fixed convexHull description; updated cvHaarDetectObjects description (maxSize parameter).

doc/imgproc_struct_shape_analysis.tex

@@ -1252,7 +1252,7 @@ void convexHull( const Mat\& points, vector<Point2f>\& hull,\par
                  bool clockwise=false );}
 \begin{description}
 \cvarg{points}{The input 2D point set, represented by \texttt{CV\_32SC2} or \texttt{CV\_32FC2} matrix, or by \texttt{vector<Point>} or \texttt{vector<Point2f>} converted to the matrix using \texttt{Mat(const vector<T>\&)} constructor.}
-\cvarg{hull}{The output convex hull. It is either a vector of points that form the hull, or a vector of 0-based point indices of the hull points in the original array (since the set of convex hull points is a subset of the original point set).}
+\cvarg{hull}{The output convex hull. It is either a vector of points that form the hull (must have the same type as the input points), or a vector of 0-based point indices of the hull points in the original array (since the set of convex hull points is a subset of the original point set).}
 \cvarg{clockwise}{If true, the output convex hull will be oriented clockwise, otherwise it will be oriented counter-clockwise. Here, the usual screen coordinate system is assumed - the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.}
 \end{description}
 

doc/objdetect.tex

@@ -211,17 +211,18 @@ CvAvgComp;
 \fi
 
 \cvdefC{
-CvSeq* cvHaarDetectObjects( \par const CvArr* image,\par CvHaarClassifierCascade* cascade,\par CvMemStorage* storage,\par double scale\_factor=1.1,\par int min\_neighbors=3,\par int flags=0,\par CvSize min\_size=cvSize(0,\par0) );
-}\cvdefPy{HaarDetectObjects(image,cascade,storage,scale\_factor=1.1,min\_neighbors=3,flags=0,min\_size=(0,0))-> detected\_objects}
+CvSeq* cvHaarDetectObjects( \par const CvArr* image,\par CvHaarClassifierCascade* cascade,\par CvMemStorage* storage,\par double scaleFactor=1.1,\par int minNeighbors=3,\par int flags=0,\par CvSize minSize=cvSize(0, 0),\par CvSize maxSize=cvSize(0,0) );
+}\cvdefPy{HaarDetectObjects(image,cascade,storage,scaleFactor=1.1,minNeighbors=3,flags=0,minSize=(0,0))-> detected\_objects}
 
 \begin{description}
 \cvarg{image}{Image to detect objects in}
 \cvarg{cascade}{Haar classifier cascade in internal representation}
 \cvarg{storage}{Memory storage to store the resultant sequence of the object candidate rectangles}
-\cvarg{scale\_factor}{The factor by which the search window is scaled between the subsequent scans, 1.1 means increasing window by 10\% }
-\cvarg{min\_neighbors}{Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than \texttt{min\_neighbors}-1 are rejected. If \texttt{min\_neighbors} is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure}
+\cvarg{scaleFactor}{The factor by which the search window is scaled between the subsequent scans, 1.1 means increasing window by 10\% }
+\cvarg{minNeighbors}{Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than \texttt{min\_neighbors}-1 are rejected. If \texttt{minNeighbors} is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure}
 \cvarg{flags}{Mode of operation. Currently the only flag that may be specified is \texttt{CV\_HAAR\_DO\_CANNY\_PRUNING}. If it is set, the function uses Canny edge detector to reject some image regions that contain too few or too much edges and thus can not contain the searched object. The particular threshold values are tuned for face detection and in this case the pruning speeds up the processing}
-\cvarg{min\_size}{Minimum window size. By default, it is set to the size of samples the classifier has been trained on ($\sim 20\times 20$ for face detection)}
+\cvarg{minSize}{Minimum window size. By default, it is set to the size of samples the classifier has been trained on ($\sim 20\times 20$ for face detection)}
+\cvarg{maxSize}{Maximum window size to use. By default, it is set to the size of the image.}
 \end{description}
 
 The function finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles. The function scans the image several times at different scales (see \cvCPyCross{SetImagesForHaarClassifierCascade}). Each time it considers overlapping regions in the image and applies the classifiers to the regions using \cvCPyCross{RunHaarClassifierCascade}. It may also apply some heuristics to reduce number of analyzed regions, such as Canny prunning. After it has proceeded and collected the candidate rectangles (regions that passed the classifier cascade), it groups them and returns a sequence of average rectangles for each large enough group. The default parameters (\texttt{scale\_factor} =1.1, \texttt{min\_neighbors} =3, \texttt{flags} =0) are tuned for accurate yet slow object detection. For a faster operation on real video images the settings are: \texttt{scale\_factor} =1.2, \texttt{min\_neighbors} =2, \texttt{flags} =\texttt{CV\_HAAR\_DO\_CANNY\_PRUNING}, \texttt{min\_size} =\textit{minimum possible face size} (for example, $\sim$ 1/4 to 1/16 of the image area in the case of video conferencing).