双运放

本文主要介绍关于snake cvSnakeImage,升级改进 opencv 3.0的知识点，对【图像处理之其他杂项（三）之cvSnakeImage改进升级兼容 适用于opencv2，，在opencv3.0以上版本中测试通过】和【双运放】有兴趣的朋友可以看下由【Coming_is_winter】投稿的技术文章，希望该技术和经验能帮到你解决你所遇的【# 图像处理其他杂项】相关技术问题。

双运放

cvSnakeImage改进升级兼容 ? 　　cvSnakeImage函数在opencv2中已经被去掉，现在函数仅有C接口版本，把函数源代码作为独立函数整合进程序中，并对其中包含的opencv2中不存在的函数宏定义进行更改替换，适合opencv2环境使用，测试环境VS2015+opencv3.2。 　　原源代码： 蚂蚁搬家 　 http://blog.csdn.net/hongxingabc/article/details/51606520　　 主动轮廓线模型Snake模型简介&openCV中cvSnakeImage()函数代码分析 ? 　　　主要改进： ? ? ?　　 1. （1） CvSepFilter pX, pY; 　　　　 （2） pX.init_deriv(TILE_SIZE + 2, CV_8UC1, CV_16SC1, 1, 0, 3); 　　　　　　? ? pY.init_deriv(TILE_SIZE + 2, CV_8UC1, CV_16SC1, 0, 1, 3); ? ? ? ? 　　（3） pX.process(&_src1, &_dx);　pY.process(&_src1, &_dy); ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?改成：cvSobel(&_src1, &_dx, 1, 0, 1); ? ??cvSobel(&_src1, &_dy, 1, 0, 1)； ? 　　　 2.加入宏定义：CV_VALUE=30； ? ? ? ? ? ? ? 3.注释掉CV_ERROR相关代码等。 ? 　　　 代码具体工作原理还未深入研究，最后不知是修改的代码哪里存在问题，还是snake的工作机制原因，算法只对特定图片的特定参数设置有反应，如测试的几张图片，只有一张云层的图片测试比较正常，可以达到预期效果，有待进一步研究，如若测试时效果不理想，不如试一下此云层图片。

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#include "cv.h"  

#define _CV_SNAKE_BIG 2.e+38f  
#define _CV_SNAKE_IMAGE 1  
#define _CV_SNAKE_GRAD  2  
#define CV_VALUE 30

/*F///
//    Name:      icvSnake8uC1R
//    Purpose:
//    Context:
//    Parameters:
//               src - source image,
//               srcStep - its step in bytes,
//               roi - size of ROI,
//               pt - pointer to snake points array
//               n - size of points array,
//               alpha - pointer to coefficient of continuity energy,
//               beta - pointer to coefficient of curvature energy,
//               gamma - pointer to coefficient of image energy,
//               coeffUsage - if CV_VALUE - alpha, beta, gamma point to single value
//                            if CV_MATAY - point to arrays
//               criteria - termination criteria.
//               scheme - image energy scheme
//                         if _CV_SNAKE_IMAGE - image intensity is energy
//                         if _CV_SNAKE_GRAD  - magnitude of gradient is energy
//    Returns:
//F*/

int icvSnake8uC1R(unsigned char *src,   //原始图像数据  
	int srcStep,         //每行的字节数  
	CvSize roi,         //图像尺寸  
	CvPoint * pt,       //轮廓点(变形对象)  
	int n,            //轮廓点的个数  
	float *alpha,       //指向α的指针，α可以是单个值，也可以是与轮廓点个数一致的数组  
	float *beta,        //β的值，同α  
	float *gamma,       //γ的值，同α  
	int coeffUsage,   //确定αβγ是用作单个值还是个数组  
	CvSize win,       //每个点用于搜索的最小的领域大小,宽度为奇数  
	CvTermCriteria criteria,   //递归迭代终止的条件准则  
	int scheme)         //确定图像能量场的数据选择，1为灰度，2为灰度梯度  
{
	int i, j, k;
	int neighbors = win.height * win.width;    //当前点领域中点的个数  

											   //当前点的位置  
	int centerx = win.width >> 1;
	int centery = win.height >> 1;

	float invn;        //n 的倒数？  
	int iteration = 0;     //迭代次数  
	int cOnverged= 0;      //收敛标志，0为非收敛  

							//能量  
	float *Econt;    //  
	float *Ecurv;   //轮廓曲线能量  
	float *Eimg;    //图像能量  
	float *E;      //  

				   //αβγ的副本  
	float _alpha, _beta, _gamma;

	/*#ifdef GRAD_SNAKE */
	float *gradient = NULL;
	uchar *map = NULL;
	int map_width = ((roi.width - 1) >> 3) + 1;
	int map_height = ((roi.height - 1) >> 3) + 1;
	//CvSepFilter pX, pY;
#define WTILE_SIZE 8  
#define TILE_SIZE (WTILE_SIZE + 2)         
	short dx[TILE_SIZE*TILE_SIZE], dy[TILE_SIZE*TILE_SIZE];
	CvMat _dx = cvMat(TILE_SIZE, TILE_SIZE, CV_16SC1, dx);
	CvMat _dy = cvMat(TILE_SIZE, TILE_SIZE, CV_16SC1, dy);
	CvMat _src = cvMat(roi.height, roi.width, CV_8UC1, src);

