minor changes in documentation

modules/core/doc/basic_structures.rst

@@ -786,6 +786,8 @@ Various Mat constructors
 
     :param cols: Number of columns in a 2D array.
 
+    :param roi: Region of interest.
+
     :param size: 2D array size:  ``Size(cols, rows)`` . In the  ``Size()``  constructor, the number of rows and the number of columns go in the reverse order.
 
     :param sizes: Array of integers specifying an n-dimensional array shape.

modules/core/doc/clustering.rst

@@ -11,9 +11,9 @@ Finds centers of clusters and groups input samples around the clusters.
 
 .. ocv:pyfunction:: cv2.kmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) -> retval, bestLabels, centers
 
-.. ocv:cfunction:: int cvKMeans2(const CvArr* samples, int nclusters, CvArr* labels, CvTermCriteria criteria, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* centers=0, double* compactness=0)
+.. ocv:cfunction:: int cvKMeans2(const CvArr* samples, int clusterCount, CvArr* labels, CvTermCriteria criteria, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* centers=0, double* compactness=0)
 
-.. ocv:pyoldfunction:: cv.KMeans2(samples, nclusters, labels, criteria)-> None
+.. ocv:pyoldfunction:: cv.KMeans2(samples, clusterCount, labels, criteria)-> None
 
     :param samples: Floating-point matrix of input samples, one row per sample.
 
@@ -25,6 +25,8 @@ Finds centers of clusters and groups input samples around the clusters.
 
     :param attempts: Flag to specify the number of times the algorithm is executed using different initial labelings. The algorithm returns the labels that yield the best compactness (see the last function parameter).
 
+    :param rng: CvRNG state initialized by RNG().
+
     :param flags: Flag that can take the following values:
 
             * **KMEANS_RANDOM_CENTERS** Select random initial centers in each attempt.
@@ -35,6 +37,8 @@ Finds centers of clusters and groups input samples around the clusters.
 
     :param centers: Output matrix of the cluster centers, one row per each cluster center.
 
+    :param compactness: The returned value that is described below.
+
 The function ``kmeans`` implements a k-means algorithm that finds the
 centers of ``clusterCount`` clusters and groups the input samples
 around the clusters. As an output,

modules/core/doc/drawing_functions.rst

@@ -190,7 +190,7 @@ Fills the area bounded by one or more polygons.
 
 .. ocv:pyfunction:: cv2.fillPoly(img, pts, color[, lineType[, shift[, offset]]]) -> None
 
-.. ocv:cfunction:: void cvFillPoly( CvArr* img, CvPoint** pts, int* npts, int contours, CvScalar color, int lineType=8, int shift=0 )
+.. ocv:cfunction:: void cvFillPoly( CvArr* img, CvPoint** pts, int* npts, int ncontours, CvScalar color, int lineType=8, int shift=0 )
 .. ocv:pyoldfunction:: cv.FillPoly(img, polys, color, lineType=8, shift=0)-> None
 
     :param img: Image.
@@ -207,6 +207,8 @@ Fills the area bounded by one or more polygons.
 
     :param shift: Number of fractional bits in the vertex coordinates.
 
+    :param offset: Optional offset of all points of the contours.
+
 The function ``fillPoly`` fills an area bounded by several polygonal contours. The function can fill complex areas, for example,
 areas with holes, contours with self-intersections (some of thier parts), and so forth.
 

modules/core/doc/dynamic_structures.rst

@@ -126,16 +126,22 @@ There are helper functions to construct the slice and to compute its length:
 
 .. ocv:cfunction:: CvSlice cvSlice( int start, int end )
 
+    :param start: Inclusive left boundary.
+
+    :param end: Exclusive right boundary.
+
 ::
 
     #define CV_WHOLE_SEQ_END_INDEX 0x3fffffff
     #define CV_WHOLE_SEQ  cvSlice(0, CV_WHOLE_SEQ_END_INDEX)
 
-..
-
 .. ocv:cfunction:: int cvSliceLength( CvSlice slice, const CvSeq* seq )
 
-  Calculates the sequence slice length
+    :param slice: The slice of sequence.
+
+    :param seq: Source sequence.
+
+Calculates the sequence slice length.
 
 Some of functions that operate on sequences take a ``CvSlice slice`` parameter that is often set to the whole sequence (CV_WHOLE_SEQ) by default. Either of the ``start_index`` and  ``end_index`` may be negative or exceed the sequence length. If they are equal, the slice is considered empty (i.e., contains no elements). Because sequences are treated as circular structures, the slice may select a
 few elements in the end of a sequence followed by a few elements at the beginning of the sequence. For example,  ``cvSlice(-2, 3)`` in the case of a 10-element sequence will select a 5-element slice, containing the pre-last (8th), last (9th), the very first (0th), second (1th) and third (2nd)