Memory Management in OpenCV

The memory management feature of OpenCV enables users to dynamically allocate, free memory and take measures to avoid memory leaks that gradually lead to out of memory problems. This article explains the concepts and shows best practices on how to manage memory in OpenCV C++ applications.

Updated March 26, 2023

Hey! If you love Computer Vision and AI, let's connect on Twitter or LinkedIn. I talk about this stuff all the time!

Welcome to the world of computer vision and OpenCV! In this tutorial, we’ll be exploring the best practices for memory management in OpenCV. Memory management is a critical aspect of computer vision, and it’s essential to understand how to use it effectively.

Firstly, let’s start by understanding what memory management is. In computer science, memory management refers to the process of allocating memory to different programs and freeing it when it’s no longer needed. It’s crucial to manage memory effectively to avoid memory leaks, which can cause your program to crash or even damage the operating system.

OpenCV provides several functions for allocating and freeing memory, but it’s important to use them correctly to avoid memory leaks. In this tutorial, we’ll explore the best practices for memory management in OpenCV.

Best Practice #1: Always Release Memory

When using OpenCV, you must always release memory after you’re done with it. OpenCV provides several functions to release memory, such as cv::Mat::release() or cv::Ptr<>::release(). You should use these functions to release memory as soon as you’re done with it, to avoid memory leaks.

Here’s an example of how to release memory in OpenCV:

cv::Mat image = cv::imread("image.jpg");
cv::imshow("Image", image);
image.release(); // Release memory

Best Practice #2: Use Smart Pointers

Smart pointers are a powerful tool for managing memory in C++. OpenCV provides several smart pointers, such as cv::Ptr<>, that can help you manage memory effectively. Smart pointers automatically release memory when they’re no longer needed, so you don’t have to worry about manually releasing memory.

Here’s an example of how to use a smart pointer in OpenCV:

cv::Ptr<cv::Mat> image = cv::makePtr<cv::Mat>(cv::imread("image.jpg"));
cv::imshow("Image", *image);

Best Practice #3: Avoid Creating Copies

Creating copies of images can consume a lot of memory, which can lead to memory leaks. Instead of creating copies, you can use image views or image references. Image views and references are lightweight and don’t consume extra memory.

Here’s an example of how to use an image view in OpenCV:

cv::Mat image = cv::imread("image.jpg");
cv::Mat image_roi = image(cv::Rect(0, 0, 100, 100));
cv::imshow("Image ROI", image_roi);

Best Practice #4: Use Memory Pools

Memory pools are a technique for managing memory that can improve performance and reduce memory fragmentation. A memory pool is a pre-allocated block of memory that you can use to allocate and deallocate memory.

OpenCV provides several memory pool functions, such as cv::fastMalloc() and cv::fastFree(). You can use these functions to create and manage memory pools in OpenCV.

Here’s an example of how to use a memory pool in OpenCV:

cv::setUseOptimized(true); // Enable optimized memory management
cv::Mat image = cv::imread("image.jpg");
cv::Mat image_roi = cv::Mat(100, 100, image.type(), cv::fastMalloc(100 * 100 * image.elemSize())); // Allocate memory from the memory pool
cv::imshow("Image ROI", image_roi);
cv::fastFree(; // Deallocate memory from the memory pool


Effective memory management is essential for ensuring the reliability and performance of computer vision applications that utilize OpenCV. By following the best practices we’ve discussed in this tutorial, such as releasing memory, using smart pointers, avoiding unnecessary copies, and implementing memory pools, you can optimize your memory usage and avoid memory leaks. Keep in mind that mastering memory management is an ongoing process that requires continuous learning and practice. However, with the knowledge and techniques covered in this tutorial, you’re now equipped with the tools to improve your memory management skills in OpenCV.