自2011年接触OpenCV已经有几年了，一直停留在写一些小程序，利用手冊完毕一些任务，一直没有深入研究当中代码，现在毕业，但各种原因未能进入图像处理行业，故现重学OpenCV，包含分析代码，学习算法，blog不定时更写，欢迎交流。
搭建好开发环境：VS2010+CV1.0
最新CV是3.0 alpha，但1.0结构简单，基础功能还是有的，并且本人比較熟悉C语言，故选择1.0。
官网下载1.0的安装文件，系统自己主动安装到C:Program Files (x86)，进入OpenCV文件夹，会发现有一个C:Program Files (x86)OpenCV\_make文件夹，里面有一个OpenCV.sln文件，这个非常好辨认，用vs2010打开文件，由于是2008的project文件，所以须要转换，还好没有报错，直接生成project，使用Debug版本号，由于原版是Release的，无法单步调试。
生成项目后，会在对应文件夹下看到文件名称为d结尾的lib和dll，依据Vs配置OpenCV的原理（网上有非常多，不再赘述），至此环境完毕，仅仅要新建项目，单步调试，就能一步步的观察函数的内部结构。
#include <cv.h>
#include <highgui.h>
int main()
{
IplImage * image = cvLoadImage("e:\1.jpg",1);
cvNamedWindow("原图");
cvNamedWindow("变换");
cvShowImage("原图",image);
cvSmooth(image,image,CV_GAUSSIAN,3,3,0,0);
cvShowImage("变换",image);
cvWaitKey();
cvDestroyWindow("原图");
cvDestroyWindow("变换");
cvReleaseImage(&image);
return 0;
}调试的话进入cvSmooth：
CV_IMPL void
cvSmooth( const void* srcarr, void* dstarr, int smooth_type,
int param1, int param2, double param3, double param4 )
{
CvBoxFilter box_filter;
CvSepFilter gaussian_filter;
CvMat* temp = 0;
CV_FUNCNAME( "cvSmooth" );
__BEGIN__;
int coi1 = 0, coi2 = 0;
CvMat srcstub, *src = (CvMat*)srcarr;
CvMat dststub, *dst = (CvMat*)dstarr;
CvSize size;
int src_type, dst_type, depth, cn;
double sigma1 = 0, sigma2 = 0;
bool have_ipp = icvFilterMedian_8u_C1R_p != 0;
CV_CALL( src = cvGetMat( src, &srcstub, &coi1 ));
CV_CALL( dst = cvGetMat( dst, &dststub, &coi2 ));
if( coi1 != 0 || coi2 != 0 )
CV_ERROR( CV_BadCOI, "" );
src_type = CV_MAT_TYPE( src->type );
dst_type = CV_MAT_TYPE( dst->type );
depth = CV_MAT_DEPTH(src_type);
cn = CV_MAT_CN(src_type);
size = cvGetMatSize(src);
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
if( smooth_type != CV_BLUR_NO_SCALE && !CV_ARE_TYPES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedFormats,
"The specified smoothing algorithm requires input and ouput arrays be of the same type" );
if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE ||
smooth_type == CV_GAUSSIAN || smooth_type == CV_MEDIAN )
{
// automatic detection of kernel size from sigma
if( smooth_type == CV_GAUSSIAN )
{
sigma1 = param3;
sigma2 = param4 ? param4 : param3;
if( param1 == 0 && sigma1 > 0 )
param1 = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
if( param2 == 0 && sigma2 > 0 )
param2 = cvRound(sigma2*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
}
if( param2 == 0 )
param2 = size.height == 1 ? 1 : param1;
if( param1 < 1 || (param1 & 1) == 0 || param2 < 1 || (param2 & 1) == 0 )
CV_ERROR( CV_StsOutOfRange,
"Both mask width and height must be >=1 and odd" );
if( param1 == 1 && param2 == 1 )
{
cvConvert( src, dst );
EXIT;
}
}
if( have_ipp && (smooth_type == CV_BLUR || smooth_type == CV_MEDIAN) &&
size.width >= param1 && size.height >= param2 && param1 > 1 && param2 > 1 )
{
CvSmoothFixedIPPFunc ipp_median_box_func = 0;
if( smooth_type == CV_BLUR )
{
ipp_median_box_func =
src_type == CV_8UC1 ? icvFilterBox_8u_C1R_p :
src_type == CV_8UC3 ? icvFilterBox_8u_C3R_p :
src_type == CV_8UC4 ? icvFilterBox_8u_C4R_p :
src_type == CV_32FC1 ? icvFilterBox_32f_C1R_p :
src_type == CV_32FC3 ? icvFilterBox_32f_C3R_p :
src_type == CV_32FC4 ? icvFilterBox_32f_C4R_p : 0;
}
else if( smooth_type == CV_MEDIAN )
{
ipp_median_box_func =
src_type == CV_8UC1 ? icvFilterMedian_8u_C1R_p :
src_type == CV_8UC3 ? icvFilterMedian_8u_C3R_p :
src_type == CV_8UC4 ? icvFilterMedian_8u_C4R_p : 0;
}
if( ipp_median_box_func )
{
CvSize el_size = { param1, param2 };
CvPoint el_anchor = { param1/2, param2/2 };
int stripe_size = 1 << 14; // the optimal value may depend on CPU cache,
// overhead of the current IPP code etc.
