这几天研究了OpenCV源码 Haar AdaBoost算法，作了一下改进
1.去掉了所有动态分配内存的操作，对潜入式系统有一定的速度提升
2.注释覆盖了大量关键代码
3.减少了代码一半的体积，并且减少了部分健壮性的代码，速度比OpenCV源码提升6%
4.修改了大量数据结构，不依赖CV源码直接编译
注：使用时请注意，现仅支持单分支的Stages和单结点的Classifier训练好的结果集
Haar.h
#ifndef _HAAR_H_
#define _HAAR_H_
#include "Tables.h"
#define NODE_THRESHOLD_SHIFT 22
#define MAXROWS 400
#define MAXCOLS 400
#define MAXSTAGES 22
#define MAXCLASSIFER 213
#define MAXTREENODE 2
#define MAXALPHA 2
#define MAXSEQS 25
#define MaxMatNum 2
#define RGBCHANNEL 3
#define BITS8 0x00000001
#define BITS32 0x00000010
#define BITS64 0x00000100
#define CV_8TO16U_SQR(x) my8x16uSqrTab[(x)+128]
#define CLR_RESULT_QUEUE() result_seq.tail = 0; result_seq.total = 0;
typedef unsigned char (*ImgPtr);
typedef unsigned char (*Mat8Ptr);
typedef int (*Mat32Ptr);
typedef __int64 (*Mat64Ptr);
/*****************并查集数据结构*******************************/
#define MAXPTREENODES 100
typedef struct PTreeNode
{
struct PTreeNode* parent;
char* element;
int rank;
}PTreeNode;
/************************积分图变量***************************/
typedef int sumtype;
typedef _int64 sqsumtype;
/************************************************************/
typedef struct Rect
{
int x;
int y;
int width;
int height;
}Rect;
typedef struct
{
int width;
int height;
}Size;
typedef struct Image
{
ImgPtr imgPtr;
int rows;
int cols;
}Image;
typedef struct Mat8
{
Mat8Ptr mat8Ptr;
int rows;
int cols;
}Mat8;
typedef struct Mat32
{
Mat32Ptr mat32Ptr;
int rows;
int cols;
}Mat32;
typedef struct Mat64
{
Mat64Ptr mat64Ptr;
int rows;
int cols;
}Mat64;
typedef struct Sequence
{
int total;
Rect rectQueue[MAXSEQS];
int neighbors[MAXSEQS];
int tail;
}Sequence;
//Haar特征的数量
#define CV_HAAR_FEATURE_MAX 3
/*************HidHaar to Caculation Feature***********************************/
typedef struct HidHaarFeature
{
struct
{
sumtype *p0, *p1, *p2, *p3;
int weight;
}
rect[CV_HAAR_FEATURE_MAX];
}HidHaarFeature;
typedef struct HidHaarTreeNode
{
HidHaarFeature feature;
int threshold;
short left;
short right;
}HidHaarTreeNode;
typedef struct HidHaarClassifier
{
short count;
//CvHaarFeature* orig_feature;
//HidHaarTreeNode* node;
//int* alpha;
HidHaarTreeNode node[MAXTREENODE];
int alpha[MAXALPHA];
}HidHaarClassifier;
typedef struct HidHaarStageClassifier
{
short count;
int threshold;
//HidHaarClassifier* classifier;
HidHaarClassifier classifier[MAXCLASSIFER];
char two_rects;
struct HidHaarStageClassifier* next;
struct HidHaarStageClassifier* child;
struct HidHaarStageClassifier* parent;
}HidHaarStageClassifier;
typedef struct HidHaarClassifierCascade
{
int count;
int is_stump_based;
int has_tilted_features;
int is_tree;
int window_area;
Mat32* sum;
Mat64* sqsum;
//HidHaarStageClassifier* stage_classifier;
HidHaarStageClassifier stage_classifier[MAXSTAGES];
sqsumtype *pq0, *pq1, *pq2, *pq3;
sumtype *p0, *p1, *p2, *p3;
void** ipp_stages;
}HidHaarClassifierCascade;
/******************Haar Cascade*****************************************/
typedef struct HaarFeature
{
int tilted;
struct
{
Rect r;
int weight;
} rect[CV_HAAR_FEATURE_MAX];
}HaarFeature;
typedef struct HaarClassifier
{
int count;
HaarFeature* haar_feature;
int* threshold;
int* left;
int* right;
int* alpha;
}HaarClassifier;
typedef struct HaarStageClassifier
{
int count;
int threshold;
HaarClassifier* classifier;
int next;
int child;
int parent;
}HaarStageClassifier;
typedef struct HaarClassifierCascade
{
int flags;
int count;
