| /*M/////////////////////////////////////////////////////////////////////////////////////// |
| // |
| // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
| // |
| // By downloading, copying, installing or using the software you agree to this license. |
| // If you do not agree to this license, do not download, install, |
| // copy or use the software. |
| // |
| // |
| // Intel License Agreement |
| // For Open Source Computer Vision Library |
| // |
| // Copyright (C) 2000, Intel Corporation, all rights reserved. |
| // Third party copyrights are property of their respective owners. |
| // |
| // Redistribution and use in source and binary forms, with or without modification, |
| // are permitted provided that the following conditions are met: |
| // |
| // * Redistribution's of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // |
| // * Redistribution's in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // |
| // * The name of Intel Corporation may not be used to endorse or promote products |
| // derived from this software without specific prior written permission. |
| // |
| // This software is provided by the copyright holders and contributors "as is" and |
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| // warranties of merchantability and fitness for a particular purpose are disclaimed. |
| // In no event shall the Intel Corporation or contributors be liable for any direct, |
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| // (including, but not limited to, procurement of substitute goods or services; |
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| // the use of this software, even if advised of the possibility of such damage. |
| // |
| //M*/ |
| |
| #include "_cxcore.h" |
| #include <float.h> |
| |
| /****************************************************************************************\ |
| * Mean value over the region * |
| \****************************************************************************************/ |
| |
| #define ICV_MEAN_CASE_C1( len ) \ |
| for( ; x <= (len) - 2; x += 2 ) \ |
| { \ |
| if( mask[x] ) \ |
| s0 += src[x], pix++; \ |
| if( mask[x+1] ) \ |
| s0 += src[x+1], pix++; \ |
| } \ |
| \ |
| for( ; x < (len); x++ ) \ |
| if( mask[x] ) \ |
| s0 += src[x], pix++ |
| |
| |
| #define ICV_MEAN_CASE_C2( len ) \ |
| for( ; x < (len); x++ ) \ |
| if( mask[x] ) \ |
| { \ |
| s0 += src[x*2]; \ |
| s1 += src[x*2+1]; \ |
| pix++; \ |
| } |
| |
| |
| #define ICV_MEAN_CASE_C3( len ) \ |
| for( ; x < (len); x++ ) \ |
| if( mask[x] ) \ |
| { \ |
| s0 += src[x*3]; \ |
| s1 += src[x*3+1]; \ |
| s2 += src[x*3+2]; \ |
| pix++; \ |
| } |
| |
| |
| #define ICV_MEAN_CASE_C4( len ) \ |
| for( ; x < (len); x++ ) \ |
