cvround源码（cvround函数）
本文目录一览：
- 1、我也求一份opencv下提取图片sift特征的项目源码，急用，谢谢您了
- 2、谁知道ENVI里面图像增强中（Enhance）高斯增强是什么原理，文档或者链接都行啊
- 3、怎样使用OpenCV进行人脸识别
- 4、请教大神。基于AdaBoost算法的人脸检测。程序怎么改可以使检测后人脸区域是矩形 （现在是圆形）
- 5、error C3861: 'AfxSocketInit': identifier not found
我也求一份opencv下提取图片sift特征的项目源码，急用，谢谢您了
#include "stdafx.h"
#include opencv2/opencv.hpp
double
compareSURFDescriptors( const float* d1, const float* d2, double best, int length )
{
double total_cost = 0;
assert( length % 4 == 0 );
for( int i = 0; i length; i += 4 )
{
double t0 = d1[i ] - d2[i ];
double t1 = d1[i+1] - d2[i+1];
double t2 = d1[i+2] - d2[i+2];
double t3 = d1[i+3] - d2[i+3];
total_cost += t0*t0 + t1*t1 + t2*t2 + t3*t3;
if( total_cost best )
break;
}
return total_cost;
}
int
naiveNearestNeighbor( const float* vec, int laplacian,
const CvSeq* model_keypoints,
const CvSeq* model_descriptors )
{
int length = (int)(model_descriptors-elem_size/sizeof(float));
int i, neighbor = -1;
double d, dist1 = 1e6, dist2 = 1e6;
CvSeqReader reader, kreader;
cvStartReadSeq( model_keypoints, kreader, 0 );
cvStartReadSeq( model_descriptors, reader, 0 );
for( i = 0; i model_descriptors-total; i++ )
{
const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
const float* mvec = (const float*)reader.ptr;
CV_NEXT_SEQ_ELEM( kreader.seq-elem_size, kreader );
CV_NEXT_SEQ_ELEM( reader.seq-elem_size, reader );
if( laplacian != kp-laplacian )
continue;
d = compareSURFDescriptors( vec, mvec, dist2, length );
if( d dist1 )
{
dist2 = dist1;
dist1 = d;
neighbor = i;
}
else if ( d dist2 )
dist2 = d;
}
if ( dist1 0.6*dist2 )
return neighbor;
return -1;
}
void
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vectorint ptpairs )
{
int i;
CvSeqReader reader, kreader;
cvStartReadSeq( objectKeypoints, kreader );
cvStartReadSeq( objectDescriptors, reader );
ptpairs.clear();
for( i = 0; i objectDescriptors-total; i++ )
{
const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
const float* descriptor = (const float*)reader.ptr;
CV_NEXT_SEQ_ELEM( kreader.seq-elem_size, kreader );
CV_NEXT_SEQ_ELEM( reader.seq-elem_size, reader );
int nearest_neighbor = naiveNearestNeighbor( descriptor, kp-laplacian, imageKeypoints, imageDescriptors );
if( nearest_neighbor = 0 )
{
ptpairs.push_back(i);
ptpairs.push_back(nearest_neighbor);
}
}
}
void
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
const CvSeq*, const CvSeq* imageDescriptors, vectorint ptpairs )
{
int length = (int)(objectDescriptors-elem_size/sizeof(float));
cv::Mat m_object(objectDescriptors-total, length, CV_32F);
cv::Mat m_image(imageDescriptors-total, length, CV_32F);
// copy descriptors
CvSeqReader obj_reader;
float* obj_ptr = m_object.ptrfloat(0);
cvStartReadSeq( objectDescriptors, obj_reader );
for(int i = 0; i objectDescriptors-total; i++ )
{
const float* descriptor = (const float*)obj_reader.ptr;
CV_NEXT_SEQ_ELEM( obj_reader.seq-elem_size, obj_reader );
memcpy(obj_ptr, descriptor, length*sizeof(float));
obj_ptr += length;
}
CvSeqReader img_reader;
float* img_ptr = m_image.ptrfloat(0);
cvStartReadSeq( imageDescriptors, img_reader );
for(int i = 0; i imageDescriptors-total; i++ )
{
const float* descriptor = (const float*)img_reader.ptr;
CV_NEXT_SEQ_ELEM( img_reader.seq-elem_size, img_reader );
memcpy(img_ptr, descriptor, length*sizeof(float));
img_ptr += length;
}
// find nearest neighbors using FLANN