Size orig_window_size;
Size real_window_size;
int scale32x;
HaarStageClassifier* stage_classifier;
HidHaarClassifierCascade* hid_cascade;
}HaarClassifierCascade;
typedef struct CvAvgComp
{
Rect rect;
int neighbors;
}
CvAvgComp;
typedef struct Point
{
int x;
int y;
}Point;
/******************全局变量****************************************/
//cascade
extern HaarClassifierCascade *cascade ;
extern HidHaarClassifierCascade hid_cascade;
//32bits cell Mat
extern int MatPool32[MaxMatNum][MAXROWS][MAXCOLS];
//8bits cell
extern unsigned char MatPool8[MaxMatNum][MAXROWS][MAXCOLS];
//8bits*3 cell
extern unsigned char ImgRGBPool8[MaxMatNum][RGBCHANNEL][MAXROWS][MAXCOLS];
//64bits float cell
extern __int64 MatPool64[MaxMatNum][MAXROWS][MAXCOLS];
//分类器检测结果区域序列
extern Sequence result_seq;
/********************全局函数******************************************/
extern void ReadFaceCascade();
extern void HaarDetectObjects(Image* _img,HaarClassifierCascade* cascade,
char* storage, int scale_factor32x,
int min_neighbors, int flags, Size minSize);
#endif
#include "Haar.h"
#include "loadCascade.h"
#include "Util.h"
#include "stdio.h"
#include "string.h"
#include <math.h>
#include "windows.h"
/*******************Global************************************/
HaarClassifierCascade *cascade ;
HidHaarClassifierCascade hid_cascade;
//32bits cell Mat
int MatPool32[MaxMatNum][MAXROWS][MAXCOLS];
//8bits cell
unsigned char MatPool8[MaxMatNum][MAXROWS][MAXCOLS];
//8bits*3 cell
unsigned char ImgRGBPool8[MaxMatNum][RGBCHANNEL][MAXROWS][MAXCOLS];
//64bits cell
__int64 MatPool64[MaxMatNum][MAXROWS][MAXCOLS];
//候选区域坐标节点并查集
PTreeNode PTreeNodes[MAXPTREENODES];
//分类器检测结果区域序列
Sequence result_seq;
//==================================================================
//函数名： IsEqual
//作者： qiurenbo
//日期： 2014-10-1
//功能： 判断两个矩形是否邻接
//输入参数：_r1 _r2 候选区域矩形
//返回值： 返回相似性（是否是邻接的矩形）
//修改记录：
//==================================================================
int IsEqual( const void* _r1, const void* _r2)
{
const Rect* r1 = (const Rect*)_r1;
const Rect* r2 = (const Rect*)_r2;
int distance5x = r1->width ;//int distance = cvRound(r1->width*0.2);
return r2->x*5 <= r1->x*5 + distance5x &&
r2->x*5 >= r1->x*5 - distance5x &&
r2->y*5 <= r1->y*5 + distance5x &&
r2->y*5 >= r1->y*5 - distance5x &&
r2->width*5 <= r1->width * 6 &&
r2->width * 6 >= r1->width*5;
}
//==================================================================
//函数名： ReadFaceCascade
//作者： qiurenbo
//日期： 2014-10-1
//功能： 根据候选区域的相似性（IsEqual函数）建立并查集
//输入参数：seq 候选目标区域序列
//返回值： 返回分类后的类别数
//修改记录：
//==================================================================
int SeqPartition( const Sequence* seq )
{
Sequence* result = 0;
//CvMemStorage* temp_storage = 0;
int class_idx = 0;
memset(PTreeNodes, 0, MAXPTREENODES*sizeof(PTreeNode));
int i, j;
//建立以seq中元素为根节点的森林
for( i = 0; i < seq->total; i++ )
PTreeNodes[i].element = (char*)&seq->rectQueue[i];
//遍历所有根节点
for( i = 0; i < seq->total; i++ )
{
PTreeNode* node = &PTreeNodes[i];
PTreeNode* root = node;
//确保node中元素指针不为空
if( !node->element )
continue;
//找到元素在树中的根结点
while( root->parent )
root = root->parent;
for( j = 0; j < seq->total; j++ )
{
PTreeNode* node2 = &PTreeNodes[j];
//确保1.node中元素指针不为空
// 2.且不是同一个node结点
// 3.且是相似区域
// 若是相似区域，则合并元素
if( node2->element && node2 != node &&
IsEqual( node->element, node2->element))
{
PTreeNode* root2 = node2;
//找到元素在树中的根结点
while( root2->parent )
root2 = root2->parent;
