vs2005hog+svm,vs2008 hog+svm 中的computer()函数出错折磨我好几天

最新推荐文章于 2023-06-06 16:30:34 发布

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#include "stdafx.h"
#include <ctype.h>
#include "cv.h"
#include "highgui.h"
#include <ml.h>
#include <iostream>
#include <fstream>
#include <string.h>
#include <vector>
#include <math.h>
#include <stdio.h>
#include "cvaux.h"
using namespace cv;
using namespace std;
class Mysvm: public CvSVM
{
public:
int get_alpha_count()
{
return this->sv_total;
}
int get_sv_dim()
{
return this->var_all;
}
int get_sv_count()
{
return this->decision_func->sv_count;
}
double* get_alpha()
{
return this->decision_func->alpha;
}
float** get_sv()
{
return this->sv;
}
float get_rho()
{
return this->decision_func->rho;
}
};
void Train()
{
char classifierSavePath[256] = "E:\\My Documents1\\hogsvm\\hogsvm\\liu.xml";
// string positivePath = "E:\\My Documents1\\hogsvm\\hogsvm\\pos296\\";
// string negativePath = "E:\\My Documents1\\hogsvm\\hogsvm\\neg297\\";
int positiveSampleCount = 18;
int negativeSampleCount = 18;
int totalSampleCount = positiveSampleCount + negativeSampleCount;
cout<<"//////////////////////////////////////////////////////////////////"<<endl;
cout<<"totalSampleCount: "<<totalSampleCount<<endl;
cout<<"positiveSampleCount: "<<positiveSampleCount<<endl;
cout<<"negativeSampleCount: "<<negativeSampleCount<<endl;
int totalCols = /*3780*/1764;
CvMat *sampleFeaturesMat = cvCreateMat(totalSampleCount , totalCols, CV_32FC1);
//64*128的训练样本，该矩阵将是totalSample*3780,64*64的训练样本，该矩阵将是totalSample*1764
cvSetZero(sampleFeaturesMat);
CvMat *sampleLabelMat = cvCreateMat(totalSampleCount, 1, CV_32FC1);//样本标识
cvSetZero(sampleLabelMat);
cout<<"************************************************************"<<endl;
cout<<"start to training positive samples..."<<endl;
//char positiveImgName[256];
//HOGDescriptor *hog=NULL;
string path;
ifstream positive_data( "E:\\My Documents1\\hogsvm\\hogsvm\\pos296\\pos.txt" );
int count = 0;
while( positive_data )
{
if( getline( positive_data, path) )
{
cv::Mat img = cv::imread(path);
if( img.data == NULL )
{
cout<<"positive image sample load error: "<<count<<" "<<path<<endl;
system("pause");
continue;
}
cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
//cv::HOGDescriptor *hog=new HOGDescriptor(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
<span style="background-color: rgb(51, 204, 255);">vector<float> featureVec(1764);//此句，去掉（1764），在vs2008上正确，但是在vs2005上就出错，在vs2005上加入（1764就正确运行）
</span> //vector<float> featureVec = new std::vector<float>();
hog->compute(img, featureVec, cv::Size(8,8),cv::Size(0,0));
//hog.compute(img, featureVec, cvSize(8,8));
int featureVecSize = featureVec.size();
//for (int j=0; j<featureVecSize; j++) //todo
for (int j = 0; j < totalCols; j++)
{
CV_MAT_ELEM( *sampleFeaturesMat, float, count, j ) = featureVec[j];
}
sampleLabelMat->data.fl[count] = 1;
count ++;/**/
}
}
positive_data.close();
cout<<"end of training for positive samples...["<<count<<"]"<<endl;
cout<<"*********************************************************"<<endl;
