网站服务器租用哪家好,个人能建设网站吗,注册网站要语音验证码的有哪些,销售渠道有哪几种目录
一#xff0c;直方图均衡
1#xff0c;直方图统计
2#xff0c;灰度变换
3#xff0c;直方图均衡
二#xff0c;可分离滤波器
1#xff0c;可分离滤波器的工厂
2#xff0c;ocvSepFilter、sepFilter2D
3#xff0c;Sobel
三#xff0c;相位相关法 phase…目录
一直方图均衡
1直方图统计
2灰度变换
3直方图均衡
二可分离滤波器
1可分离滤波器的工厂
2ocvSepFilter、sepFilter2D
3Sobel
三相位相关法 phaseCorrelate
1phaseCorrelate
2汉宁窗
四匹配器
1纯虚类DescriptorMatcher
2子类FlannBasedMatcher
3knnMatch算法 一直方图均衡
opencv-4.2.0\modules\imgproc\src\histogram.cpp 中的代码
1直方图统计
class EqualizeHistCalcHist_Invoker : public cv::ParallelLoopBody
{
public:enum {HIST_SZ 256};EqualizeHistCalcHist_Invoker(cv::Mat src, int* histogram, cv::Mutex* histogramLock): src_(src), globalHistogram_(histogram), histogramLock_(histogramLock){ }void operator()( const cv::Range rowRange ) const CV_OVERRIDE{int localHistogram[HIST_SZ] {0, };const size_t sstep src_.step;int width src_.cols;int height rowRange.end - rowRange.start;if (src_.isContinuous()){width * height;height 1;}for (const uchar* ptr src_.ptruchar(rowRange.start); height--; ptr sstep){int x 0;for (; x width - 4; x 4){int t0 ptr[x], t1 ptr[x1];localHistogram[t0]; localHistogram[t1];t0 ptr[x2]; t1 ptr[x3];localHistogram[t0]; localHistogram[t1];}for (; x width; x)localHistogram[ptr[x]];}cv::AutoLock lock(*histogramLock_);for( int i 0; i HIST_SZ; i )globalHistogram_[i] localHistogram[i];}static bool isWorthParallel( const cv::Mat src ){return ( src.total() 640*480 );}private:EqualizeHistCalcHist_Invoker operator(const EqualizeHistCalcHist_Invoker);cv::Mat src_;int* globalHistogram_;cv::Mutex* histogramLock_;
};
类继承了ParallelLoopBody可以做并行加速。
灰度级HIST_SZ 256
构造函数保存三个参数。
仿函数是统计直方图。
isWorthParallel函数是判断是否启用并行加速。
2灰度变换
class EqualizeHistLut_Invoker : public cv::ParallelLoopBody
{
public:EqualizeHistLut_Invoker( cv::Mat src, cv::Mat dst, int* lut ): src_(src),dst_(dst),lut_(lut){ }void operator()( const cv::Range rowRange ) const CV_OVERRIDE{const size_t sstep src_.step;const size_t dstep dst_.step;int width src_.cols;int height rowRange.end - rowRange.start;int* lut lut_;if (src_.isContinuous() dst_.isContinuous()){width * height;height 1;}const uchar* sptr src_.ptruchar(rowRange.start);uchar* dptr dst_.ptruchar(rowRange.start);for (; height--; sptr sstep, dptr dstep){int x 0;for (; x width - 4; x 4){int v0 sptr[x];int v1 sptr[x1];int x0 lut[v0];int x1 lut[v1];dptr[x] (uchar)x0;dptr[x1] (uchar)x1;v0 sptr[x2];v1 sptr[x3];x0 lut[v0];x1 lut[v1];dptr[x2] (uchar)x0;dptr[x3] (uchar)x1;}for (; x width; x)dptr[x] (uchar)lut[sptr[x]];}}static bool isWorthParallel( const cv::Mat src ){return ( src.total() 640*480 );}private:EqualizeHistLut_Invoker operator(const EqualizeHistLut_Invoker);cv::Mat src_;cv::Mat dst_;int* lut_;
};
构造函数保存三个参数。
仿函数是根据灰度变换表lut把原图变成目标图。
3直方图均衡
void cv::equalizeHist( InputArray _src, OutputArray _dst )