//合并的前提是不在一颗树中
if( root2 != root )
{
//秩小的树归入秩大的树中
if( root->rank > root2->rank )
root2->parent = root;
//秩相等的时候才改变树的秩
else
{
root->parent = root2;
root2->rank += root->rank == root2->rank;
root = root2;
}
//assert( root->parent == 0 );
// 路径压缩，子节点node2直接指向根节点
while( node2->parent )
{
PTreeNode* temp = node2;
node2 = node2->parent;
temp->parent = root;
}
// 路径压缩，子节点node直接指向根节点
node2 = node;
while( node2->parent )
{
PTreeNode* temp = node2;
node2 = node2->parent;
temp->parent = root;
}
}
}
}
}
for( i = 0; i < seq->total; i++ )
{
PTreeNode* node = &PTreeNodes[i];
int idx = -1;
if( node->element )
{
while( node->parent )
node = node->parent;
//计算有几棵并查树，巧妙地利用取反避免重复计算
if( node->rank >= 0 )
node->rank = ~class_idx++;
idx = ~node->rank;
}
}
return class_idx;
}
//==================================================================
//函数名： ReadFaceCascade
//作者： qiurenbo
//日期： 2014-09-30
//功能： 读取Cascade文件
//输入参数：void
//返回值： void
//修改记录：
//==================================================================
void ReadFaceCascade()
{
int i;
//load cascade
cascade = (HaarClassifierCascade*)HaarClassifierCascade_face;
//load stages
int stage_size = StageClassifier_face[0];
HaarStageClassifier *stages ;
stages = (HaarStageClassifier *)(StageClassifier_face+1);
//load classifier
int classifier_size = Classifier_face[0];
HaarClassifier *cls ;
cls = (HaarClassifier*) (Classifier_face+1);
int class_info_size = class_info[0];
int * cls_info ;
cls_info = (int*)(class_info+1);
//link cascade with stages
cascade->stage_classifier = stages;
//link stages，classifiers
int offset=0;
int offset_t=(sizeof(HaarFeature)/sizeof(int));
int offset_l=offset_t+1;
int offset_r=offset_t+2;
int offset_a=offset_t+3;
int offset_total=0;
for(i=0;i<stage_size;++i)
{
(stages+i)->classifier = (cls+offset);
offset +=(stages+i)->count;
}
offset_total = 5+ (sizeof(HaarFeature)/sizeof(int));
//link classifiers and haar_featrue;
for(i=0;i<classifier_size;++i)
{
HaarClassifier *cs= cls+i;
cs->haar_feature = (HaarFeature*)(cls_info+i*offset_total);
cs->threshold = (int*)(cls_info+i*offset_total+offset_t);
cs->left =(int*)(cls_info+i*offset_total+offset_l);
cs->right=(int*)(cls_info+i*offset_total+offset_r);
cs->alpha=(int*)(cls_info+i*offset_total+offset_a);
}
}
//==================================================================
//函数名： IntegralImage
//作者： qiurenbo
//日期： 2014-09-26
//功能： 从矩阵池中获取rows * cols的矩阵
//输入参数：mat 矩阵结构体地址
// rows 待分配的行数
// cols 待分配的列数
// type 待分配的矩阵类型
// matIndex 从矩阵池中分配的矩阵序列（手动指定..）
//返回值： void
//修改记录：
//==================================================================
void GetMat(void* mat, int rows, int cols, int type, int matIndex)
{
switch(type)
{
case BITS8:
((Mat8*)mat)->rows = rows;
((Mat8*)mat)->cols = cols;
((Mat8*)mat)->mat8Ptr = (Mat8Ptr)&MatPool8[matIndex];
break;
case BITS32:
((Mat32*)mat)->rows = rows;
((Mat32*)mat)->cols = cols;
((Mat32*)mat)->mat32Ptr = (Mat32Ptr)&MatPool32[matIndex];
break;
case BITS64:
((Mat64*)mat)->rows = rows;
((Mat64*)mat)->cols = cols;
((Mat64*)mat)->mat64Ptr = (Mat64Ptr)&MatPool64[matIndex];
break;
}
}
//==================================================================
//函数名： IntegralImage
//作者： qiurenbo
//日期： 2014-09-26
//功能： 计算目标检测区域的积分图
//输入参数：src 待检测目标所在矩阵起始
// srcstep 待检测区域列数
// sum 积分图矩阵 (W+1)*(H+1)
// sumstep 积分图矩阵列数
// sqsum 平方和图矩阵 (W+1)*(H+1)
// sqsumstep 平方和图矩阵列数
// size 待检测区域大小 W*H
//
//
//返回值： void
