【OpenCV3】threshold()函数详解
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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*/
};
标签：ithresh,imaxval,value,OpenCV3,详解,THRESH,threshold,type,CV 来源： https://www.cnblogs.com/hs-pingfan/p/10494264.html