opencv_traincascade训练时长、预计算疑问及numThreads失效问题咨询

Let’s walk through each of your questions with practical context from working with OpenCV’s traincascade tool:

What is Precomputation Time? #

Precomputation is the behind-the-scenes prep work that often kicks off at the start of training to speed up the actual classifier building process. Specifically, it includes:

• Calculating integral images for all positive and negative samples (a foundational step for Haar/LBP feature extraction)
• Precomputing and caching all possible feature values for your samples
• Organizing these features into structures that the AdaBoost training loop can quickly access

This step is memory-heavy (which explains your 11.5GB usage) because it stores massive amounts of feature data upfront. It’s also typically single-threaded, which ties directly into your CPU usage observation.

Why is the numThreads Parameter Being Ignored? #

There are a few key reasons this happens:

• Stage-specific multi-threading: The numThreads parameter only activates during the actual AdaBoost training stages (the part where it builds weak classifiers). The precomputation phase I mentioned earlier is almost always single-threaded in OpenCV’s traincascade implementation—so your CPU sitting at 14% (likely one core maxed out) is totally normal right now.
• OpenCV version limitations: If you’re using an older OpenCV version (pre-3.x), numThreads support was either incomplete or non-existent for traincascade. Even in newer versions, multi-threading doesn’t apply to every part of the pipeline.
• Stuck preprocessing: In rare cases, if your sample dataset has corrupted files or misconfigured paths, the tool might get stuck in a single-threaded validation step before even reaching the multi-threaded training phase. Double-check your sample lists to rule this out.

How Long Until Training Finishes? #

It’s hard to give an exact number, but here’s how to gauge progress:

1. Wait for precomputation to finish: Once this phase ends, you’ll see logs about starting stage 1 of your classifier, and CPU usage should jump to match your numThreads setting. If you have thousands of samples, precomputation taking an hour (or longer) is common—especially if you’re using Haar features (slower than LBP).
2. Estimate from stage progress: Once training starts, each stage will output a log line when it’s done. Time the first 1-2 stages, then multiply by your total number of stages (-numStages parameter) to get a rough estimate.
3. Watch memory usage: After precomputation, memory usage may drop a bit as temporary feature caches are cleared, which is a sign you’re moving into the actual training loop.

内容的提问来源于stack exchange，提问作者user6283344