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saskatchewancatch 2017-09-08 19:55:19 -06:00
parent fda1e76776
commit 8877e3aedb
1 changed files with 4 additions and 28 deletions
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modules/core/include/opencv2/core.hpp
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@ -644,20 +644,6 @@ CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stdde
This version of cv::norm calculates the absolute norm of src1. The type of norm to calculate is specified using cv::NormTypes.
If normType is not specified, NORM_L2 is used.
--done edit--
\f[norm = \forkfour{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) }
{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)}\f]
If normType is not specified, NORM_L2 is used.
or an absolute or relative difference norm if src2 is there:
As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
is calculated as follows
@ -676,14 +662,16 @@ The following graphic shows all values for the three norm functions \f$\| r(x) \
It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$.
![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png)
The function cv::norm returns the calculated norm.
When the mask parameter is specified and it is not empty, the norm is
If normType is not specified, NORM_L2 is used.
calculated only over the region specified by the mask.
Multi-channel input arrays are treated as single-channel arrays, that is,
the results for all channels are combined.
Hamming norms can only be calculated with CV_8U depth arrays.
@param src1 first input array.
@param normType type of the norm (see cv::NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
@ -696,18 +684,6 @@ This version of cv::norm calculates the absolute difference norm
or the relative difference norm of arrays src1 and src2.
The type of norm to calculate is specified using cv::NormTypes.
\f[norm = \forkfour{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) }
\f]
or
\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }\f]
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param normType type of the norm (cv::NormTypes).