cout<<"start to train negative samples..."<<endl;
ifstream negative_data("E:\\My Documents1\\hogsvm\\hogsvm\\neg297\\neg.txt");
count = 0;
while(negative_data)
{
if( getline( negative_data, path ))
{
cv::Mat img = cv::imread(path);
if(img.data == NULL)
{
cout<<"negative image sample load error: "<<path<<endl;
continue;
}
cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
vector<float> featureVec(1764);
hog.compute(img,featureVec,cv::Size(8,8));//计算HOG特征
int featureVecSize = featureVec.size();
//for ( int j=0; j<featureVecSize; j ++) //todo
for (int j = 0; j< totalCols; j++)
{
CV_MAT_ELEM( *sampleFeaturesMat, float, count + positiveSampleCount, j ) = featureVec[ j ];
}
sampleLabelMat->data.fl[ count + positiveSampleCount ] = -1;
count ++;
}
}
negative_data.close();
cout<<"end of training for negative samples...["<<count<<"]"<<endl;
cout<<"********************************************************"<<endl;
cout<<"start to train for SVM classifier..."<<endl;
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, FLT_EPSILON);
params.C = 0.01;
Mysvm svm;
svm.train( sampleFeaturesMat, sampleLabelMat, NULL, NULL, params ); //用SVM线性分类器训练//sampleFeaturesMat保存各样本的特征值，sampleLabelMat保存图片类型
svm.save(classifierSavePath);
cvReleaseMat(&sampleFeaturesMat);
cvReleaseMat(&sampleLabelMat);
int supportVectorSize = svm.get_support_vector_count();//获得支持向量的个数
cout<<"support vector size of SVM："<<supportVectorSize<<endl;
cout<<"************************ end of training for SVM ******************"<<endl;
CvMat *sv,*alp,*re;//所有样本特征向量
sv = cvCreateMat(supportVectorSize , totalCols, CV_32FC1);
alp = cvCreateMat(1 , supportVectorSize, CV_32FC1);
re = cvCreateMat(1 , totalCols, CV_32FC1);
CvMat *res = cvCreateMat(1 , 1, CV_32FC1);
cvSetZero(sv);
cvSetZero(re);
for(int i=0; i<supportVectorSize; i++)
{
memcpy( (float*)(sv->data.fl+i*totalCols), svm.get_support_vector(i), totalCols*sizeof(float)); //get_support_vector获得对应的索引编号的支持向量
}
double* alphaArr = svm.get_alpha();
int alphaCount = svm.get_alpha_count();
cout<<"alpharr"<<*alphaArr<<endl;
for(int i=0; i<supportVectorSize; i++)
{
alp->data.fl[i] = alphaArr[i];
}
cvMatMul(alp, sv, re);
int posCount = 0;
for (int i=0; i<totalCols; i++)
{
re->data.fl[i] *= -1;
}
FILE* fp = fopen("E:\\My Documents1\\hogsvm\\hogsvm\\num.txt","wb");
if( NULL == fp )
{
return;
}
for(int i=0; i<totalCols; i++)
{
fprintf(fp,"%f \n",re->data.fl[i]);
}
float rho = svm.get_rho();
fprintf(fp, "%f", rho);
cout<<"E:\\My Documents1\\hogsvm\\hogsvm\\num.txt 保存完毕"<<endl;//保存HOG能识别的分类器
fclose(fp);
return;
}
/*void Detect()
{
CvCapture* cap = cvCreateFileCapture("D:\\people.avi");
//CvCapture* cap = cvCreateFileCapture("E:/My Documents1/detecthog+svm/detecthog+svm/00.jpg");
if (!cap)
{
cout<<"avi file load error..."<<endl;
system("pause");
exit(-1);
}
vector<float> x;
ifstream fileIn("D:/image/WorkTest/hogSVMDetector-peopleFlow.txt", ios::in);
float val = 0.0f;
while(!fileIn.eof())
{
fileIn>>val;
x.push_back(val);
}
fileIn.close();
vector<cv::Rect> found;
cv::HOGDescriptor hog(cv::Size(20,20), cv::Size(10,10), cv::Size(5,5), cv::Size(5,5), 9);
