OpenCV : 基于切线方向的边缘增强算法

使用切线方法，对切线方向上的边缘进行强化：

代码：

		//在种子点方向上寻找合适的梯度，用于寻找边缘

		//对low_Gray, high_gray之间的点寻找边缘

		void FindBestGradient(

			cv::Mat &_src, cv::Mat &_dst,

			cv::Point2f  &seed,

			float low_Gray,

			float high_gray,

			int aperture_size, bool oPenEnhence  )

		{

			//角度矩阵

			cv::Mat df = cv::Mat::zeros( _src.rows,_src.cols, CV_32FC1 );

			//梯度矩阵

			cv::Mat dg = cv::Mat::zeros( _src.rows,_src.cols, CV_32FC1 );

			//原始图像

			cv::Mat ds = _src.clone();

			//目标图像 uchar型

			cv::Mat dd = _src.clone();

			//1.根据角度计算梯度//得到梯度矩阵

			//使用N*1的算子

			int n = aperture_size;//必须为奇数

			//对每个柱进行初始化

			//搜索柱:在射线方向上搜索l_Search 个像素；宽度为

			int l_Search = n;

			int w_Search = 1;

			std::vector<std::vector<std::pair<cv::Point ,float>  >  >  beam;

			beam.resize( l_Search );

			for (int i=0;i< beam.size();++i)

			{

				beam[i].resize(w_Search);

			}//初始化柱

			//设定系数//生成模板

			double gap = 2.0/ (n-1);

			std::vector< double > mask(l_Search);

			for (int i=0;i< mask.size();++i)

			{

				mask[i] = -1 + i*gap ;

			}

			//2.生成角度图像

			//在射线方向上寻找//方法不是太好，但是没有寻找到简单有效的方法

			for ( int y=0 ;y< ds.rows;++y )

			{

				float* ptr = (float*)( df.data + y * df.step);

				unsigned char* pS = ( unsigned char* )( ds.data + y * ds.step);

				for ( int x=0; x< ds.cols; ++x )

				{

					//计算角度

					if ( (int)(*pS) > low_Gray && (int)(*pS) <high_gray )

					{

						*ptr = (float)(cvWish::cosCv(seed,cv::Point2f( x,y ) ) );

					}

					else

					{

						*ptr = 0.00000000000f;

					}

					++ptr;

					++pS;

				}

			}

			//计算差值-导数

			for (int y=0 ;y< ds.rows;++y)

			{

				float* pf = (float*)( df.data + y * df.step);

				float* pg = (float*)( dg.data + y * dg.step);

				unsigned char* pd = (unsigned char*)( dd.data + y * dd.step);

				for (int x=0;x< ds.cols;++x )

				{

					//计算角度

					if ( abs((float)(*pf)) > 0.00000001 )

					{

						//cvWish::BeamInit(l_Search,w_Search,cv::Point2f( x,y ),df.at<float >(y,x),beam,0);//0表示从中部开始搜索

						cvWish::BeamInit(l_Search,w_Search,cv::Point2f( x,y ), *pf ,beam,0);//0表示从中部开始搜索

						cvWish::BeamNormal(dg.cols, dg.rows , beam);

						*pg = 0;

						for ( int k =0; k< l_Search; ++k ){

							*pg += (float)( mask[k]* ds.at<unsigned char>(beam[k][0].first.y,beam[k][0].first.x)  );

						}

						int s = abs ( ( (int)(*pg ) )%255 ) ;

						*pd = (unsigned char) (s);

					}

					else

					{

						*pd = (unsigned char) (0);

					}

					++pf;

					++pg;

					++pd;

				}

			}

			cv::Mat edgeMat = dd;

			cv::Mat angleMat= df;

			int maskSize = 5;

			if ( oPenEnhence )

			{

				int num = 1;

				for (int i=0;i< num;++i)

				{

					EnhanceEdgeByTangent( edgeMat ,angleMat,  maskSize);

				}

			}

			else

			{

			}

			_dst = edgeMat.clone();

			return;

		}

边缘强化函数：

//使用边缘增强--沿切线方向增强

		//方向性，边缘限制

		void EnhanceEdgeByTangent( cv::Mat &edgeMat ,cv::Mat &angleMat, int maskSize)

		{

			cv::Mat ds = edgeMat;

			cv::Mat dd = edgeMat.clone();

			cv::Mat df = angleMat;

			cv::Mat dg = angleMat.clone();//导数图，最终转化为 灰度图 dd

			const int l_Search  = maskSize;

			const int w_Search =        1;

			//初始化柱

			std::vector<std::vector<std::pair<cv::Point ,float>  >  >  beam;

			beam.resize( l_Search );

			for (int i=0;i< beam.size();++i)

			{

				beam[i].resize(w_Search);

			}//初始化柱

			//设定系数//生成模板

			double gap = 2.0/ ( l_Search - 1);

			std::vector< double > mask(l_Search);

			for (int i=0;i< mask.size();++i)

			{

				mask[i] =abs( 1- abs( -1 + i*gap ) );

			}

			//强化边缘

			for (int y=0 ;y< ds.rows;++y)

			{

				float* pf = (float*)( df.data + y * df.step);

				float* pg = (float*)( dg.data + y * dg.step);

				unsigned char* pd = (unsigned char*)( dd.data + y * dd.step);

				for ( int x=0; x< ds.cols; ++x )

				{

					//计算角度

					if ( abs((float)(*pf)) > 0.00000001 )

					{

						float angle = *pf + PI_1_2 ;//切线方向，加 PI_1_2

						angle = angle>=PI_4_2? angle - PI_4_2:angle;

						cvWish::BeamInit( l_Search, w_Search, cv::Point2f( x,y ), angle , beam, 0 );

						cvWish::BeamNormal(dg.cols, dg.rows , beam);

						*pg = 0;

						const int gl= ds.at<unsigned char>(y,x) ;//当前像素值

						int vvv = dd.at<unsigned char>(y,x);

						for ( int k =0; k< l_Search; ++k ){

							vvv += gl* mask[k];

						}

						if ( vvv>255 )

						{

							vvv=255 ;

						}

						dd.at<unsigned char>(y,x) = vvv;

						//*pd = (unsigned char) (s);

					}

					else

					{

						*pd = (unsigned char) (0);

					}

					++pf;

					++pg;

					++pd;

				}

			}

			edgeMat = dd.clone();

			return ;

		}

图片效果：

巴特西

OpenCV : 基于切线方向的边缘增强算法

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