1.我用的是VS2013，配置的是OpenCV3.4.0，运行如下程序的时候就说有许多未定义的标识符，琢磨了好久就是不知道哪里出了问题。求大神帮我看看程序。看看哪里出了问题，，需要的话我可以把整个程序发过去，（有偿）
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <stdio.h>
#include <stdlib.h>
//#include "cvaux.h"
#include "codeb.h"
int CVCONTOUR_APPROX_LEVEL = 2;
int CVCLOSE_ITR = 1;
#define CV_CVX_WHITE CV_RGB(0xff,0xff,0xff)
#define CV_CVX_BLACK CV_RGB(0x00,0x00,0x00)
codeBook* cA;
codeBook* cC;
codeBook* cD;
int maxMod[CHANNELS];
int minMod[CHANNELS];
unsigned cbBounds[CHANNELS];
bool ch[CHANNELS];
int nChannels = CHANNELS;
int imageLen = 0;
uchar *pColor;
int Td;
int Tadd;
int Tdel;
int T=50;
int Fadd=35;
int Tavgstale=50;
int Fd=2;
int Tavgstale_cD=50;
int fgcount=0;
float beta=0.1f;
float gamma=0.1f;
float forgratio=0.0f;
float Tadap_update=0.4f;
int clear_stale_entries(codeBook &c);
uchar background_Diff(uchar *p, codeBook &c, int numChannels, int *minMod, int *maxMod);
int update_codebook_model(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels);
int trainig_codebook(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels);
int training_clear_stale_entries(codeBook &c);
int det_update_codebook_cC(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels);
int det_update_codebook_cD(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels, int numframe);
int realtime_clear_stale_entries_cC(codeBook &c, int FrmNum);
int realtime_clear_stale_entries_cD(codeBook &c, int FrmNum);
int cD_to_cC(codeBook &d, codeBook &c, int FrmNum);
uchar background_diff_realtime(uchar* p,codeBook& c,int numChannels,int* minMod,int* maxMod);
void help() {
printf(
"***Keep the focus on the video windows, NOT the consol***\n"
"INTERACTIVE PARAMETERS:\n"
"\tESC,q,Q - quit the program\n"
"\th - print this help\n"
"\tp - pause toggle\n"
"\ts - single step\n"
"\tr - run mode (single step off)\n"
"=== CODEBOOK PARAMS ===\n"
"\ty,u,v- only adjust channel 0(y) or 1(u) or 2(v) respectively\n"
"\ta - adjust all 3 channels at once\n"
"\tb - adjust both 2 and 3 at once\n"
"\ti,o - bump upper threshold up,down by 1\n"
"\tk,l - bump lower threshold up,down by 1\n"
"\tz,x - bump Fadd threshold up,down by 1\n"
"\tn,m - bump Tavgstale threshold up,down by 1\n"
"\t Fadd小更新快,Tavgstale大更新快\n"
);
}
int count_Segmentation(codeBook *c, IplImage *I, int numChannels, int *minMod, int *maxMod)
{
int count = 0,i;
uchar *pColor;
int imageLen = I->width * I->height;
//GET BASELINE NUMBER OF FG PIXELS FOR Iraw
pColor = (uchar *)((I)->imageData);
for(i=0; i<imageLen; i++)
{
if(background_Diff(pColor, c[i], numChannels, minMod, maxMod))
count++;
pColor += 3;
}
fgcount=count;
return(fgcount);
}
void connected_Components(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers)
{
static CvMemStorage* mem_storage = NULL;
static CvSeq* contours = NULL;
//CLEAN UP RAW MASK
cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_OPEN, CVCLOSE_ITR );
cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_CLOSE, CVCLOSE_ITR );
//FIND CONTOURS AROUND ONLY BIGGER REGIONS
if( mem_storage==NULL ) mem_storage = cvCreateMemStorage(0);
else cvClearMemStorage(mem_storage);
CvContourScanner scanner = cvStartFindContours(mask,mem_storage,sizeof(CvContour),CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);
CvSeq* c;
int numCont = 0;
while( (c = cvFindNextContour( scanner )) != NULL )
{
double len = cvContourPerimeter( c );
double q = (mask->height + mask->width) /perimScale; //calculate perimeter len threshold
if( len < q ) //Get rid of blob if it's perimeter is too small
{
cvSubstituteContour( scanner, NULL );
}
else //Smooth it's edges if it's large enough
{
CvSeq* c_new;
if(poly1_hull0) //Polygonal approximation of the segmentation