hog.setSVMDetector(x);
IplImage* img = NULL;
cvNamedWindow("img", 0);
while(img=cvQueryFrame(cap))
{
hog.detectMultiScale(img, found, 0, cv::Size(5,5), cv::Size(10,10), 1.05, 2);
if (found.size() > 0)
{
for (int i=0; i<found.size(); i++)
{
CvRect tempRect = cvRect(found[i].x, found[i].y, found[i].width, found[i].height);
cvRectangle(img, cvPoint(tempRect.x,tempRect.y),
cvPoint(tempRect.x+tempRect.width,tempRect.y+tempRect.height),CV_RGB(255,0,0), 2);
}
}
}
cvReleaseCapture(&cap);
}
*/
int face()
{
Mat img;
FILE* f = 0;
char _filename[1024] = "3.jpg";
char _filePath[1024];
sprintf(_filePath, "E:\\My Documents1\\hogsvm\\hogsvm\\%s", _filename);
img = imread(_filePath);
if( !img.data )
{
return -1;
}
cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);//("D:\\pedestrianDetect-peopleFlow.xml");
//hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());//得到检测器
//vector<float> detector;// = load_lear_model("D:\\hogSVMDetector-peopleFlow.txt");
//CvLatentSvmDetector* detector = cvLoadLatentSvmDetector("result.xml");
vector<float> detector;
ifstream detector_data("E:\\My Documents1\\hogsvm\\hogsvm\\num.txt", ios::in);
int count = 0;
string buf;
float tmpFlaot;
while(!detector_data.eof())
{
detector_data >> tmpFlaot;
detector.push_back(tmpFlaot);
count ++;
}
hog.setSVMDetector(detector);
namedWindow("people detector", 1);
for(;;)
{
char* filename = _filename;
if(f)
{
if(!fgets(filename, (int)sizeof(_filename)-2, f))
break;
//while(*filename && isspace(*filename))
// ++filename;
if(filename[0] == '#')
continue;
int l = strlen(filename);
while(l > 0 && isspace(filename[l-1]))
--l;
filename[l] = '\0';
img = imread(filename);
}
printf("%s:\n", filename);
if(!img.data)
continue;
fflush(stdout);
vector<Rect> found, found_filtered;
double t = (double)getTickCount();
// run the detector with default parameters. to get a higher hit-rate
// (and more false alarms, respectively), decrease the hitThreshold and
// groupThreshold (set groupThreshold to 0 to turn off the grouping completely).
hog.detectMultiScale(img, found, 0, Size(8,8), Size(0,0), 1.05, 2);
t = (double)getTickCount() - t;
printf("tdetection time = %gms\n", t*1000./cv::getTickFrequency());
size_t i, j;
for( i = 0; i < found.size(); i++ )
{
Rect r = found[i];
for( j = 0; j < found.size(); j++ )
if( j != i && (r & found[j]) == r)
break;
if( j == found.size() )
found_filtered.push_back(r);
}
count = 0;
for( i = 0; i < found_filtered.size(); i++ )
{
Rect r = found_filtered[i];
// the HOG detector returns slightly larger rectangles than the real objects.
// so we slightly shrink the rectangles to get a nicer output.
r.x += cvRound(r.width*0.1);
r.width = cvRound(r.width*0.8);
r.y += cvRound(r.height*0.07);
r.height = cvRound(r.height*0.8);
rectangle(img, r.tl(), r.br(), cv::Scalar(255,0,0), 3);
count++;
}
cout<<"all count round:"<<count<<endl;
imshow("detector", img);
int c = cvWaitKey(0) & 255;
if( c == 'q' || c == 'Q' || !f)
break;
}
if(f)
fclose(f);
return 0;
}
int main()
{
Train();
//Detect();
face();
return 0;
}

