Mentioned in doc if a function is parallelized with the TBB library (issue #421)

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

@@ -616,7 +616,7 @@ Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
     :param flags: Method for solving a PnP problem (see  :ocv:func:`solvePnP` ).
 
 The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections ``imagePoints`` and the projected (using
-:ocv:func:`projectPoints` ) ``objectPoints``. The use of RANSAC makes the function resistant to outliers.
+:ocv:func:`projectPoints` ) ``objectPoints``. The use of RANSAC makes the function resistant to outliers. The function is parallelized with the TBB library.
 
 
 
@@ -1127,8 +1127,7 @@ Computes disparity using the BM algorithm for a rectified stereo pair.
 
     :param state: The pre-initialized ``CvStereoBMState`` structure in the case of the old API.
 
-The method executes the BM algorithm on a rectified stereo pair. See the ``stereo_match.cpp`` OpenCV sample on how to prepare images and call the method. Note that the method is not constant, thus you should not use the same ``StereoBM`` instance from within different threads simultaneously.
-
+The method executes the BM algorithm on a rectified stereo pair. See the ``stereo_match.cpp`` OpenCV sample on how to prepare images and call the method. Note that the method is not constant, thus you should not use the same ``StereoBM`` instance from within different threads simultaneously. The function is parallelized with the TBB library.
 
 
 

modules/imgproc/doc/filtering.rst

@@ -858,7 +858,7 @@ The function dilates the source image using the specified structuring element th
 
     \texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')
 
-The function supports the in-place mode. Dilation can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
+The function supports the in-place mode. Dilation can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently. The function is parallelized with the TBB library.
 
 .. seealso::
 
@@ -898,7 +898,7 @@ The function erodes the source image using the specified structuring element tha
 
     \texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')
 
-The function supports the in-place mode. Erosion can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
+The function supports the in-place mode. Erosion can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently. The function is parallelized with the TBB library.
 
 .. seealso::
 
@@ -1233,7 +1233,7 @@ Morphological gradient:
 
     \texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}
 
-Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
+Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently. The function is parallelized with the TBB library.
 
 .. seealso::
 

modules/imgproc/doc/miscellaneous_transformations.rst

@@ -436,7 +436,7 @@ The functions ``distanceTransform`` calculate the approximate or precise
 distance from every binary image pixel to the nearest zero pixel.
 For zero image pixels, the distance will obviously be zero.
 
-When ``maskSize == CV_DIST_MASK_PRECISE`` and ``distanceType == CV_DIST_L2`` , the function runs the algorithm described in [Felzenszwalb04]_.
+When ``maskSize == CV_DIST_MASK_PRECISE`` and ``distanceType == CV_DIST_L2`` , the function runs the algorithm described in [Felzenszwalb04]_. This algorithm is parallelized with the TBB library.
 
 In other cases, the algorithm
 [Borgefors86]_
@@ -706,7 +706,9 @@ Also, the special value ``THRESH_OTSU`` may be combined with
 one of the above values. In this case, the function determines the optimal threshold
 value using the Otsu's algorithm and uses it instead of the specified ``thresh`` .
 The function returns the computed threshold value.
-Currently, the Otsu's method is implemented only for 8-bit images.
+Currently, the Otsu's method is implemented only for 8-bit images. 
+
+The function is parallelized with the TBB library except the Otsu's method.
 
 .. image:: pics/threshold.png
 

modules/ml/doc/decision_trees.rst

@@ -239,6 +239,8 @@ There are four ``train`` methods in :ocv:class:`CvDTree`:
 
 * The **last** method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed :ocv:class:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``subsampleIdx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``subsampleIdx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
 
+The function is parallelized with the TBB library.
+
 
 
 CvDTree::predict

modules/ml/doc/k_nearest_neighbors.rst

@@ -79,6 +79,8 @@ In case of C++ interface you can use output pointers to empty matrices and the f
 
 If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.
 
+The function is parallelized with the TBB library.
+
 CvKNearest::get_max_k
 ---------------------
 Returns the number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.

modules/ml/doc/neural_networks.rst

@@ -238,6 +238,9 @@ Trains/updates MLP.
 
 This method applies the specified training algorithm to computing/adjusting the network weights. It returns the number of done iterations.
 
+The RPROP training algorithm is parallelized with the TBB library.
+
+
 CvANN_MLP::predict
 ------------------
 Predicts responses for input samples.
@@ -275,4 +278,4 @@ Returns neurons weights of the particular layer.
 .. ocv:function:: double* CvANN_MLP::get_weights(int layer)
 
     :param layer: Index of the particular layer.
-    
\ No newline at end of file
+    

modules/ml/doc/normal_bayes_classifier.rst

@@ -60,3 +60,4 @@ Predicts the response for sample(s).
 
 The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples``. In case of multiple input vectors, there should be one output vector ``results``. The predicted class for a single input vector is returned by the method.
 
+The function is parallelized with the TBB library.

modules/ml/doc/random_trees.rst

@@ -112,6 +112,8 @@ Trains the Random Trees model.
 
 The method :ocv:func:`CvRTrees::train` is very similar to the method :ocv:func:`CvDTree::train` and follows the generic method :ocv:func:`CvStatModel::train` conventions. All the parameters specific to the algorithm training are passed as a :ocv:class:`CvRTParams` instance. The estimate of the training error (``oob-error``) is stored in the protected class member ``oob_error``.
 
+The function is parallelized with the TBB library.
+
 CvRTrees::predict
 -----------------
 Predicts the output for an input sample.

modules/ml/doc/support_vector_machines.rst

@@ -242,6 +242,9 @@ Predicts the response for input sample(s).
 
 If you pass one sample then prediction result is returned. If you want to get responses for several samples then you should pass the ``results`` matrix where prediction results will be stored.
 
+The function is parallelized with the TBB library.
+
+
 CvSVM::get_default_grid
 -----------------------
 Generates a grid for SVM parameters.

modules/nonfree/doc/feature_detection.rst

@@ -170,3 +170,4 @@ Detects keypoints and computes SURF descriptors for them.
     
     :param params: SURF algorithm parameters in OpenCV 1.x API.
 
+The function is parallelized with the TBB library.

modules/objdetect/doc/cascade_classification.rst

@@ -213,6 +213,7 @@ Detects objects of different sizes in the input image. The detected objects are
 
     :param maxSize: Maximum possible object size. Objects larger than that are ignored.
 
+The function is parallelized with the TBB library.
 
 
 CascadeClassifier::setImage

modules/stitching/doc/matching.rst

@@ -179,6 +179,8 @@ Performs images matching.
 
     :param mask: Mask indicating which image pairs must be matched
 
+The function is parallelized with the TBB library.
+
 .. seealso:: :ocv:struct:`detail::MatchesInfo`
 
 detail::FeaturesMatcher::isThreadSafe

modules/video/doc/motion_analysis_and_object_tracking.rst

@@ -41,8 +41,7 @@ Calculates an optical flow for a sparse feature set using the iterative Lucas-Ka
 
     :param minEigThreshold: The algorithm computes a minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]_) divided by number of pixels in a window. If this value is less then ``minEigThreshold`` then a corresponding feature is filtered out and its flow is not computed. So it allows to remove bad points earlier and speed up the computation.
             
-The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
-[Bouguet00]_.
+The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]_. The function is parallelized with the TBB library.