Refactored Tegra related macro usage
This commit is contained in:
Andrey Kamaev
parent
48ea65e69c
commit
bdfd4c3062
@ -1942,7 +1942,53 @@ reduceC_( const Mat& srcmat, Mat& dstmat )
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typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
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}
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#define reduceSumR8u32s reduceR_<uchar, int, OpAdd<int> >
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#define reduceSumR8u32f reduceR_<uchar, float, OpAdd<int> >
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#define reduceSumR8u64f reduceR_<uchar, double,OpAdd<int> >
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#define reduceSumR16u32f reduceR_<ushort,float, OpAdd<float> >
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#define reduceSumR16u64f reduceR_<ushort,double,OpAdd<double> >
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#define reduceSumR16s32f reduceR_<short, float, OpAdd<float> >
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#define reduceSumR16s64f reduceR_<short, double,OpAdd<double> >
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#define reduceSumR32f32f reduceR_<float, float, OpAdd<float> >
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#define reduceSumR32f64f reduceR_<float, double,OpAdd<double> >
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#define reduceSumR64f64f reduceR_<double,double,OpAdd<double> >
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#define reduceMaxR8u reduceR_<uchar, uchar, OpMax<uchar> >
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#define reduceMaxR16u reduceR_<ushort,ushort,OpMax<ushort> >
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#define reduceMaxR16s reduceR_<short, short, OpMax<short> >
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#define reduceMaxR32f reduceR_<float, float, OpMax<float> >
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#define reduceMaxR64f reduceR_<double,double,OpMax<double> >
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#define reduceMinR8u reduceR_<uchar, uchar, OpMin<uchar> >
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#define reduceMinR16u reduceR_<ushort,ushort,OpMin<ushort> >
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#define reduceMinR16s reduceR_<short, short, OpMin<short> >
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#define reduceMinR32f reduceR_<float, float, OpMin<float> >
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#define reduceMinR64f reduceR_<double,double,OpMin<double> >
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#define reduceSumC8u32s reduceC_<uchar, int, OpAdd<int> >
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#define reduceSumC8u32f reduceC_<uchar, float, OpAdd<int> >
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#define reduceSumC8u64f reduceC_<uchar, double,OpAdd<int> >
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#define reduceSumC16u32f reduceC_<ushort,float, OpAdd<float> >
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#define reduceSumC16u64f reduceC_<ushort,double,OpAdd<double> >
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#define reduceSumC16s32f reduceC_<short, float, OpAdd<float> >
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#define reduceSumC16s64f reduceC_<short, double,OpAdd<double> >
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#define reduceSumC32f32f reduceC_<float, float, OpAdd<float> >
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#define reduceSumC32f64f reduceC_<float, double,OpAdd<double> >
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#define reduceSumC64f64f reduceC_<double,double,OpAdd<double> >
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#define reduceMaxC8u reduceC_<uchar, uchar, OpMax<uchar> >
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#define reduceMaxC16u reduceC_<ushort,ushort,OpMax<ushort> >
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#define reduceMaxC16s reduceC_<short, short, OpMax<short> >
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#define reduceMaxC32f reduceC_<float, float, OpMax<float> >
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#define reduceMaxC64f reduceC_<double,double,OpMax<double> >
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#define reduceMinC8u reduceC_<uchar, uchar, OpMin<uchar> >
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#define reduceMinC16u reduceC_<ushort,ushort,OpMin<ushort> >
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#define reduceMinC16s reduceC_<short, short, OpMin<short> >
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#define reduceMinC32f reduceC_<float, float, OpMin<float> >
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#define reduceMinC64f reduceC_<double,double,OpMin<double> >
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void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
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{
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Mat src = _src.getMat();
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@ -1958,7 +2004,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
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Mat dst = _dst.getMat(), temp = dst;
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CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
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op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
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op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
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CV_Assert( src.channels() == dst.channels() );
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if( op == CV_REDUCE_AVG )
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@ -1977,75 +2023,51 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
