|
|
|
@ -204,9 +204,8 @@ |
|
|
|
|
\> \texttt{IplImage oldC1 = newC; CvMat oldC2 = newC;}\\ |
|
|
|
|
|
|
|
|
|
\textbf{... (with copying the data)}\\ |
|
|
|
|
\> \texttt{Mat image\_copy = image.clone();}\\ |
|
|
|
|
\> \texttt{Mat P(10, 1, CV\_32FC2, Scalar(1, 1));}\\ |
|
|
|
|
\> \texttt{vector<Point2f> ptvec = Mat\_<Point2f>(P);}\\ |
|
|
|
|
\> \texttt{Mat newC2 = cvarrToMat(oldC0).clone();}\\ |
|
|
|
|
\> \texttt{vector<Point2f> ptvec = Mat\_<Point2f>(iP);}\\ |
|
|
|
|
|
|
|
|
|
\>\\ |
|
|
|
|
\textbf{Access matrix elements}\\ |
|
|
|
@ -387,7 +386,7 @@ implements the core of Levenberg-Marquardt optimization algorithm. |
|
|
|
|
\end{tabular} |
|
|
|
|
|
|
|
|
|
\begin{tabbing} |
|
|
|
|
Exa\=mple. Filter image in-place with a 3x3 high-pass filter\\ |
|
|
|
|
Exa\=mple. Filter image in-place with a 3x3 high-pass kernel\\ |
|
|
|
|
\> (preserve negative responses by shifting the result by 128):\\ |
|
|
|
|
\texttt{filter2D(image, image, image.depth(), (Mat\_<float>(3,3)<<}\\ |
|
|
|
|
\> \texttt{-1, -1, -1, -1, 9, -1, -1, -1, -1), Point(1,1), 128);}\\ |
|
|
|
|