OpenCV 2.3.1:如何判断haartraining是卡住了还是还在工作(在小示例中)
OpenCV 2.3.1:如何判断haartraining是卡住了还是还在工作(在小示例中)
提问于 2012-01-02 12:24:57
我在Mac上使用OpenCV 2.3.1，这是我第一次使用opencv_haartraining。
我正在试着训练一个非常快速的例子。我只用了23个正面的例子和45个负面的例子。然而，opencv_haartraining已经100%使用了我2010年Macbook Air的一个内核至少30个小时！
以下是相关文件：
目前为止生成的bg ()示例和中间结果的目录http://stanford.edu/~jonr1/haartraining_test_1/bg_negative_examples.txt
- 目录http://stanford.edu/~jonr1/haartraining_test_1/vec_positive_samples/vec_positive_samples.vec
- bg(负)示例vec文件
vec文件是通过遵循本教程http://note.sonots.com/SciSoftware/haartraining.html生成的，使用作者的程序mergevec组合由createsamples单独生成的vec文件。
opencv_haartraining的输出为：
Data dir name: /Users/jon/Tabletop/haartraining_test_1/results
Vec file name: /Users/jon/Tabletop/haartraining_test_1/vec_positive_samples/vec_positive_samples.vec
BG file name: /var/folders/85/96xv8qxx5ssc7ndg50s5lp480000gn/T/tmpZ2bASi.txt, is a vecfile: no
Num pos: 115
Num neg: 45
Num stages: 20
Num splits: 2 (tree as weak classifier)
Mem: 200 MB
Symmetric: TRUE
Min hit rate: 0.995000
Max false alarm rate: 0.500000
Weight trimming: 0.950000
Equal weights: FALSE
Mode: BASIC
Width: 20
Height: 20
Applied boosting algorithm: GAB
Error (valid only for Discrete and Real AdaBoost): misclass
Max number of splits in tree cascade: 0
Min number of positive samples per cluster: 500
Required leaf false alarm rate: 9.53674e-07
Tree Classifier
Stage
+---+
| 0|
+---+
Number of features used : 41910
Parent node: NULL
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 1
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-| 0.910420| 1.000000| 0.044444| 0.012500|
+----+----+-+---------+---------+---------+---------+
Stage training time: 2.00
Number of used features: 2
Parent node: NULL
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+
| 0|
+---+
0
Parent node: 0
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.283019
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.965048| 1.000000| 1.000000| 0.018750|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.903213| 1.000000| 0.288889| 0.025000|
+----+----+-+---------+---------+---------+---------+
Stage training time: 3.00
Number of used features: 4
Parent node: 0
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+
| 0| 1|
+---+---+
0---1
Parent node: 1
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.338346
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.961620| 1.000000| 1.000000| 0.043750|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.660077| 1.000000| 0.622222| 0.043750|
+----+----+-+---------+---------+---------+---------+
| 3| 88%|-| 0.142538| 1.000000| 0.044444| 0.012500|
+----+----+-+---------+---------+---------+---------+
Stage training time: 4.00
Number of used features: 6
Parent node: 1
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+
| 0| 1| 2|
+---+---+---+
0---1---2
Parent node: 2
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.145631
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.975839| 1.000000| 0.777778| 0.025000|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.904803| 1.000000| 0.244444| 0.037500|
+----+----+-+---------+---------+---------+---------+
Stage training time: 3.00
Number of used features: 4
Parent node: 2
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+
| 0| 1| 2| 3|
+---+---+---+---+
0---1---2---3
Parent node: 3
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.0293926
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.981092| 1.000000| 1.000000| 0.031250|
+----+----+-+---------+---------+---------+---------+
| 2| 91%|+|-0.820519| 1.000000| 0.333333| 0.031250|
+----+----+-+---------+---------+---------+---------+
Stage training time: 3.00
Number of used features: 4
Parent node: 3
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+
| 0| 1| 2| 3| 4|
+---+---+---+---+---+
0---1---2---3---4
Parent node: 4
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.0244965
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.964250| 1.000000| 1.000000| 0.025000|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-1.801320| 1.000000| 1.000000| 0.025000|
+----+----+-+---------+---------+---------+---------+
| 3| 88%|-|-0.938272| 1.000000| 0.177778| 0.006250|
+----+----+-+---------+---------+---------+---------+
Stage training time: 4.00
Number of used features: 6
Parent node: 4
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5|
+---+---+---+---+---+---+
0---1---2---3---4---5
Parent node: 5
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.0100245
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.975839| 1.000000| 1.000000| 0.037500|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.109149| 1.000000| 0.133333| 0.037500|
