【OpenCV3】threshold()函数详解

threshold()函数源码

double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double maxval, int type )
{
      

// enum
//{
// CV_THRESH_BINARY =0, /**< value = value > threshold ? max_value : 0 */
// CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */
// CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */
// CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */
// CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */
// CV_THRESH_MASK =7,
// CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value;
// combine the flag with one of the above CV_THRESH_* values */
// CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value;
// combine the flag with one of the above CV_THRESH_* values, but not
// with CV_THRESH_OTSU */
//};

    CV_INSTRUMENT_REGION();

    CV_OCL_RUN_(_src.dims() <= 2 && _dst.isUMat(),
                ocl_threshold(_src, _dst, thresh, maxval, type), thresh)

    Mat src = _src.getMat();
    int automatic_thresh = (type & ~CV_THRESH_MASK);// 排除前五种可能，判断是否是CV_THRESH_OTSU \ CV_THRESH_TRIANGLE(8,16)
    type &= THRESH_MASK; // THRESH_MASK(7) 得到当前二值化的类型（前五种），0,1,2,3,4

    CV_Assert( automatic_thresh != (CV_THRESH_OTSU | CV_THRESH_TRIANGLE) );
    if( automatic_thresh == CV_THRESH_OTSU )// 判断是否是CV_THRESH_OTSU(8)
    {
      
　　　　// 　使用算法选择最佳阈值；将标志与上述cv_thresh_*值之一相结合 计算最佳阈值
        CV_Assert( src.type() == CV_8UC1 );
        thresh = getThreshVal_Otsu_8u( src );
    }
    else if( automatic_thresh == CV_THRESH_TRIANGLE )// 判断是否是CV_THRESH_TRIANGLE(16)
   {
      
　　　　// 使用三角算法选择最优阈值；将标志与上述cv_thresh_*值之一组合，但不使用cv_thresh_otsu 计算最佳阈值
        CV_Assert( src.type() == CV_8UC1 );
        thresh = getThreshVal_Triangle_8u( src );
    }

    _dst.create( src.size(), src.type() );// 创建目标图像
    Mat dst = _dst.getMat();

    if( src.depth() == CV_8U )// 如果原始图像的深度为8位无符号
    {
        int ithresh = cvFloor(thresh);// 将thresh向下取整
        thresh = ithresh;
        int imaxval = cvRound(maxval); // 将maxval向最接近的整数取整
        if( type == THRESH_TRUNC )
            imaxval = ithresh;
        imaxval = saturate_cast(imaxval);

        if( ithresh < 0 || ithresh >= 255 )
        {
         if( type == THRESH_BINARY || type == THRESH_BINARY_INV ||
                ((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < 0) ||
                (type == THRESH_TOZERO && ithresh >= 255) )
            {
                int v = type == THRESH_BINARY ? (ithresh >= 255 ? 0 : imaxval) :
                        type == THRESH_BINARY_INV ? (ithresh >= 255 ? imaxval : 0) :
                        /*type == THRESH_TRUNC ? imaxval :*/ 0;
                dst.setTo(v);
            }
            else
                src.copyTo(dst);
            return thresh;
        }
        CV_OVX_RUN(!ovx::skipSmallImages(src.cols, src.rows),
                  openvx_threshold(src, dst, ithresh, imaxval, type), (double)ithresh)

        thresh = ithresh;
        maxval = imaxval;
    }
    else if( src.depth() == CV_16S )// 如果原始图像的深度为16位short类型
    {
        int ithresh = cvFloor(thresh);
        thresh = ithresh;
        int imaxval = cvRound(maxval);
        if( type == THRESH_TRUNC )
            imaxval = ithresh;
        imaxval = saturate_cast<short>(imaxval);

        if( ithresh < SHRT_MIN || ithresh >= SHRT_MAX )
        {
            if( type == THRESH_BINARY || type == THRESH_BINARY_INV ||
               ((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < SHRT_MIN) ||
               (type == THRESH_TOZERO && ithresh >= SHRT_MAX) )
            {
                int v = type == THRESH_BINARY ? (ithresh >= SHRT_MAX ? 0 : imaxval) :
                type == THRESH_BINARY_INV ? (ithresh >= SHRT_MAX ? imaxval : 0) :
                /*type == THRESH_TRUNC ? imaxval :*/ 0;
                dst.setTo(v);
            }
            else
                src.copyTo(dst);
            return thresh;
        }
        thresh = ithresh;
        maxval = imaxval;
    }
    else if (src.depth() == CV_16U )// 如果原始图像的深度为16位无符号
    {
        int ithresh = cvFloor(thresh);
        thresh = ithresh;
        int imaxval = cvRound(maxval);
        if (type == THRESH_TRUNC)
            imaxval = ithresh;
        imaxval = saturate_cast<ushort>(imaxval);

        int ushrt_min = 0;
        if (ithresh < ushrt_min || ithresh >= (int)USHRT_MAX)
        {
            if (type == THRESH_BINARY || type == THRESH_BINARY_INV ||
               ((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < ushrt_min) ||
               (type == THRESH_TOZERO && ithresh >= (int)USHRT_MAX))
            {
                int v = type == THRESH_BINARY ? (ithresh >= (int)USHRT_MAX ? 0 : imaxval) :
                        type == THRESH_BINARY_INV ? (ithresh >= (int)USHRT_MAX ? imaxval : 0) :
                  /*type == THRESH_TRUNC ? imaxval :*/ 0;
                dst.setTo(v);
            }
            else
                src.copyTo(dst);
            return thresh;
        }
        thresh = ithresh;
        maxval = imaxval;
    }
    else if( src.depth() == CV_32F )// 如果原始图像的深度为32位浮点型
        ;
    else if( src.depth() == CV_64F )// 如果原始图像的深度为64位浮点型
        ;
    else
        CV_Error( CV_StsUnsupportedFormat, "" ); // 不能识别的图像格式

    parallel_for_(Range(0, dst.rows),
                  ThresholdRunner(src, dst, thresh, maxval, type),
                  dst.total()/(double)(1<<16));
    return thresh;
}

 threshold()函数二值化的方法（types）/** Threshold types */

enum
{
    CV_THRESH_BINARY      =0,  /**< value = value > threshold ? max_value : 0       正向二值化*/
    CV_THRESH_BINARY_INV  =1,  /**< value = value > threshold ? 0 : max_value       反向二值化*/
    CV_THRESH_TRUNC       =2,  /**< value = value > threshold ? threshold : value   */
    CV_THRESH_TOZERO      =3,  /**< value = value > threshold ? value : 0           */
    CV_THRESH_TOZERO_INV  =4,  /**< value = value > threshold ? 0 : value           */
    CV_THRESH_MASK        =7,　　// 掩码
    CV_THRESH_OTSU        =8, /**< use Otsu algorithm to choose the optimal threshold value;
                                 combine the flag with one of the above CV_THRESH_* values 
　　　　　　　　　　　　　　　　　　　　使用算法选择最佳阈值；将标志与上述cv_thresh_*值之一相结合*/
    CV_THRESH_TRIANGLE    =16  /**< use Triangle algorithm to choose the optimal threshold value;
                                 combine the flag with one of the above CV_THRESH_* values, but not
                                 with CV_THRESH_OTSU 
　　　　　　　　　　　　　　　　　　　　使用三角算法选择最优阈值；将标志与上述cv_thresh_*值之一组合，但不使用cv_thresh_otsu*/
};

 

转载于:https://www.cnblogs.com/hs-pingfan/p/10494264.html