{CV_INSTRUMENT_REGION();CV_Assert( _src.type() CV_8UC1 );if (_src.empty())return;CV_OCL_RUN(_src.dims() 2 _dst.isUMat(),ocl_equalizeHist(_src, _dst))Mat src _src.getMat();_dst.create( src.size(), src.type() );Mat dst _dst.getMat();CV_OVX_RUN(!ovx::skipSmallImagesVX_KERNEL_EQUALIZE_HISTOGRAM(src.cols, src.rows),openvx_equalize_hist(src, dst))Mutex histogramLockInstance;const int hist_sz EqualizeHistCalcHist_Invoker::HIST_SZ;int hist[hist_sz] {0,};int lut[hist_sz];EqualizeHistCalcHist_Invoker calcBody(src, hist, histogramLockInstance);EqualizeHistLut_Invoker lutBody(src, dst, lut);cv::Range heightRange(0, src.rows);if(EqualizeHistCalcHist_Invoker::isWorthParallel(src))parallel_for_(heightRange, calcBody);elsecalcBody(heightRange);int i 0;while (!hist[i]) i;int total (int)src.total();if (hist[i] total){dst.setTo(i);return;}float scale (hist_sz - 1.f)/(total - hist[i]);int sum 0;for (lut[i] 0; i hist_sz; i){sum hist[i];lut[i] saturate_castuchar(sum * scale);}if(EqualizeHistLut_Invoker::isWorthParallel(src))parallel_for_(heightRange, lutBody);elselutBody(heightRange);
}
先是直方图统计然后是对于纯色图片的特殊处理直方图均衡结果等于原图再是计算灰度变换表lut最后把原图变成目标图。 二可分离滤波器
1可分离滤波器的工厂
PtrFilterEngine createSeparableLinearFilter(int _srcType, int _dstType,InputArray __rowKernel, InputArray __columnKernel,Point _anchor, double _delta,int _rowBorderType, int _columnBorderType,const Scalar _borderValue)
{Mat _rowKernel __rowKernel.getMat(), _columnKernel __columnKernel.getMat();_srcType CV_MAT_TYPE(_srcType);_dstType CV_MAT_TYPE(_dstType);int sdepth CV_MAT_DEPTH(_srcType), ddepth CV_MAT_DEPTH(_dstType);int cn CV_MAT_CN(_srcType);CV_Assert( cn CV_MAT_CN(_dstType) );int rsize _rowKernel.rows _rowKernel.cols - 1;int csize _columnKernel.rows _columnKernel.cols - 1;if( _anchor.x 0 )_anchor.x rsize/2;if( _anchor.y 0 )_anchor.y csize/2;int rtype getKernelType(_rowKernel,_rowKernel.rows 1 ? Point(_anchor.x, 0) : Point(0, _anchor.x));int ctype getKernelType(_columnKernel,_columnKernel.rows 1 ? Point(_anchor.y, 0) : Point(0, _anchor.y));Mat rowKernel, columnKernel;bool isBitExactMode false;int bdepth std::max(CV_32F,std::max(sdepth, ddepth));int bits 0;if( sdepth CV_8U ((rtype KERNEL_SMOOTHKERNEL_SYMMETRICAL ctype KERNEL_SMOOTHKERNEL_SYMMETRICAL ddepth CV_8U) ||((rtype (KERNEL_SYMMETRICALKERNEL_ASYMMETRICAL)) (ctype (KERNEL_SYMMETRICALKERNEL_ASYMMETRICAL)) (rtype ctype KERNEL_INTEGER) ddepth CV_16S)) ){int bits_ ddepth CV_8U ? 8 : 0;bool isValidBitExactRowKernel createBitExactKernel_32S(_rowKernel, rowKernel, bits_);bool isValidBitExactColumnKernel createBitExactKernel_32S(_columnKernel, columnKernel, bits_);if (!isValidBitExactRowKernel){CV_LOG_DEBUG(NULL, createSeparableLinearFilter: bit-exact row-kernel cant be applied: ksize _rowKernel.total());}else if (!isValidBitExactColumnKernel){CV_LOG_DEBUG(NULL, createSeparableLinearFilter: bit-exact column-kernel cant be applied: ksize _columnKernel.total());}else{bdepth CV_32S;bits bits_;bits * 2;_delta * (1 bits);isBitExactMode true;}}if (!isBitExactMode){if( _rowKernel.type() ! bdepth )_rowKernel.convertTo( rowKernel, bdepth );elserowKernel _rowKernel;if( _columnKernel.type() ! bdepth )_columnKernel.convertTo( columnKernel, bdepth );elsecolumnKernel _columnKernel;}int _bufType CV_MAKETYPE(bdepth, cn);PtrBaseRowFilter _rowFilter getLinearRowFilter(_srcType, _bufType, rowKernel, _anchor.x, rtype);PtrBaseColumnFilter _columnFilter getLinearColumnFilter(_bufType, _dstType, columnKernel, _anchor.y, ctype, _delta, bits );return PtrFilterEngine( new FilterEngine(PtrBaseFilter(), _rowFilter, _columnFilter,_srcType, _dstType, _bufType, _rowBorderType, _columnBorderType, _borderValue ));
