awesome-reviewers/_reviewers/opencv-framework-synchronization-practices.md

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title	description	repository	label	language	comments_count	repository_stars
Framework synchronization practices	When implementing concurrent code, always use the framework's provided synchronization primitives rather than direct language features. This ensures compatibility across all build configurations and platforms:	opencv/opencv	Concurrency	C++	4	82865

When implementing concurrent code, always use the framework's provided synchronization primitives rather than direct language features. This ensures compatibility across all build configurations and platforms:

1. Use OpenCV synchronization mechanisms like cv::getInitializationMutex() and cv::AutoLock instead of standard C++ constructs like std::mutex and std::lock_guard:

// Instead of this:
static std::mutex mtx;
std::lock_guard<std::mutex> lock(mtx);

// Use this:
cv::AutoLock lock(cv::getInitializationMutex());

2. Respect system threading settings with cv::getNumThreads() rather than hardcoding thread counts:

// Instead of this:
m_parallel_runner = JxlThreadParallelRunnerMake(nullptr, 8); // Hardcoded value

// Use this:
m_parallel_runner = JxlThreadParallelRunnerMake(nullptr, cv::getNumThreads());

3. Be aware that thread counts vary by platform (e.g., Android defaults to 2, Linux uses all cores) and that nested parallel operations may be serialized.

4. When using static resources in multithreaded contexts, ensure proper synchronization or consider alternatives like on-demand initialization that avoid static state altogether.

These practices ensure your code remains compatible with special configurations like OPENCV_DISABLE_THREAD_SUPPORT while maintaining optimal performance across various platforms.