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if( op == CV_REDUCE_SUM )
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{
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if(sdepth == CV_8U && ddepth == CV_32S)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR8uAdd(tegra::reduceR8uAdd);
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#else
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func = reduceR_<uchar,int,OpAdd<int> >;
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#endif
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func = GET_OPTIMIZED(reduceSumR8u32s);
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else if(sdepth == CV_8U && ddepth == CV_32F)
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func = reduceR_<uchar,float,OpAdd<int> >;
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func = reduceSumR8u32f;
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else if(sdepth == CV_8U && ddepth == CV_64F)
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func = reduceR_<uchar,double,OpAdd<int> >;
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func = reduceSumR8u64f;
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else if(sdepth == CV_16U && ddepth == CV_32F)
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func = reduceR_<ushort,float,OpAdd<float> >;
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func = reduceSumR16u32f;
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else if(sdepth == CV_16U && ddepth == CV_64F)
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func = reduceR_<ushort,double,OpAdd<double> >;
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func = reduceSumR16u64f;
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else if(sdepth == CV_16S && ddepth == CV_32F)
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func = reduceR_<short,float,OpAdd<float> >;
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func = reduceSumR16s32f;
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else if(sdepth == CV_16S && ddepth == CV_64F)
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func = reduceR_<short,double,OpAdd<double> >;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR32fAdd(tegra::reduceR32fAdd);
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#else
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func = reduceR_<float,float,OpAdd<float> >;
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#endif
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func = reduceSumR16s64f;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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func = GET_OPTIMIZED(reduceSumR32f32f);
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else if(sdepth == CV_32F && ddepth == CV_64F)
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func = reduceR_<float,double,OpAdd<double> >;
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func = reduceSumR32f64f;
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceR_<double,double,OpAdd<double> >;
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func = reduceSumR64f64f;
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}
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else if(op == CV_REDUCE_MAX)
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{
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if(sdepth == CV_8U && ddepth == CV_8U)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR8uMax(tegra::reduceR8uMax);
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#else
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func = reduceR_<uchar, uchar, OpMax<uchar> >;
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#endif
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceR_<ushort, ushort, OpMax<ushort> >;
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func = GET_OPTIMIZED(reduceMaxR8u);
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceMaxR16u;
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else if(sdepth == CV_16S && ddepth == CV_16S)
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func = reduceR_<short, short, OpMax<short> >;
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func = reduceMaxR16s;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR32fMax(tegra::reduceR32fMax);
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#else
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func = reduceR_<float, float, OpMax<float> >;
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#endif
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceR_<double, double, OpMax<double> >;
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func = GET_OPTIMIZED(reduceMaxR32f);
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceMaxR64f;
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}
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else if(op == CV_REDUCE_MIN)
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{
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if(sdepth == CV_8U && ddepth == CV_8U)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR8uMin(tegra::reduceR8uMin);
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#else
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func = reduceR_<uchar, uchar, OpMin<uchar> >;
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#endif
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func = GET_OPTIMIZED(reduceMinR8u);
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceR_<ushort, ushort, OpMin<ushort> >;
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func = reduceMinR16u;
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else if(sdepth == CV_16S && ddepth == CV_16S)
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func = reduceR_<short, short, OpMin<short> >;
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func = reduceMinR16s;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceR32fMin(tegra::reduceR32fMin);
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#else