cv::Mat m_indices(objectDescriptors-total, 2, CV_32S);
cv::Mat m_dists(objectDescriptors-total, 2, CV_32F);
cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4)); // using 4 randomized kdtrees
flann_index.knnSearch(m_object, m_indices, m_dists, 2, cv::flann::SearchParams(64) ); // maximum number of leafs checked
int* indices_ptr = m_indices.ptrint(0);
float* dists_ptr = m_dists.ptrfloat(0);
for (int i=0;im_indices.rows;++i) {
if (dists_ptr[2*i]0.6*dists_ptr[2*i+1]) {
ptpairs.push_back(i);
ptpairs.push_back(indices_ptr[2*i]);
}
}
}
/* a rough implementation for object location */
int
locatePlanarObject( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors,
const CvPoint src_corners[4], CvPoint dst_corners[4] )
{
double h[9];
CvMat _h = cvMat(3, 3, CV_64F, h);
vectorint ptpairs;
vectorCvPoint2D32f pt1, pt2;
CvMat _pt1, _pt2;
int i, n;
#ifdef USE_FLANN
flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#else
findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#endif
n = (int)(ptpairs.size()/2);
if( n 4 )
return 0;
pt1.resize(n);
pt2.resize(n);
for( i = 0; i n; i++ )
{
pt1[i] = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))-pt;
pt2[i] = ((CvSURFPoint*)cvGetSeqElem(imageKeypoints,ptpairs[i*2+1]))-pt;
}
_pt1 = cvMat(1, n, CV_32FC2, pt1[0] );
_pt2 = cvMat(1, n, CV_32FC2, pt2[0] );
if( !cvFindHomography( _pt1, _pt2, _h, CV_RANSAC, 5 ))
return 0;
for( i = 0; i 4; i++ )
{
double x = src_corners[i].x, y = src_corners[i].y;
double Z = 1./(h[6]*x + h[7]*y + h[8]);
double X = (h[0]*x + h[1]*y + h[2])*Z;
double Y = (h[3]*x + h[4]*y + h[5])*Z;
dst_corners[i] = cvPoint(cvRound(X), cvRound(Y));
}
return 1;
}
int main(int argc, char** argv)
{
const char* object_filename = argc == 3 ? argv[1] : "box.png";
const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
if( !object || !image )
{
fprintf( stderr, "Can not load %s and/or %s\n",
object_filename, scene_filename );
exit(-1);
}
CvMemStorage* storage = cvCreateMemStorage(0);
cvNamedWindow("Object", 1);
cvNamedWindow("Object Correspond", 1);
static CvScalar colors[] =
{
{{0,0,255}},
{{0,128,255}},
{{0,255,255}},
{{0,255,0}},
{{255,128,0}},
{{255,255,0}},
{{255,0,0}},
{{255,0,255}},
{{255,255,255}}
};
IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
cvCvtColor( object, object_color, CV_GRAY2BGR );
CvSeq* objectKeypoints = 0, *objectDescriptors = 0;
CvSeq* imageKeypoints = 0, *imageDescriptors = 0;
int i;
CvSURFParams params = cvSURFParams(500, 1);
double tt = (double)cvGetTickCount();
cvExtractSURF( object, 0, objectKeypoints, objectDescriptors, storage, params );
printf("Object Descriptors: %d\n", objectDescriptors-total);
cvExtractSURF( image, 0, imageKeypoints, imageDescriptors, storage, params );
printf("Image Descriptors: %d\n", imageDescriptors-total);
tt = (double)cvGetTickCount() - tt;
printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));
CvPoint src_corners[4] = {{0,0}, {object-width,0}, {object-width, object-height}, {0, object-height}};
CvPoint dst_corners[4];
IplImage* correspond = cvCreateImage( cvSize(image-width, object-height+image-height), 8, 1 );
cvSetImageROI( correspond, cvRect( 0, 0, object-width, object-height ) );
cvCopy( object, correspond );
cvSetImageROI( correspond, cvRect( 0, object-height, correspond-width, correspond-height ) );
cvCopy( image, correspond );
