在 OpenCV 中有效地将大型 Mat 加载到内存中

Efficiently load a large Mat into memory in OpenCV(在 OpenCV 中有效地将大型 Mat 加载到内存中)

问题描述

是否有比 OpenCV 中的 FileStorage 方法更有效的方法将大型 Mat 对象加载到内存中?

Is there a more efficient way to load a large Mat object into memory than the FileStorage method in OpenCV?

我有一个包含 192 列和 100 万行的大型 Mat，我想将其本地存储在一个文件中并加载到内存中，然后我的应用程序启动.使用 FileStorage 没有问题，但我想知道是否有更有效的方法来做到这一点.目前在Visual Studio中使用Debug模式将Mat加载到内存大约需要5分钟，在Release模式下需要大约3分钟，数据文件大小约为1.2GB.

I have a large Mat with 192 columns and 1 million rows I want to store locally in a file and load into memory then my application starts. There is no problem using the FileStorage, but I was wondering if there exists a more efficient method to do this. At the moment it takes about 5 minutes to load the Mat into memory using the Debug mode in Visual Studio and around 3 minutes in the Release mode and the size of the data file is around 1.2GB.

FileStorage 方法是唯一可用于执行此任务的方法吗?

Is the FileStorage method the only method available to do this task?

推荐答案

100x 加速你是否满意?

Are you ok with a 100x speedup?

您应该以二进制格式保存和加载图像.您可以使用下面代码中的 matwrite 和 matread 函数来实现.

You should save and load your images in binary format. You can do that with the matwrite and matread function in the code below.

我测试了从 FileStorage 和二进制文件加载，对于 250K 行、192 列的较小图像，输入 CV_8UC1 我得到了这些结果(时间在女士):

I tested both loading from a FileStorage and the binary file, and for a smaller image with 250K rows, 192 columns, type CV_8UC1 I got these results (time in ms):

// Mat: 250K rows, 192 cols, type CV_8UC1
Using FileStorage: 5523.45
Using Raw:         50.0879    

使用我得到的二进制模式(时间以毫秒为单位)在具有 100 万行和 192 列的图像上:

On a image with 1M rows and 192 cols using the binary mode I got (time in ms):

// Mat: 1M rows, 192 cols, type CV_8UC1
Using FileStorage: (can't load, out of memory)
Using Raw:         197.381

注意

1. 永远不要在调试中衡量性能.
2. 加载矩阵的 3 分钟似乎太多了，即使对于 FileStorage 也是如此.但是，切换到二进制模式会带来很多好处.

1. Never measure performance in debug.
2. 3 minutes to load a matrix seems way too much, even for FileStorages. However, you'll gain a lot switching to binary mode.

这里是带有 matwrite 和 matread 函数的代码，以及测试:

Here the code with the functions matwrite and matread, and the test:

#include <opencv2opencv.hpp>
#include <iostream>
#include <fstream>

using namespace std;
using namespace cv;


void matwrite(const string& filename, const Mat& mat)
{
    ofstream fs(filename, fstream::binary);

    // Header
    int type = mat.type();
    int channels = mat.channels();
    fs.write((char*)&mat.rows, sizeof(int));    // rows
    fs.write((char*)&mat.cols, sizeof(int));    // cols
    fs.write((char*)&type, sizeof(int));        // type
    fs.write((char*)&channels, sizeof(int));    // channels

    // Data
    if (mat.isContinuous())
    {
        fs.write(mat.ptr<char>(0), (mat.dataend - mat.datastart));
    }
    else
    {
        int rowsz = CV_ELEM_SIZE(type) * mat.cols;
        for (int r = 0; r < mat.rows; ++r)
        {
            fs.write(mat.ptr<char>(r), rowsz);
        }
    }
}

Mat matread(const string& filename)
{
    ifstream fs(filename, fstream::binary);

    // Header
    int rows, cols, type, channels;
    fs.read((char*)&rows, sizeof(int));         // rows
    fs.read((char*)&cols, sizeof(int));         // cols
    fs.read((char*)&type, sizeof(int));         // type
    fs.read((char*)&channels, sizeof(int));     // channels

    // Data
    Mat mat(rows, cols, type);
    fs.read((char*)mat.data, CV_ELEM_SIZE(type) * rows * cols);

    return mat;
}

int main()
{
    // Save the random generated data
    {
        Mat m(1024*256, 192, CV_8UC1);
        randu(m, 0, 1000);

        FileStorage fs("fs.yml", FileStorage::WRITE);
        fs << "m" << m;

        matwrite("raw.bin", m);
    }

    // Load the saved matrix

    {
        // Method 1: using FileStorage
        double tic = double(getTickCount());

        FileStorage fs("fs.yml", FileStorage::READ);
        Mat m1;
        fs["m"] >> m1;

        double toc = (double(getTickCount()) - tic) * 1000. / getTickFrequency();
        cout << "Using FileStorage: " << toc << endl; 
    }

    {
        // Method 2: usign raw binary data
        double tic = double(getTickCount());

        Mat m2 = matread("raw.bin");

        double toc = (double(getTickCount()) - tic) * 1000. / getTickFrequency();
        cout << "Using Raw: " << toc << endl;
    }

    int dummy;
    cin >> dummy;

    return 0;
}

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