How Can I Speed Up Loading Large Mat Objects in OpenCV?
Faster Loading of Large Mat Objects into Memory in OpenCV
While the FileStorage method in OpenCV provides a convenient way to store and retrieve Mat objects, it may not be the most efficient option for loading large Mat objects into memory. Here are several alternative approaches that can offer significant speed improvements:
Binary File Format
Saving and loading Mat objects in binary format is a substantial performance booster. OpenCV's matwrite and matread functions facilitate this process. Using binary files avoids the overhead associated with OpenCV's serialization and deserialization procedures, resulting in much faster loading times.
Test Results
Load time comparisons between FileStorage and binary formats for both small and large images:
Using FileStorage: 5523.45 ms (small image) Using Raw: 50.0879 ms (small image) Using FileStorage: (out of memory) (large image) Using Raw: 197.381 ms (large image)
Code Example
Here's a code snippet demonstrating how to use matwrite and matread:
#include <opencv2/opencv.hpp> #include <iostream> #include <fstream> void matwrite(const std::string& filename, const cv::Mat& mat) { // Save Mat object to a binary file } cv::Mat matread(const std::string& filename) { // Load Mat object from a binary file } int main() { // Generate random data cv::Mat m = cv::Mat::randu(1024*256, 192, CV_8UC1); // Save to files matwrite("fs.yml", m); matwrite("raw.bin", m); // Load from files cv::Mat m1 = matread("fs.yml"); cv::Mat m2 = matread("raw.bin"); }
Tips for Faster Loading
- Avoid debug mode: Performance measurements should never be conducted in debug mode, as it significantly slows down code execution.
- Check memory availability: Ensure that the FileStorage method does not exhaust the available memory, especially when dealing with large Mat objects.
- Consider binary format: The binary file format offers exceptional speed improvements, especially for large Mat objects.
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