c_new = cvApproxPoly(c,sizeof(CvContour),mem_storage,CV_POLY_APPROX_DP, CVCONTOUR_APPROX_LEVEL,0);
else //Convex Hull of the segmentation
c_new = cvConvexHull2(c,mem_storage,CV_CLOCKWISE,1);
cvSubstituteContour( scanner, c_new );
numCont++;
}
}
contours = cvEndFindContours( &scanner );
// PAINT THE FOUND REGIONS BACK INTO THE IMAGE
cvZero( mask );
IplImage *maskTemp;
//CALC CENTER OF MASS AND OR BOUNDING RECTANGLES
if(num != NULL)
{
int N = *num, numFilled = 0, i=0;
CvMoments moments;
double M00, M01, M10;
maskTemp = cvCloneImage(mask);
for(i=0, c=contours; c != NULL; c = c->h_next,i++ )
{
if(i < N) //Only process up to *num of them
{
cvDrawContours(maskTemp,c,CV_CVX_WHITE, CV_CVX_WHITE,-1,CV_FILLED,8);
//Find the center of each contour
if(centers != NULL)
{
cvMoments(maskTemp,&moments,1);
M00 = cvGetSpatialMoment(&moments,0,0);
M10 = cvGetSpatialMoment(&moments,1,0);
M01 = cvGetSpatialMoment(&moments,0,1);
centers[i].x = (int)(M10/M00);
centers[i].y = (int)(M01/M00);
}
//Bounding rectangles around blobs
if(bbs != NULL)
{
bbs[i] = cvBoundingRect(c);
}
cvZero(maskTemp);
numFilled++;
}
//Draw filled contours into mask
cvDrawContours(mask,c,CV_CVX_WHITE,CV_CVX_WHITE,-1,CV_FILLED,8); //draw to central mask
} //end looping over contours
*num = numFilled;
cvReleaseImage( &maskTemp);
}
else
{
for( c=contours; c != NULL; c = c->h_next )
{
cvDrawContours(mask,c,CV_CVX_WHITE, CV_CVX_BLACK,-1,CV_FILLED,8);
}
}
}
////////////////////////////
int main(int argc, char** argv)
{
IplImage* temp1 = NULL;
IplImage* temp2 = NULL;
IplImage* result = NULL;
IplImage* result1 = NULL;
IplImage* result2 = NULL;
CvBGStatModel* bg_model = 0;
CvBGStatModel* bg_model1=0;
IplImage* rawImage = 0;
IplImage* yuvImage = 0;
IplImage* rawImage1 = 0;
IplImage* pFrImg = 0;
IplImage* pFrImg1= 0;
IplImage* pFrImg2= 0;
IplImage* ImaskCodeBookCC = 0;
CvCapture* capture = 0;
int c,n;
maxMod[0] = 25;
minMod[0] = 35;
maxMod[1] = 8;
minMod[1] = 8;
maxMod[2] = 8;
minMod[2] = 8;
argc=2;
argv[1]="intelligentroom_raw.avi";
if( argc > 2 )
{
fprintf(stderr, "Usage: bkgrd [video_file_name]\n");
return -1;
}
if (argc ==1)
if( !(capture = cvCaptureFromCAM(-1)))
{
fprintf(stderr, "Can not open camera.\n");
return -2;
}
if(argc == 2)
if( !(capture = cvCaptureFromFile(argv[1])))
{
fprintf(stderr, "Can not open video file %s\n", argv[1]);
return -2;
}
bool pause = false;
bool singlestep = false;
if( capture )
{
cvNamedWindow( "原视频序列图像", 1 );
cvNamedWindow("不实时更新的Codebook算法[本文]",1);
cvNamedWindow("实时更新的Codebook算法[本文]",1);
cvNamedWindow("基于MOG的方法[Chris Stauffer'2001]",1);
cvNamedWindow("三帧差分", 1);
cvNamedWindow("基于Bayes decision的方法[Liyuan Li'2003]", 1);
cvMoveWindow("原视频序列图像", 0, 0);
cvMoveWindow("不实时更新的Codebook算法[本文]", 360, 0);
cvMoveWindow("实时更新的Codebook算法[本文]", 720, 350);
cvMoveWindow("基于MOG的方法[Chris Stauffer'2001]", 0, 350);
cvMoveWindow("三帧差分", 720, 0);
cvMoveWindow("基于Bayes decision的方法[Liyuan Li'2003]",360, 350);
int nFrmNum = -1;
for(;;)
{
if(!pause)
{
rawImage = cvQueryFrame( capture );
++nFrmNum;
printf("第%d帧\n",nFrmNum);
if(!rawImage)
break;
}
if(singlestep)
{
pause = true;
}
if(0 == nFrmNum)
{
printf(". . . wait for it . . .\n");
temp1 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 3);
temp2 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 3);
result1 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);
result2 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);
result = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);
bg_model = cvCreateGaussianBGModel(rawImage);
bg_model1 = cvCreateFGDStatModel(rawImage);
rawImage1 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 3 );
