使用cvLoadImage导致内存泄漏的解决方法

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我是靠谱客的博主奋斗白猫，最近开发中收集的这篇文章主要介绍使用cvLoadImage导致内存泄漏的解决方法，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

最近在做一个视频跟踪的项目，里面需要用cvLoadImage从一个文件夹中循环的读取图片。程序编译通过，但在运行的时候内存一直处于增长状态，最后直接报出内存溢出。查了一下发现是cvLoadImage的原因。具体代码如下：

for( string::size_type i = 0; i != img_path.size(); i++ )
{
path=".\TrainData\"+img_path[i];
src=cvLoadImage(path.c_str(),1);
if( src == NULL )
{
cout<<" can not load the image: "<<img_path[i].c_str()<<endl;
continue;
}
cout<<" processing "<<img_path[i].c_str()<<endl;
cvResize(src,trainImg);
//读取图片
hog->compute(trainImg, descriptors,Size(1,1), Size(0,0)); //调用计算函数开始计算
cout<<"HOG dims: "<<descriptors.size()<<endl;
n=0;
for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
{
cvmSet(data_mat,i,n,*iter);//把HOG存储下来
n++;
}
//cout<<SVMtrainMat->rows<<endl;
cvmSet( res_mat, i, 0, img_catg[i] );
cout<<" end processing "<<img_path[i].c_str()<<" "<<img_catg[i]<<endl;
}

查了一下cvLoadImage的源代码（在moudelshighguisrcloadsave.cpp中）：

CV_IMPL IplImage*
cvLoadImage( const char* filename, int iscolor )
{
return (IplImage*)cv::imread_(filename, iscolor, cv::LOAD_IMAGE );
}

static void*
imread_( const string& filename, int flags, int hdrtype, Mat* mat=0 )
{
IplImage* image = 0;
CvMat *matrix = 0;
Mat temp, *data = &temp;
ImageDecoder decoder = findDecoder(filename);
if( decoder.empty() )
return 0;
decoder->setSource(filename);
if( !decoder->readHeader() )
return 0;
CvSize size;
size.width = decoder->width();
size.height = decoder->height();
int type = decoder->type();
if( flags != -1 )
{
if( (flags & CV_LOAD_IMAGE_ANYDEPTH) == 0 )
type = CV_MAKETYPE(CV_8U, CV_MAT_CN(type));
if( (flags & CV_LOAD_IMAGE_COLOR) != 0 ||
((flags & CV_LOAD_IMAGE_ANYCOLOR) != 0 && CV_MAT_CN(type) > 1) )
type = CV_MAKETYPE(CV_MAT_DEPTH(type), 3);
else
type = CV_MAKETYPE(CV_MAT_DEPTH(type), 1);
}
if( hdrtype == LOAD_CVMAT || hdrtype == LOAD_MAT )
{
if( hdrtype == LOAD_CVMAT )
{
matrix = cvCreateMat( size.height, size.width, type );
temp = cvarrToMat(matrix);
}
else
{
mat->create( size.height, size.width, type );
data = mat;
}
}
else
{
image = cvCreateImage( size, cvIplDepth(type), CV_MAT_CN(type) );
temp = cvarrToMat(image);
}
if( !decoder->readData( *data ))
{
cvReleaseImage( &image );
cvReleaseMat( &matrix );
if( mat )
mat->release();
return 0;
}
return hdrtype == LOAD_CVMAT ? (void*)matrix :
hdrtype == LOAD_IMAGE ? (void*)image : (void*)mat;
}

每次cvLoadImage时，都会开辟一块新的内存，如果不释放，就会导致内存泄露，更改代码如下


for( string::size_type i = 0; i != img_path.size(); i++ )
{
path=".\TrainData\"+img_path[i];
src=cvLoadImage(path.c_str(),1);
if( src == NULL )
{
cout<<" can not load the image: "<<img_path[i].c_str()<<endl;
continue;
}
cout<<" processing "<<img_path[i].c_str()<<endl;
cvResize(src,trainImg);
//读取图片
hog->compute(trainImg, descriptors,Size(1,1), Size(0,0)); //调用计算函数开始计算
cout<<"HOG dims: "<<descriptors.size()<<endl;
//CvMat* SVMtrainMat=cvCreateMat(descriptors.size(),1,CV_32FC1);
n=0;
for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
{
cvmSet(data_mat,i,n,*iter);//把HOG存储下来
n++;
}
//cout<<SVMtrainMat->rows<<endl;
cvmSet( res_mat, i, 0, img_catg[i] );
//{
//	vector<float>().swap(descriptors); //释放vector内存
//}
cout<<" end processing "<<img_path[i].c_str()<<" "<<img_catg[i]<<endl;
cvReleaseImage( &src );
src=NULL;
}