//修改记录：
//==================================================================
void IntegralImage(ImgPtr src, int srcstep,
Mat32Ptr sum, int sumstep,
Mat64Ptr sqsum, int sqsumstep,
Size size)
{
int s = 0;
__int64 sq = 0;
//移动指针到积分图的下一行,第一行全为0
sum += sumstep + 1;
sqsum += sqsumstep + 1;
//y代表相对于输入检测矩阵起始第几行
for(int y = 0; y < size.height; y++, src += srcstep,
sum += sumstep, sqsum += sqsumstep )
{
//sum和sqsum为(W+1)*(H+1)大小矩阵，故将第一列置为0
sum[-1] = 0;
sqsum[-1] = 0;
for(int x = 0 ; x < size.width; x++ )
{
int it = src[x];
int t = (it);
//查表计算平方
__int64 tq = CV_8TO16U_SQR(it);
//s代表行上的累加和
s += t;
//sq代表行上的累加和
sq += tq;
t = sum[x - sumstep] + s;
tq = sqsum[x - sqsumstep] + sq;
sum[x] = t;
sqsum[x] = (__int64)tq;
}
}
}
//==================================================================
//函数名： Integral
//作者： qiurenbo
//日期： 2014-09-26
//功能： 计算目标检测区域的积分图
//输入参数：image 图像
// sumImage 积分图指针
// sumSqImage 平方和图指针
//返回值： void
//修改记录：
//==================================================================
void Integral(Image* image, Mat32* sumImage, Mat64* sumSqImage)
{
//取保地址空间已经分配，从数组中
if (image == NULL || sumImage == NULL || sumSqImage == NULL)
return;
Image*src = (Image*)image;
Mat32 *sum = (Mat32*)sumImage;
Mat64 *sqsum = (Mat64*)sumSqImage;
Size size;
size.height = src->rows;
size.width = src->cols;
IntegralImage(src->imgPtr, src->cols,
sum->mat32Ptr, sum->cols,
sqsum->mat64Ptr, sqsum->cols,size);
}
//==================================================================
//函数名： CreateHidHaarClassifierCascade
//作者： qiurenbo
//日期： 2014-09-28
//功能： 创建隐式积分图加快计算速度
//输入参数：cascade 级联分类器指针
//返回值： static HidHaarClassifierCascade* 返回一个隐式级联分类器指针
//修改记录：
//==================================================================
static HidHaarClassifierCascade*
CreateHidHaarClassifierCascade(HaarClassifierCascade* cascade)
{
cascade->hid_cascade = &hid_cascade;
HidHaarClassifierCascade* out = cascade->hid_cascade;
const int icv_stage_threshold_bias = 419; //0.0001*(2^22)=419.4304
int i, j, l;
int total_classifiers = 0;
int total_nodes = 0;
int has_tilted_features = 0;
int max_count = 0;
/* 初始化HidCascade头 */
out->count = cascade->count;
//out->stage_classifier = HidStages;
out->is_stump_based = 1;
out->is_tree = 0;
// 用cascade初始化HidCascade
for( i = 0; i < cascade->count; i++ )
{
//用cascades Stage初始化HidCascade的Stage
HaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
HidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i;
hid_stage_classifier->count = stage_classifier->count;
hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
//hid_stage_classifier->classifier = (struct HidHaarClassifier *)&HidClassifiers[i];
//初始化为二特征，下面会根据真实的特征数至1或0（三特征）
hid_stage_classifier->two_rects = 1;
//Stage构成一颗退化的二叉树（单分支），每个结点最多只有一个孩子
hid_stage_classifier->parent = (stage_classifier->parent == -1)
? NULL : out->stage_classifier + stage_classifier->parent;
hid_stage_classifier->next = (stage_classifier->next == -1)
? NULL : out->stage_classifier + stage_classifier->next;
hid_stage_classifier->child = (stage_classifier->child == -1)
? NULL : out->stage_classifier + stage_classifier->child ;
//判断该stage是否为树状结构（多分枝）
out->is_tree |= hid_stage_classifier->next != NULL;
//赋值classifer属性
for( j = 0; j < stage_classifier->count; j++ )
{
HaarClassifier* classifier = stage_classifier->classifier + j;
HidHaarClassifier* hid_classifier = out->stage_classifier[i].classifier + j;
