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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

2，ocvSepFilter、sepFilter2D

3，Sobel

三，相位相关法 phaseCorrelate

1，phaseCorrelate

2，汉宁窗

四，匹配器

1，纯虚类DescriptorMatcher

2，子类FlannBasedMatcher

3，knnMatch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_.ptr<uchar>(rowRange.start); height--; ptr += sstep){int x = 0;for (; x <= width - 4; x += 4){int t0 = ptr[x], t1 = ptr[x+1];localHistogram[t0]++; localHistogram[t1]++;t0 = ptr[x+2]; t1 = ptr[x+3];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_.ptr<uchar>(rowRange.start);uchar* dptr = dst_.ptr<uchar>(rowRange.start);for (; height--; sptr += sstep, dptr += dstep){int x = 0;for (; x <= width - 4; x += 4){int v0 = sptr[x];int v1 = sptr[x+1];int x0 = lut[v0];int x1 = lut[v1];dptr[x] = (uchar)x0;dptr[x+1] = (uchar)x1;v0 = sptr[x+2];v1 = sptr[x+3];x0 = lut[v0];x1 = lut[v1];dptr[x+2] = (uchar)x0;dptr[x+3] = (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::skipSmallImages<VX_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_cast<uchar>(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

Ptr<FilterEngine> 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_SMOOTH+KERNEL_SYMMETRICAL &&ctype == KERNEL_SMOOTH+KERNEL_SYMMETRICAL &&ddepth == CV_8U) ||((rtype & (KERNEL_SYMMETRICAL+KERNEL_ASYMMETRICAL)) &&(ctype & (KERNEL_SYMMETRICAL+KERNEL_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 can't be applied: ksize=" << _rowKernel.total());}else if (!isValidBitExactColumnKernel){CV_LOG_DEBUG(NULL, "createSeparableLinearFilter: bit-exact column-kernel can't 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);Ptr<BaseRowFilter> _rowFilter = getLinearRowFilter(_srcType, _bufType, rowKernel, _anchor.x, rtype);Ptr<BaseColumnFilter> _columnFilter = getLinearColumnFilter(_bufType, _dstType, columnKernel, _anchor.y, ctype, _delta, bits );return Ptr<FilterEngine>( new FilterEngine(Ptr<BaseFilter>(), _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。

2，ocvSepFilter、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);Ptr<FilterEngine> 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

3，Sobel

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。

1，phaseCorrelate

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;AutoBuffer<double> _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.ptr<float>(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.ptr<double>(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::vector<DMatch>& matches, InputArray mask=noArray() ) const;
CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,CV_OUT std::vector<std::vector<DMatch> >& matches, int k,InputArray mask=noArray(), bool compactResult=false ) const;
CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,InputArray mask=noArray(), bool compactResult=false ) const;
CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,InputArrayOfArrays masks=noArray() );
CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,InputArrayOfArrays masks=noArray(), bool compactResult=false );
CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,InputArrayOfArrays masks=noArray(), bool compactResult=false );
。。。。。。
};

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 Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
......
};

（1）clone

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。

3，knnMatch算法

void DescriptorMatcher::knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,std::vector<std::vector<DMatch> >& matches, int knn,InputArray mask, bool compactResult ) const
{CV_INSTRUMENT_REGION();Ptr<DescriptorMatcher> tempMatcher = clone(true);tempMatcher->add(trainDescriptors);tempMatcher->knnMatch( queryDescriptors, matches, knn, std::vector<Mat>(1, mask.getMat()), compactResult );
}
void DescriptorMatcher::knnMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& 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。