )

// update MHI

cvCvtScale( mhi, dst, 255./MHI_DURATION,

(MHI_DURATION - timestamp)*255./MHI_DURATION )

cvCvtScale( mhi, dst, 255./MHI_DURATION, 0 )

// 中值滤波，消除小的噪声

cvSmooth( dst, dst, CV_MEDIAN, 3, 0, 0, 0 )

// 向下采样，去掉噪声

cvPyrDown( dst, pyr, 7 )

cvDilate( pyr, pyr, 0, 1 )

// 做膨胀 *** 作，消除目标的不连续空洞

cvPyrUp( pyr, dst, 7 )

//

// 下面的程序段用来找到轮廓

//

// Create dynamic structure and sequence.

stor = cvCreateMemStorage(0)

cont = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint) , stor)

// 找到所有轮廓

cvFindContours( dst, stor, &cont, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0))

// 直接使用CONTOUR中的矩形来画轮廓

for(contcont = cont->h_next)

{

CvRect r = ((CvContour*)cont)->rect

if(r.height * r.width >CONTOUR_MAX_AERA) // 面积小的方形抛弃掉

{

cvRectangle( img, cvPoint(r.x,r.y),

cvPoint(r.x + r.width, r.y + r.height),

CV_RGB(255,0,0), 1, CV_AA,0)

}

} // free memory

cvReleaseMemStorage(&stor)

cvReleaseImage( &pyr )

}

int main(int argc, char** argv)

{

IplImage* motion = 0

CvCapture* capture = 0//视频获取结构

if( argc == 1 || (argc == 2 &&strlen(argv[1]) == 1 &&isdigit(argv[1][0])))

//原型：extern int isdigit(char c) //用法：#include <ctype.h> 功能：判断字符c是否为数字说明：当c为数字0-9时，返回非零值，否则返回零。

capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 1 )

else if( argc == 2 )

capture = cvCaptureFromAVI( argv[1] )

if( capture )

{

cvNamedWindow( "Motion", 1 )

for()

{

IplImage* image

if( !cvGrabFrame( capture )) //从摄像头或者视频文件中抓取帧

break

image = cvRetrieveFrame( capture )

//取回由函数cvGrabFrame抓取的图像,返回由函数cvGrabFrame 抓取的图像的指针

if( image )

{

if( !motion )

{

motion = cvCreateImage( cvSize(image->width,image->height), 8, 1 )

cvZero( motion )

motion->origin = image->origin

///* 0 - 顶—左结构, 1 - 底—左结构 (Windows bitmaps 风格) */

}

}

update_mhi( image, motion, 60 )

cvShowImage( "Motion", image )

if( cvWaitKey(10) >= 0 )

break

}

cvReleaseCapture( &capture )

cvDestroyWindow( "Motion" )

}

return 0

}

你这个要看凯祥环燃银境怎么样的，opencv提供了人脸检测和行人检测，如果摄像头离人比较近，人脸检测的效果会好一点，如果比较远可以得到人的全身图像，就需要行人检测了，人脸检测比较容易，用的是haarcascade方法，楼皮孙宴上的答案就是，行人检测也可以用haarcascade，但是效果一般，比较好的是用HOG，网上仔细找找也有代码的。

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