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func = reduceR_<float, float, OpMin<float> >;
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#endif
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func = GET_OPTIMIZED(reduceMinR32f);
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceR_<double, double, OpMin<double> >;
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func = reduceMinR64f;
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}
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}
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else
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@ -2053,67 +2075,63 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
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if(op == CV_REDUCE_SUM)
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{
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if(sdepth == CV_8U && ddepth == CV_32S)
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#ifdef HAVE_TEGRA_OPTIMIZATION
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func = tegra::getTegraOptimized_reduceC8uAdd(tegra::reduceC8uAdd);
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#else
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func = reduceC_<uchar,int,OpAdd<int> >;
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#endif
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func = GET_OPTIMIZED(reduceSumC8u32s);
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else if(sdepth == CV_8U && ddepth == CV_32F)
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func = reduceC_<uchar,float,OpAdd<int> >;
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func = reduceSumC8u32f;
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else if(sdepth == CV_8U && ddepth == CV_64F)
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func = reduceC_<uchar,double,OpAdd<int> >;
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func = reduceSumC8u64f;
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else if(sdepth == CV_16U && ddepth == CV_32F)
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func = reduceC_<ushort,float,OpAdd<float> >;
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func = reduceSumC16u32f;
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else if(sdepth == CV_16U && ddepth == CV_64F)
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func = reduceC_<ushort,double,OpAdd<double> >;
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func = reduceSumC16u64f;
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else if(sdepth == CV_16S && ddepth == CV_32F)
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func = reduceC_<short,float,OpAdd<float> >;
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func = reduceSumC16s32f;
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else if(sdepth == CV_16S && ddepth == CV_64F)
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func = reduceC_<short,double,OpAdd<double> >;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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func = reduceC_<float,float,OpAdd<float> >;
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func = reduceSumC16s64f;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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func = GET_OPTIMIZED(reduceSumC32f32f);
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else if(sdepth == CV_32F && ddepth == CV_64F)
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func = reduceC_<float,double,OpAdd<double> >;
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func = reduceSumC32f64f;
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceC_<double,double,OpAdd<double> >;
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func = reduceSumC64f64f;
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}
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else if(op == CV_REDUCE_MAX)
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{
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if(sdepth == CV_8U && ddepth == CV_8U)
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func = reduceC_<uchar, uchar, OpMax<uchar> >;
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceC_<ushort, ushort, OpMax<ushort> >;
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func = GET_OPTIMIZED(reduceMaxC8u);
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceMaxC16u;
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else if(sdepth == CV_16S && ddepth == CV_16S)
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func = reduceC_<short, short, OpMax<short> >;
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func = reduceMaxC16s;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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func = reduceC_<float, float, OpMax<float> >;
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func = GET_OPTIMIZED(reduceMaxC32f);
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceC_<double, double, OpMax<double> >;
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func = reduceMaxC64f;
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}
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else if(op == CV_REDUCE_MIN)
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{
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if(sdepth == CV_8U && ddepth == CV_8U)
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func = reduceC_<uchar, uchar, OpMin<uchar> >;
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func = GET_OPTIMIZED(reduceMinC8u);
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else if(sdepth == CV_16U && ddepth == CV_16U)
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func = reduceC_<ushort, ushort, OpMin<ushort> >;
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func = reduceMinC16u;
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else if(sdepth == CV_16S && ddepth == CV_16S)
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func = reduceC_<short, short, OpMin<short> >;