| if( mask[x] ) \ |
| { \ |
| s0 += src[x*4]; \ |
| s1 += src[x*4+1]; \ |
| s2 += src[x*4+2]; \ |
| s3 += src[x*4+3]; \ |
| pix++; \ |
| } |
| |
| |
| #define ICV_MEAN_COI_CASE( len, cn ) \ |
| for( ; x <= (len) - 2; x += 2 ) \ |
| { \ |
| if( mask[x] ) \ |
| s0 += src[x*(cn)], pix++; \ |
| if( mask[x+1] ) \ |
| s0+=src[(x+1)*(cn)], pix++; \ |
| } \ |
| \ |
| for( ; x < (len); x++ ) \ |
| if( mask[x] ) \ |
| s0 += src[x*(cn)], pix++; |
| |
| |
| ////////////////////////////////////// entry macros ////////////////////////////////////// |
| |
| #define ICV_MEAN_ENTRY_COMMON() \ |
| int pix = 0; \ |
| step /= sizeof(src[0]) |
| |
| #define ICV_MEAN_ENTRY_C1( sumtype ) \ |
| sumtype s0 = 0; \ |
| ICV_MEAN_ENTRY_COMMON() |
| |
| #define ICV_MEAN_ENTRY_C2( sumtype ) \ |
| sumtype s0 = 0, s1 = 0; \ |
| ICV_MEAN_ENTRY_COMMON() |
| |
| #define ICV_MEAN_ENTRY_C3( sumtype ) \ |
| sumtype s0 = 0, s1 = 0, s2 = 0; \ |
| ICV_MEAN_ENTRY_COMMON() |
| |
| #define ICV_MEAN_ENTRY_C4( sumtype ) \ |
| sumtype s0 = 0, s1 = 0, s2 = 0, s3 = 0; \ |
| ICV_MEAN_ENTRY_COMMON() |
| |
| |
| #define ICV_MEAN_ENTRY_BLOCK_COMMON( block_size ) \ |
| int remaining = block_size; \ |
| ICV_MEAN_ENTRY_COMMON() |
| |
| #define ICV_MEAN_ENTRY_BLOCK_C1( sumtype, worktype, block_size )\ |
| sumtype sum0 = 0; \ |
| worktype s0 = 0; \ |
| ICV_MEAN_ENTRY_BLOCK_COMMON( block_size ) |
| |
| #define ICV_MEAN_ENTRY_BLOCK_C2( sumtype, worktype, block_size )\ |
| sumtype sum0 = 0, sum1 = 0; \ |
| worktype s0 = 0, s1 = 0; \ |
| ICV_MEAN_ENTRY_BLOCK_COMMON( block_size ) |
| |
| #define ICV_MEAN_ENTRY_BLOCK_C3( sumtype, worktype, block_size )\ |
| sumtype sum0 = 0, sum1 = 0, sum2 = 0; \ |
| worktype s0 = 0, s1 = 0, s2 = 0; \ |
| ICV_MEAN_ENTRY_BLOCK_COMMON( block_size ) |
| |
| #define ICV_MEAN_ENTRY_BLOCK_C4( sumtype, worktype, block_size )\ |
| sumtype sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0; \ |
| worktype s0 = 0, s1 = 0, s2 = 0, s3 = 0; \ |
| ICV_MEAN_ENTRY_BLOCK_COMMON( block_size ) |
| |
| |
| /////////////////////////////////////// exit macros ////////////////////////////////////// |
| |
| #define ICV_MEAN_EXIT_COMMON() \ |
| double scale = pix ? 1./pix : 0 |
| |
| #define ICV_MEAN_EXIT_C1( tmp ) \ |
| ICV_MEAN_EXIT_COMMON(); \ |
| mean[0] = scale*(double)tmp##0 |
| |
| #define ICV_MEAN_EXIT_C2( tmp ) \ |
| ICV_MEAN_EXIT_COMMON(); \ |
| double t0 = scale*(double)tmp##0; \ |
| double t1 = scale*(double)tmp##1; \ |
| mean[0] = t0; \ |
| mean[1] = t1 |
| |
| #define ICV_MEAN_EXIT_C3( tmp ) \ |
| ICV_MEAN_EXIT_COMMON(); \ |
| double t0 = scale*(double)tmp##0; \ |
| double t1 = scale*(double)tmp##1; \ |
| double t2 = scale*(double)tmp##2; \ |
| mean[0] = t0; \ |
| mean[1] = t1; \ |
| mean[2] = t2 |
| |