yuvImage = cvCloneImage(rawImage);
pFrImg = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
pFrImg1 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
pFrImg2 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
ImaskCodeBookCC = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
imageLen = rawImage->width*rawImage->height;
cA = new codeBook [imageLen];
cC = new codeBook [imageLen];
cD = new codeBook [imageLen];
for(int f = 0; f<imageLen; f++)
{
cA[f].numEntries = 0; cA[f].t = 0;
cC[f].numEntries = 0; cC[f].t = 0;
cD[f].numEntries = 0; cD[f].t = 0;
}
for(int nc=0; nc<nChannels;nc++)
{
cbBounds[nc] = 10;
}
ch[0] = true;
ch[1] = true;
ch[2] = true;
}
if( rawImage )
{
if(!pause)
{
cvSmooth(rawImage, rawImage1, CV_GAUSSIAN,3,3);
cvChangeDetection(temp1, temp2, result1);
cvChangeDetection(rawImage1, temp1, result2);
cvAnd(result1, result2, result, NULL);
cvCopy(temp1,temp2, NULL);
cvCopy(rawImage,temp1, NULL);
cvUpdateBGStatModel( rawImage, bg_model );
cvUpdateBGStatModel( rawImage, bg_model1 );
}
cvCvtColor( rawImage1, yuvImage, CV_BGR2YCrCb );
if( !pause && nFrmNum >= 1 && nFrmNum < T )
{
pColor = (uchar *)((yuvImage)->imageData);
for(int c=0; c<imageLen; c++)
{
update_codebook_model(pColor, cA[c],cbBounds,nChannels);
trainig_codebook(pColor, cC[c],cbBounds,nChannels);
pColor += 3;
}
}
if( nFrmNum == T)
{
for(c=0; c<imageLen; c++)
{
clear_stale_entries(cA[c]);
training_clear_stale_entries(cC[c]);
}
}
if(nFrmNum > T)
{
pColor = (uchar *)((yuvImage)->imageData);
uchar maskPixelCodeBook;
uchar maskPixelCodeBook1;
uchar maskPixelCodeBook2;
uchar *pMask = (uchar *)((pFrImg)->imageData);
uchar *pMask1 = (uchar *)((pFrImg1)->imageData);
uchar *pMask2 = (uchar *)((pFrImg2)->imageData);
for(int c=0; c<imageLen; c++)
{
//本文中不带自动背景更新的算法输出
maskPixelCodeBook1=background_Diff(pColor, cA[c],nChannels,minMod,maxMod);
*pMask1++ = maskPixelCodeBook1;
//本文中带自动背景更新的算法输出
if ( !pause && det_update_codebook_cC(pColor, cC[c],cbBounds,nChannels))
{
det_update_codebook_cD(pColor, cD[c],cbBounds,nChannels, nFrmNum);
realtime_clear_stale_entries_cD(cD[c], nFrmNum);
cD_to_cC(cD[c], cC[c], (nFrmNum - T)/5);
}
else
{
realtime_clear_stale_entries_cC(cC[c], nFrmNum);
}
maskPixelCodeBook2=background_Diff(pColor, cC[c],nChannels,minMod,maxMod);
*pMask2++ = maskPixelCodeBook2;
pColor += 3;
}
cvCopy(pFrImg2,ImaskCodeBookCC);
if(!pause)
{
count_Segmentation(cC,yuvImage,nChannels,minMod,maxMod);
forgratio = (float) (fgcount)/ imageLen;
}
}
bg_model1->foreground->origin=1;
bg_model->foreground->origin=1;
pFrImg->origin=1;
pFrImg1->origin=1;
pFrImg2->origin=1;
ImaskCodeBookCC->origin=1;
result->origin=1;
//connected_Components(pFrImg1,1,40);
//connected_Components(pFrImg2,1,40);
cvShowImage("基于MOG的方法[Chris Stauffer'2001]", bg_model->foreground);
cvShowImage( "原视频序列图像", rawImage );
cvShowImage("三帧差分", result);
cvShowImage( "不实时更新的Codebook算法[本文]",pFrImg1);
cvShowImage("实时更新的Codebook算法[本文]",pFrImg2);
cvShowImage("基于Bayes decision的方法[Liyuan Li'2003]", bg_model1->foreground);
c = cvWaitKey(1)&0xFF;
//End processing on ESC, q or Q
if(c == 27 || c == 'q' || c == 'Q')
break;
//Else check for user input
switch(c)
{
case 'h':
help();
break;
case 'p':
pause ^= 1;
break;
case 's':
singlestep = 1;
pause = false;
break;
case 'r':
pause = false;
singlestep = false;
break;
//CODEBOOK PARAMS
case 'y':
case '0':
ch[0] = 1;
ch[1] = 0;
ch[2] = 0;
printf("CodeBook YUV Channels active: ");
for(n=0; n<nChannels; n++)
printf("%d, ",ch[n]);
printf("\n");
break;
case 'u':
case '1':
ch[0] = 0;
ch[1] = 1;
ch[2] = 0;
printf("CodeBook YUV Channels active: ");
for(n=0; n<nChannels; n++)
printf("%d, ",ch[n]);
printf("\n");