#include "stdafx.h"
#include <ctype.h>
#include "cv.h"     
#include "highgui.h"    
#include <ml.h>     
#include <iostream>     
#include <fstream>     
#include <string.h>     
#include <vector> 
#include <math.h>
#include <stdio.h>
#include "cvaux.h"
using namespace cv;    
using namespace std;

class Mysvm: public CvSVM
{
public:
	int get_alpha_count()
	{
		return this->sv_total;
	}

	int get_sv_dim()
	{
		return this->var_all;
	}

	int get_sv_count()
	{
		return this->decision_func->sv_count;
	}

	double* get_alpha()
	{
		return this->decision_func->alpha;
	}

	float** get_sv()
	{
		return this->sv;
	}

	float get_rho()
	{
		return this->decision_func->rho;
	}
};

void Train()
{
	char classifierSavePath[256] = "E:\\My Documents1\\hogsvm\\hogsvm\\liu.xml";

// 	string positivePath = "E:\\My Documents1\\hogsvm\\hogsvm\\pos296\\";
// 	string negativePath = "E:\\My Documents1\\hogsvm\\hogsvm\\neg297\\";

	int positiveSampleCount = 18;
	int negativeSampleCount = 18;
	int totalSampleCount = positiveSampleCount + negativeSampleCount;

	cout<<"//////////////////////////////////////////////////////////////////"<<endl;
	cout<<"totalSampleCount: "<<totalSampleCount<<endl;
	cout<<"positiveSampleCount: "<<positiveSampleCount<<endl;
	cout<<"negativeSampleCount: "<<negativeSampleCount<<endl;

	int totalCols = /*3780*/1764;
	CvMat *sampleFeaturesMat = cvCreateMat(totalSampleCount , totalCols, CV_32FC1);
	//64*128的训练样本，该矩阵将是totalSample*3780,64*64的训练样本，该矩阵将是totalSample*1764
	cvSetZero(sampleFeaturesMat);  
	CvMat *sampleLabelMat = cvCreateMat(totalSampleCount, 1, CV_32FC1);//样本标识  
	cvSetZero(sampleLabelMat);  

	cout<<"************************************************************"<<endl;
	cout<<"start to training positive samples..."<<endl;

	//char positiveImgName[256];
	//HOGDescriptor *hog=NULL;

	string path;   
	ifstream positive_data( "E:\\My Documents1\\hogsvm\\hogsvm\\pos296\\pos.txt" ); 
	int count = 0;
	while( positive_data )    
	{    
		if( getline( positive_data, path) )  
		{
			cv::Mat img = cv::imread(path);
			if( img.data == NULL )
			{
				cout<<"positive image sample load error: "<<count<<" "<<path<<endl;
				system("pause");
				continue;
			}

			cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
			//cv::HOGDescriptor *hog=new HOGDescriptor(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
			vector<float> featureVec(1764);//此句，去掉（1764），在vs2008上正确，但是在vs2005上就出错，在vs2005上加入（1764就正确运行） 
			//vector<float> featureVec = new std::vector<float>(); 
			hog->compute(img, featureVec, cv::Size(8,8),cv::Size(0,0)); 
			//hog.compute(img, featureVec, cvSize(8,8));  

			int featureVecSize = featureVec.size();

			//for (int j=0; j<featureVecSize; j++)  //todo
			for (int j = 0; j < totalCols; j++)
			{    
				CV_MAT_ELEM( *sampleFeaturesMat, float, count, j ) = featureVec[j]; 
			}  
			sampleLabelMat->data.fl[count] = 1;
			count ++;/**/
		}
	}
	positive_data.close();
	cout<<"end of training for positive samples...["<<count<<"]"<<endl;

	cout<<"*********************************************************"<<endl;
	cout<<"start to train negative samples..."<<endl;

	ifstream negative_data("E:\\My Documents1\\hogsvm\\hogsvm\\neg297\\neg.txt");
	count = 0;
	while(negative_data)
	{
		if( getline( negative_data, path ))  
		{
			cv::Mat img = cv::imread(path);
			if(img.data == NULL)
			{
				cout<<"negative image sample load error: "<<path<<endl;
				continue;
			}

			cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);  
			vector<float> featureVec(1764); 

			hog.compute(img,featureVec,cv::Size(8,8));//计算HOG特征
			int featureVecSize = featureVec.size();  

			//for ( int j=0; j<featureVecSize; j ++)  //todo
			for (int j = 0; j< totalCols; j++)
			{  
				CV_MAT_ELEM( *sampleFeaturesMat, float, count + positiveSampleCount, j ) = featureVec[ j ];
			}  

			sampleLabelMat->data.fl[ count + positiveSampleCount ] = -1;
			count ++;
		}
	}  

	negative_data.close();

	cout<<"end of training for negative samples...["<<count<<"]"<<endl;
	cout<<"********************************************************"<<endl;
	cout<<"start to train for SVM classifier..."<<endl;

