我是第一次使用 opencv_haartraining
,在 Mac OS X Lion 上使用 OpenCV 2.3.1。
我正在尝试训练一个非常快速的示例。我只使用了 23 个正面例子和 45 个负面例子。然而,opencv_haartraining
已经 100% 使用了我 2010 Macbook Air 的一个内核至少 30 小时!
相关文件如下:
- http://stanford.edu/~jonr1/haartraining_test_1/目录
- 正样本的vec文件http://stanford.edu/~jonr1/haartraining_test_1/vec_positive_samples/vec_positive_samples.vec
vec 文件是按照本教程 http://note.sonots.com/SciSoftware/haartraining.html 生成的,使用该作者的程序 mergevec
组合由 createsamples
单独生成的 vec 文件。
opencv_haartraining 的输出是:
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 分钟内产生的。产生此输出后,它继续以 100% 的一个核心运行 30 小时(到目前为止),没有进一步的输出。
我的问题是:我如何判断 haartraining 在这种特殊情况下是否崩溃,更一般地说,有谁知道如何修改 cvhaartraining.cpp
以便它定期输出其状态?感谢一百万!
(相关问题,均无答案:
)
最佳答案
OpenCV Yahoo 技术组上也有类似的线程,其中包含 michael_p_horton 的代码,用于提供一些额外的反馈以确定代码是否进入无限循环:tech.groups.yahoo.com/group/OpenCV/message/45080
总结上述主题,haartraining 可以在两个地方发挥作用。
第一个很容易通过检查输出来捕捉——您需要增加 HR(命中率)并减少 FA(误报)。如果这没有发生,训练将进入无限循环。
但是,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 组的答案,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 */
If this starts displaying messages like "0%, 0 negatives of 972 required, 10000000 images tested" you've entered an endless loop.
最后一点——对于 OpenCV 2.4,相关代码在 icvGetHaarTrainingData
函数中。
关于OpenCV 2.3.1 : how to tell if haartraining is stuck or still working (on TINY example),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/8697385/