cvResetImageROI( correspond );
#ifdef USE_FLANN
printf("Using approximate nearest neighbor search\n");
#endif
if( locatePlanarObject( objectKeypoints, objectDescriptors, imageKeypoints,
imageDescriptors, src_corners, dst_corners ))
{
for( i = 0; i 4; i++ )
{
CvPoint r1 = dst_corners[i%4];
CvPoint r2 = dst_corners[(i+1)%4];
cvLine( correspond, cvPoint(r1.x, r1.y+object-height ),
cvPoint(r2.x, r2.y+object-height ), colors[8] );
}
}
vectorint ptpairs;
#ifdef USE_FLANN
flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#else
findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#endif
for( i = 0; i (int)ptpairs.size(); i += 2 )
{
CvSURFPoint* r1 = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, ptpairs[i] );
CvSURFPoint* r2 = (CvSURFPoint*)cvGetSeqElem( imageKeypoints, ptpairs[i+1] );
cvLine( correspond, cvPointFrom32f(r1-pt),
cvPoint(cvRound(r2-pt.x), cvRound(r2-pt.y+object-height)), colors[8] );
}
cvShowImage( "Object Correspond", correspond );
for( i = 0; i objectKeypoints-total; i++ )
{
CvSURFPoint* r = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, i );
CvPoint center;
int radius;
center.x = cvRound(r-pt.x);
center.y = cvRound(r-pt.y);
radius = cvRound(r-size*1.2/9.*2);
cvCircle( object_color, center, radius, colors[0], 1, 8, 0 );
}
cvShowImage( "Object", object_color );
cvWaitKey(0);
cvDestroyWindow("Object");
cvDestroyWindow("Object Correspond");
return 0;
}
谁知道ENVI里面图像增强中（Enhance）高斯增强是什么原理，文档或者链接都行啊
高斯滤波（高斯平滑）是图像处理，计算机视觉里面最常见的操作。平时，我们都是用matlab或者opencv的函数调用：imfilter或者cvSmooth，并不关心底层的实现。然而当开发者要实做高斯滤波的时候，往往就会很迷惘，往往会被以下几个问题困扰：
给定sigma，即标准偏差，怎么确定离散化后滤波器的窗口大小？
给定窗口大小，怎么计算高斯核的sigma，即标准方差？
怎么实现可分离滤波器？
我在google上搜了一下，还真没几个人把实现的细节讲清楚了。这里，我尝试结合三份源码，做个小小的总结。
三份源码分别是：
opencv中的cvfilter.cpp
autopano-sift-c中的GaussianConvolution.c
GIMP中的blur-gauss.c和unsharp-mask.c
在图像处理中，高斯滤波一般有两种实现方式，一是用离散化窗口滑窗卷积，另一种通过傅里叶变换。最常见的就是第一种滑窗实现，只有当离散化的窗口非常大，用滑窗计算量非常大（即使用可分离滤波器的实现）的情况下，可能会考虑基于傅里叶变化的实现方法。这里我们只讨论第一种方法。
二维高斯函数的形式是这样的：
f(x,y) = A e^{- \left(\frac{(x-x_o)^2}{2\sigma_x^2} + \frac{(y-y_o)^2}{2\sigma_y^2} \right)}.
有着如下的形状，形状很激凸，怎么会叫高斯平滑呢，分明是高斯激凸嘛：
基本上，离散化的主旨就是保留高斯函数中心能量最集中的中间部分，忽略四周能量很小的平坦区域。这只是个很感性的描述，具体实现起来，就会出现千奇百怪的版本。下面结合三份源码，看看现实世界里的高斯平滑到底长的什么样子。
首先是第一个问题：给定sigma，怎么计算窗口大小？
直接上opencv的源码，在cvFilter函数中：
param1 = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
opencv认为半径为3*sigma的窗口就是高斯函数能量最集中的区域。（为什么在图像深度不是8U的时候，使用4*sigma半径的窗口就不得而知了，有高人指点一下？）
autopan0-sift-c是图像拼接软件hugin里面的sift实现，在实现DoG的时候需要做不同尺度的高斯平滑，实现如下，在GaussianConvolution_new1函数中：
dim = 1 + 2 * ((int) (3.0 * sigma));
可见autopano也是实现的3*sigma半径的窗口。
在GIMP里，实现比较奇特，在blur_gauss.c的make_rle_curve函数里面，
const gdouble sigma2 = 2 * sigma * sigma;
const gdouble l = sqrt (-sigma2 * log (1.0 / 255.0));
int n = ceil (l) * 2;
if ((n % 2) == 0)
n += 1;
我是没看懂那个 log (1.0 / 255.0)是干嘛的。。。惭愧。效果来看，这个实现的窗口半径是约等于2.2*sigma。
然后是第二个问题：给定窗口大小，怎么计算sigma？
opencv的实现，在cvFilter.cpp的init_gaussian_kernel函数中：
sigmaX = sigma 0 ? sigma : (n/2 – 1)*0.3 + 0.8;
再次不可解。。。乘以0.3还可以接受，加上0.8是为嘛啊？
autopano没有实现这个特性。
GIMP的实现：
/* we want to generate a matrix that goes out a certain radius
* from the center, so we have to go out ceil(rad-0.5) pixels,
* inlcuding the center pixel. Of course, that’s only in one direction,
* so we have to go the same amount in the other direction, but not count
* the center pixel again. So we double the previous result and subtract
* one.