break;
case 'v':
case '2':
ch[0] = 0;
ch[1] = 0;
ch[2] = 1;
printf("CodeBook YUV Channels active: ");
for(n=0; n<nChannels; n++)
printf("%d, ",ch[n]);
printf("\n");
break;
case 'a': //All
case '3':
ch[0] = 1;
ch[1] = 1;
ch[2] = 1;
printf("CodeBook YUV Channels active: ");
for(n=0; n<nChannels; n++)
printf("%d, ",ch[n]);
printf("\n");
break;
case 'b': //both u and v together
ch[0] = 0;
ch[1] = 1;
ch[2] = 1;
printf("CodeBook YUV Channels active: ");
for(n=0; n<nChannels; n++)
printf("%d, ",ch[n]);
printf("\n");
break;
case 'z':
printf(" Fadd加1 ");
Fadd += 1;
printf("Fadd=%.4d\n",Fadd);
break;
case 'x':
printf(" Fadd减1 ");
Fadd -= 1;
printf("Fadd=%.4d\n",Fadd);
break;
case 'n':
printf(" Tavgstale加1 ");
Tavgstale += 1;
printf("Tavgstale=%.4d\n",Tavgstale);
break;
case 'm':
printf(" Tavgstale减1 ");
Tavgstale -= 1;
printf("Tavgstale=%.4d\n",Tavgstale);
break;
case 'i': //modify max classification bounds (max bound goes higher)
for(n=0; n<nChannels; n++)
{
if(ch[n])
maxMod[n] += 1;
printf("%.4d,",maxMod[n]);
}
printf(" CodeBook High Side\n");
break;
case 'o': //modify max classification bounds (max bound goes lower)
for(n=0; n<nChannels; n++)
{
if(ch[n])
maxMod[n] -= 1;
printf("%.4d,",maxMod[n]);
}
printf(" CodeBook High Side\n");
break;
case 'k': //modify min classification bounds (min bound goes lower)
for(n=0; n<nChannels; n++)
{
if(ch[n])
minMod[n] += 1;
printf("%.4d,",minMod[n]);
}
printf(" CodeBook Low Side\n");
break;
case 'l': //modify min classification bounds (min bound goes higher)
for(n=0; n<nChannels; n++)
{
if(ch[n])
minMod[n] -= 1;
printf("%.4d,",minMod[n]);
}
printf(" CodeBook Low Side\n");
break;
}
}
}
cvReleaseCapture( &capture );
cvReleaseBGStatModel((CvBGStatModel**)&bg_model);
cvReleaseBGStatModel((CvBGStatModel**)&bg_model1);
cvDestroyWindow( "原视频序列图像" );
cvDestroyWindow( "不实时更新的Codebook算法[本文]");
cvDestroyWindow( "实时更新的Codebook算法[本文]");
cvDestroyWindow( "基于MOG的方法[Chris Stauffer'2001]");
cvDestroyWindow( "三帧差分" );
cvDestroyWindow( "基于Bayes decision的方法[Liyuan Li'2003]");
cvReleaseImage(&temp1);
cvReleaseImage(&temp2);
cvReleaseImage(&result);
cvReleaseImage(&result1);
cvReleaseImage(&result2);
cvReleaseImage(&pFrImg);
cvReleaseImage(&pFrImg1);
cvReleaseImage(&pFrImg2);
if(yuvImage) cvReleaseImage(&yuvImage);
if(rawImage) cvReleaseImage(&rawImage);
if(rawImage1) cvReleaseImage(&rawImage1);
if(ImaskCodeBookCC) cvReleaseImage(&ImaskCodeBookCC);
delete [] cA;
delete [] cC;
delete [] cD;
}
else
{
printf("\n\nDarn, Something wrong with the parameters\n\n"); help();
}
return 0;
}
int clear_stale_entries(codeBook &c)
{
int staleThresh = c.t>>1;
int *keep = new int [c.numEntries];
int keepCnt = 0;
for(int i=0; i<c.numEntries; i++)
{
if(c.cb[i]->stale > staleThresh)
keep[i] = 0;
else
{
keep[i] = 1;
keepCnt += 1;
}
}
c.t = 0;
code_element **foo = new code_element* [keepCnt];
int k=0;
for(int ii=0; ii<c.numEntries; ii++)
{
if(keep[ii])
{
foo[k] = c.cb[ii];
foo[k]->t_last_update = 0;
k++;
}
}
delete [] keep;
delete [] c.cb;
c.cb = foo;
int numCleared = c.numEntries - keepCnt;
c.numEntries = keepCnt;
return(numCleared);
}
uchar background_Diff(uchar *p, codeBook &c, int numChannels, int *minMod, int *maxMod)
{
int matchChannel;
int i;
for(i=0; i<c.numEntries; i++)
{
matchChannel = 0;
for(int n=0; n<numChannels; n++)
{
if((c.cb[i]->min[n] - minMod[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->max[n] + maxMod[n]))
{
matchChannel++;
}
else
{
break;
}
}
if(matchChannel == numChannels)
{
break;
}
}
if(i >= c.numEntries) return(255);
return(0);
}
int update_codebook_model(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels)
{
if(c.numEntries == 0) c.t = 0;