	CvSVMParams params;  
	params.svm_type = CvSVM::C_SVC;  
	params.kernel_type = CvSVM::LINEAR;  
	params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, FLT_EPSILON);
	params.C = 0.01;

	Mysvm svm;
	svm.train( sampleFeaturesMat, sampleLabelMat, NULL, NULL, params ); //用SVM线性分类器训练//sampleFeaturesMat保存各样本的特征值，sampleLabelMat保存图片类型 

	svm.save(classifierSavePath);

	cvReleaseMat(&sampleFeaturesMat);
	cvReleaseMat(&sampleLabelMat);

	int supportVectorSize = svm.get_support_vector_count();//获得支持向量的个数
	cout<<"support vector size of SVM："<<supportVectorSize<<endl;
	cout<<"************************ end of training for SVM ******************"<<endl;

	CvMat *sv,*alp,*re;//所有样本特征向量 
	sv  = cvCreateMat(supportVectorSize , totalCols, CV_32FC1);
	alp = cvCreateMat(1 , supportVectorSize, CV_32FC1);
	re  = cvCreateMat(1 , totalCols, CV_32FC1);
	CvMat *res  = cvCreateMat(1 , 1, CV_32FC1);

	cvSetZero(sv);
	cvSetZero(re);

	for(int i=0; i<supportVectorSize; i++)
	{
		memcpy( (float*)(sv->data.fl+i*totalCols), svm.get_support_vector(i), totalCols*sizeof(float)); //get_support_vector获得对应的索引编号的支持向量
	}

	double* alphaArr = svm.get_alpha();
	int alphaCount = svm.get_alpha_count();
	cout<<"alpharr"<<*alphaArr<<endl;

	for(int i=0; i<supportVectorSize; i++)
	{
		alp->data.fl[i] = alphaArr[i];
	}
	cvMatMul(alp, sv, re);

	int posCount = 0;
	for (int i=0; i<totalCols; i++)
	{
		re->data.fl[i] *= -1;
	}

	FILE* fp = fopen("E:\\My Documents1\\hogsvm\\hogsvm\\num.txt","wb");
	if( NULL == fp )
	{
		return;
	}
	for(int i=0; i<totalCols; i++)
	{
		fprintf(fp,"%f \n",re->data.fl[i]);
	}
	float rho = svm.get_rho();
	fprintf(fp, "%f", rho);
	cout<<"E:\\My Documents1\\hogsvm\\hogsvm\\num.txt 保存完毕"<<endl;//保存HOG能识别的分类器
	fclose(fp);

	return;
}

/*void Detect()
{
CvCapture* cap = cvCreateFileCapture("D:\\people.avi");
//CvCapture* cap = cvCreateFileCapture("E:/My Documents1/detecthog+svm/detecthog+svm/00.jpg");
if (!cap)
{
cout<<"avi file load error..."<<endl;
system("pause");
exit(-1);
}

vector<float> x;
ifstream fileIn("D:/image/WorkTest/hogSVMDetector-peopleFlow.txt", ios::in);
float val = 0.0f;
while(!fileIn.eof())
{
fileIn>>val;
x.push_back(val);
}
fileIn.close();

vector<cv::Rect>  found;
cv::HOGDescriptor hog(cv::Size(20,20), cv::Size(10,10), cv::Size(5,5), cv::Size(5,5), 9);
hog.setSVMDetector(x);