| #define ICV_MEAN_EXIT_C4( tmp ) \ |
| ICV_MEAN_EXIT_COMMON(); \ |
| double t0 = scale*(double)tmp##0; \ |
| double t1 = scale*(double)tmp##1; \ |
| mean[0] = t0; \ |
| mean[1] = t1; \ |
| t0 = scale*(double)tmp##2; \ |
| t1 = scale*(double)tmp##3; \ |
| mean[2] = t0; \ |
| mean[3] = t1 |
| |
| #define ICV_MEAN_EXIT_BLOCK_C1() \ |
| sum0 += s0; \ |
| ICV_MEAN_EXIT_C1( sum ) |
| |
| #define ICV_MEAN_EXIT_BLOCK_C2() \ |
| sum0 += s0; sum1 += s1; \ |
| ICV_MEAN_EXIT_C2( sum ) |
| |
| #define ICV_MEAN_EXIT_BLOCK_C3() \ |
| sum0 += s0; sum1 += s1; \ |
| sum2 += s2; \ |
| ICV_MEAN_EXIT_C3( sum ) |
| |
| #define ICV_MEAN_EXIT_BLOCK_C4() \ |
| sum0 += s0; sum1 += s1; \ |
| sum2 += s2; sum3 += s3; \ |
| ICV_MEAN_EXIT_C4( sum ) |
| |
| ////////////////////////////////////// update macros ///////////////////////////////////// |
| |
| #define ICV_MEAN_UPDATE_COMMON( block_size )\ |
| remaining = block_size |
| |
| #define ICV_MEAN_UPDATE_C1( block_size ) \ |
| ICV_MEAN_UPDATE_COMMON( block_size ); \ |
| sum0 += s0; \ |
| s0 = 0 |
| |
| #define ICV_MEAN_UPDATE_C2( block_size ) \ |
| ICV_MEAN_UPDATE_COMMON( block_size ); \ |
| sum0 += s0; sum1 += s1; \ |
| s0 = s1 = 0 |
| |
| #define ICV_MEAN_UPDATE_C3( block_size ) \ |
| ICV_MEAN_UPDATE_COMMON( block_size ); \ |
| sum0 += s0; sum1 += s1; sum2 += s2; \ |
| s0 = s1 = s2 = 0 |
| |
| #define ICV_MEAN_UPDATE_C4( block_size ) \ |
| ICV_MEAN_UPDATE_COMMON( block_size ); \ |
| sum0 += s0; sum1 += s1; \ |
| sum2 += s2; sum3 += s3; \ |
| s0 = s1 = s2 = s3 = 0 |
| |
| |
| #define ICV_IMPL_MEAN_BLOCK_FUNC_2D( flavor, cn, \ |
| arrtype, sumtype, worktype, block_size ) \ |
| IPCVAPI_IMPL( CvStatus, icvMean_##flavor##_C##cn##MR, \ |
| ( const arrtype* src, int step, \ |
| const uchar* mask, int maskstep, \ |
| CvSize size, double* mean ), \ |
| (src, step, mask, maskstep, size, mean)) \ |
| { \ |
| ICV_MEAN_ENTRY_BLOCK_C##cn( sumtype, worktype, block_size );\ |
| \ |
| for( ; size.height--; src += step, mask += maskstep ) \ |
| { \ |
| int x = 0; \ |
| while( x < size.width ) \ |
| { \ |
| int limit = MIN( remaining, size.width - x ); \ |
| remaining -= limit; \ |
| limit += x; \ |
| ICV_MEAN_CASE_C##cn( limit ); \ |
| if( remaining == 0 ) \ |
| { \ |
| ICV_MEAN_UPDATE_C##cn( block_size ); \ |
| } \ |
| } \ |
| } \ |
| \ |
| { ICV_MEAN_EXIT_BLOCK_C##cn(); } \ |
| return CV_OK; \ |
| } |
| |
| |
| #define ICV_IMPL_MEAN_FUNC_2D( flavor, cn, \ |
| arrtype, sumtype, worktype ) \ |
| IPCVAPI_IMPL( CvStatus, icvMean_##flavor##_C##cn##MR, \ |
| ( const arrtype* src, int step, \ |
| const uchar* mask, int maskstep, \ |
| CvSize size, double* mean), \ |
| (src, step, mask, maskstep, size, mean)) \ |
| { \ |
| ICV_MEAN_ENTRY_C##cn( sumtype ); \ |
| \ |