c.t += 1;
unsigned int high[3],low[3];
int matchChannel;
float avg[3];
for(int i=0; i<c.numEntries; i++)
{
matchChannel = 0;
for(int n=0; n<numChannels; n++)
{
if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n]))
{
matchChannel++;
}
}
if(matchChannel == numChannels)
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (c.cb[i]->f * c.cb[i]->avg[n] + *(p+n))/(c.cb[i]->f + 1);
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
c.cb[i]->f += 1;
c.cb[i]->t_last_update = c.t;
int negRun = c.t - c.cb[i]->t_last_update;
if(c.cb[i]->stale < negRun) c.cb[i]->stale = negRun;
break;
}
}
for(int n=0; n<numChannels; n++)
{
high[n] = *(p+n)+*(cbBounds+n);
if(high[n] > 255) high[n] = 255;
low[n] = *(p+n)-*(cbBounds+n);
if(low[n] < 0) low[n] = 0;
}
if(i == c.numEntries)
{
code_element **foo = new code_element* [c.numEntries+1];
for(int ii=0; ii<c.numEntries; ii++)
{
foo[ii] = c.cb[ii];
}
foo[c.numEntries] = new code_element;
if(c.numEntries) delete [] c.cb;
c.cb = foo;
for(int n=0; n<numChannels; n++)
{
c.cb[c.numEntries]->avg[n] = *(p+n);
c.cb[c.numEntries]->max[n] = *(p+n);
c.cb[c.numEntries]->min[n] = *(p+n);
c.cb[c.numEntries]->learnHigh[n] = high[n];
c.cb[c.numEntries]->learnLow[n] = low[n];
}
c.cb[c.numEntries]->f = 1;
c.cb[c.numEntries]->stale = c.t-1;
c.cb[c.numEntries]->t_first_update = c.t;
c.cb[c.numEntries]->t_last_update = c.t;
c.numEntries += 1;
}
for(int s=0; s<c.numEntries; s++)
{
int negRun = c.t - c.cb[s]->t_last_update + c.cb[s]->t_first_update -1 ;
if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;
}
for(n=0; n<numChannels; n++)
{
if(c.cb[i]->learnHigh[n] < high[n]) c.cb[i]->learnHigh[n] += 1;
if(c.cb[i]->learnLow[n] > low[n]) c.cb[i]->learnLow[n] -= 1;
}
return(i);
}
int trainig_codebook(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels)
{
if(c.numEntries == 0) c.t = 0;
c.t += 1;
unsigned int high[3],low[3];
int matchChannel;
float avg[3];
for(int i=0; i<c.numEntries; i++)
{
matchChannel = 0;
for(int n=0; n<numChannels; n++)
{
if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n]))
{
matchChannel++;
}
}
if(matchChannel == numChannels)
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (c.cb[i]->f * c.cb[i]->avg[n] + *(p+n))/(c.cb[i]->f + 1);
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
c.cb[i]->f += 1;
c.cb[i]->t_last_update = c.t;
int negRun = c.t - c.cb[i]->t_last_update;
if(c.cb[i]->stale < negRun) c.cb[i]->stale = negRun;
if (i!=0)
{
code_element **fo = new code_element* [c.numEntries];
fo[0] = c.cb[i];
for(int h=0; h<i; h++)
{
fo[h+1] = c.cb[h];
}
for(int h=i+1; h<c.numEntries; h++)
{
fo[h] = c.cb[h];
}
if(c.numEntries) delete [] c.cb;
c.cb = fo;
}
break;
}
}
for(int n=0; n<numChannels; n++)
{
high[n] = *(p+n)+*(cbBounds+n);
if(high[n] > 255) high[n] = 255;
low[n] = *(p+n)-*(cbBounds+n);
if(low[n] < 0) low[n] = 0;
}
if(i == c.numEntries)
{
code_element **foo = new code_element* [c.numEntries+1];
for(int ii=0; ii<c.numEntries; ii++)
{
foo[ii] = c.cb[ii];
}
foo[c.numEntries] = new code_element;
if(c.numEntries) delete [] c.cb;
c.cb = foo;
for(n=0; n<numChannels; n++)
{
c.cb[c.numEntries]->avg[n] = *(p+n);
c.cb[c.numEntries]->max[n] = *(p+n);
c.cb[c.numEntries]->min[n] = *(p+n);
c.cb[c.numEntries]->learnHigh[n] = high[n];
c.cb[c.numEntries]->learnLow[n] = low[n];
}
c.cb[c.numEntries]->f = 1;
c.cb[c.numEntries]->stale = c.t-1;
c.cb[c.numEntries]->t_first_update = c.t;
c.cb[c.numEntries]->t_last_update = c.t;
c.numEntries += 1;
}
for(int s=0; s<c.numEntries; s++)
{
int negRun = c.t - c.cb[s]->t_last_update + c.cb[s]->t_first_update -1 ;
if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;
}