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func = reduceMinC16s;
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else if(sdepth == CV_32F && ddepth == CV_32F)
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func = reduceC_<float, float, OpMin<float> >;
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func = GET_OPTIMIZED(reduceMinC32f);
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else if(sdepth == CV_64F && ddepth == CV_64F)
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func = reduceC_<double, double, OpMin<double> >;
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func = reduceMinC64f;
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}
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}
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if( !func )
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CV_Error( CV_StsUnsupportedFormat,
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"Unsupported combination of input and output array formats" );
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"Unsupported combination of input and output array formats" );
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func( src, temp );
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if( op0 == CV_REDUCE_AVG )
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if( op0 == CV_REDUCE_AVG )
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temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
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}
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}
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//////////////////////////////////////// sort ///////////////////////////////////////////
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@ -169,7 +169,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn )
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return nzm;
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}
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static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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@ -201,7 +201,7 @@ static SumFunc sumTab[] =
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(SumFunc)GET_OPTIMIZED(sum32f), (SumFunc)sum64f,
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0
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};
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template<typename T>
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static int countNonZero_(const T* src, int len )
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{
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@ -229,7 +229,7 @@ static int countNonZero64f( const double* src, int len )
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{ return countNonZero_(src, len); }
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typedef int (*CountNonZeroFunc)(const uchar*, int);
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static CountNonZeroFunc countNonZeroTab[] =
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{
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(CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u), (CountNonZeroFunc)(countNonZero8u),
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@ -238,7 +238,7 @@ static CountNonZeroFunc countNonZeroTab[] =
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(CountNonZeroFunc)countNonZero64f, 0
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};
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template<typename T, typename ST, typename SQT>
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static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int len, int cn )
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{
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@ -393,7 +393,7 @@ static SumSqrFunc sumSqrTab[] =
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};
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}
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cv::Scalar cv::sum( InputArray _src )
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{
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Mat src = _src.getMat();
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@ -419,12 +419,12 @@ cv::Scalar cv::sum( InputArray _src )
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blockSize = std::min(blockSize, intSumBlockSize);
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_buf.allocate(cn);
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buf = _buf;
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for( k = 0; k < cn; k++ )
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buf[k] = 0;
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esz = src.elemSize();
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}
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for( size_t i = 0; i < it.nplanes; i++, ++it )
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{
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for( j = 0; j < total; j += blockSize )
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@ -464,7 +464,7 @@ int cv::countNonZero( InputArray _src )
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return nz;
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}
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cv::Scalar cv::mean( InputArray _src, InputArray _mask )
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{
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Mat src = _src.getMat(), mask = _mask.getMat();
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@ -523,7 +523,7 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask )
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return s*(nz0 ? 1./nz0 : 0);
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}
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void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
