used new device layer for cv::cuda::norm
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								modules/cudaarithm/src/cuda/norm.cu
									
									
									
									
									
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| /*M/////////////////////////////////////////////////////////////////////////////////////// | ||||
| // | ||||
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| //                           License Agreement | ||||
| //                For Open Source Computer Vision Library | ||||
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| // This software is provided by the copyright holders and contributors "as is" and | ||||
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| // (including, but not limited to, procurement of substitute goods or services; | ||||
| // loss of use, data, or profits; or business interruption) however caused | ||||
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| // | ||||
| //M*/ | ||||
|  | ||||
| #include "opencv2/opencv_modules.hpp" | ||||
|  | ||||
| #ifndef HAVE_OPENCV_CUDEV | ||||
|  | ||||
| #error "opencv_cudev is required" | ||||
|  | ||||
| #else | ||||
|  | ||||
| #include "opencv2/cudaarithm.hpp" | ||||
| #include "opencv2/cudev.hpp" | ||||
|  | ||||
| using namespace cv::cudev; | ||||
|  | ||||
| namespace | ||||
| { | ||||
|     double normDiffInf(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf) | ||||
|     { | ||||
|         const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1; | ||||
|         const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2; | ||||
|         GpuMat_<int>& buf = (GpuMat_<int>&) _buf; | ||||
|  | ||||
|         gridFindMinMaxVal(abs_(cvt_<int>(src1) - cvt_<int>(src2)), buf); | ||||
|  | ||||
|         int data[2]; | ||||
|         buf.download(cv::Mat(1, 2, buf.type(), data)); | ||||
|  | ||||
|         return data[1]; | ||||
|     } | ||||
|  | ||||
|     double normDiffL1(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf) | ||||
|     { | ||||
|         const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1; | ||||
|         const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2; | ||||
|         GpuMat_<int>& buf = (GpuMat_<int>&) _buf; | ||||
|  | ||||
|         gridCalcSum(abs_(cvt_<int>(src1) - cvt_<int>(src2)), buf); | ||||
|  | ||||
|         int data; | ||||
|         buf.download(cv::Mat(1, 1, buf.type(), &data)); | ||||
|  | ||||
|         return data; | ||||
|     } | ||||
|  | ||||
|     double normDiffL2(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf) | ||||
|     { | ||||
|         const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1; | ||||
|         const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2; | ||||
|         GpuMat_<double>& buf = (GpuMat_<double>&) _buf; | ||||
|  | ||||
|         gridCalcSum(sqr_(cvt_<double>(src1) - cvt_<double>(src2)), buf); | ||||
|  | ||||
|         double data; | ||||
|         buf.download(cv::Mat(1, 1, buf.type(), &data)); | ||||
|  | ||||
|         return std::sqrt(data); | ||||
|     } | ||||
| } | ||||
|  | ||||
| double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normType) | ||||
| { | ||||
|     typedef double (*func_t)(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf); | ||||
|     static const func_t funcs[] = | ||||
|     { | ||||
|         0, normDiffInf, normDiffL1, 0, normDiffL2 | ||||
|     }; | ||||
|  | ||||
|     GpuMat src1 = _src1.getGpuMat(); | ||||
|     GpuMat src2 = _src2.getGpuMat(); | ||||
|  | ||||
|     CV_Assert( src1.type() == CV_8UC1 ); | ||||
|     CV_Assert( src1.size() == src2.size() && src1.type() == src2.type() ); | ||||
|     CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 ); | ||||
|  | ||||
|     return funcs[normType](src1, src2, buf); | ||||
| } | ||||
|  | ||||
| #endif | ||||
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	 Vladislav Vinogradov
					Vladislav Vinogradov