diff --git a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst index 6c11a26e2..4d40ea74b 100644 --- a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst +++ b/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. diff --git a/modules/imgproc/doc/filtering.rst b/modules/imgproc/doc/filtering.rst index 380e13030..9fa6d1699 100644 --- a/modules/imgproc/doc/filtering.rst +++ b/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:: diff --git a/modules/imgproc/doc/miscellaneous_transformations.rst b/modules/imgproc/doc/miscellaneous_transformations.rst index 85288b050..28e5c4602 100644 --- a/modules/imgproc/doc/miscellaneous_transformations.rst +++ b/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 diff --git a/modules/ml/doc/decision_trees.rst b/modules/ml/doc/decision_trees.rst index fdb74c2bf..eb4f5b402 100644 --- a/modules/ml/doc/decision_trees.rst +++ b/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 diff --git a/modules/ml/doc/k_nearest_neighbors.rst b/modules/ml/doc/k_nearest_neighbors.rst index 40059d98f..6e721190a 100644 --- a/modules/ml/doc/k_nearest_neighbors.rst +++ b/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`. diff --git a/modules/ml/doc/neural_networks.rst b/modules/ml/doc/neural_networks.rst index 7546dc786..27de50552 100644 --- a/modules/ml/doc/neural_networks.rst +++ b/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 + diff --git a/modules/ml/doc/normal_bayes_classifier.rst b/modules/ml/doc/normal_bayes_classifier.rst index 6e2195862..5c0d1b112 100644 --- a/modules/ml/doc/normal_bayes_classifier.rst +++ b/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. diff --git a/modules/ml/doc/random_trees.rst b/modules/ml/doc/random_trees.rst index 5d389c6f0..20495eb02 100644 --- a/modules/ml/doc/random_trees.rst +++ b/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. diff --git a/modules/ml/doc/support_vector_machines.rst b/modules/ml/doc/support_vector_machines.rst index 555b9291a..fc12ce5c4 100644 --- a/modules/ml/doc/support_vector_machines.rst +++ b/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. diff --git a/modules/nonfree/doc/feature_detection.rst b/modules/nonfree/doc/feature_detection.rst index c3ab805e1..1071fdaa1 100644 --- a/modules/nonfree/doc/feature_detection.rst +++ b/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. diff --git a/modules/objdetect/doc/cascade_classification.rst b/modules/objdetect/doc/cascade_classification.rst index aa19ec8a1..8079fbc2a 100644 --- a/modules/objdetect/doc/cascade_classification.rst +++ b/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 diff --git a/modules/stitching/doc/matching.rst b/modules/stitching/doc/matching.rst index 06a8ac54f..d5a68cde1 100644 --- a/modules/stitching/doc/matching.rst +++ b/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 diff --git a/modules/video/doc/motion_analysis_and_object_tracking.rst b/modules/video/doc/motion_analysis_and_object_tracking.rst index 652955361..da793358e 100644 --- a/modules/video/doc/motion_analysis_and_object_tracking.rst +++ b/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.