Read OpenCV Mat from raw file [duplicate] - opencv

Question

Is there a more efficient way to load a large Mat object into memory than the FileStorage method in OpenCV?
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.
Is the FileStorage method the only method available to do this task?

Answer 1

Are you ok with a 100x speedup?
You should save and load your images in binary format. You can do that with the matwrite and matread function in the code below.
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
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
NOTE
Never measure performance in debug.
3 minutes to load a matrix seems way too much, even for FileStorages. However, you'll gain a lot switching to binary mode.
Here the code with the functions matwrite and matread, and the test:
#include <opencv2\opencv.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;
}