带着强烈的兴趣，上周开始人脸识别的尝试与学习，并且将具体的操作过程记录了下来
链接如下：http://blog.csdn.net/u011583927/article/details/44627493
这周开始了对于算法的深入学习，下面进入正题。
Haar特征的原理是什么？
Haar特征分为三类：边缘特征、线性特征、中心特征和对角线特征，组合成特征模板。特征模板内有白色和黑色两种矩形，并定义该模板的特征值为白色矩形像素和减去黑色矩形像素和（在opencv实现中为黑色-白色）。Haar特征值反映了图像的灰度变化情况。例如：脸部的一些特征能由矩形特征简单的描述，如：眼睛要比脸颊颜色要深，鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。但矩形特征只对一些简单的图形结构，如边缘、线段较敏感，所以只能描述特定走向（水平、垂直、对角）的结构。（本段文字及下面两幅图引用自http://blog.csdn.net/zouxy09/article/details/7929570）
Viola提出的haar特征：
Lienhart等牛们提出的Haar-like特征：
矩形特征可位于图像任意位置，大小也可以任意改变，所以矩形特征值是矩形模版类别、矩形位置和矩形大小这三个因素的函数，当然对于新提出的有旋转角度的haar特征，还要把旋转的因素考虑进去。
所以一个Haar特征的数据结构应该包含以下内容：
*haar特征模板类型
*是否有旋转
*矩阵位置及大小
CvIntHaarFeatures是如何构成的？
在Opencv中，我们用CvTHaarFeature和CvFastHaarFeature作为描述单个特征的数据结构，用CvIntHaarFeatures作为一个封装的类型，通过这个类型中的两个指针（分别是CvTHaarFeature*和CvFastHaarFeature*指针）可以间接遍寻到存储的所有的特征。下面来看下它们的具体构造
CvTHaarFeature的数据结构：
//CvTHaarFeature：由（至多三个）矩形表示特征位置
typedef struct CvTHaarFeature
{
char desc[CV_HAAR_FEATURE_DESC_MAX]; //描述haar特征模板类型的变量
int tilted; //标识是否有旋转，通过desc字符数组开头是否为tilted判断
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX]; //三个矩形来描述特征位置
} CvTHaarFeature;
创建一个CvTHaarFeature特征：
/*例：haarFeature = cvHaarFeature("tilted_haar_y2",
x, y, dx,2*dy, -1,
x, y,dx, dy, +2 );*/
CV_INLINECvTHaarFeature cvHaarFeature(const char* desc,
int x0, int y0, int w0,int h0, float wt0,
int x1, int y1, int w1,int h1, float wt1,
int x2, int y2, int w2,int h2, float wt2 )
{
CvTHaarFeature hf;
assert( CV_HAAR_FEATURE_MAX >= 3 );
assert( strlen( desc ) <CV_HAAR_FEATURE_DESC_MAX );
strcpy( &(hf.desc[0]), desc );
hf.tilted = ( hf.desc[0] == 't' );
hf.rect[0].r.x = x0;
hf.rect[0].r.y = y0;
hf.rect[0].r.width = w0;
hf.rect[0].r.height = h0;
hf.rect[0].weight = wt0;
hf.rect[1].r.x = x1;
hf.rect[1].r.y = y1;
hf.rect[1].r.width = w1;
hf.rect[1].r.height = h1;
hf.rect[1].weight = wt1;
hf.rect[2].r.x = x2;
hf.rect[2].r.y = y2;
hf.rect[2].r.width = w2;
hf.rect[2].r.height = h2;
hf.rect[2].weight = wt2;
return hf;
}
CvFastHaarFeature的数据结构：
//与CvTHaarFeature类似，不同的是通过4个点来描述特征矩形的位置大小信息
typedef struct CvFastHaarFeature
{
int tilted;
struct
{
int p0, p1, p2, p3;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvFastHaarFeature;
CvIntHaarFeatures的数据结构：
typedef struct CvIntHaarFeatures
{
CvSize winsize;
int count;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
} CvIntHaarFeatures;
了解了如何构成，我们就来创建，icvCreateIntHaarFeatures（）方法的具体实现：
接下来就是最重要的一步，如何创建我们想要得到的所有特征信息及CvIntHaarFeatures，下面是icvCreateIntHaarFeatures方法的具体实现和详细注释
由于opencv和C++都是初学，用了很长时间写了大量注释，0基础也绝对能看懂，希望能对大家有帮助
/*
* icvCreateIntHaarFeatures
*
* Create internal representation of haar features
*
* mode:
* 0 - BASIC = Viola提出的原始举行特征
* 1 - CORE = All upright 所有垂直的haar特征
* 2 - ALL = All features 所有haar特征
*symmetric: 目标图形是否为垂直对称
*/
static
CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
int mode,
int symmetric )
{
CvIntHaarFeatures* features = NULL;
CvTHaarFeature haarFeature;
/*内存存储器是一个可用来存储诸如序列,轮廓,图形,子划分等动态增长数据结构的底层结构。它是由一系列以同等大小的内存块构成,呈列表型*/
CvMemStorage* storage = NULL;
CvSeq* seq = NULL;
CvSeqWriter writer;
int s0 = 36; /* minimum total area size of basic haar feature */
int s1 = 12; /* minimum total area size of tilted（倾斜的） haar features 2 */
int s2 = 18; /* minimum total area size of tilted haar features 3 */
int s3 = 24; /* minimum total area size of tilted haar features 4 */
