【OpenCV3】threshold()函数详解

系统管理员 2021-12-22 00:35 414阅读 0赞

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<uchar>(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<VX_KERNEL_THRESHOLD>(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