for(int n=0; n<numChannels; n++)
{
if(c.cb[i]->learnHigh[n] < high[n]) c.cb[i]->learnHigh[n] += 1;
if(c.cb[i]->learnLow[n] > low[n]) c.cb[i]->learnLow[n] -= 1;
}
return(i);
}
int training_clear_stale_entries(codeBook &c)
{
int staleThresh = c.t>>1;
int *keep = new int [c.numEntries];
int keepCnt = 0;
for(int i=0; i<c.numEntries; i++)
{
if(c.cb[i]->stale > staleThresh)
keep[i] = 0;
else
{
keep[i] = 1;
keepCnt += 1;
}
}
code_element **foo = new code_element* [keepCnt];
int k=0;
for(int ii=0; ii<c.numEntries; ii++)
{
if(keep[ii])
{
foo[k] = c.cb[ii];
k++;
}
}
delete [] keep;
delete [] c.cb;
c.cb = foo;
int numCleared = c.numEntries - keepCnt;
c.numEntries = keepCnt;
return(numCleared);
}
int det_update_codebook_cC(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels)
{
c.t += 1;
int matchChannel;
float avg[3];
int learnLow[3],learnHigh[3];
for(int i=0; i<c.numEntries; i++)
{
matchChannel = 0;
for(int n=0; n<numChannels; n++)
{
if (forgratio >= Tadap_update )
{
learnLow[n] = c.cb[i]->learnLow[n] * (1 - gamma);
c.cb[i]->learnLow[n] = learnLow[n];
learnHigh[n] = c.cb[i]->learnHigh[n] * (1 + gamma);
c.cb[i]->learnHigh[n] = learnHigh[n];
}
if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n]))
{
matchChannel++;
}
}
if(matchChannel == numChannels)
{
if (forgratio >= Tadap_update )
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (1 - beta) * c.cb[i]->avg[n] + *(p+n) * beta;
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
}
else
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (c.cb[i]->f * c.cb[i]->avg[n] + *(p+n))/(c.cb[i]->f + 1);
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
}
int negRun = c.t - c.cb[i]->t_last_update;
if(c.cb[i]->stale < negRun) c.cb[i]->stale = negRun;
c.cb[i]->t_last_update = c.t;
c.cb[i]->f += 1;
break;
}
}
if( i == c.numEntries) return (i);
return(0);
}
int det_update_codebook_cD(uchar* p,codeBook& c,unsigned* cbBounds,int numChannels, int numframe)
{
if(c.numEntries == 0) c.t = numframe -1;
c.t += 1;
unsigned int high[3],low[3];
int matchChannel;
float avg[3];
int learnLow[3],learnHigh[3];
for(int i=0; i<c.numEntries; i++)
{
matchChannel = 0;
for(int n=0; n<numChannels; n++)
{
if (forgratio >= Tadap_update )
{
learnLow[n] = c.cb[i]->learnLow[n] * (1 - gamma);
c.cb[i]->learnLow[n] = learnLow[n];
learnHigh[n] = c.cb[i]->learnHigh[n] * (1 + gamma);
c.cb[i]->learnHigh[n] = learnHigh[n];
}
if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n]))
{
matchChannel++;
}
}
if(matchChannel == numChannels)
{
if (forgratio >= Tadap_update )
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (1 - beta) * c.cb[i]->avg[n] + *(p+n) * beta;
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
}
else
{
for(int n=0; n<numChannels; n++)
{
avg[n] = (c.cb[i]->f * c.cb[i]->avg[n] + *(p+n))/(c.cb[i]->f + 1);
c.cb[i]->avg[n] = avg[n];
if(c.cb[i]->max[n] < *(p+n))
{
c.cb[i]->max[n] = *(p+n);
}
else if(c.cb[i]->min[n] > *(p+n))
{
c.cb[i]->min[n] = *(p+n);
}
}
}
int negRun = c.t - c.cb[i]->t_last_update;
if(c.cb[i]->stale < negRun) c.cb[i]->stale = negRun;
c.cb[i]->f += 1;
c.cb[i]->t_last_update = c.t;
break;
}
}
for(int n=0; n<numChannels; n++)
{
high[n] = *(p+n)+*(cbBounds+n);
if(high[n] > 255) high[n] = 255;
low[n] = *(p+n)-*(cbBounds+n);
if(low[n] < 0) low[n] = 0;
}
if(i == c.numEntries)
{
code_element **foo = new code_element* [c.numEntries+1];
for(int ii=0; ii<c.numEntries; ii++)
{
foo[ii] = c.cb[ii];
}
foo[c.numEntries] = new code_element;
if(c.numEntries)
delete [] c.cb;
c.cb = foo;
for(int n=0; n<numChannels; n++)
{
c.cb[c.numEntries]->avg[n] = *(p+n);
c.cb[c.numEntries]->max[n] = *(p+n);
c.cb[c.numEntries]->min[n] = *(p+n);
