曾经有一刻，一直在纠结opencv->haartraining中的条件宏ICV_DEF_FIND_STUMP_THRESHOLD_SQ的使用，该宏的作用是寻找特征值数组中的最佳阈值，使得error最小。
ICV_DEF_FIND_STUMP_THRESHOLD_SQ代码如下：
/* least sum of squares error */
#define ICV_DEF_FIND_STUMP_THRESHOLD_SQ( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( sq_##suffix, type, \
/* calculate error (sum of squares) */ \
/* err = sum( w * (y - left(rigt)Val)^2 ) */ \
curlerror = wyyl + curleft * curleft * wl - 2.0F * curleft * wyl; \
currerror = (*sumwyy) - wyyl + curright * curright * wr - 2.0F * curright * wyr; \
)
其中，ICV_DEF_FIND_STUMP_THRESHOLD定义如下：
#define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \
CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \
uchar* data, size_t datastep, \
uchar* wdata, size_t wstep, \
uchar* ydata, size_t ystep, \
uchar* idxdata, size_t idxstep, int num, \
float* lerror, \
float* rerror, \
float* threshold, float* left, float* right, \
float* sumw, float* sumwy, float* sumwyy ) \
{ \
int found = 0; \
float wyl = 0.0F; \
float wl = 0.0F; \
float wyyl = 0.0F; \
float wyr = 0.0F; \
float wr = 0.0F; \
\
float curleft = 0.0F; \
float curright = 0.0F; \
float* prevval = NULL; \
float* curval = NULL; \
float curlerror = 0.0F; \
float currerror = 0.0F; \
float wposl; \
float wposr; \
\
int i = 0; \
int idx = 0; \
\
wposl = wposr = 0.0F; \
if( *sumw == FLT_MAX ) \
{ \
/* calculate sums */ \
float *y = NULL; \
float *w = NULL; \
float wy = 0.0F; \
\
*sumw = 0.0F; \
*sumwy = 0.0F; \
*sumwyy = 0.0F; \
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
w = (float*) (wdata + idx * wstep); \
*sumw += *w; \
y = (float*) (ydata + idx * ystep); \
wy = (*w) * (*y); \
*sumwy += wy; \
*sumwyy += wy * (*y); \
} \
} \
\
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
curval = (float*) (data + idx * datastep); \
/* for debug purpose */ \
if( i > 0 ) assert( (*prevval) <= (*curval) ); \
\
wyr = *sumwy - wyl; \
wr = *sumw - wl; \
\
if( wl > 0.0 ) curleft = wyl / wl; \
else curleft = 0.0F; \
\
if( wr > 0.0 ) curright = wyr / wr; \
else curright = 0.0F; \
\
error \
\
if( curlerror + currerror < (*lerror) + (*rerror) ) \
{ \
(*lerror) = curlerror; \
(*rerror) = currerror; \
*threshold = *curval; \
if( i > 0 ) { \
*threshold = 0.5F * (*threshold + *prevval); \
} \
*left = curleft; \
*right = curright; \
found = 1; \
} \
\
do \
{ \
wl += *((float*) (wdata + idx * wstep)); \
wyl += (*((float*) (wdata + idx * wstep))) \
* (*((float*) (ydata + idx * ystep))); \
wyyl += *((float*) (wdata + idx * wstep)) \
* (*((float*) (ydata + idx * ystep))) \
* (*((float*) (ydata + idx * ystep))); \
} \
while( (++i) < num && \
( *((float*) (data + (idx = \
(int) ( *((type*) (idxdata + i*idxstep))) ) * datastep)) \
== *curval ) ); \
--i; \
prevval = curval; \
} /* for each value */ \
\
return found; \
}
当时觉得很奇怪，内部条件宏ICV_DEF_FIND_STUMP_THRESHOLD括号内明明只有 suffix, type, error 三个参数，怎么调用传递的时候
ICV_DEF_FIND_STUMP_THRESHOLD( sq_##suffix, type, \
/* calculate error (sum of squares) */ \
/* err = sum( w * (y - left(rigt)Val)^2 ) */ \
curlerror = wyyl + curleft * curleft * wl - 2.0F * curleft * wyl; \
currerror = (*sumwyy) - wyyl + curright * curright * wr - 2.0F * curright * wyr; \
里面却是上面这个样子，前面两个参数，外加两句代码行？
后来弄明白了，我们注意看ICV_DEF_FIND_STUMP_THRESHOLD中的error的相关使用就知道了，在ICV_DEF_FIND_STUMP_THRESHOLD中间，孤零零的一句代码
\
error \
\
原来error不能说的上是条件宏的一个参数，他就是一个代码段，调用的时候，相当于直接把代码段粘贴到上面的位置！
为啥要这么用呢，原因就是error代表的代码段，复用率十分高，所以索性直接写成这样的形式，C语言，真的是灵活异常哎。。。。
类似的宏还有：
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32f, float )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32f, float )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32f, float )
此外，sq_##suffix起到了连接字符串的功能，如果suffix是16s，那么sq_##suffix实际上就是sq_16s，再然后，直接指向相关函数。
opencv之莫名其妙的条件宏ICV_DEF_FIND_STUMP_THRESHOLD_SQ解释~
原文地址：http://blog.csdn.net/wsj998689aa/article/details/42242565
