OpenCV：如何了解HaarTraining的进度状态

本文介绍了OpenCV：如何了解HaarTraining的进度状态的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在使用OpenCV 2.2（使用OpenMP编译）运行HaarTraining流程。培训进度显示：

I'm running a HaarTraining process using OpenCV 2.2 (compiled with OpenMP). The training progress shows:

+----+----+-+---------+---------+---------+---------+ |7468| 44%|-|-2.712450| 1.000000| 0.719459| 0.108393| +----+----+-+---------+---------+---------+---------+ |7469| 44%|-|-2.712449| 1.000000| 0.719459| 0.108393| +----+----+-+---------+---------+---------+---------+ .......

什么是数字7469意味着（我只有7000个样本）？我怎么知道这个过程何时结束？

What does the number 7469 mean (I only have 7000 samples)? How can I know when the process is going to finish?

谢谢！

推荐答案

N表示此级联的当前功能。 （参见 cv-kolaric.blogspot/2008 /03/output-from-haartrainingexe.html ）

N means the current feature for this cascade. (see cv-kolaric.blogspot/2008/03/output-from-haartrainingexe.html)

+----+----+-+---------+---------+---------+---------+ | N |%%SMP|F| ST.THR | HR | FA | EXP. ERR| +----+----+-+---------+---------+---------+---------+ N = current feature for this cascade (seq->total), %%SMP = percentage of samples used, if trimmings enabled (v_wt) F = '+' if isFilpped, if symmetry is specified (v_flipped), '-' otherwise ST.THR = stage threshold, HR = Hit Rate based on Stage threshold (v_hitrate / numpos), FA = False alarm based on Stage threshold (v_falsealarm / numneg) EXP.ERR = Strong classification error of adaboost algorithm, based on threshold=0 (v_experr)

计算所有样本的不同功能。例如。根据样本之间的差异，计算出更多的特征，看起来你的正面图像彼此差异太大，无法对它们进行分类。

Different features are calculated for all your samples. E.g. x-y derratives, contrast-ratios etc. etc. Depending on the difference between your samples, more features are calculated, and it seems your positive images are just too different from each other, to categorize them.

尝试在训练前先预处理图像。例如。删除背景，查找边缘，阈值图像，洪水填充。任何可以使您的正面图像设置看起来更相似的东西

Try to pre-process your images first, before training. E.g. remove background, find edges, threshold the images, flood-fill. Anything that can make your positive image set look more alike