人脸识别之人脸检测（三）--Haar特征原理及实现
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本文主要由于OpenCV的haartraining程序，对haar特征的补充及代码注释。
原文：http://www.aiuxian.com/article/p-2476165.html
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://www.aiuxian.com/article/p-1897852.html）
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(constchar* 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*/staticCvIntHaarFeatures* 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) ); 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