* The radius parameter that is passed to this function is used as
* the standard deviation, and the radius of effect is the
* standard deviation * 2. It’s a little confusing.
*/
radius = fabs (radius) + 1.0;
std_dev = radius;
radius = std_dev * 2;
/* go out ‘radius’ in each direction */
matrix_length = 2 * ceil (radius – 0.5) + 1;
注释讲的很清楚了，基本上就是认为sigma应该等于窗口半径的一半。
看完这三分源码，结论就是，关于sigma和半径，你爱怎么算就怎么算吧，差不多就行。。。（额。。费了半天劲，就得到这么一句废话啊）。
第三个问题是可分离滤波器：
由于高斯函数可以写成可分离的形式，因此可以采用可分离滤波器实现来加速。所谓的可分离滤波器，就是可以把多维的卷积化成多个一维卷积。具体到二维的高斯滤波，就是指先对行做一维卷积，再对列做一维卷积。这样就可以将计算复杂度从O(M*M*N*N)降到O(2*M*M*N)，M，N分别是图像和滤波器的窗口大小。问题是实现时候怎么计算一维的卷积核呢？
其实很简单，按照前面计算出来的窗口大小，计算所有离散点上一维高斯函数的权值，最后别忘了将权值之和归一化到1.
有码有真相，来自opencv：
for( i = 0; i = n/2; i++ )
{
double t = fixed_kernel ? (double)fixed_kernel[i] : exp(scale2X*i*i);
if( type == CV_32FC1 )
{
cf[(n/2+i)*step] = (float)t;
sum += cf[(n/2+i)*step]*2;
}
else
{
cd[(n/2+i)*step] = t;
sum += cd[(n/2+i)*step]*2;
}
}
sum = 1./sum;
for( i = 0; i = n/2; i++ )
{
if( type == CV_32FC1 )
cf[(n/2+i)*step] = cf[(n/2-i)*step] = (float)(cf[(n/2+i)*step]*sum);
else
cd[(n/2+i)*step] = cd[(n/2-i)*step] = cd[(n/2+i)*step]*sum;
}
怎样使用OpenCV进行人脸识别
网上帖子不是很多么，这个是我自己简单写的，注释的部分删了吧，就可以简单识别人脸了
#include opencv2\core\core.hpp
#include opencv2\imgproc\imgproc.hpp
#include opencv2\highgui\highgui.hpp
#include opencv2\video\background_segm.hpp
#include opencv2/objdetect/objdetect.hpp
#include iostream
using namespace cv;
void detectAndDraw( Mat img, CascadeClassifier cascade,
CascadeClassifier nestedCascade,
double scale, bool tryflip );
//Mat imageresize(Mat image, Size size);
/*int main(){
//VideoCapture cap(0); //打开默认摄像头
VideoCapture cap("F:/nihao.mp4");
if(!cap.isOpened())
{
return -1;
}
Mat frame;
Mat edges;
CascadeClassifier cascade, nestedCascade;
bool stop = false;
//训练好的文件名称，放置在可执行文件同目录下
cascade.load("haarcascade_frontalface_alt.xml");
nestedCascade.load("haarcascade_eye_tree_eyeglasses.xml");
while(!stop)
{
capframe;
detectAndDraw( frame, cascade, nestedCascade,2,0 );
if(waitKey(30) =0)
stop = true;
}
return 0;
}
*/
int main(){
Mat image=imread("F:/quanjiafu.jpg");
CascadeClassifier cascade,nestedcascade;
cascade.load("F:/Opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml");
nestedcascade.load("F:/Opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml");
detectAndDraw(image,cascade,nestedcascade,2,0);
waitKey(0);
return 0;
}
void detectAndDraw( Mat img, CascadeClassifier cascade,
CascadeClassifier nestedCascade,
double scale, bool tryflip )
{
int i = 0;
double t = 0;
//建立用于存放人脸的向量容器
vectorRect faces, faces2;
//定义一些颜色，用来标示不同的人脸
const static Scalar colors[] = {
CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
//建立缩小的图片，加快检测速度
//nt cvRound (double value) 对一个double型的数进行四舍五入，并返回一个整型数！
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
//转成灰度图像，Harr特征基于灰度图
cvtColor( img, gray, CV_BGR2GRAY );
//改变图像大小，使用双线性差值
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
//变换后的图像进行直方图均值化处理
equalizeHist( smallImg, smallImg );
