阈值化

素颜马尾好姑娘i 2022-06-17 08:07 179阅读 0赞

阈值可以被视作最简单的图像分割方法属于像素级分割。设定阈值后图像中每个像素预期比较选择保留或剔除，从而达到所需要的部分图像。

阈值是对灰度图像进行操作得到的二值图像（二值图像中所有的像素只能从0和1这两个值中取）

阈值函数有两种：

1、固定阈值：Threshold（）函数

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

第一个参数：输入图像数组

第二个参数：输出图像数组

第三个参数：设定阈值

第四个参数：设定的最大值

第五个参数：设置取阈值的方法

double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double maxval, int type )
    {
        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);
        type &= THRESH_MASK;
    
        CV_Assert( automatic_thresh != (CV_THRESH_OTSU | CV_THRESH_TRIANGLE) );
        if( automatic_thresh == CV_THRESH_OTSU )
        {
            CV_Assert( src.type() == CV_8UC1 );
            thresh = getThreshVal_Otsu_8u( src );
        }
        else if( automatic_thresh == CV_THRESH_TRIANGLE )
        {
            CV_Assert( src.type() == CV_8UC1 );
            thresh = getThreshVal_Triangle_8u( src );
        }
    
        _dst.create( src.size(), src.type() );
        Mat dst = _dst.getMat();
    
        if( src.depth() == CV_8U )
        {
            int ithresh = cvFloor(thresh);
            thresh = ithresh;
            int imaxval = cvRound(maxval);
            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;
            }
            thresh = ithresh;
            maxval = imaxval;
        }
        else if( src.depth() == CV_16S )
        {
            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_32F )
            ;
        else
            CV_Error( CV_StsUnsupportedFormat, "" );
    
        parallel_for_(Range(0, dst.rows),
                      ThresholdRunner(src, dst, thresh, maxval, type),
                      dst.total()/(double)(1<<16));
        return thresh;
    }

2、自适应阈值：adaptiveThreshold（）函数

void cv::adaptiveThreshold( InputArray _src, OutputArray _dst, double maxValue,                              int method, int type, int blockSize, double delta ) 
     
     
     第一个参数：输入图像数组
     第二个参数：输出图像数组
     第三个参数：设定的最大值
     第四个参数：算法
    
    
     第五个参数：阈值类型 THRESH_BINARY,THRESH_BINARY_INV
     第六个参数：一个像素的领域尺寸
    
    
     第七个参数：减去平均或加权平均值后的平均值
    
    
     
     void cv::adaptiveThreshold( InputArray _src, OutputArray _dst, double maxValue,
                                int method, int type, int blockSize, double delta )
    {
        Mat src = _src.getMat();
        CV_Assert( src.type() == CV_8UC1 );
        CV_Assert( blockSize % 2 == 1 && blockSize > 1 );
        Size size = src.size();
    
        _dst.create( size, src.type() );
        Mat dst = _dst.getMat();
    
        if( maxValue < 0 )
        {
            dst = Scalar(0);
            return;
        }
    
        Mat mean;
    
        if( src.data != dst.data )
            mean = dst;
    
        if( method == ADAPTIVE_THRESH_MEAN_C )
            boxFilter( src, mean, src.type(), Size(blockSize, blockSize),
                       Point(-1,-1), true, BORDER_REPLICATE );
        else if( method == ADAPTIVE_THRESH_GAUSSIAN_C )
            GaussianBlur( src, mean, Size(blockSize, blockSize), 0, 0, BORDER_REPLICATE );
        else
            CV_Error( CV_StsBadFlag, "Unknown/unsupported adaptive threshold method" );
    
        int i, j;
        uchar imaxval = saturate_cast<uchar>(maxValue);
        int idelta = type == THRESH_BINARY ? cvCeil(delta) : cvFloor(delta);
        uchar tab[768];
    
        if( type == CV_THRESH_BINARY )
            for( i = 0; i < 768; i++ )
                tab[i] = (uchar)(i - 255 > -idelta ? imaxval : 0);
        else if( type == CV_THRESH_BINARY_INV )
            for( i = 0; i < 768; i++ )
                tab[i] = (uchar)(i - 255 <= -idelta ? imaxval : 0);
        else
            CV_Error( CV_StsBadFlag, "Unknown/unsupported threshold type" );
    
        if( src.isContinuous() && mean.isContinuous() && dst.isContinuous() )
        {
            size.width *= size.height;
            size.height = 1;
        }
    
        for( i = 0; i < size.height; i++ )
        {
            const uchar* sdata = src.ptr(i);
            const uchar* mdata = mean.ptr(i);
            uchar* ddata = dst.ptr(i);
    
            for( j = 0; j < size.width; j++ )
                ddata[j] = tab[sdata[j] - mdata[j] + 255];
        }
    }