0th days of learning opencv

I have been in contact with opencv for several years since 2011 and have been writing some small programs. I have used the manual to complete some tasks and have not studied the code in depth. Now I graduated from, but failed to enter the image processing industry for various reasons, so we are now re-learning opencv, including analyzing code, learning algorithms, and writing blogs from time to time. Please contact us.

Build Environment: vs2010 + cv1.0

The latest CV is 3.0 alpha, but the 1.0 structure is simple, the basic functions are still there, And I am familiar with the C language, so choose 1.0.

Download the 1.0 installation file from the official website. The system automatically installs the file to c: \ Program Files (x86). Go to the opencv directory and you will find a c: \ Program Files (x86) file) \ opencv \ _ make directory, which contains opencv. SLN file. This is very recognizable. open the file with vs2010. Because it is a 2008 project file, you need to convert it. Fortunately, no error is reported. directly generate the project and use the debug version, because the original version is release, one-step debugging is not possible.

After the project is generated, the lib and DLL files ending with D are displayed in the corresponding directory. Based on the Principle of opencv configuration in vs (there are many online files and I will not repeat them here). Now the environment is complete, as long as you create a new project and perform one-step debugging, you can observe the internal structure of the function step by step.

# Include <cv. h> # include For debugging, go to cvsmooth:


CV_IMPL voidcvSmooth( 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 );}







0th days of learning opencv