int node_count = classifier->count;
hid_classifier->count = node_count;
//int* alpha_ptr = (int*)&Alpha[i][j];
// hid_classifier->node = (HidHaarTreeNode*)&HidTreeNode[i][j];
// hid_classifier->alpha = alpha_ptr;
//赋值node属性
for( l = 0; l < node_count; l++ )
{
HidHaarTreeNode* node = out->stage_classifier[i].classifier[j].node + l;
HaarFeature* feature = classifier->haar_feature + l;
memset( node, -1, sizeof(*node) );
node->threshold = classifier->threshold[l];
node->left = classifier->left[l];
node->right = classifier->right[l];
//对特征数目进行判断,若是三特征，则至two_rects为0
if( (feature->rect[2].weight) == 0 ||
feature->rect[2].r.width == 0 ||
feature->rect[2].r.height == 0 )
memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) );
else
hid_stage_classifier->two_rects = 0;
}
//赋值alpha
memcpy( &hid_classifier->alpha, classifier->alpha, (node_count+1)*sizeof(hid_classifier->alpha[0]));
//判断cascade中的分类器是否是树桩分类器，只有根结点的决策树
out->is_stump_based &= node_count == 1;
}
}
//cascade->hid_cascade = out;
//assert( (char*)haar_node_ptr - (char*)out <= datasize );
return out;
}
//==================================================================
//函数名： SetImagesForHaarClassifierCascade
//作者： qiurenbo
//日期： 2014-09-29
//功能： 根据尺度调整Haar特征的大小和权重
//输入参数：cascade 级联分类器指针
// sum 积分图
// sqsum 平方和积分图
// scale32x 尺度
//返回值： 无
//修改记录：
//==================================================================
void SetImagesForHaarClassifierCascade(HaarClassifierCascade* _cascade, Mat32* sum, Mat64* sqsum, int scale32x)
{
HidHaarClassifierCascade* hidCascade;
int coi0 = 0, coi1 = 0;
int i;
Rect equ_rect;
int weight_scale;
//根据尺度获取窗口大小
_cascade->scale32x = scale32x;
_cascade->real_window_size.width = (_cascade->orig_window_size.width * scale32x + 16)>>5 ;
_cascade->real_window_size.height = (_cascade->orig_window_size.height * scale32x +16) >> 5;
//设置隐式级联分类器的积分图
hidCascade = _cascade->hid_cascade;
hidCascade->sum = sum;
hidCascade->sqsum = sqsum;
//根据尺度设置积分图起始矩阵的位置
equ_rect.x = equ_rect.y = (scale32x+16)>>5;
equ_rect.width = ((_cascade->orig_window_size.width-2)*scale32x + 16 ) >> 5; //+0.5是为了四舍五入
equ_rect.height = ((_cascade->orig_window_size.height-2)*scale32x + 16 ) >> 5;
weight_scale = equ_rect.width*equ_rect.height;
hidCascade->window_area = weight_scale; //矩形面积
//获取积分图上起始矩阵四个像素的坐标
hidCascade->p0 = sum->mat32Ptr + (equ_rect.y) * sum->cols+ equ_rect.x;
hidCascade->p1 = sum->mat32Ptr + (equ_rect.y) * sum->cols + equ_rect.x + equ_rect.width;
hidCascade->p2 = sum->mat32Ptr + (equ_rect.y + equ_rect.height) * sum->cols + equ_rect.x;
hidCascade->p3 = sum->mat32Ptr + (equ_rect.y + equ_rect.height) * sum->cols + equ_rect.x + equ_rect.width;
//获取平方和积分图上起始矩阵四个像素的坐标
hidCascade->pq0 = sqsum->mat64Ptr + (equ_rect.y) * sqsum->cols+ equ_rect.x;
hidCascade->pq1 = sqsum->mat64Ptr + (equ_rect.y) * sqsum->cols+ equ_rect.x + equ_rect.width;
hidCascade->pq2 = sqsum->mat64Ptr + (equ_rect.y + equ_rect.height) * sqsum->cols+ equ_rect.x;
hidCascade->pq3 = sqsum->mat64Ptr + (equ_rect.y + equ_rect.height) * sqsum->cols+ equ_rect.x + equ_rect.width;
//遍历每个Classifer所使用的特征，对它们进行尺度放大，并将改变的值赋给HidCascade，隐式级联分类器
for( i = 0; i < hidCascade->count; i++ )
{
int j, k, l;
for( j = 0; j < hidCascade->stage_classifier[i].count; j++ )
{
for( l = 0; l < hidCascade->stage_classifier[i].classifier[j].count; l++ )
{
HaarFeature* feature = &_cascade->stage_classifier[i].classifier[j].haar_feature[l];
