How Can I Efficiently Load Large Matrices in OpenCV?

Efficient Loading of Large Mats in OpenCV: Alternative to FileStorage

While FileStorage offers a convenient method to load and store matrices, it may not be the most efficient for handling large datasets. For such scenarios, exploring alternative approaches becomes crucial.

MatWrite and MatRead: A Binary Advantage

OpenCV provides two auxiliary functions, matwrite and matread, specifically designed for saving and loading matrices in binary format. This approach bypasses the overhead associated with YAML parsing and stream-based writing, leading to significant performance gains.

Performance Comparison

Testing the loading speed on a matrix with 250K rows and 192 columns yielded astonishing results. The binary method achieved a remarkable 100x speedup compared to FileStorage, reducing the loading time from over 5 seconds to a mere 50 milliseconds under debug mode.

For even larger matrices with 1 million rows, the FileStorage method struggled with out-of-memory issues. Meanwhile, the binary approach loaded the matrix in approximately 197 milliseconds, demonstrating its efficiency for handling such large matrices.

Recommendations

For optimal performance when loading massive matrices into memory, consider employing the matwrite and matread functions instead of FileStorage. These functions offer a simple and lightning-fast solution, significantly reducing the time required for data manipulation.

It's important to remember that performance measurements should never be conducted in debug mode, as it introduces overheads that can skew the results. Furthermore, the observed loading time may vary depending on hardware specifications.

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