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threshold()函数源码
我要评论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*/
};
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