在OpenCV中自适应确定canny算法的分割门限

   在OpenCV中用canny算子进行边缘检测速度很快，不过有点不爽的就是高低阈值需要输入。在matlab中，如果不指定阈值的话，由函数自适应确定，因此仿照matlab中的做法，对canny函数进行了修改，以便当用户没有指定高低阈值时，由函数自适应确定阈值。

我在OpenCv原码库中增加了一个函数，用于确定高低阈值。

// 仿照matlab，自适应求高低两个门限
CV_IMPL void AdaptiveFindThreshold(CvMat *dx, CvMat *dy, double *low, double *high)
{
 CvSize size;
 IplImage *imge=0;
 int i,j;
 CvHistogram *hist;
 int hist_size = 255;
    float range_0[]={0,256};
    float* ranges[] = { range_0 };
 double  PercentOfPixelsNotEdges = 0.7;
 size = cvGetSize(dx);
 imge = cvCreateImage(size, IPL_DEPTH_32F, 1);
 // 计算边缘的强度, 并存于图像中
 float maxv = 0;
 for(i = 0; i < size.height; i++ )
 {
  const short* _dx = (short*)(dx->data.ptr + dx->step*i);
        const short* _dy = (short*)(dy->data.ptr + dy->step*i);
  float* _image = (float *)(imge->imageData + imge->widthStep*i);
  for(j = 0; j < size.width; j++)
  {
   _image[j] = (float)(abs(_dx[j]) + abs(_dy[j]));
   maxv = maxv < _image[j] ? _image[j]: maxv;
  }
 }

 // 计算直方图
 range_0[1] = maxv;
 hist_size = (int)(hist_size > maxv ? maxv:hist_size);
 hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1);
 cvCalcHist( &imge, hist, 0, NULL );
 int total = (int)(size.height * size.width * PercentOfPixelsNotEdges);
 float sum=0;
 int icount = hist->mat.dim[0].size;

 float *h = (float*)cvPtr1D( hist->bins, 0 );
 for(i = 0; i < icount; i++)
 {
  sum += h[i];
  if( sum > total )
   break;
 }
// 计算高低门限
 *high = (i+1) * maxv / hist_size ;
 *low = *high * 0.4;
 cvReleaseImage( &imge );
 cvReleaseHist(&hist);
}

在把cvCanny函数进行以下修改。
在函数体中，当程序用两个sobel算子计算完水平和垂直两个方向的梯度强度过后加入以下代码
// 自适应确定阈值
 if(low_thresh == -1 && high_thresh == -1)
 {
  AdaptiveFindThreshold(dx, dy, &low_thresh, &high_thresh);
 }

// 仿照matlab，自适应求高低两个门限 CV_IMPL void AdaptiveFindThreshold(CvMat *dx, CvMat *dy, double *low, double *high) { CvSize size; IplImage *imge=0; int i,j; CvHistogram *hist; int hist_size = 255; float range_0[]={0,256}; float* ranges[] = { range_0 }; double PercentOfPixelsNotEdges = 0.7; size = cvGetSize(dx); imge = cvCreateImage(size, IPL_DEPTH_32F, 1); // 计算边缘的强度, 并存于图像中 float maxv = 0; for(i = 0; i < size.height; i++ ) { const short* _dx = (short*)(dx->data.ptr + dx->step*i); const short* _dy = (short*)(dy->data.ptr + dy->step*i); float* _image = (float *)(imge->imageData + imge->widthStep*i); for(j = 0; j < size.width; j++) { _image[j] = (float)(abs(_dx[j]) + abs(_dy[j])); maxv = maxv < _image[j] ? _image[j]: maxv; } } // 计算直方图 range_0[1] = maxv; hist_size = (int)(hist_size > maxv ? maxv:hist_size); hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1); cvCalcHist( &imge, hist, 0, NULL ); int total = (int)(size.height * size.width * PercentOfPixelsNotEdges); float sum=0; int icount = hist->mat.dim[0].size; float *h = (float*)cvPtr1D( hist->bins, 0 ); for(i = 0; i < icount; i++) { sum += h[i]; if( sum > total ) break; } // 计算高低门限 *high = (i+1) * maxv / hist_size ; *low = *high * 0.4; cvReleaseImage( &imge ); cvReleaseHist(&hist); } 在把cvCanny函数进行以下修改。 在函数体中，当程序用两个sobel算子计算完水平和垂直两个方向的梯度强度过后加入以下代码 // 自适应确定阈值 if(low_thresh == -1 && high_thresh == -1) { AdaptiveFindThreshold(dx, dy, &low_thresh, &high_thresh); }

 

这样，在调用cvCanny函数时，指定高低门限为－1，则cvCanny函数就自适应确定门限。

 

最后，别忘了重新编译cv库，对lib和dll库进行更新。

that's all！

http://blog.chinaunix.net/u/30231/showart_233944.html