	/* inner buffer of convolution process */
	//char ConvBuffer[400];  

	/*#endif */

	//检点参数的合理性  
	/* check bad arguments */
	//if (src == NULL)
	//	return CV_NULLPTR_ERR;
	//if ((roi.height <= 0) || (roi.width <= 0))
	//	return CV_BADSIZE_ERR;
	//if (srcStep > 1);
			int right = MIN(roi.width - 1 - pt[i].x, win.width >> 1);
			int upper = MIN(pt[i].y, win.height >> 1);
			int bottom = MIN(roi.height - 1 - pt[i].y, win.height >> 1);
			//初始化Econt  
			maxEcOnt= 0;
			minEcOnt= _CV_SNAKE_BIG;
			//在合理的搜索范围内进行Econt的计算  
			for (j = -upper; j <= bottom; j++)
			{
				for (k = -left; k <= right; k++)
				{
					int diffx, diffy;
					float energy;
					//在轮廓点集的首尾相接处作相应处理,求轮廓点差分  
					if (i == 0)
					{
						diffx = pt[n - 1].x - (pt[i].x + k);
						diffy = pt[n - 1].y - (pt[i].y + j);
					}
					else
						//在其他地方作一般处理  

					{
						diffx = pt[i - 1].x - (pt[i].x + k);
						diffy = pt[i - 1].y - (pt[i].y + j);
					}
					//将邻域陈列坐标转成Econt数组的下标序号，计算邻域中每点的Econt  
					//Econt的值等于平均距离和此点和上一点的距离的差的绝对值（这是怎么来的？）  
					Econt[(j + centery) * win.width + k + centerx] = energy =
						(float)fabs(ave_d -
							cvSqrt((float)(diffx * diffx + diffy * diffy)));
					//求出所有邻域点中的Econt的最大值和最小值  
					maxEcOnt= MAX(maxEcont, energy);
					minEcOnt= MIN(minEcont, energy);
				}
			}
			//求出邻域点中最大值和最小值之差,并对所有的邻域点的Econt进行标准归一化,若最大值最小  
			//相等，则邻域中的点Econt全相等，Econt归一化束缚为0  
			tmp = maxEcont - minEcont;
			tmp = (tmp == 0) ? 0 : (1 / tmp);
			for (k = 0; k = criteria.max_iter))
			cOnverged= 1;
		//到大相应精度时，停止迭代（与第一个条件有相同效果）  
		if ((criteria.type & CV_TERMCRIT_EPS) && (moved <= criteria.epsilon))
			cOnverged= 1;
	}

	//释放各个缓冲区  
	cvFree(&Econt);
	cvFree(&Ecurv);
	cvFree(&Eimg);
	cvFree(&E);

	if (scheme == _CV_SNAKE_GRAD)
	{
		cvFree(&gradient);
		cvFree(&map);
	}
	return 1;
}


void cvSnakeImage(const IplImage* src, CvPoint* points,
	int length, float *alpha,
	float *beta, float *gamma,
	int coeffUsage, CvSize win,
	CvTermCriteria criteria, int calcGradient)
{

	CV_FUNCNAME("cvSnakeImage");

	//__BEGIN__;

	uchar *data;
	CvSize size;
	int step;

	//if (src->nChannels != 1)
		//CV_ERROR(CV_BadNumChannels, "input image has more than one channel");

	//if (src->depth != IPL_DEPTH_8U)
		//CV_ERROR(CV_BadDepth, cvUnsupportedFormat);

	cvGetRawData(src, &data, &step, &size);

	icvSnake8uC1R(data, step, size, points, length,
		alpha, beta, gamma, coeffUsage, win, criteria,
		calcGradient ? _CV_SNAKE_GRAD : _CV_SNAKE_IMAGE);
	//__END__;
}

/* end of file */





//测试应用程序

//#include "stdafx.h"  
#include 
  
    #include 
   
     #include 
    
      #include 
     
       #include 
      
        #include 
       
         IplImage *image = 0; //原始图像 IplImage *image2 = 0; //原始图像copy using namespace std; int Thresholdness = 41; int ialpha = 20; int ibeta = 20; int igamma = 20; void onChange(int pos) { if (image2) cvReleaseImage(&image2); if (image) cvReleaseImage(&image); image2 = cvLoadImage("E://素材//sfsd.jpg", 1); //显示图片 image = cvLoadImage("E://素材//sfsd.jpg", 0); cvThreshold(image, image, Thresholdness, 205, CV_THRESH_BINARY); //分割域值 CvMemStorage* storage = cvCreateMemStorage(0); CvSeq* cOntours= 0; cvFindContours(image, storage, &contours, sizeof(CvContour), //寻找初始化轮廓 CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); if (!contours) return; int length = contours->total; //cout <
      
     
    
   
  

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图1 ?测试效果图

图2 ?测试原图

本文《图像处理之其他杂项（三）之cvSnakeImage改进升级兼容 适用于opencv2，，在opencv3.0以上版本中测试通过》版权归Coming_is_winter所有，引用图像处理之其他杂项（三）之cvSnakeImage改进升级兼容 适用于opencv2，，在opencv3.0以上版本中测试通过需遵循CC 4.0 BY-SA版权协议。