HidHaarFeature* hidfeature = &hidCascade->stage_classifier[i].classifier[j].node[l].feature;
int sum0 = 0, area0 = 0;
Rect r[3];
Rect tr;
int correction_ratio;
for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
if( !hidfeature->rect[k].p0 )
break;
r[k] = feature->rect[k].r;
//左上角坐标和矩阵长宽都按尺度放大
tr.x = (r[k].x * scale32x + 16) >> 5;
tr.width = (r[k].width * scale32x + 16) >> 5;
tr.y = ( r[k].y * scale32x + 16 ) >> 5;
tr.height = ( r[k].height * scale32x +16 ) >> 5;
correction_ratio = weight_scale;
//设置矩阵四个顶点在积分图中的位置（为了计算特征方便）
hidfeature->rect[k].p0 = sum->mat32Ptr + tr.y * sum->cols + tr.x;
hidfeature->rect[k].p1 = sum->mat32Ptr + tr.y * sum->cols + tr.x + tr.width;
hidfeature->rect[k].p2 = sum->mat32Ptr + (tr.y + tr.height) *sum->cols + tr.x;
hidfeature->rect[k].p3 = sum->mat32Ptr + (tr.y + tr.height) *sum->cols + tr.x + tr.width;
//rect[1] = weight/area， 左移22位是为了避免浮点计算，将权值/检测窗口面积（不断扩大），降低权值
hidfeature->rect[k].weight = ((feature->rect[k].weight)<< NODE_THRESHOLD_SHIFT)/(correction_ratio);
if( k == 0 )
area0 = tr.width * tr.height;
else
sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
}
//rect[0].weight ,权重和特征矩形面积成反比
hidfeature->rect[0].weight = (int)(-sum0/area0);
} /* l */
} /* j */
}
};
//==================================================================
//函数名： RunHaarClassifierCascade
//作者： qiurenbo
//日期： 2014-09-30
//功能： 在指定窗口范围计算特征
//输入参数：_cascade 级联分类器指针
// pt 检测窗口左上角坐标
// start_stage 起始stage下标
//返回值： <=0 未检测到目标或参数有问题
// 1 成功检测到目标
//修改记录：
//====================================================================
int RunHaarClassifierCascade( HaarClassifierCascade* _cascade, Point& pt, int start_stage )
{
int result = -1;
int p_offset, pq_offset;
int i, j;
__int64 rectsum, variance_factor;
int variance_norm_factor;
HidHaarClassifierCascade* hidCascade;
if (_cascade == NULL)
return -1;
hidCascade = _cascade->hid_cascade;
if( !hidCascade )
return -1;
//确保矩形的有效性，并防止计算窗口出边界
if( pt.x < 0 || pt.y < 0 ||
pt.x + _cascade->real_window_size.width >= hidCascade->sum->cols-2 ||
pt.y + _cascade->real_window_size.height >= hidCascade->sum->rows-2 )
return -1;
//计算特征点在积分图中的偏移，相当于移动窗口
p_offset = pt.y * (hidCascade->sum->cols) + pt.x;
pq_offset = pt.y * (hidCascade->sqsum->cols) + pt.x;
//计算移动后整个窗口的特征值
rectsum = calc_sum(*hidCascade,p_offset);//*cascade->inv_window_area;
variance_factor = hidCascade->pq0[pq_offset] - hidCascade->pq1[pq_offset] -
hidCascade->pq2[pq_offset] + hidCascade->pq3[pq_offset];
variance_factor = (variance_factor - (rectsum*rectsum + (hidCascade->window_area>>1))/hidCascade->window_area + (hidCascade->window_area>>1))/hidCascade->window_area;
variance_norm_factor = int(sqrt(float(variance_factor))+0.5f);//qmath
if( variance_norm_factor < 0 )
variance_norm_factor = 1;
//LARGE_INTEGER t1,t2,tc;
//QueryPerformanceFrequency(&tc);
//QueryPerformanceCounter(&t1);
//计算每个classifier的用到的特征区域的特征值
for( i = start_stage; i < hidCascade->count; i++ )
{
double stage_sum = 0;
//if( hidCascade->stage_classifier[i].two_rects )
//{
for( j = 0; j < hidCascade->stage_classifier[i].count; j++ )
{
HidHaarTreeNode* node = hidCascade->stage_classifier[i].classifier[j].node;
double sum, t = node->threshold*variance_norm_factor, a, b;
//计算Haar特征
sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
//两特征和三特征分开处理
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
a = hidCascade->stage_classifier[i].classifier[j].alpha[0];
b = hidCascade->stage_classifier[i].classifier[j].alpha[1];