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{
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Mat src = _src.getMat(), mask = _mask.getMat();
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@ -610,7 +610,7 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input
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Mat dst = _dst.getMat();
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int dcn = (int)dst.total();
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CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
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(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
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(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
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double* dptr = dst.ptr<double>();
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for( k = 0; k < cn; k++ )
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dptr[k] = sptr[k];
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@ -701,7 +701,7 @@ static void minMaxIdx_32f(const float* src, const uchar* mask, float* minval, fl
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static void minMaxIdx_64f(const double* src, const uchar* mask, double* minval, double* maxval,
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size_t* minidx, size_t* maxidx, int len, size_t startidx )
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{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
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typedef void (*MinMaxIdxFunc)(const uchar*, const uchar*, int*, int*, size_t*, size_t*, int, size_t);
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static MinMaxIdxFunc minmaxTab[] =
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@ -712,7 +712,7 @@ static MinMaxIdxFunc minmaxTab[] =
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(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_32f), (MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_64f),
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0
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};
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static void ofs2idx(const Mat& a, size_t ofs, int* idx)
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{
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int i, d = a.dims;
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@ -732,7 +732,7 @@ static void ofs2idx(const Mat& a, size_t ofs, int* idx)
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idx[i] = -1;
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}
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}
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}
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void cv::minMaxIdx(InputArray _src, double* minVal,
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@ -784,9 +784,9 @@ void cv::minMaxIdx(InputArray _src, double* minVal,
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if( maxIdx )
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ofs2idx(src, maxidx, maxIdx);
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}
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void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal,
|
||||
Point* minLoc, Point* maxLoc, InputArray mask )
|
||||
Point* minLoc, Point* maxLoc, InputArray mask )
|
||||
{
|
||||
Mat img = _img.getMat();
|
||||
CV_Assert(img.dims <= 2);
|
||||
@ -797,7 +797,7 @@ void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal,
|
||||
if( maxLoc )
|
||||
std::swap(maxLoc->x, maxLoc->y);
|
||||
}
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
* norm *
|
||||
\****************************************************************************************/
|
||||
@ -842,7 +842,7 @@ float normL2Sqr_(const float* a, const float* b, int n)
|
||||
return d;
|
||||
}
|
||||
|
||||
|
||||
|
||||
float normL1_(const float* a, const float* b, int n)
|
||||
{
|
||||
int j = 0; float d = 0.f;
|
||||
@ -870,7 +870,7 @@ float normL1_(const float* a, const float* b, int n)
|
||||
for( ; j <= n - 4; j += 4 )
|
||||
{
|
||||
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
|
||||
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
||||
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -910,7 +910,7 @@ int normL1_(const uchar* a, const uchar* b, int n)
|
||||
for( ; j <= n - 4; j += 4 )
|
||||
{
|
||||
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
|
||||
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
||||
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -930,7 +930,7 @@ static const uchar popCountTable[] =
|
||||
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
|
||||
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
|
||||
};
|
||||
|
||||
|
||||
static const uchar popCountTable2[] =
|
||||
{
|
||||
0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
|
||||
@ -942,7 +942,7 @@ static const uchar popCountTable2[] =
|
||||
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
||||
2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4
|
||||
};
|
||||
|
||||
|
||||
static const uchar popCountTable4[] =
|
||||
{
|
||||
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
||||
@ -954,7 +954,7 @@ static const uchar popCountTable4[] =
|
||||
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
||||
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
|
||||
};
|
||||
|
||||
|
||||
int normHamming(const uchar* a, const uchar* b, int n)
|
||||
{
|
||||
int i = 0, result = 0;
|
||||
@ -979,12 +979,12 @@ int normHamming(const uchar* a, const uchar* b, int n)
|
||||
#endif
|
||||
for( ; i <= n - 4; i += 4 )
|
||||
result += popCountTable[a[i] ^ b[i]] + popCountTable[a[i+1] ^ b[i+1]] +
|
||||
popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
|
||||
popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
|
||||
for( ; i < n; i++ )
|
||||
result += popCountTable[a[i] ^ b[i]];
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
|
||||