c.cb[c.numEntries]->learnHigh[n] = high[n];
c.cb[c.numEntries]->learnLow[n] = low[n];
}
c.cb[c.numEntries]->f = 1;
c.cb[c.numEntries]->stale = 0;
c.cb[c.numEntries]->t_first_update = c.t;
c.cb[c.numEntries]->t_last_update = c.t;
c.numEntries += 1;
}
for(int s=0; s<c.numEntries; s++)
{
int negRun = c.t - c.cb[s]->t_last_update;
if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;
}
for(int n=0; n<numChannels; n++)
{
if(c.cb[i]->learnHigh[n] < high[n]) c.cb[i]->learnHigh[n] += 1;
if(c.cb[i]->learnLow[n] > low[n]) c.cb[i]->learnLow[n] -= 1;
}
return(i);
}
int realtime_clear_stale_entries_cC(codeBook &c, int FrmNum)
{
int staleThresh = FrmNum/2;
int *keep = new int [c.numEntries];
int keepCnt = 0;
for(int i=0; i<c.numEntries; i++)
{
if(c.cb[i]->stale > staleThresh)
keep[i] = 0;
else
{
keep[i] = 1;
keepCnt += 1;
}
}
c.t = 0;
code_element **foo = new code_element* [keepCnt];
int k=0;
for(int ii=0; ii<c.numEntries; ii++)
{
if(keep[ii])
{
foo[k] = c.cb[ii];
k++;
}
}
delete [] keep;
delete [] c.cb;
c.cb = foo;
int numCleared = c.numEntries - keepCnt;
c.numEntries = keepCnt;
return(numCleared);
}
int realtime_clear_stale_entries_cD(codeBook &c, int FrmNum)
{
int *keep = new int [c.numEntries];
int keepCnt = 0;
for(int i=0; i<c.numEntries; i++)
{
if(c.cb[i]->f <=Fd && c.cb[i]->stale >=Tavgstale_cD)
keep[i] = 0;
else
{
keep[i] = 1;
keepCnt += 1;
}
}
code_element **foo = new code_element* [keepCnt];
int k=0;
for(int ii=0; ii<c.numEntries; ii++)
{
if(keep[ii])
{
foo[k] = c.cb[ii];
k++;
}
}
delete [] keep;
delete [] c.cb;
c.cb = foo;
int numCleared = c.numEntries - keepCnt;
c.numEntries = keepCnt;
return(numCleared);
}
int cD_to_cC(codeBook &d, codeBook &c, int FrmNum)
{
int *keep_d = new int [d.numEntries];
int keepCnt = 0;
for(int i=0; i<d.numEntries; i++)
{
int convertThresh = (FrmNum - T)/d.cb[i]->f;
if(d.cb[i]->f >=Fadd && convertThresh <=Tavgstale)
{
keep_d[i] = 0;
}
else
{
keep_d[i] = 1;
keepCnt += 1;
}
}
code_element **foo_d = new code_element* [keepCnt];
int k=0;
for(int ii=0; ii<d.numEntries; ii++)
{
if(keep_d[ii])
{
foo_d[k] = d.cb[ii];
k++;
}
else
{
code_element **foo_c = new code_element* [c.numEntries+1];
for(int jj=0; jj<c.numEntries; jj++)
{
foo_c[jj] = c.cb[jj];
}
foo_c[c.numEntries] = new code_element;
delete [] c.cb;
c.cb = foo_c;
c.cb[c.numEntries] = d.cb[ii];
c.numEntries +=1;
}
}
delete [] keep_d;
delete [] d.cb;
d.cb = foo_d;
int numconverted = d.numEntries - keepCnt;
d.numEntries = keepCnt;
return(numconverted);
}
下面是报错：
1>------ 已启动生成: 项目: Realtime_online_cb_det, 配置: Debug x64 ------
1> Realtime_online_cb_det.cpp
1>Realtime_online_cb_det.cpp(185): error C2065: “CvBGStatModel”: 未声明的标识符
1>Realtime_online_cb_det.cpp(185): error C2065: “bg_model”: 未声明的标识符
1>Realtime_online_cb_det.cpp(186): error C2065: “CvBGStatModel”: 未声明的标识符
1>Realtime_online_cb_det.cpp(186): error C2065: “bg_model1”: 未声明的标识符
1>Realtime_online_cb_det.cpp(271): error C2065: “bg_model”: 未声明的标识符
1>Realtime_online_cb_det.cpp(271): error C3861: “cvCreateGaussianBGModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(272): error C2065: “bg_model1”: 未声明的标识符
1>Realtime_online_cb_det.cpp(272): error C3861: “cvCreateFGDStatModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(308): error C3861: “cvChangeDetection”: 找不到标识符
1>Realtime_online_cb_det.cpp(309): error C3861: “cvChangeDetection”: 找不到标识符
1>Realtime_online_cb_det.cpp(315): error C2065: “bg_model”: 未声明的标识符
1>Realtime_online_cb_det.cpp(315): error C3861: “cvUpdateBGStatModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(316): error C2065: “bg_model1”: 未声明的标识符
1>Realtime_online_cb_det.cpp(316): error C3861: “cvUpdateBGStatModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(381): error C2065: “bg_model1”: 未声明的标识符