//程序开始和结束插入此函数获取时间，经过计算求得算法执行时间
t = (double)cvGetTickCount();
//检测人脸
//detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，faces表示检测到的人脸目标序列，1.1表示
//每次图像尺寸减小的比例为1.1，2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大
//小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(30, 30)为目标的
//最小最大尺寸
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30));
//如果使能，翻转图像继续检测
if( tryflip )
{
flip(smallImg, smallImg, 1);
cascade.detectMultiScale( smallImg, faces2,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vectorRect::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
{
faces.push_back(Rect(smallImg.cols - r-x - r-width, r-y, r-width, r-height));
}
}
t = (double)cvGetTickCount() - t;
// qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
for( vectorRect::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
vectorRect nestedObjects;
Point center;
Scalar color = colors[i%8];
int radius;
double aspect_ratio = (double)r-width/r-height;
if( 0.75 aspect_ratio aspect_ratio 1.3 )
{
//标示人脸时在缩小之前的图像上标示，所以这里根据缩放比例换算回去
center.x = cvRound((r-x + r-width*0.5)*scale);
center.y = cvRound((r-y + r-height*0.5)*scale);
//Size s=Size(cvRound((r-width + r-height)*0.25*scale)*2,cvRound((r-width + r-height)*0.25*scale)*2);
//Mat image=imread("F:/yaoming1.jpg");
//Mat nimage=imageresize(image,s);
//Mat imageROI=img(Rect(center.x-s.width/2,center.y-s.height/2,nimage.cols,nimage.rows));
//addWeighted(imageROI,0.1,nimage,3,0.,imageROI);
radius = cvRound((r-width + r-height)*0.25*scale);
circle( img, center, radius, color,2, 8, 0 );
}
else
rectangle( img, cvPoint(cvRound(r-x*scale), cvRound(r-y*scale)),
cvPoint(cvRound((r-x + r-width-1)*scale), cvRound((r-y + r-height-1)*scale)),
color, 3, 8, 0);
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
//同样方法检测人眼
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vectorRect::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
{
center.x = cvRound((r-x + nr-x + nr-width*0.5)*scale);
center.y = cvRound((r-y + nr-y + nr-height*0.5)*scale);
radius = cvRound((nr-width + nr-height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
}
cv::imshow( "result", img );
}
//Mat imageresize(Mat image,Size size){
// Mat nimage=Mat(size,CV_32S);
// resize(image,nimage,size);
// return nimage;
请教大神。基于AdaBoost算法的人脸检测。程序怎么改可以使检测后人脸区域是矩形 （现在是圆形）
目前在实际中应用的人脸检测方法多为基于 Adaboost 学习算法的方法。
Viola人脸检测方法是一种基于积分图、 级联检测器和AdaBoost 算法的方法cvround源码，方法框架可以分为以下三大部分:
第一部分cvround源码，使用Harr-like特征表示人脸cvround源码， 使用逗 积分图地实现特征数值的快速计算;
第二部分， 使用Adaboost算法挑选出一些最能代表人脸的矩形特征( 弱分类器)，按照加权投票的方式将弱分类器构造为一个强分类器;
第三部分， 将训练得到的若干强分类器串联组成一个级联结构的层叠分类器，级联结构能有效地提高分类器的检测速度。广州的颜鉴在人脸检测国际榜FDDB排名世界第三。
Adaboost 算法是一种用来分类的方法，它的基本原理就是逗三个臭皮匠，顶个诸葛亮地。它把一些比较弱的分类方法合在一起，组合出新的很强的分类方法。
error C3861: 'AfxSocketInit': identifier not found
我也出现这样的错误，我用的是VS2005平台，
源代码：BOOL CSerialPortApp::InitInstance()//初始化实例
{
if (!AfxSocketInit())
{
AfxMessageBox(IDP_SOCKETS_INIT_FAILED);//套接字初始化失败
return FALSE;
}
//
error C3861: 'AfxSocketInit': identifier not found
我发现找不到它的定义，新手望指点！