{
|
||||
if( cellSize == 1 )
|
||||
@ -999,13 +999,13 @@ int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
|
||||
int i = 0, result = 0;
|
||||
for( ; i <= n - 4; i += 4 )
|
||||
result += tab[a[i] ^ b[i]] + tab[a[i+1] ^ b[i+1]] +
|
||||
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
|
||||
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
|
||||
for( ; i < n; i++ )
|
||||
result += tab[a[i] ^ b[i]];
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
template<typename T, typename ST> int
|
||||
normInf_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
||||
{
|
||||
@ -1026,7 +1026,7 @@ normInf_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
||||
*_result = result;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
template<typename T, typename ST> int
|
||||
normL1_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
||||
{
|
||||
@ -1071,7 +1071,7 @@ normL2_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
||||
*_result = result;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
template<typename T, typename ST> int
|
||||
normDiffInf_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn)
|
||||
{
|
||||
@ -1140,16 +1140,16 @@ normDiffL2_(const T* src1, const T* src2, const uchar* mask, ST* _result, int le
|
||||
|
||||
|
||||
#define CV_DEF_NORM_FUNC(L, suffix, type, ntype) \
|
||||
static int norm##L##_##suffix(const type* src, const uchar* mask, ntype* r, int len, int cn) \
|
||||
static int norm##L##_##suffix(const type* src, const uchar* mask, ntype* r, int len, int cn) \
|
||||
{ return norm##L##_(src, mask, r, len, cn); } \
|
||||
static int normDiff##L##_##suffix(const type* src1, const type* src2, \
|
||||
const uchar* mask, ntype* r, int len, int cn) \
|
||||
static int normDiff##L##_##suffix(const type* src1, const type* src2, \
|
||||
const uchar* mask, ntype* r, int len, int cn) \
|
||||
{ return normDiff##L##_(src1, src2, mask, r, (int)len, cn); }
|
||||
|
||||
|
||||
#define CV_DEF_NORM_ALL(suffix, type, inftype, l1type, l2type) \
|
||||
CV_DEF_NORM_FUNC(Inf, suffix, type, inftype) \
|
||||
CV_DEF_NORM_FUNC(L1, suffix, type, l1type) \
|
||||
CV_DEF_NORM_FUNC(L2, suffix, type, l2type)
|
||||
CV_DEF_NORM_FUNC(Inf, suffix, type, inftype) \
|
||||
CV_DEF_NORM_FUNC(L1, suffix, type, l1type) \
|
||||
CV_DEF_NORM_FUNC(L2, suffix, type, l2type)
|
||||
|
||||
CV_DEF_NORM_ALL(8u, uchar, int, int, int)
|
||||
CV_DEF_NORM_ALL(8s, schar, int, int, int)
|
||||
@ -1159,7 +1159,7 @@ CV_DEF_NORM_ALL(32s, int, int, double, double)
|
||||
CV_DEF_NORM_ALL(32f, float, float, double, double)
|
||||
CV_DEF_NORM_ALL(64f, double, double, double, double)
|
||||
|
||||
|
||||
|
||||
typedef int (*NormFunc)(const uchar*, const uchar*, uchar*, int, int);
|
||||
typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int);
|
||||
|
||||
@ -1202,7 +1202,7 @@ static NormDiffFunc normDiffTab[3][8] =
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
|
||||
double cv::norm( InputArray _src, int normType, InputArray _mask )
|
||||
{
|
||||
Mat src = _src.getMat(), mask = _mask.getMat();
|
||||
@ -1221,18 +1221,18 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
|
||||
if( normType == NORM_L2 )
|
||||
{
|
||||
double result = 0;
|
||||
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
|
||||
return std::sqrt(result);
|
||||
}
|
||||
if( normType == NORM_L1 )
|
||||
{
|
||||
double result = 0;
|
||||
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
|
||||
return result;
|
||||
}
|
||||
{
|
||||
float result = 0;
|
||||
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
|
||||
return result;
|
||||
|
||||
}
|
||||
@ -1257,7 +1257,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
|
||||
NAryMatIterator it(arrays, ptrs);
|
||||
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
|
||||
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
|
||||
(normType == NORM_L2 && depth <= CV_8S);
|
||||
(normType == NORM_L2 && depth <= CV_8S);
|
||||
int isum = 0;
|
||||
int *ibuf = &result.i;
|
||||
size_t esz = 0;
|
||||
@ -1275,8 +1275,8 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
|
||||
for( j = 0; j < total; j += blockSize )
|
||||
{
|
||||
int bsz = std::min(total - j, blockSize);
|
||||
func( ptrs[0], ptrs[1], (uchar*)ibuf, bsz, cn );
|
||||
count += bsz;
|
||||
func( ptrs[0], ptrs[1], (uchar*)ibuf, bsz, cn );
|
||||
count += bsz;
|
||||
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
||||
{
|
||||
result.d += isum;
|
||||
@ -1304,7 +1304,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
|
||||
return result.d;
|
||||
}
|
||||
|
||||
|
||||
|
||||
double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask )
|
||||
{
|
||||
if( normType & CV_RELATIVE )
|
||||
@ -1329,18 +1329,18 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
|
||||
if( normType == NORM_L2 )
|
||||
{
|
||||
double result = 0;
|
||||
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
return std::sqrt(result);
|
||||
}
|
||||
if( normType == NORM_L1 )
|
||||
{
|
||||
double result = 0;
|
||||
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
return result;
|
||||
}
|
||||
{
|
||||
float result = 0;
|
||||
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
|
||||
return result;
|
||||
}
|
||||
}
|
||||
@ -1365,7 +1365,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
|
||||
NAryMatIterator it(arrays, ptrs);
|
||||
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
|
||||
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
|
||||
(normType == NORM_L2 && depth <= CV_8S);
|
||||
(normType == NORM_L2 && depth <= CV_8S);
|
||||
unsigned isum = 0;
|
||||
unsigned *ibuf = &result.u;
|
||||
size_t esz = 0;
|
||||
@ -1496,7 +1496,7 @@ cvMinMaxLoc( const void* imgarr, double* _minVal, double* _maxVal,
|
||||
cv::extractImageCOI(imgarr, img);
|
||||
|
||||
cv::minMaxLoc( img, _minVal, _maxVal,
|
||||
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
|
||||
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
|
||||
}
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user