当 Haar 训练完成第 0 阶段，输出一个 txt 文件并且没有 .xml 并决定再次执行第 0 阶段时，这意味着什么？
谢谢
顺便说一句：这可能与我在使用 opencv_traincascade 时遇到问题的另一篇文章有关，所以我决定使用 haartraining，这发生了：Haar Training: error (-215)_img.row * _img.cols == vecSize在功能上
以下是来自一次 Haar 培训执行的回复。它完成第 0 阶段，然后再次开始，如下所示：
第一阶段 0
Number of features used : 3951220
Parent node: NULL
*** 1 cluster ***
POS: 2000 2000 1.000000
NEG: 2000 1
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.342012| 1.000000| 1.000000| 0.276750|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.527221| 1.000000| 1.000000| 0.265750|
+----+----+-+---------+---------+---------+---------+
| 3|100%|-|-0.691320| 1.000000| 1.000000| 0.249750|
+----+----+-+---------+---------+---------+---------+
| 4| 85%|+|-0.824964| 1.000000| 1.000000| 0.225000|
+----+----+-+---------+---------+---------+---------+
| 5| 91%|-|-1.065741| 1.000000| 1.000000| 0.207000|
+----+----+-+---------+---------+---------+---------+
| 6| 85%|+|-1.199052| 1.000000| 1.000000| 0.192500|
+----+----+-+---------+---------+---------+---------+
| 7| 85%|-|-1.348132| 1.000000| 1.000000| 0.180000|
+----+----+-+---------+---------+---------+---------+
| 8| 91%|+|-1.410564| 1.000000| 1.000000| 0.179000|
+----+----+-+---------+---------+---------+---------+
| 9| 88%|-|-1.158043| 0.997000| 0.810500| 0.162000|
+----+----+-+---------+---------+---------+---------+
| 10| 80%|+|-1.249312| 0.996000| 0.769000| 0.160750|
+----+----+-+---------+---------+---------+---------+
| 11| 79%|-|-1.386396| 0.997000| 0.777000| 0.145250|
+----+----+-+---------+---------+---------+---------+
| 12| 78%|+|-1.388796| 0.996000| 0.687000| 0.144500|
+----+----+-+---------+---------+---------+---------+
| 13| 78%|-|-1.338245| 0.996000| 0.724000| 0.139000|
+----+----+-+---------+---------+---------+---------+
| 14| 77%|+|-1.275169| 0.995500| 0.672500| 0.138000|
+----+----+-+---------+---------+---------+---------+
| 15| 76%|-|-1.414586| 0.996000| 0.698500| 0.127000|
+----+----+-+---------+---------+---------+---------+
| 16| 76%|+|-1.484858| 0.995500| 0.616500| 0.121250|
+----+----+-+---------+---------+---------+---------+
| 17| 75%|-|-1.545480| 0.996000| 0.591500| 0.113750|
+----+----+-+---------+---------+---------+---------+
| 18| 73%|+|-1.519740| 0.995500| 0.585000| 0.111250|
+----+----+-+---------+---------+---------+---------+
| 19| 73%|-|-1.434103| 0.995500| 0.527500| 0.105750|
+----+----+-+---------+---------+---------+---------+
| 20| 72%|+|-1.475413| 0.995500| 0.536000| 0.105750|
+----+----+-+---------+---------+---------+---------+
| 21| 72%|-|-1.503680| 0.995500| 0.537500| 0.102500|
+----+----+-+---------+---------+---------+---------+
| 22| 71%|+|-1.521223| 0.995500| 0.529000| 0.096000|
+----+----+-+---------+---------+---------+---------+
| 23| 71%|-|-1.496952| 0.995500| 0.484000| 0.089250|
+----+----+-+---------+---------+---------+---------+
Stage training time: 49991.00
Number of used features: 23
第二阶段 0 进行中：
Tree Classifier
Stage
+---+
| 0|
+---+
0
Parent node: 0
*** 1 cluster ***
POS: 2000 2009 0.995520
NEG: 2000 0.517464
BACKGROUND PROCESSING TIME: 0.00
Precalculation time: 0.00
+----+----+-+---------+---------+---------+---------+
| N |%SMP|F| ST.THR | HR | FA | EXP. ERR|
+----+----+-+---------+---------+---------+---------+
| 1|100%|-|-0.224272| 1.000000| 1.000000| 0.332750|
+----+----+-+---------+---------+---------+---------+
| 2|100%|+|-0.268457| 1.000000| 1.000000| 0.303750|
+----+----+-+---------+---------+---------+---------+
| 3| 97%|-|-0.381251| 1.000000| 1.000000| 0.262000|
+----+----+-+---------+---------+---------+---------+
| 4| 93%|+|-0.683000| 1.000000| 1.000000| 0.264750|
+----+----+-+---------+---------+---------+---------+
| 5| 93%|-|-0.935299| 1.000000| 1.000000| 0.263750|
+----+----+-+---------+---------+---------+---------+
| 6| 87%|+|-1.005094| 0.998000| 0.965500| 0.270000|
+----+----+-+---------+---------+---------+---------+
| 7| 86%|-|-1.095243| 0.998000| 0.967500| 0.244500|
+----+----+-+---------+---------+---------+---------+
| 8| 84%|+|-1.152430| 0.998000| 0.968500| 0.233750|
+