+----+----+-+---------+---------+---------+---------+
Stage training time: 3.00
Number of used features: 4
Parent node: 5
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5| 6|
+---+---+---+---+---+---+---+
0---1---2---3---4---5---6
Parent node: 6
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.00587774
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.870814| 1.000000| 0.800000| 0.050000|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.437010| 1.000000| 0.200000| 0.050000|
+----+----+-+---------+---------+---------+---------+
Stage training time: 3.00
Number of used features: 4
Parent node: 6
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5| 6| 7|
+---+---+---+---+---+---+---+---+
0---1---2---3---4---5---6---7
Parent node: 7
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.00269655
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.825750| 1.000000| 1.000000| 0.087500|
+----+----+-+---------+---------+---------+---------+
| 2| 89%|+|-1.098274| 1.000000| 0.911111| 0.093750|
+----+----+-+---------+---------+---------+---------+
| 3| 99%|-|-0.387003| 1.000000| 0.222222| 0.050000|
+----+----+-+---------+---------+---------+---------+
Stage training time: 5.00
Number of used features: 6
Parent node: 7
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5| 6| 7| 8|
+---+---+---+---+---+---+---+---+---+
0---1---2---3---4---5---6---7---8
Parent node: 8
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.000656714
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.780975| 1.000000| 1.000000| 0.125000|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-1.143491| 1.000000| 0.866667| 0.125000|
+----+----+-+---------+---------+---------+---------+
| 3|100%|-|-1.267461| 1.000000| 0.355556| 0.037500|
+----+----+-+---------+---------+---------+---------+
Stage training time: 5.00
Number of used features: 6
Parent node: 8
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5| 6| 7| 8| 9|
+---+---+---+---+---+---+---+---+---+---+
0---1---2---3---4---5---6---7---8---9
Parent node: 9
*** 1 cluster ***
POS: 115 115 1.000000
NEG: 45 0.000245695
BACKGROUND PROCESSING TIME: 1.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.982759| 1.000000| 1.000000| 0.006250|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+| 0.017238| 1.000000| 0.000000| 0.000000|
+----+----+-+---------+---------+---------+---------+
Stage training time: 2.00
Number of used features: 4
Parent node: 9
Chosen number of splits: 0
Total number of splits: 0
Tree Classifier
Stage
+---+---+---+---+---+---+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5| 6| 7| 8| 9| 10|
+---+---+---+---+---+---+---+---+---+---+---+
0---1---2---3---4---5---6---7---8---9--10
Parent node: 10
*** 1 cluster ***
POS: 115 115 1.000000所有这些输出都是在运行的前5分钟产生的。在产生这个输出之后，它已经以一个内核的100%运行了30个小时(到目前为止)，没有进一步的输出。
我的问题是:在这种情况下，我如何判断cvhaartraining.cpp 是否已崩溃，更广泛地说，是否有人知道如何修改 haartraining 以使其定期输出其状态？万分感谢！
(相关问题，均无答案：
)
回答 5
Stack Overflow用户
回答已采纳
发布于 2012-10-21 21:19:47
在OpenCV雅虎技术小组上也有类似的帖子，有一个由michael_p_horton提供的代码，用于提供一些额外的反馈，以确定代码是否进入了无限循环：tech.groups.yahoo.com/group/OpenCV/message/45080
综上所述，有两个地方可以让haartraining变得狂野。
第一个问题很容易通过检查输出来捕获-您需要增加HR (命中率)和减少FA (错误警报)。如果没有发生这种情况，训练就会进入无限循环。
然而，根据maxenglander的回答，您遇到的问题是icvGetHaarTrainingDataFromBG中的无限循环。要检查这一点，您需要深入研究cvhaartraining.cpp代码并添加一些调试输出。
要引用Yahoo groups的答案，您需要按以下方式修改icvGetHaarTrainingDataFromBG实现(查找cascade eval()行，然后添加CV_VERBOSE代码)：
icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );
if( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F )
break;
/* Display progress on negative image selection */
#ifdef CV_VERBOSE
if( thread_consumed_count % 1000 == 0 )
{
fprintf( stderr, "%3d%%, %d negatives of %d required, %d images
tested\r", (int) ( 100.0 * (i - first) / count ), (i-first), count,
thread_consumed_count );
fflush( stderr );
}
#endif /* CV_VERBOSE */如果开始显示"0%，0负片，10000000张图片测试“这样的消息，你就进入了一个无尽的循环。
最后要注意的是，对于OpenCV 2.4，相关代码在icvGetHaarTrainingData函数中。
Stack Overflow用户
发布于 2012-01-11 01:43:24
从haartraining/cvhaartraining.cpp上看，在取消之前，您的进程看起来就在这一行：
negcount = icvGetHaarTrainingDataFromBG( training_data, poscount, nneg,
(CvIntHaarClassifier*) tcc, &false_alarm, bg_vecfile ? bgfilename : NULL );您可以深入到icvGetHaarTrainingDataFromBG调用的函数，添加printf语句，然后重新编译。我的训练运行也被挂起了(我使用的是opencv_traincascade)，我想看看哪里卡住了。我可能会尝试在traincascade/traincascade.cpp中加入一些printf语句，看看为什么我的运行挂起了。
最大值
Stack Overflow用户
发布于 2012-04-12 21:45:23
查看haartraining的描述，您会发现默认值为
-minpos <min_number_of_positive_samples_per_cluster = 500>因为你只有115个阳性样本，这可能是你的问题…也许您没有指定这个参数，这会导致训练永远不会结束。尝试将其设置为75
haartraining <your params> -minpos 75这只是我的想法，没有经过验证。请让我知道你的发现
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/8697385
复制相关文章
相似问题