IplImage* img = NULL;
cvNamedWindow("img", 0);
while(img=cvQueryFrame(cap))
{
hog.detectMultiScale(img, found, 0, cv::Size(5,5), cv::Size(10,10), 1.05, 2);
if (found.size() > 0)
{

for (int i=0; i<found.size(); i++)
{
CvRect tempRect = cvRect(found[i].x, found[i].y, found[i].width, found[i].height);

cvRectangle(img, cvPoint(tempRect.x,tempRect.y),
cvPoint(tempRect.x+tempRect.width,tempRect.y+tempRect.height),CV_RGB(255,0,0), 2);
}
}
}
cvReleaseCapture(&cap);
}
*/


int face()  
{  
	Mat img;  
	FILE* f = 0;  
	char _filename[1024] = "3.jpg"; 
	char _filePath[1024];

	sprintf(_filePath, "E:\\My Documents1\\hogsvm\\hogsvm\\%s", _filename);
	img = imread(_filePath);  

	if( !img.data )   
	{    
		return -1;   
	}  

	cv::HOGDescriptor hog(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);//("D:\\pedestrianDetect-peopleFlow.xml");  
	//hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());//得到检测器 
	//vector<float> detector;// = load_lear_model("D:\\hogSVMDetector-peopleFlow.txt");
	//CvLatentSvmDetector* detector = cvLoadLatentSvmDetector("result.xml");
	vector<float> detector;

	ifstream detector_data("E:\\My Documents1\\hogsvm\\hogsvm\\num.txt", ios::in);
	int count = 0;
	string buf;
	float tmpFlaot;
	while(!detector_data.eof())
	{
		detector_data >> tmpFlaot;
		detector.push_back(tmpFlaot);
		count ++;
	}
	hog.setSVMDetector(detector);
	namedWindow("people detector", 1); 

	for(;;)  
	{  
		char* filename = _filename;  
		if(f)  
		{  
			if(!fgets(filename, (int)sizeof(_filename)-2, f))  
				break;  
			//while(*filename && isspace(*filename))   
			//  ++filename;   
			if(filename[0] == '#')  
				continue;  
			int l = strlen(filename);  
			while(l > 0 && isspace(filename[l-1]))  
				--l;  
			filename[l] = '\0';  
			img = imread(filename);  
		}  
		printf("%s:\n", filename);  
		if(!img.data)  
			continue;  

		fflush(stdout);  
		vector<Rect> found, found_filtered;  
		double t = (double)getTickCount();  
		// run the detector with default parameters. to get a higher hit-rate   
		// (and more false alarms, respectively), decrease the hitThreshold and   
		// groupThreshold (set groupThreshold to 0 to turn off the grouping completely).   
		hog.detectMultiScale(img, found, 0, Size(8,8), Size(0,0), 1.05, 2);  
		t = (double)getTickCount() - t;  
		printf("tdetection time = %gms\n", t*1000./cv::getTickFrequency());  
		size_t i, j;  
		for( i = 0; i < found.size(); i++ )  
		{  
			Rect r = found[i];  
			for( j = 0; j < found.size(); j++ )  
				if( j != i && (r & found[j]) == r)  
					break;  
			if( j == found.size() )  
				found_filtered.push_back(r);  
		} 
		count = 0;
		for( i = 0; i < found_filtered.size(); i++ )  
		{  
			Rect r = found_filtered[i];  
			// the HOG detector returns slightly larger rectangles than the real objects.   
			// so we slightly shrink the rectangles to get a nicer output.   
			r.x += cvRound(r.width*0.1);  
			r.width = cvRound(r.width*0.8);  
			r.y += cvRound(r.height*0.07);  
			r.height = cvRound(r.height*0.8);  
			rectangle(img, r.tl(), r.br(), cv::Scalar(255,0,0), 3);  
			count++;
		} 
		cout<<"all count round:"<<count<<endl;
		imshow("detector", img);  
		int c = cvWaitKey(0) & 255;  
		if( c == 'q' || c == 'Q' || !f)  
			break;  
	}  
	if(f)  
		fclose(f);  
	return 0;  
}  


int main()
{	
	Train();
	//Detect();
	face();

	return 0;
}

说明：

1、在vs2005工程目录下面，要加入一个pmmintrin.h文件，在vs2008中就不需要，vs2008中本身就有这个文件，而05中没有

2、此程序在vs2008上运行正确，在vs2005上出错，原因，把vector<float> featureVec; 修改为 vector<float> featureVec(1764);就可以运行

只要是>1764就可以。