OpenCV中threshold自动阈值，类似matlab中的graythresh

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在opencv工程里面使用otsu分割灰度图像类似于matlab里的graythresh,

opencv里面提供了otsu threshold的源代码“icvGetThreshVal_Otsu”，我们可以根据源代码进行自动阈值处理

icvGetThreshVal_Otsu的源代码如下：

//opencv Source code:  static double icvGetThreshVal_Otsu( const CvHistogram* hist )  {      double max_val = 0;            CV_FUNCNAME( "icvGetThreshVal_Otsu" );      __BEGIN__;      int i, count;      const float* h;      double sum = 0, mu = 0;      bool uniform = false;      double low = 0, high = 0, delta = 0;      float* nu_thresh = 0;      double mu1 = 0, q1 = 0;      double max_sigma = 0;      if( !CV_IS_HIST(hist) || CV_IS_SPARSE_HIST(hist) || hist->mat.dims != 1 )          CV_ERROR( CV_StsBadArg,          "The histogram in Otsu method must be a valid dense 1D histogram" );      count = hist->mat.dim[0].size;      h = (float*)cvPtr1D( hist->bins, 0 );      if( !CV_HIST_HAS_RANGES(hist) || CV_IS_UNIFORM_HIST(hist) )      {          if( CV_HIST_HAS_RANGES(hist) )          {              low = hist->thresh[0][0];              high = hist->thresh[0][1];          }          else          {              low = 0;              high = count;          }          delta = (high-low)/count;          low += delta*0.5;          uniform = true;      }      else          nu_thresh = hist->thresh2[0];      for( i = 0; i < count; i++ )      {          sum += h;          if( uniform )              mu += (i*delta + low)*h;          else              mu += (nu_thresh[i*2] + nu_thresh[i*2+1])*0.5*h;      }            sum = fabs(sum) > FLT_EPSILON ? 1./sum : 0;      mu *= sum;      mu1 = 0;      q1 = 0;      for( i = 0; i < count; i++ )      {          double p_i, q2, mu2, val_i, sigma;          p_i = h*sum;          mu1 *= q1;          q1 += p_i;          q2 = 1. - q1;          if( MIN(q1,q2) < FLT_EPSILON || MAX(q1,q2) > 1. - FLT_EPSILON )              continue;          if( uniform )              val_i = i*delta + low;          else              val_i = (nu_thresh[i*2] + nu_thresh[i*2+1])*0.5;          mu1 = (mu1 + val_i*p_i)/q1;          mu2 = (mu - q1*mu1)/q2;          sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);          if( sigma > max_sigma )          {              max_sigma = sigma;              max_val = val_i;          }      }      __END__;      return max_val;  }

对源代码进行改写，得到我们想要的代码：

double getThreshVal_Otsu_8u( const cv::Mat& _src ){cv::Size size = _src.size();if ( _src.isContinuous() ){size.width *= size.height;size.height = 1;}const int N = 256;int i, j, h[N] = {0};for ( i = 0; i < size.height; i++ ){const uchar* src = _src.data + _src.step*i;for ( j = 0; j <= size.width - 4; j += 4 ){int v0 = src[j], v1 = src[j+1];h[v0]++; h[v1]++;v0 = src[j+2]; v1 = src[j+3];h[v0]++; h[v1]++;}for ( ; j < size.width; j++ )h[src[j]]++;}double mu = 0, scale = 1./(size.width*size.height);for ( i = 0; i < N; i++ )mu += i*h[i];mu *= scale;double mu1 = 0, q1 = 0;double max_sigma = 0, max_val = 0;for ( i = 0; i < N; i++ ){double p_i, q2, mu2, sigma;p_i = h[i]*scale;mu1 *= q1;q1 += p_i;q2 = 1. - q1;if ( std::min(q1,q2) < FLT_EPSILON || std::max(q1,q2) > 1. - FLT_EPSILON )continue;mu1 = (mu1 + i*p_i)/q1;mu2 = (mu - q1*mu1)/q2;sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);if ( sigma > max_sigma ){max_sigma = sigma;max_val = i;}}return max_val;}

直接进行调用上述函数，即可：

m_threshold = getThreshVal_Otsu_8u(matMedian);

在进行二值化：

threshold(matMedian,matBinary,m_threshold,255,0);   // 二值化

详细请参考OpenCV Miscellaneous Image Transformations

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