Opencv中的内存管理

Question

我试图找到字节数cvCreateImage(size, d, nChan); 函数调用分配给返回的指针。（说尺寸：宽度=1200，高度=600，d=32，nChan=3）

opencv 分配一个对齐的内存位置还是只是一个随机的内存位置？

使用函数cvReleaseImage释放的内存是否立即可供正在运行的应用程序使用？（假设一个程序在一个循环中创建和释放图像，循环大约 30000 次，是否会导致内存不足错误或任何碎片错误）

Answer 1

您可以在OpenCV 源代码中查找问题的答案。

cvCreateImage()分配一个sizeof(IplImage)（当前 112 字节）的头。数据有点复杂；如果你想image->imageSize在alloc.cpp中 得到一个确切的答案 grep ，但它大约是size.height * size.width * d * nChan / 8（例如，8,640,000）。

内存最终fastMalloc()在 alloc.cpp 中分配并与 16 字节边界对齐 - 但您返回的指针是该边界上方的指针的大小。

cvReleaseImage减少引用计数，并且仅在引用计数达到零时才释放数据。如果您的程序只是在循环中创建和发布图像，那么除了性能之外，您应该没有问题。当您在下一次迭代中再次需要它时，取消分配图像内存是一种糟糕的设计。

确切的分配/解除分配行为将取决于您的 OpenCV 是否使用 CV_USE_SYSTEM_MALLOC 构建。

如果您使用的CV ::垫代替的IplImage，OpenCV的照顾所有的内存管理问题的全自动。我强烈建议您重写代码以使用 C++ API（即使用 Mat 的）

例如，这里是您在上述评论中链接的代码的翻译：

// Based on http://mehdi.rabah.free.fr/SSIM/SSIM.cpp
// Converted to OpenCV C++ API by B...

/*
 * The equivalent of Zhou Wang's SSIM matlab code using OpenCV.
 * from http://www.cns.nyu.edu/~zwang/files/research/ssim/index.html
 * The measure is described in :
 * "Image quality assessment: From error measurement to structural similarity"
 * C++ code by Rabah Mehdi. http://mehdi.rabah.free.fr/SSIM
 *
 * This implementation is under the public domain.
 * @see http://creativecommons.org/licenses/publicdomain/
 * The original work may be under copyrights. 
 */

//#include <cv.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>

//#include <highgui.h>
#include "opencv2/highgui/highgui.hpp"

//#include <iostream.h>
#include <iostream>
using namespace std;


/*
 * Parameters : complete path to the two image to be compared
 * The file format must be supported by your OpenCV build
 */
int main(int argc, char** argv)
{
    if(argc!=3)
        return -1;

    // default settings
    double C1 = 6.5025, C2 = 58.5225;

    /*
    IplImage
        *img1=NULL, *img2=NULL, *img1_img2=NULL,
        *img1_temp=NULL, *img2_temp=NULL,
        *img1_sq=NULL, *img2_sq=NULL,
        *mu1=NULL, *mu2=NULL,
        *mu1_sq=NULL, *mu2_sq=NULL, *mu1_mu2=NULL,
        *sigma1_sq=NULL, *sigma2_sq=NULL, *sigma12=NULL,
        *ssim_map=NULL, *temp1=NULL, *temp2=NULL, *temp3=NULL;
    */

    /***************************** INITS **********************************/
    //img1_temp = cvLoadImage(argv[1]);
    cv::Mat img1 = cv::imread(argv[1]);
    //img2_temp = cvLoadImage(argv[2]);
    cv::Mat img2 = cv::imread(argv[2]);


    //if(img1_temp==NULL || img2_temp==NULL)
    if(img1.empty() || img2.empty())
        return -1;

    //int x=img1_temp->width, y=img1_temp->height;
    //int nChan=img1_temp->nChannels, d=IPL_DEPTH_32F;
    //CvSize size = cvSize(x, y);

    //img1 = cvCreateImage( size, d, nChan);
    //img2 = cvCreateImage( size, d, nChan);

    //cvConvert(img1_temp, img1);
    img1.convertTo(img1, CV_32F);
    //cvConvert(img2_temp, img2);
    img2.convertTo(img2, CV_32F);
    //cvReleaseImage(&img1_temp);
    //cvReleaseImage(&img2_temp);


    //img1_sq = cvCreateImage( size, d, nChan);
    cv::Mat img1_sq;
    //img2_sq = cvCreateImage( size, d, nChan);
    cv::Mat img2_sq;
    //img1_img2 = cvCreateImage( size, d, nChan);
    cv::Mat img1_img2;

    //cvPow( img1, img1_sq, 2 );
    cv::pow(img1, 2, img1_sq);
    //cvPow( img2, img2_sq, 2 );
    cv::pow(img1, 2, img1_sq);
    //cvMul( img1, img2, img1_img2, 1 );
    cv::multiply(img1, img2, img1_img2);

    //mu1 = cvCreateImage( size, d, nChan);
    cv::Mat mu1;
    //mu2 = cvCreateImage( size, d, nChan);
    cv::Mat mu2;

