OpenCV C++与C接口性能对比及IplImage与Mat结合方案咨询

First off, let’s clear up your biggest misunderstanding about the C++ Canny function: it does NOT copy your image every time you call it. The InputArray and OutputArray parameters are lightweight wrappers that act as references to existing data (like a Mat), not copies. Unless you’re passing a temporary object that needs implicit conversion, your original frame data is reused directly. So that "24 copies per second" concern is unfounded—you can use the C++ interface without that overhead.

Now let’s tackle your other questions:

Can I combine IplImage and Mat? #

Absolutely. OpenCV provides seamless conversions between the two types, and they can even share the same underlying pixel data (no copy involved if you set it up right):

• To convert an IplImage* to Mat:

  IplImage* ipl_img = cvLoadImage("frame.jpg");
  Mat mat_img(ipl_img, false); // The 'false' flag means share data, don't copy
  

• To convert a Mat to IplImage:

  Mat mat_img = imread("frame.jpg");
  IplImage ipl_img = mat_img; // This creates an IplImage wrapper without copying data
  

Just be careful with memory management: if you’re using a shared-data Mat from an IplImage, don’t free the IplImage until you’re done with the Mat, and vice versa.

What’s the overhead of converting IplImage to Mat? #

If you use the shared-data conversion (like the example above with false), the overhead is negligible—it’s just creating a new wrapper object that points to the existing data buffer. There’s no pixel copying involved. The only time you’d get overhead is if you force a copy (by passing true to the Mat constructor), but that’s unnecessary for most high-speed use cases.

Is using IplImage a reasonable choice for speed? #

In short: No, not for new code. Here’s why:

• The C++ Mat interface is actively maintained, optimized, and has access to all the latest OpenCV features (many new algorithms aren’t available in the C API).
• Mat handles automatic memory management, which reduces the risk of memory leaks—critical for long-running video processing pipelines.
• Performance-wise, the C++ interface is just as fast (if not faster) than the C API. OpenCV’s C++ functions are optimized with modern C++ features and compiler optimizations, and they avoid the overhead of C-style pointer checks in many cases.
• The C API (including cvCanny) is deprecated in newer OpenCV versions, so you’ll run into compatibility issues if you try to upgrade later.

If your main goal is maximum speed, focus on optimizing your C++ pipeline instead:

• Reuse Mat objects for frames (allocate once, reuse for each frame instead of creating new ones every time).
• Use contiguous Mat data (avoid submatrices that aren’t contiguous if possible).
• Enable compiler optimizations (like -O3 in GCC/Clang or /O2 in MSVC) to get the most out of your code.

内容的提问来源于stack exchange，提问作者yooo123