How Can I Efficiently Load Large OpenCV Mats?

Efficient Loading of Large Mats in OpenCV: A Comprehensive Guide

When dealing with extensive memory-intensive Mat objects, efficiency in loading them becomes paramount. While OpenCV's FileStorage method provides a straightforward approach, it may not always suffice.

Alternative: Binary Data Loading

For significantly improved performance, consider saving and loading Mat objects in a raw binary format. This bypasses the overhead associated with FileStorage, leading to substantial time savings.

Implementation with matwrite and matread Functions

To implement binary loading, utilize the matwrite and matread functions:

void matwrite(const string& filename, const Mat& mat) { ... }
Mat matread(const string& filename) { ... }

Performance Comparison

Benchmarks reveal a vast difference in loading speeds between FileStorage and binary loading:

Using FileStorage: 5523.45 ms
Using Raw:         50.0879 ms

Using FileStorage: (out of memory)
Using Raw:         197.381 ms

Additional Notes

• Performance measurements should be conducted in release mode, not debug mode.
• Memory limitations may prevent loading large Mats using FileStorage, while binary loading offers a viable solution.

Code Example

The following code demonstrates the use of matwrite and matread for saving, loading, and performance testing:

#include <opencv2/opencv.hpp>
#include <iostream>
#include <fstream>

using namespace std;
using namespace cv;

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

    matwrite("raw.bin", m);

    // Load saved matrix
    double tic = getTickCount();
    Mat m1 = matread("raw.bin");

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

By employing binary loading for large Mat objects, you can significantly enhance efficiency and reduce loading times, optimizing your OpenCV applications for optimal performance.

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