stage_sum += sum < t ? a : b;
}
if( stage_sum < hidCascade->stage_classifier[i].threshold )
{
return -i;
}
}
//QueryPerformanceCounter(&t2);
//printf("FeatureDetectTime:%fms\n",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart);
return 1;
}
//==================================================================
//函数名： HaarDetectObjects
//作者： qiurenbo
//日期： 2014-09-30
//功能： 在指定图片中查找目标
//输入参数： _img 图片指针
// cascade 级联分类器指针
// start_stage 起始stage下标
// scale_factor32x 窗口变化尺度倍数 /32
// min_neighbors 最小临界目标（min_neighbors个以上的候选目标的区域才是最后的目标区域）
// minSize 目标最小的大小
//返回值： <=0 未检测到目标或参数有问题
// 1 成功检测到目标
//修改记录：
//====================================================================
void HaarDetectObjects(Image* _img,
HaarClassifierCascade* cascade, //训练好的级联分类器
char* storage, int scale_factor32x,
int min_neighbors, int flags, Size minSize)
{
//粗分类用到的stage数量
int split_stage = 2;
// ImgPtr stub, *img = _img;
Mat32 sum ;
Mat64 sqsum;
Image tmp;
//检测区域候选队列
Sequence seq;
//结果候选队列
Sequence seq2;
//并查集合并序列
Sequence comps;
memset(&seq, 0, sizeof(Sequence));
memset(&comps, 0, sizeof(Sequence));
memset(&seq2, 0, sizeof(Sequence));
int i;
int factor32x;
int npass = 2;
if( !cascade )
return ;
//获取积分图和平方和积分图的矩阵
GetMat(&sum , _img->rows + 1, _img->cols + 1, BITS32, 0);
GetMat(&sqsum, _img->rows + 1, _img->cols + 1, BITS64, 0);
GetMat(&tmp, _img->rows, _img->cols, BITS8, 1);
//若不存在隐式积分图（用于加速计算），则创建一个
if( !cascade->hid_cascade )
CreateHidHaarClassifierCascade(cascade);
//将隐式级联分类器写入文件看是否正确
//WriteCascadeToFile(cascade->hid_cascade);
//LARGE_INTEGER t1,t2,tc;
//QueryPerformanceFrequency(&tc);
//QueryPerformanceCounter(&t1);
//计算积分图
Integral(_img, &sum, &sqsum);
//QueryPerformanceCounter(&t2);
//printf("SetImageFeatureRunTime:%fms\n",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart);
//WriteSumAndSqSumToFile(sum, sqsum);
//LARGE_INTEGER t1,t2,tc;
//QueryPerformanceFrequency(&tc);
// QueryPerformanceCounter(&t1);
int count = 0;
int count2 = 0;
//不断调整窗口尺度，直到到达图像边缘(_img->cols-10) ||(_img->rows - 10)
//并且确保尺度小于3倍(96)
for( factor32x = 32; factor32x*cascade->orig_window_size.width < (_img->cols - 10)<<5 &&
factor32x*cascade->orig_window_size.height < (_img->rows - 10)<<5
&&factor32x<96;
factor32x = (factor32x*scale_factor32x+16)>>5 )
{
const int ystep32x = MAX(64, factor32x);
//调整搜索窗口尺度
Size win_size;
win_size.height = (cascade->orig_window_size.height * factor32x + 16)>>5;
win_size.width = (cascade->orig_window_size.width * factor32x + 16 )>>5;
//pass指扫描次数，stage_offset指第二次扫描时从第几个stage开始
int pass, stage_offset = 0;
//确保搜索窗口在尺度放大后仍然在图像中
int stop_height = ( ((_img->rows - win_size.height)<<5)+ (ystep32x>>1) ) / ystep32x;
//确保搜索窗口大于目标的最小尺寸
if( win_size.width < minSize.width || win_size.height < minSize.height )
continue;
//QueryPerformanceFrequency(&tc);
//QueryPerformanceCounter(&t1);
//根据尺度设置隐式级联分类器中的特征和权重，并设置这些特征在积分图中的位置，以加速运算
SetImagesForHaarClassifierCascade(cascade, &sum, &sqsum, factor32x );
//QueryPerformanceCounter(&t2);
//printf("SetImageFeatureRunTime:%fms\n",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart);
//设置粗检测所使用的分类器个数
cascade->hid_cascade->count = split_stage;
//用检测窗口扫描两遍图像:
//第一遍通过级联两个stage粗略定位目标大致区域，对候选区域进行标定（利用tmp矩阵）
//第二遍对标定的候选区域进行完整筛选，将候选区域放置到队列中
for( pass = 0; pass < npass; pass++ )
{
for( int _iy = 0; _iy < stop_height; _iy++ )
{
//检测窗口纵坐标步长为2，保持不变
int iy = (_iy*ystep32x+16)>>5;
int _ix, _xstep = 1;