}
前2个参数是输入输出图像的格式接下来2个参数是核分离出来的行向量和列向量。
函数返回一个FilterEngine对象其中保存了一些需要的信息。
2ocvSepFilter、sepFilter2D
static void ocvSepFilter(int stype, int dtype, int ktype,uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step,int width, int height, int full_width, int full_height,int offset_x, int offset_y,uchar * kernelx_data, int kernelx_len,uchar * kernely_data, int kernely_len,int anchor_x, int anchor_y, double delta, int borderType)
{Mat kernelX(Size(kernelx_len, 1), ktype, kernelx_data);Mat kernelY(Size(kernely_len, 1), ktype, kernely_data);PtrFilterEngine f createSeparableLinearFilter(stype, dtype, kernelX, kernelY,Point(anchor_x, anchor_y),delta, borderType ~BORDER_ISOLATED);Mat src(Size(width, height), stype, src_data, src_step);Mat dst(Size(width, height), dtype, dst_data, dst_step);f-apply(src, dst, Size(full_width, full_height), Point(offset_x, offset_y));
};
先创建FilterEngine对象然后调用它的apply方法进行滤波。
void sepFilter2D(int stype, int dtype, int ktype,uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step,int width, int height, int full_width, int full_height,int offset_x, int offset_y,uchar * kernelx_data, int kernelx_len,uchar * kernely_data, int kernely_len,int anchor_x, int anchor_y, double delta, int borderType)
{bool res replacementSepFilter(stype, dtype, ktype,src_data, src_step, dst_data, dst_step,width, height, full_width, full_height,offset_x, offset_y,kernelx_data, kernelx_len,kernely_data, kernely_len,anchor_x, anchor_y, delta, borderType);if (res)return;ocvSepFilter(stype, dtype, ktype,src_data, src_step, dst_data, dst_step,width, height, full_width, full_height,offset_x, offset_y,kernelx_data, kernelx_len,kernely_data, kernely_len,anchor_x, anchor_y, delta, borderType);
}
调用ocvSepFilter
3Sobel
void cv::Sobel( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,int ksize, double scale, double delta, int borderType )
{CV_INSTRUMENT_REGION();int stype _src.type(), sdepth CV_MAT_DEPTH(stype), cn CV_MAT_CN(stype);if (ddepth 0)ddepth sdepth;int dtype CV_MAKE_TYPE(ddepth, cn);_dst.create( _src.size(), dtype );int ktype std::max(CV_32F, std::max(ddepth, sdepth));Mat kx, ky;getDerivKernels( kx, ky, dx, dy, ksize, false, ktype );if( scale ! 1 ){// usually the smoothing part is the slowest to compute,// so try to scale it instead of the faster differentiating partif( dx 0 )kx * scale;elseky * scale;}CV_OCL_RUN(ocl::isOpenCLActivated() _dst.isUMat() _src.dims() 2 ksize 3 (size_t)_src.rows() ky.total() (size_t)_src.cols() kx.total(),ocl_sepFilter3x3_8UC1(_src, _dst, ddepth, kx, ky, delta, borderType));CV_OCL_RUN(ocl::isOpenCLActivated() _dst.isUMat() _src.dims() 2 (size_t)_src.rows() kx.total() (size_t)_src.cols() kx.total(),ocl_sepFilter2D(_src, _dst, ddepth, kx, ky, Point(-1, -1), delta, borderType))Mat src _src.getMat();Mat dst _dst.getMat();Point ofs;Size wsz(src.cols, src.rows);if(!