    //mu1_sq = cvCreateImage( size, d, nChan);
    cv::Mat mu1_sq;
    //mu2_sq = cvCreateImage( size, d, nChan);
    cv::Mat mu2_sq;
    //mu1_mu2 = cvCreateImage( size, d, nChan);
    cv::Mat mu1_mu2;


    //sigma1_sq = cvCreateImage( size, d, nChan);
    cv::Mat sigma1_sq;
    //sigma2_sq = cvCreateImage( size, d, nChan);
    cv::Mat sigma2_sq;
    //sigma12 = cvCreateImage( size, d, nChan);
    cv::Mat sigma12;

    //temp1 = cvCreateImage( size, d, nChan);
    //temp2 = cvCreateImage( size, d, nChan);
    //temp3 = cvCreateImage( size, d, nChan);

    //ssim_map = cvCreateImage( size, d, nChan);
    cv::Mat ssim_map;
    /*************************** END INITS **********************************/


    //////////////////////////////////////////////////////////////////////////
    // PRELIMINARY COMPUTING
    //cvSmooth( img1, mu1, CV_GAUSSIAN, 11, 11, 1.5 );
    cv::GaussianBlur(img1, mu1, cv::Size(11, 11), 1.5);
    //cvSmooth( img2, mu2, CV_GAUSSIAN, 11, 11, 1.5 );
    cv::GaussianBlur(img2, mu2, cv::Size(11, 11), 1.5);

    //cvPow( mu1, mu1_sq, 2 );
    cv::pow(mu1, 2, mu1_sq);
    //cvPow( mu2, mu2_sq, 2 );
    cv::pow(mu2, 2, mu2_sq);
    //cvMul( mu1, mu2, mu1_mu2, 1 );
    cv::multiply(mu1, mu2, mu1_mu2);

    //cvSmooth( img1_sq, sigma1_sq, CV_GAUSSIAN, 11, 11, 1.5 );
    cv::GaussianBlur(img1_sq, sigma1_sq, cv::Size(11, 11), 1.5);
    //cvAddWeighted( sigma1_sq, 1, mu1_sq, -1, 0, sigma1_sq );
    cv::addWeighted(sigma1_sq, 1.0, mu1_sq, -1.0, 0.0, sigma1_sq);

    //cvSmooth( img2_sq, sigma2_sq, CV_GAUSSIAN, 11, 11, 1.5 );
    cv::GaussianBlur(img2_sq, sigma2_sq, cv::Size(11, 11), 1.5);
    //cvAddWeighted( sigma2_sq, 1, mu2_sq, -1, 0, sigma2_sq );
    cv::addWeighted(sigma2_sq, 1.0, mu2_sq, -1.0, 0.0, sigma2_sq);

    //cvSmooth( img1_img2, sigma12, CV_GAUSSIAN, 11, 11, 1.5 );
    cv::GaussianBlur(img1_img2, sigma12, cv::Size(11, 11), 1.5);
    //cvAddWeighted( sigma12, 1, mu1_mu2, -1, 0, sigma12 );
    cv::addWeighted(sigma12, 1.0, mu1_mu2, -1.0, 0.0, sigma12);


    //////////////////////////////////////////////////////////////////////////
    // FORMULA

    // (2*mu1_mu2 + C1)
    //cvScale( mu1_mu2, temp1, 2 );
    //cvAddS( temp1, cvScalarAll(C1), temp1 );
    cv::Mat temp3 = 2 * mu1_mu2 + C1;

    // (2*sigma12 + C2)
    //cvScale( sigma12, temp2, 2 );
    //cvAddS( temp2, cvScalarAll(C2), temp2 );

    // ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
    //cvMul( temp1, temp2, temp3, 1 );
    temp3 = temp3.mul(2 * sigma12 + C2);

    // (mu1_sq + mu2_sq + C1)
    //cvAdd( mu1_sq, mu2_sq, temp1 );
    //cvAddS( temp1, cvScalarAll(C1), temp1 );
    cv::Mat temp1 = mu1_sq + mu2_sq + C1;

    // (sigma1_sq + sigma2_sq + C2)
    //cvAdd( sigma1_sq, sigma2_sq, temp2 );
    //cvAddS( temp2, cvScalarAll(C2), temp2 );

    // ((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
    //cvMul( temp1, temp2, temp1, 1 );
    temp1 = temp1.mul(sigma1_sq + sigma2_sq + C2);

    // ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
    //cvDiv( temp3, temp1, ssim_map, 1 );
    cv::divide(temp3, temp1, ssim_map);


    //CvScalar index_scalar = cvAvg( ssim_map );
    cv::Scalar index_scalar = cv::mean(ssim_map);

    // through observation, there is approximately 
    // 1% error max with the original matlab program

    cout << "(R, G & B SSIM index)" << endl ;
    cout << index_scalar.val[2] * 100 << "%" << endl ;
    cout << index_scalar.val[1] * 100 << "%" << endl ;
    cout << index_scalar.val[0] * 100 << "%" << endl ;

    /*
    // if you use this code within a program
    // don't forget to release the IplImages
    */
    return 0;
}