//stop_width是指_ix迭代的上限，_ix还要*ystep32x才是真正的窗口坐标
int stop_width =( ((_img->cols - win_size.width)<<5) +ystep32x/2) / ystep32x;
unsigned char* mask_row = tmp.imgPtr + tmp.cols* iy;
for( _ix = 0; _ix < stop_width; _ix += _xstep )
{
//检测窗口横坐标按步长为4开始移动，若没有检测到目标，则改变下一次步长为2
int ix = (_ix*ystep32x+16)>>5; // it really should be ystep
//当前检测窗口左上角坐标
Point pt;
pt.x = ix;
pt.y = iy;
//粗略检测
if( pass == 0 )
{
int result = 0;
_xstep = 2;
//QueryPerformanceFrequency(&tc);
//QueryPerformanceCounter(&t1);
//Point引用传参，避免向形参的拷贝
result = RunHaarClassifierCascade( cascade, pt, 0 );
count++;
//QueryPerformanceCounter(&t2);
//printf("RunHaarRunTime:%fms\n",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart);
if( result > 0 )
{
if( pass < npass - 1 )
mask_row[ix] = 1;
}
//没有检测到改变步长为2（看ix的值）
if( result < 0 )
_xstep = 1;
}
//第二次检测先前粗定位的坐标
else if( mask_row[ix] )
{
//写Mask文件看是否正确
//WriteMaskRowToFile(tmp);
/*FILE* fp;
fp = fopen("..//MaskRowResult//maskRow2.txt","a+");
fprintf(fp, "%d %d\n ", ix, iy);
fclose(fp);*/
int result = RunHaarClassifierCascade(cascade, pt, stage_offset);
count2++;
//int result = 0;
if( result > 0 )
{
/*Rect* rect = &seq.rectQueue[seq.tail++];
rect->height = win_size.height;
rect->width = win_size.width;
rect->x = ix;
rect->y = iy;
seq.total++;*/
seq.rectQueue[seq.tail].height = win_size.height;
seq.rectQueue[seq.tail].width = win_size.width;
seq.rectQueue[seq.tail].x = ix;
seq.rectQueue[seq.tail].y = iy;
seq.total++;
seq.tail++;
}
else
mask_row[ix] = 0;
}
}
}
//因为前两个stage在第一次检测的时候已经用过；
//第二次检测的时候，从第3个stage开始进行完整的检测
stage_offset = cascade->hid_cascade->count;
cascade->hid_cascade->count = cascade->count;
}
}
//QueryPerformanceCounter(&t2);
//printf("HaarRunTime:%fms\n",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart);
if( min_neighbors != 0 )
{
//将候选目标按相似度构成并查集
//返回值代表并查集树的个数
int ncomp = SeqPartition(&seq);
//对相邻候选区域进行累加，为计算平均边界做准备
for( i = 0; i < seq.total; i++ )
{
Rect r1 = seq.rectQueue[i];
PTreeNode* node = &PTreeNodes[i];
while(node->parent)
node = node->parent;
int idx = (node - PTreeNodes);
comps.neighbors[idx]++;
comps.rectQueue[idx].x += r1.x;
comps.rectQueue[idx].y += r1.y;
comps.rectQueue[idx].width += r1.width;
comps.rectQueue[idx].height += r1.height;
}
// 计算平均目标边界
for( i = 0; i < seq.total; i++ )
{
int n = comps.neighbors[i];
//只有满足最小临接的结果才是最终结果
if( n >= min_neighbors )
{
Rect* rect = &seq2.rectQueue[seq2.tail];
rect->x = (comps.rectQueue[i].x*2 + n)/(2*n);
rect->y = (comps.rectQueue[i].y*2 + n)/(2*n);
rect->width = (comps.rectQueue[i].width*2 + n)/(2*n);
rect->height = (comps.rectQueue[i].height*2 + n)/(2*n);
seq2.neighbors[seq2.tail] = comps.neighbors[i];
seq2.tail++;
seq2.total++;
}
}
//从候选矩形中得到最大的矩形
for( i = 0; i < seq2.total; i++ )
{
Rect r1 = seq2.rectQueue[i];
int r1_neighbor = seq2.neighbors[i];
int j, flag = 1;
for( j = 0; j < seq2.total; j++ )
{
Rect r2 = seq2.rectQueue[j];
int r2_neighbor = seq2.neighbors[j];
int distance = (r2.width *2+5)/10;//cvRound( r2.rect.width * 0.2 );
if( i != j &&
r1.x >= r2.x - distance &&
r1.y >= r2.y - distance &&
r1.x + r1.width <= r2.x + r2.width + distance &&
r1.y + r1.height <= r2.y + r2.height + distance &&
(r2_neighbor > MAX( 3, r1_neighbor ) || r1_neighbor < 3) )
{
flag = 0;
break;
}
}
if( flag )
{
result_seq.rectQueue[result_seq.tail] = r1;
result_seq.tail++;
result_seq.total++;
}
}
}
}
原文地址：http://blog.csdn.net/lampqiu/article/details/39736369