1>Realtime_online_cb_det.cpp(381): error C2227: “->foreground”的左边必须指向类/结构/联合/泛型类型
1> 类型是“unknown-type”
1>Realtime_online_cb_det.cpp(381): error C2227: “->origin”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(382): error C2065: “bg_model”: 未声明的标识符
1>Realtime_online_cb_det.cpp(382): error C2227: “->foreground”的左边必须指向类/结构/联合/泛型类型
1> 类型是“unknown-type”
1>Realtime_online_cb_det.cpp(382): error C2227: “->origin”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(391): error C2065: “bg_model”: 未声明的标识符
1>Realtime_online_cb_det.cpp(391): error C2227: “->foreground”的左边必须指向类/结构/联合/泛型类型
1> 类型是“unknown-type”
1>Realtime_online_cb_det.cpp(391): error C2660: “cvShowImage”: 函数不接受 1 个参数
1>Realtime_online_cb_det.cpp(396): error C2065: “bg_model1”: 未声明的标识符
1>Realtime_online_cb_det.cpp(396): error C2227: “->foreground”的左边必须指向类/结构/联合/泛型类型
1> 类型是“unknown-type”
1>Realtime_online_cb_det.cpp(396): error C2660: “cvShowImage”: 函数不接受 1 个参数
1>Realtime_online_cb_det.cpp(529): error C2065: “CvBGStatModel”: 未声明的标识符
1>Realtime_online_cb_det.cpp(529): error C2059: 语法错误:“)”
1>Realtime_online_cb_det.cpp(530): error C2065: “CvBGStatModel”: 未声明的标识符
1>Realtime_online_cb_det.cpp(530): error C2059: 语法错误:“)”
1>Realtime_online_cb_det.cpp(529): error C3861: “cvReleaseBGStatModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(530): error C3861: “cvReleaseBGStatModel”: 找不到标识符
1>Realtime_online_cb_det.cpp(677): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(712): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(714): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(714): error C2227: “->learnHigh”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(714): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(715): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(715): error C2227: “->learnLow”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(715): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(717): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(789): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(799): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(801): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(802): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(803): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(804): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(805): error C2065: “n”: 未声明的标识符
1>Realtime_online_cb_det.cpp(824): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(824): error C2227: “->learnHigh”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(825): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(825): error C2227: “->learnLow”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(827): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(880): warning C4244: “=”: 从“float”转换到“int”，可能丢失数据
1>Realtime_online_cb_det.cpp(882): warning C4244: “=”: 从“float”转换到“int”，可能丢失数据
1>Realtime_online_cb_det.cpp(938): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(962): warning C4244: “=”: 从“float”转换到“int”，可能丢失数据
1>Realtime_online_cb_det.cpp(964): warning C4244: “=”: 从“float”转换到“int”，可能丢失数据
1>Realtime_online_cb_det.cpp(1024): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(1061): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(1061): error C2227: “->learnHigh”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(1062): error C2065: “i”: 未声明的标识符
1>Realtime_online_cb_det.cpp(1062): error C2227: “->learnLow”的左边必须指向类/结构/联合/泛型类型
1>Realtime_online_cb_det.cpp(1064): error C2065: “i”: 未声明的标识符
========== 生成: 成功 0 个，失败 1 个，最新 0 个，跳过 0 个 ==========