const uchar* shifted_ptr;
int y, dy = 0;
int temp_step, dst_step = dst->step;
CV_CALL( temp = icvIPPFilterInit( src, stripe_size, el_size ));
shifted_ptr = temp->data.ptr +
el_anchor.y*temp->step + el_anchor.x*CV_ELEM_SIZE(src_type);
temp_step = temp->step ? temp->step : CV_STUB_STEP;
for( y = 0; y < src->rows; y += dy )
{
dy = icvIPPFilterNextStripe( src, temp, y, el_size, el_anchor );
IPPI_CALL( ipp_median_box_func( shifted_ptr, temp_step,
dst->data.ptr + y*dst_step, dst_step, cvSize(src->cols, dy),
el_size, el_anchor ));
}
EXIT;
}
}
if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE )
{
CV_CALL( box_filter.init( src->cols, src_type, dst_type,
smooth_type == CV_BLUR, cvSize(param1, param2) ));
CV_CALL( box_filter.process( src, dst ));
}
else if( smooth_type == CV_MEDIAN )
{
if( depth != CV_8U || cn != 1 && cn != 3 && cn != 4 )
CV_ERROR( CV_StsUnsupportedFormat,
"Median filter only supports 8uC1, 8uC3 and 8uC4 images" );
IPPI_CALL( icvMedianBlur_8u_CnR( src->data.ptr, src->step,
dst->data.ptr, dst->step, size, param1, cn ));
}
else if( smooth_type == CV_GAUSSIAN )
{
CvSize ksize = { param1, param2 };
float* kx = (float*)cvStackAlloc( ksize.width*sizeof(kx[0]) );
float* ky = (float*)cvStackAlloc( ksize.height*sizeof(ky[0]) );
CvMat KX = cvMat( 1, ksize.width, CV_32F, kx );
CvMat KY = cvMat( 1, ksize.height, CV_32F, ky );
CvSepFilter::init_gaussian_kernel( &KX, sigma1 );
if( ksize.width != ksize.height || fabs(sigma1 - sigma2) > FLT_EPSILON )
CvSepFilter::init_gaussian_kernel( &KY, sigma2 );
else
KY.data.fl = kx;
if( have_ipp && size.width >= param1*3 &&
size.height >= param2 && param1 > 1 && param2 > 1 )
{
int done;
CV_CALL( done = icvIPPSepFilter( src, dst, &KX, &KY,
cvPoint(ksize.width/2,ksize.height/2)));
if( done )
EXIT;
}
CV_CALL( gaussian_filter.init( src->cols, src_type, dst_type, &KX, &KY ));
CV_CALL( gaussian_filter.process( src, dst ));
}
else if( smooth_type == CV_BILATERAL )
{
if( param1 < 0 || param2 < 0 )
CV_ERROR( CV_StsOutOfRange,
"Thresholds in bilaral filtering should not bee negative" );
param1 += param1 == 0;
param2 += param2 == 0;
if( depth != CV_8U || cn != 1 && cn != 3 )
CV_ERROR( CV_StsUnsupportedFormat,
"Bilateral filter only supports 8uC1 and 8uC3 images" );
IPPI_CALL( icvBilateralFiltering_8u_CnR( src->data.ptr, src->step,
dst->data.ptr, dst->step, size, param1, param2, cn ));
}
__END__;
cvReleaseMat( &temp );
}