(borderType BORDER_ISOLATED))src.locateROI( wsz, ofs );CALL_HAL(sobel, cv_hal_sobel, src.ptr(), src.step, dst.ptr(), dst.step, src.cols, src.rows, sdepth, ddepth, cn,ofs.x, ofs.y, wsz.width - src.cols - ofs.x, wsz.height - src.rows - ofs.y, dx, dy, ksize, scale, delta, borderType~BORDER_ISOLATED);CV_OVX_RUN(true,openvx_sobel(src, dst, dx, dy, ksize, scale, delta, borderType))//CV_IPP_RUN_FAST(ipp_Deriv(src, dst, dx, dy, ksize, scale, delta, borderType));sepFilter2D(src, dst, ddepth, kx, ky, Point(-1, -1), delta, borderType );
}
前三个参数是输入图像、输出图像及深度接下来2个参数是微分的阶。
三相位相关法 phaseCorrelate
phaseCorrelate函数是利用相位相关法给两张图片做频域配准。
1phaseCorrelate
modules\imgproc\src\phasecorr.cpp
cv::Point2d cv::phaseCorrelate(InputArray _src1, InputArray _src2, InputArray _window, double* response)
{CV_INSTRUMENT_REGION();Mat src1 _src1.getMat();Mat src2 _src2.getMat();Mat window _window.getMat();CV_Assert( src1.type() src2.type());CV_Assert( src1.type() CV_32FC1 || src1.type() CV_64FC1 );CV_Assert( src1.size src2.size);if(!window.empty()){CV_Assert( src1.type() window.type());CV_Assert( src1.size window.size);}int M getOptimalDFTSize(src1.rows);int N getOptimalDFTSize(src1.cols);Mat padded1, padded2, paddedWin;if(M ! src1.rows || N ! src1.cols){copyMakeBorder(src1, padded1, 0, M - src1.rows, 0, N - src1.cols, BORDER_CONSTANT, Scalar::all(0));copyMakeBorder(src2, padded2, 0, M - src2.rows, 0, N - src2.cols, BORDER_CONSTANT, Scalar::all(0));if(!window.empty()){copyMakeBorder(window, paddedWin, 0, M - window.rows, 0, N - window.cols, BORDER_CONSTANT, Scalar::all(0));}}else{padded1 src1;padded2 src2;paddedWin window;}Mat FFT1, FFT2, P, Pm, C;// perform window multiplication if availableif(!paddedWin.empty()){// apply window to both images before proceeding...multiply(paddedWin, padded1, padded1);multiply(paddedWin, padded2, padded2);}// execute phase correlation equation// Reference: http://en.wikipedia.org/wiki/Phase_correlationdft(padded1, FFT1, DFT_REAL_OUTPUT);dft(padded2, FFT2, DFT_REAL_OUTPUT);mulSpectrums(FFT1, FFT2, P, 0, true);magSpectrums(P, Pm);divSpectrums(P, Pm, C, 0, false); // FF* / |FF*| (phase correlation equation completed here...)idft(C, C); // gives us the nice peak shift location...fftShift(C); // shift the energy to the center of the frame.// locate the highest peakPoint peakLoc;minMaxLoc(C, NULL, NULL, NULL, peakLoc);// get the phase shift with sub-pixel accuracy, 5x5 window seems about right here...Point2d t;t weightedCentroid(C, peakLoc, Size(5, 5), response);// max response is M*N (not exactly, might be slightly larger due to rounding errors)if(response)*response / M*N;// adjust shift relative to image center...Point2d center((double)padded1.cols / 2.0, (double)padded1.rows / 2.0);return (center - t);
}
前两个参数是传2张图片第三个是应用窗函数去除图像的边界效应文档中推荐使用汉宁窗。
2汉宁窗
void cv::createHanningWindow(OutputArray _dst, cv::Size winSize, int type)