| for( ; size.height--; src += step, mask += maskstep ) \ |
| { \ |
| int x = 0; \ |
| ICV_MEAN_CASE_C##cn( size.width ); \ |
| } \ |
| \ |
| { ICV_MEAN_EXIT_C##cn( s ); } \ |
| return CV_OK; \ |
| } |
| |
| |
| #define ICV_IMPL_MEAN_BLOCK_FUNC_2D_COI( flavor, \ |
| arrtype, sumtype, worktype, block_size ) \ |
| static CvStatus CV_STDCALL \ |
| icvMean_##flavor##_CnCMR( const arrtype* src, int step, \ |
| const uchar* mask, int maskstep, \ |
| CvSize size, int cn, \ |
| int coi, double* mean ) \ |
| { \ |
| ICV_MEAN_ENTRY_BLOCK_C1( sumtype, worktype, block_size ); \ |
| src += coi - 1; \ |
| \ |
| for( ; size.height--; src += step, mask += maskstep ) \ |
| { \ |
| int x = 0; \ |
| while( x < size.width ) \ |
| { \ |
| int limit = MIN( remaining, size.width - x ); \ |
| remaining -= limit; \ |
| limit += x; \ |
| ICV_MEAN_COI_CASE( limit, cn ); \ |
| if( remaining == 0 ) \ |
| { \ |
| ICV_MEAN_UPDATE_C1( block_size ); \ |
| } \ |
| } \ |
| } \ |
| \ |
| { ICV_MEAN_EXIT_BLOCK_C1(); } \ |
| return CV_OK; \ |
| } |
| |
| |
| #define ICV_IMPL_MEAN_FUNC_2D_COI( flavor, \ |
| arrtype, sumtype, worktype ) \ |
| static CvStatus CV_STDCALL \ |
| icvMean_##flavor##_CnCMR( const arrtype* src, int step, \ |
| const uchar* mask, int maskstep, \ |
| CvSize size, int cn, \ |
| int coi, double* mean ) \ |
| { \ |
| ICV_MEAN_ENTRY_C1( sumtype ); \ |
| src += coi - 1; \ |
| \ |
| for( ; size.height--; src += step, mask += maskstep ) \ |
| { \ |
| int x = 0; \ |
| ICV_MEAN_COI_CASE( size.width, cn ); \ |
| } \ |
| \ |
| { ICV_MEAN_EXIT_C1( s ); } \ |
| return CV_OK; \ |
| } |
| |
| |
| #define ICV_IMPL_MEAN_BLOCK_ALL( flavor, arrtype, sumtype, \ |
| worktype, block_size ) \ |
| ICV_IMPL_MEAN_BLOCK_FUNC_2D( flavor, 1, arrtype, sumtype, \ |
| worktype, block_size ) \ |
| ICV_IMPL_MEAN_BLOCK_FUNC_2D( flavor, 2, arrtype, sumtype, \ |
| worktype, block_size ) \ |
| ICV_IMPL_MEAN_BLOCK_FUNC_2D( flavor, 3, arrtype, sumtype, \ |
| worktype, block_size ) \ |
| ICV_IMPL_MEAN_BLOCK_FUNC_2D( flavor, 4, arrtype, sumtype, \ |
| worktype, block_size ) \ |
| ICV_IMPL_MEAN_BLOCK_FUNC_2D_COI( flavor, arrtype, sumtype, \ |
| worktype, block_size ) |
| |
| #define ICV_IMPL_MEAN_ALL( flavor, arrtype, sumtype, worktype ) \ |
| ICV_IMPL_MEAN_FUNC_2D( flavor, 1, arrtype, sumtype, worktype ) \ |
| ICV_IMPL_MEAN_FUNC_2D( flavor, 2, arrtype, sumtype, worktype ) \ |
| ICV_IMPL_MEAN_FUNC_2D( flavor, 3, arrtype, sumtype, worktype ) \ |
| ICV_IMPL_MEAN_FUNC_2D( flavor, 4, arrtype, sumtype, worktype ) \ |
| ICV_IMPL_MEAN_FUNC_2D_COI( flavor, arrtype, sumtype, worktype ) |
| |
| ICV_IMPL_MEAN_BLOCK_ALL( 8u, uchar, int64, unsigned, 1 << 24 ) |
| ICV_IMPL_MEAN_BLOCK_ALL( 16u, ushort, int64, unsigned, 1 << 16 ) |
| ICV_IMPL_MEAN_BLOCK_ALL( 16s, short, int64, int, 1 << 16 ) |