int x = 0;
int y = 0;
int dx = 0;
int dy = 0;
#if 0
float factor = 1.0F;
factor = ((float) winsize.width) * winsize.height / (24 * 24);
s0 = (int) (s0 * factor);
s1 = (int) (s1 * factor);
s2 = (int) (s2 * factor);
s3 = (int) (s3 * factor);
#else
//程序必然走这边，为什么这么写？
s0 = 1;
s1 = 1;
s2 = 1;
s3 = 1;
#endif
/* CV_VECTOR_CREATE( vec, CvIntHaarFeature, size, maxsize ) */
storage = cvCreateMemStorage();
//功能：创建新序列，并初始化写入部分
/*我的理解：这里其实是定义了writer工具每次写入数据的大小，以及写入到哪个内存存储器
在之后调用 CV_WRITE_SEQ_ELEM( haarFeature, writer )时就可以自动将一个haarFeature类型的数据写入内存存储器中*/
cvStartWriteSeq( 0, sizeof( CvSeq ), sizeof( haarFeature ), storage, &writer );
/*矩形特征可位于图像任意位置，大小也可以任意改变，所以矩形特征值是矩形模版类别、矩形位置和矩形大小这三个因素的函数*/
for( x = 0; x < winsize.width; x++ )
{
for( y = 0; y < winsize.height; y++ )
{
//x，y确定了特征矩形的左上角坐标
for( dx = 1; dx <= winsize.width; dx++ )
{
for( dy = 1; dy <= winsize.height; dy++ )
{
//dx，dy确定了特征矩形的大小
//下面需要按照不同的特征模板类型分别讨论，在模板不越界的情况下，添
加该特征
// haar_x2 对应上图中的（a）特征模板，黑色为+，白色为-
if ( (x+dx*2 <= winsize.width) && (y+dy <= winsize.height) ) {
if (dx*2*dy < s0) continue;
if (!symmetric || (x+x+dx*2 <=winsize.width))
{
//目标图像不为垂直对称或目标垂直对称但满足上式条件
//若目标不垂直对称，显然要计算当前矩形特征的特征值
//若对称，则只计算左半部分全部位于标准样本左半边的矩形特征的特征值
haarFeature = cvHaarFeature( "haar_x2",
x, y, dx*2, dy, -1,
x+dx, y, dx , dy, +2 );
/* CV_VECTOR_PUSH( vec, CvIntHaarFeature, haarFeature, size, maxsize, step ) */
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// haar_y2 对应上图中的（b）特征模板
if ( (x+dx <= winsize.width) && (y+dy*2 <= winsize.height) ) {
if (dx*2*dy < s0) continue;
if (!symmetric || (x+x+dx <= winsize.width)) {
haarFeature = cvHaarFeature( "haar_y2",
x, y, dx, dy*2, -1,
x, y+dy, dx, dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// haar_x3 对应上图中的（c）特征模板
if ( (x+dx*3 <= winsize.width) && (y+dy <= winsize.height) ) {
if (dx*3*dy < s0) continue;
if (!symmetric || (x+x+dx*3 <=winsize.width)) {
haarFeature = cvHaarFeature( "haar_x3",
x, y, dx*3, dy, -1,
x+dx, y, dx, dy, +3 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// haar_y3 对应上图中的（d）特征模板
if ( (x+dx <= winsize.width) && (y+dy*3 <= winsize.height) ) {
if (dx*3*dy < s0) continue;
if (!symmetric || (x+x+dx <= winsize.width)) {
haarFeature = cvHaarFeature( "haar_y3",
x, y, dx, dy*3, -1,
x, y+dy, dx, dy, +3 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
if( mode != 0 /*BASIC*/ ) {
// haar_x4 对应上图中的（2b）特征模板
if ( (x+dx*4 <= winsize.width) && (y+dy <= winsize.height) ) {
if (dx*4*dy < s0) continue;
if (!symmetric || (x+x+dx*4 <=winsize.width)) {
haarFeature = cvHaarFeature( "haar_x4",
x, y, dx*4, dy, -1,
x+dx, y, dx*2, dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// haar_y4 对应上图中的（2d）特征模板
if ( (x+dx <= winsize.width ) && (y+dy*4 <= winsize.height) ) {
if (dx*4*dy < s0) continue;
if (!symmetric || (x+x+dx <=winsize.width)) {
haarFeature = cvHaarFeature( "haar_y4",
x, y, dx, dy*4, -1,
x, y+dy, dx, dy*2, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
}
// x2_y2 对应上图中的（e）特征模板
if ( (x+dx*2 <= winsize.width) && (y+dy*2 <= winsize.height) ) {
if (dx*4*dy < s0) continue;
if (!symmetric || (x+x+dx*2 <=winsize.width)) {
haarFeature = cvHaarFeature( "haar_x2_y2",
x , y, dx*2, dy*2, -1,
x , y , dx , dy, +2,
x+dx, y+dy, dx , dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
