OpenCV 2.3.1:如何判断haartraining是否卡住或仍在工作(在TINY示例中)

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OpenCV 2.3.1:如何判断haartraining是否卡住或仍在工作(在TINY示例中)

Alc*_*ive 17 opencv

我是opencv_haartraining第一次在Mac OS X Lion上使用OpenCV 2.3.1.

我正在努力训练一个非常快速的例子.我只使用了23个正面例子和45个负面例子.然而opencv_haartraining,我的2010 Macbook Air的一个核心100%使用了至少30个小时!

以下是相关文件:

目录http://stanford.edu/~jonr1/haartraining_test_1/
正样本的vec文件http://stanford.edu/~jonr1/haartraining_test_1/vec_positive_samples/vec_positive_samples.vec
- bg(负面)示例http://stanford.edu/~jonr1/haartraining_test_1/bg_negative_examples.txt
- 到目前为止产生的中间结果 http://stanford.edu/~jonr1/haartraining_test_1/results/

vec文件是按照本教程http://note.sonots.com/SciSoftware/haartraining.html制作的,使用该作者的程序mergevec组合单独生成的vec文件createsamples.

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

Run Code Online (Sandbox Code Playgroud)

所有这些输出都是在运行的前5分钟产生的.产生这个输出之后,它继续以100%的一个核心运行30个小时(到目前为止),没有进一步的输出.

我的问题是:如何判断haartraining在这种特殊情况下是否已经崩溃,更一般地说,是否有人知道如何修改cvhaartraining.cpp以便定期输出其状态？太感谢了!

(相关问题,两者都没有答案:

)

Answer 1

sta*_*tiv 4

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

\n\n

总结一下上面提到的线索，haartraining 有两个地方可以发挥作用。

\n\n

第一个很容易通过检查输出 \xe2\x80\x93 来捕获，您需要增加 HR（命中率）并减少 FA（误报）。如果这没有发生，训练就会进入无限循环。

\n\n

然而，根据 maxenglander 的回答，您遇到的问题是icvGetHaarTrainingDataFromBG. 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。

\n\n

要引用Yahoo groups的答案，您需要修改icvGetHaarTrainingDataFromBG按以下方式修改实现（查找cascade-eval()行，然后添加CV_VERBOSE代码）：

\n\n

icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );\nif( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F )\n    break;\n\n/* Display progress on negative image selection */\n#ifdef CV_VERBOSE\nif( thread_consumed_count % 1000 == 0 )\n{\n    fprintf( stderr, "%3d%%, %d negatives of %d required, %d images\n    tested\\r", (int) ( 100.0 * (i - first) / count ), (i-first), count,\n    thread_consumed_count );\n    fflush( stderr );\n}\n#endif /* CV_VERBOSE */\n

Run Code Online (Sandbox Code Playgroud)\n\n

\n
如果开始显示诸如“0%，需要 972\n 的 0 幅底片，已测试 10000000 张图像”之类的消息，则您已进入无限循环。
\n

\n\n

最后一点注意 \xe2\x80\x93 与 OpenCV 2.4 相关代码位于icvGetHaarTrainingData。

\n

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