\cvarg{points}{Array of vertices}
\end{description}
-The function calculates the vertices of the input 2d box. Here is the function code:
+The function calculates the vertices of the input 2d box.
+
+\ifC
+Here is the function code:
\begin{lstlisting}
void cvBoxPoints( CvBox2D box, CvPoint2D32f pt[4] )
pt[3].y = 2*box.center.y - pt[1].y;
}
\end{lstlisting}
+\fi
\cvCPyFunc{CalcPGH}
Calculates a pair-wise geometrical histogram for a contour.
\cvarg{signature1}{First signature, a $\texttt{size1}\times \texttt{dims}+1$ floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used}
\cvarg{signature2}{Second signature of the same format as \texttt{signature1}, though the number of rows may be different. The total weights may be different, in this case an extra "dummy" point is added to either \texttt{signature1} or \texttt{signature2}}
\cvarg{distance\_type}{Metrics used; \texttt{CV\_DIST\_L1, CV\_DIST\_L2}, and \texttt{CV\_DIST\_C} stand for one of the standard metrics; \texttt{CV\_DIST\_USER} means that a user-defined function \texttt{distance\_func} or pre-calculated \texttt{cost\_matrix} is used}
-\cvarg{distance\_func}{The user-defined distance function. It takes coordinates of two points and returns the distance between the points}
+\ifC
+\cvarg{distance\_func}{The user-supplied distance function. It takes coordinates of two points and returns the distance between the points
+\texttt{
+typedef float (*CvDistanceFunction)(const float* f1, const float* f2, void* userdata);
+}
+}
+\else
+\cvarg{distance\_func}{The user-supplied distance function. It takes coordinates of two points \texttt{pt0} and \texttt{pt1}, and returns the distance between the points, with sigature
+\texttt{
+func(pt0, pt1, userdata) -> float
+}
+}
+\fi
\cvarg{cost\_matrix}{The user-defined $\texttt{size1}\times \texttt{size2}$ cost matrix. At least one of \texttt{cost\_matrix} and \texttt{distance\_func} must be NULL. Also, if a cost matrix is used, lower boundary (see below) can not be calculated, because it needs a metric function}
\cvarg{flow}{The resultant $\texttt{size1} \times \texttt{size2}$ flow matrix: $\texttt{flow}_{i,j}$ is a flow from $i$ th point of \texttt{signature1} to $j$ th point of \texttt{signature2}}
\cvarg{lower\_bound}{Optional input/output parameter: lower boundary of distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (i.e. the signature matrices have a single column). The user \textbf{must} initialize \texttt{*lower\_bound}. If the calculated distance between mass centers is greater or equal to \texttt{*lower\_bound} (it means that the signatures are far enough) the function does not calculate EMD. In any case \texttt{*lower\_bound} is set to the calculated distance between mass centers on return. Thus, if user wants to calculate both distance between mass centers and EMD, \texttt{*lower\_bound} should be set to 0}
\cvarg{userdata}{Pointer to optional data that is passed into the user-defined distance function}
\end{description}
-\begin{lstlisting}
-typedef float (*CvDistanceFunction)(const float* f1, const float* f2, void* userdata);
-\end{lstlisting}
-
The function computes the earth mover distance and/or
a lower boundary of the distance between the two weighted point
configurations. One of the applications described in \cvCPyCross{RubnerSept98} is
\cvclass{CvConvexityDefect}\label{CvConvexityDefect}
+\ifC
Structure describing a single contour convexity defect.
\begin{lstlisting}
float depth; /* distance between the farthest point and the convex hull */
} CvConvexityDefect;
\end{lstlisting}
+\else
+
+A single contour convexity defect, represented by a tuple \texttt{(start, end, depthpoint, depth)}.
+
+\begin{description}
+\cvarg{start}{(x, y) point of the contour where the defect begins}
+\cvarg{end}{(x, y) point of the contour where the defect ends}
+\cvarg{depthpoint}{(x, y) point farthest from the convex hull point within the defect}
+\cvarg{depth}{distance between the farthest point and the convex hull}
+\end{description}
+
+\fi
% ===== Picture. Convexity defects of hand contour. =====
\includegraphics[width=0.5\textwidth]{pics/defects.png}
The function finds the convex hull of a 2D point set using Sklansky's algorithm. If \texttt{storage} is memory storage, the function creates a sequence containing the hull points or pointers to them, depending on \texttt{return\_points} value and returns the sequence on output. If \texttt{storage} is a CvMat, the function returns NULL.
-% ===== Example. Building convex hull for a sequence or array of points =====
+\ifC
+
+Example. Building convex hull for a sequence or array of points
+
\begin{lstlisting}
#include "cv.h"
#include "highgui.h"
}
}
\end{lstlisting}
+\fi
\cvCPyFunc{ConvexityDefects}
Finds the convexity defects of a contour.
\cvarg{offset}{Offset, by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context}
\end{description}
+The function retrieves contours from the binary image using the algorithm
+\cite{Suzuki85}. The contours are a useful tool for shape analysis and
+object detection and recognition.
+
The function retrieves contours from the
binary image and returns the number of retrieved contours. The
pointer \texttt{first\_contour} is filled by the function. It will
\texttt{h\_next} and \texttt{v\_next} links. The sample in the
\cvCPyCross{DrawContours} discussion shows how to use contours for
connected component detection. Contours can be also used for shape
-analysis and object recognition - see \texttt{squares.c} in the OpenCV
-sample directory.
+analysis and object recognition - see
+\ifC
+\texttt{squares.c}
+\else
+\texttt{squares.py}
+\fi
+in the OpenCV sample directory.
+\textbf{Note:} the source \texttt{image} is modified by this function.
\ifC % {
These values are proved to be invariant to the image scale, rotation, and reflection except the seventh one, whose sign is changed by reflection. Of course, this invariance was proved with the assumption of infinite image resolution. In case of a raster images the computed Hu invariants for the original and transformed images will be a bit different.
+\ifPy
+\begin{lstlisting}
+>>> import cv
+>>> original = cv.LoadImageM("building.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
+>>> print cv.GetHuMoments(cv.Moments(original))
+(0.0010620951868446141, 1.7962726159653835e-07, 1.4932744974469421e-11, 4.4832441315737963e-12, -1.0819359198251739e-23, -9.5726503811945833e-16, -3.5050592804744648e-23)
+>>> flipped = cv.CloneMat(original)
+>>> cv.Flip(original, flipped)
+>>> print cv.GetHuMoments(cv.Moments(flipped))
+(0.0010620951868446141, 1.796272615965384e-07, 1.4932744974469935e-11, 4.4832441315740249e-12, -1.0819359198259393e-23, -9.572650381193327e-16, 3.5050592804745877e-23)
+\end{lstlisting}
+\fi
\cvCPyFunc{GetNormalizedCentralMoment}
Retrieves the normalized central moment from the moment state structure.
\cvdefC{
void cvMoments( \par const CvArr* arr,\par CvMoments* moments,\par int binary=0 );
}
-\cvdefPy{Moments(arr, binary) -> moments}
+\cvdefPy{Moments(arr, binary = 0) -> moments}
\begin{description}
\cvarg{arr}{Image (1-channel or 3-channel with COI set) or polygon (CvSeq of points or a vector of points)}
The function retrieves contours from the
binary image using the algorithm \cite{Suzuki85}. The contours are a useful tool for shape analysis and object detection and recognition. See \texttt{squares.c} in the OpenCV sample directory.
+\textbf{Note:} the source \texttt{image} is modified by this function.
+
\cvCppFunc{drawContours}
Draws contours' outlines or filled contours.
projects / opencv.git / blobdiff