if (mode != 0 /*BASIC*/) {
// point 对应上图中的（3a）特征模板
if ( (x+dx*3 <= winsize.width) && (y+dy*3 <= winsize.height) ) {
if (dx*9*dy < s0) continue;
if (!symmetric || (x+x+dx*3 <=winsize.width)) {
haarFeature = cvHaarFeature( "haar_point",
x , y, dx*3, dy*3, -1,
x+dx, y+dy, dx , dy , +9);
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
}
if (mode == 2 /*ALL*/) {
// tilted haar_x2 (x, y, w, h, b, weight)
//对应上图中的（1c）特征模板
if ( (x+2*dx <= winsize.width) && (y+2*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*2*dy < s1) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x2",
x, y, dx*2, dy, -1,
x, y, dx , dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y2 (x, y, w, h, b, weight)
//对应上图中的（1d）特征模板
if ( (x+dx <= winsize.width) && (y+dx+2*dy <= winsize.height) && (x-2*dy>= 0) ) {
if (dx*2*dy < s1) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y2",
x, y, dx, 2*dy, -1,
x, y, dx, dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_x3 (x, y, w, h, b, weight)
if ( (x+3*dx <= winsize.width) && (y+3*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*3*dy < s2) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x3",
x, y, dx*3, dy, -1,
x+dx, y+dx, dx , dy, +3 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y3 (x, y, w, h, b, weight)
if ( (x+dx <= winsize.width) && (y+dx+3*dy <= winsize.height) && (x-3*dy>= 0) ) {
if (dx*3*dy < s2) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y3",
x, y, dx, 3*dy, -1,
x-dy, y+dy, dx, dy, +3 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_x4 (x, y, w, h, b, weight)
if ( (x+4*dx <= winsize.width) && (y+4*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*4*dy < s3) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x4",
x, y, dx*4, dy, -1,
x+dx, y+dx, dx*2, dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y4 (x, y, w, h, b, weight)
if ( (x+dx <= winsize.width) && (y+dx+4*dy <= winsize.height) && (x-4*dy>= 0) ) {
if (dx*4*dy < s3) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y4",
x, y, dx, 4*dy, -1,
x-dy, y+dy, dx, 2*dy, +2 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
/*
// tilted point
if ( (x+dx*3 <= winsize.width - 1) && (y+dy*3 <= winsize.height - 1) && (x-3*dy>= 0)) {
if (dx*9*dy < 36) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_point",
x, y, dx*3, dy*3, -1,
x, y+dy, dx , dy, +9 );
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
*/
}
}
}
}
}
/*我的理解：当前已经完成了数据的写入，但是是存储在内存存储器中的，调用此方法将存储器中的所有数据转移到cvSeq中*/
seq = cvEndWriteSeq( &writer );
在OpenCV中临时缓存用cvAlloc和cvFree函数分配和回收.函数应注意适当对齐,对未释放的内存保持跟踪，检查溢出。
features = (CvIntHaarFeatures*) cvAlloc( sizeof( CvIntHaarFeatures ) +
( sizeof( CvTHaarFeature ) + sizeof( CvFastHaarFeature ) ) * seq->total );
features->feature = (CvTHaarFeature*) (features + 1);
features->fastfeature = (CvFastHaarFeature*) ( features->feature + seq->total );
features->count = seq->total;
features->winsize = winsize;
cvCvtSeqToArray( seq, (CvArr*) features->feature );
cvReleaseMemStorage( &storage );
//特征的rect由坐标表示转换为由像素索引表示
icvConvertToFastHaarFeature( features->feature, features->fastfeature,
features->count, (winsize.width + 1) );
return features;
}
由于我是小白，感觉上面代码中的最后几行逻辑可能比较绕，卡在那里好久终于理解。
所以做了一张图，可以借助下图帮助理解
<span style="font-family: Arial, Helvetica, sans-serif;">看到这里，对于Haar特征的理论以及实际使用应该理解的不错了吧，希望能和大家一起交流</span>