| ICV_IMPL_MEAN_ALL( 32s, int, double, double ) |
| ICV_IMPL_MEAN_ALL( 32f, float, double, double ) |
| ICV_IMPL_MEAN_ALL( 64f, double, double, double ) |
| |
| #define icvMean_8s_C1MR 0 |
| #define icvMean_8s_C2MR 0 |
| #define icvMean_8s_C3MR 0 |
| #define icvMean_8s_C4MR 0 |
| #define icvMean_8s_CnCMR 0 |
| |
| CV_DEF_INIT_BIG_FUNC_TAB_2D( Mean, MR ) |
| CV_DEF_INIT_FUNC_TAB_2D( Mean, CnCMR ) |
| |
| CV_IMPL CvScalar |
| cvAvg( const void* img, const void* maskarr ) |
| { |
| CvScalar mean = {{0,0,0,0}}; |
| |
| static CvBigFuncTable mean_tab; |
| static CvFuncTable meancoi_tab; |
| static int inittab = 0; |
| |
| CV_FUNCNAME("cvAvg"); |
| |
| __BEGIN__; |
| |
| CvSize size; |
| double scale; |
| |
| if( !maskarr ) |
| { |
| CV_CALL( mean = cvSum(img)); |
| size = cvGetSize( img ); |
| size.width *= size.height; |
| scale = size.width ? 1./size.width : 0; |
| |
| mean.val[0] *= scale; |
| mean.val[1] *= scale; |
| mean.val[2] *= scale; |
| mean.val[3] *= scale; |
| } |
| else |
| { |
| int type, coi = 0; |
| int mat_step, mask_step; |
| |
| CvMat stub, maskstub, *mat = (CvMat*)img, *mask = (CvMat*)maskarr; |
| |
| if( !inittab ) |
| { |
| icvInitMeanMRTable( &mean_tab ); |
| icvInitMeanCnCMRTable( &meancoi_tab ); |
| inittab = 1; |
| } |
| |
| if( !CV_IS_MAT(mat) ) |
| CV_CALL( mat = cvGetMat( mat, &stub, &coi )); |
| |
| if( !CV_IS_MAT(mask) ) |
| CV_CALL( mask = cvGetMat( mask, &maskstub )); |
| |
| if( !CV_IS_MASK_ARR(mask) ) |
| CV_ERROR( CV_StsBadMask, "" ); |
| |
| if( !CV_ARE_SIZES_EQ( mat, mask ) ) |
| CV_ERROR( CV_StsUnmatchedSizes, "" ); |
| |
| type = CV_MAT_TYPE( mat->type ); |
| size = cvGetMatSize( mat ); |
| |
| mat_step = mat->step; |
| mask_step = mask->step; |
| |
| if( CV_IS_MAT_CONT( mat->type & mask->type )) |
| { |
| size.width *= size.height; |
| size.height = 1; |
| mat_step = mask_step = CV_STUB_STEP; |
| } |
| |
| if( CV_MAT_CN(type) == 1 || coi == 0 ) |
| { |
| CvFunc2D_2A1P func; |
| |
| if( CV_MAT_CN(type) > 4 ) |
| CV_ERROR( CV_StsOutOfRange, "The input array must have at most 4 channels unless COI is set" ); |
| |
| func = (CvFunc2D_2A1P)(mean_tab.fn_2d[type]); |
| |
| if( !func ) |
| CV_ERROR( CV_StsBadArg, cvUnsupportedFormat ); |
| |
| IPPI_CALL( func( mat->data.ptr, mat_step, mask->data.ptr, |
| mask_step, size, mean.val )); |
| } |
| else |
| { |
| CvFunc2DnC_2A1P func = (CvFunc2DnC_2A1P)( |
| meancoi_tab.fn_2d[CV_MAT_DEPTH(type)]); |
| |
| if( !func ) |
| CV_ERROR( CV_StsBadArg, cvUnsupportedFormat ); |
| |
| IPPI_CALL( func( mat->data.ptr, mat_step, mask->data.ptr, |
| mask_step, size, CV_MAT_CN(type), coi, mean.val )); |
| } |
| } |
| |
| __END__; |
| |
| return mean; |
| } |
| |
| /* End of file */ |