{CV_INSTRUMENT_REGION();CV_Assert( type CV_32FC1 || type CV_64FC1 );CV_Assert( winSize.width 1 winSize.height 1 );_dst.create(winSize, type);Mat dst _dst.getMat();int rows dst.rows, cols dst.cols;AutoBufferdouble _wc(cols);double* const wc _wc.data();double coeff0 2.0 * CV_PI / (double)(cols - 1), coeff1 2.0f * CV_PI / (double)(rows - 1);for(int j 0; j cols; j)wc[j] 0.5 * (1.0 - cos(coeff0 * j));if(dst.depth() CV_32F){for(int i 0; i rows; i){float* dstData dst.ptrfloat(i);double wr 0.5 * (1.0 - cos(coeff1 * i));for(int j 0; j cols; j)dstData[j] (float)(wr * wc[j]);}}else{for(int i 0; i rows; i){double* dstData dst.ptrdouble(i);double wr 0.5 * (1.0 - cos(coeff1 * i));for(int j 0; j cols; j)dstData[j] wr * wc[j];}}// perform batch sqrt for SSE performance gainscv::sqrt(dst, dst);
}四匹配器
opencv-4.2.0\modules\features2d\src\matchers.cpp中的代码
1纯虚类DescriptorMatcher
内含3种匹配算法
class CV_EXPORTS_W DescriptorMatcher : public Algorithm
{
public:
CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,CV_OUT std::vectorDMatch matches, InputArray masknoArray() ) const;
CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,CV_OUT std::vectorstd::vectorDMatch matches, int k,InputArray masknoArray(), bool compactResultfalse ) const;
CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,CV_OUT std::vectorstd::vectorDMatch matches, float maxDistance,InputArray masknoArray(), bool compactResultfalse ) const;
CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vectorDMatch matches,InputArrayOfArrays masksnoArray() );
CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vectorstd::vectorDMatch matches, int k,InputArrayOfArrays masksnoArray(), bool compactResultfalse );
CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vectorstd::vectorDMatch matches, float maxDistance,InputArrayOfArrays masksnoArray(), bool compactResultfalse );
。。。。。。
};
DescriptorMatcher内含纯虚函数clone()
match里面还是调knnMatch所以实际上是knnMatch和radiusMatch两种算法。
2子类FlannBasedMatcher
继承DescriptorMatcher
class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
{
public:CV_WRAP FlannBasedMatcher( const Ptrflann::IndexParams indexParamsmakePtrflann::KDTreeIndexParams(),const Ptrflann::SearchParams searchParamsmakePtrflann::SearchParams() );
......
};
1clone
创建一个实例
2算法
算法没有重载也没有重写直接是父类的函数。
3knnMatch算法
void DescriptorMatcher::knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,std::vectorstd::vectorDMatch matches, int knn,InputArray mask, bool compactResult ) const
{CV_INSTRUMENT_REGION();PtrDescriptorMatcher tempMatcher clone(true);tempMatcher-add(trainDescriptors);tempMatcher-knnMatch( queryDescriptors, matches, knn, std::vectorMat(1, mask.getMat()), compactResult );
}
void DescriptorMatcher::knnMatch( InputArray queryDescriptors, std::vectorstd::vectorDMatch matches, int knn,InputArrayOfArrays masks, bool compactResult )
{CV_INSTRUMENT_REGION();if( empty() || queryDescriptors.empty() )return;CV_Assert( knn 0 );checkMasks( masks, queryDescriptors.size().height );train();knnMatchImpl( queryDescriptors, matches, knn, masks, compactResult );
}
核心功能用impl技术存在knnMatchImpl里面了。
