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人脸识别（三）--Haar特征原理及实现

2016-05-20 14:55 337 查看

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：

图片分享：

Lienhart等牛们提出的Haar-like特征：

图片分享：

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) );
return features;}

这边有一个新版分类器harr特征训练的解释。
/article/10938099.html

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