我想使用的OpenCV的opencv_traincascade生成一个哈尔级联。 到目前为止，我有87个独特的正样本和测试目的39个负样本。 我产生与opencv_createsamples的.vec文件，它工作得很好。 当我运行opencv_traincascade它总是被后几个阶段卡住了，不管我怎么更改参数。 我的电话是这样的：
opencv_traincascade -data /opencvimgs/haarcascades/data/ -vec /opencvimgs/haarcascades/out.vec -bg /opencvimgs/haarcascades/neg.txt -numPos 87 -numNeg 39
我试图增加和减少minHitRate和maxFalseAlarmRate以及numPos和numNeg没有任何成功。 它可能运行几个阶段，但随后似乎在英辉循环再挂起。 我怎样才能解决这个问题？
下面的输出是什么PROGRAMM写入控制台：
opencv_traincascade -data /opencvimgs/haarcascades/data/ -vec
/opencvimgs/haarcascades/out.vec -bg /opencvimgs/haarcascades/neg.txt -numPos 87 -numNeg 39
PARAMETERS:
cascadeDirName: /opencvimgs/haarcascades/data/
vecFileName: /opencvimgs/haarcascades/out.vec
bgFileName: /opencvimgs/haarcascades/neg.txt
numPos: 87
numNeg: 39
numStages: 20
precalcValBufSize[Mb] : 256
precalcIdxBufSize[Mb] : 256
stageType: BOOST
featureType: HAAR
sampleWidth: 24
sampleHeight: 24
boostType: GAB
minHitRate: 0.995
maxFalseAlarmRate: 0.5
weightTrimRate: 0.95
maxDepth: 1
maxWeakCount: 100
mode: BASIC
===== TRAINING 0-stage =====
<BEGIN
POS count : consumed 87 : 87
NEG count : acceptanceRatio 39 : 1
Precalculation time: 1
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
===== TRAINING 1-stage =====
<BEGIN
POS count : consumed 87 : 87
NEG count : acceptanceRatio 39 : 0.0697674
Precalculation time: 1
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
===== TRAINING 2-stage =====
<BEGIN
POS count : consumed 87 : 87
NEG count : acceptanceRatio 39 : 0.00945455
Precalculation time: 1
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
===== TRAINING 3-stage =====
<BEGIN
POS count : consumed 87 : 87
NEG count : acceptanceRatio 39 : 0.000326907
Precalculation time: 1
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
===== TRAINING 4-stage =====
<BEGIN
POS count : consumed 87 : 87
