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Commit 36af349a authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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corrected a few bugs in refman

parent 6d810b13
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modules/imgproc/doc/imgproc.rst
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...@@ -12,6 +12,7 @@ imgproc. Image Processing ...@@ -12,6 +12,7 @@ imgproc. Image Processing
miscellaneous_transformations miscellaneous_transformations
histograms histograms
structural_analysis_and_shape_descriptors structural_analysis_and_shape_descriptors
planar_subdivisions
motion_analysis_and_object_tracking motion_analysis_and_object_tracking
feature_detection feature_detection
object_detection object_detection
modules/imgproc/doc/planar_subdivisions.rst
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...@@ -224,7 +224,7 @@ Returns one of the edges related to the given edge. ...@@ -224,7 +224,7 @@ Returns one of the edges related to the given edge.
* **CV_PREV_AROUND_RIGHT** previous around the right facet (reversed ``eDnext`` ) * **CV_PREV_AROUND_RIGHT** previous around the right facet (reversed ``eDnext`` )
.. image:: ../pics/quadedge.png .. image:: pics/quadedge.png
The function returns one of the edges related to the input edge. The function returns one of the edges related to the input edge.
...@@ -237,8 +237,6 @@ Returns next edge around the edge origin ...@@ -237,8 +237,6 @@ Returns next edge around the edge origin
:param edge: Subdivision edge (not a quad-edge) :param edge: Subdivision edge (not a quad-edge)
.. image:: ../pics/quadedge.png
The function returns the next edge around the edge origin: The function returns the next edge around the edge origin:
``eOnext`` ``eOnext``
on the picture above if on the picture above if
...@@ -312,8 +310,6 @@ Returns another edge of the same quad-edge. ...@@ -312,8 +310,6 @@ Returns another edge of the same quad-edge.
* **3** the reversed rotated edge (reversed ``eRot`` (in green)) * **3** the reversed rotated edge (reversed ``eRot`` (in green))
.. image:: ../pics/quadedge.png
The function returns one of the edges of the same quad-edge as the input edge. The function returns one of the edges of the same quad-edge as the input edge.
SubdivDelaunay2DInsert SubdivDelaunay2DInsert
......
modules/ml/doc/boosting.rst
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...@@ -142,7 +142,7 @@ Default and training constructors. ...@@ -142,7 +142,7 @@ Default and training constructors.
.. ocv:function:: CvBoost::CvBoost( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvBoostParams params=CvBoostParams() ) .. ocv:function:: CvBoost::CvBoost( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvBoostParams params=CvBoostParams() )
.. ocv:cfunction:: CvBoost::CvBoost( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() ) .. ocv:function::CvBoost::CvBoost( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() )
.. ocv:pyfunction:: cv2.Boost(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> <Boost object> .. ocv:pyfunction:: cv2.Boost(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> <Boost object>
...@@ -155,11 +155,11 @@ Trains a boosted tree classifier. ...@@ -155,11 +155,11 @@ Trains a boosted tree classifier.
.. ocv:function:: bool CvBoost::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvBoostParams params=CvBoostParams(), bool update=false ) .. ocv:function:: bool CvBoost::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:pyfunction:: cv2.Boost.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval .. ocv:function::bool CvBoost::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:cfunction:: bool CvBoost::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams(), bool update=false ) .. ocv:function::bool CvBoost::train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:cfunction:: bool CvBoost::train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false ) .. ocv:pyfunction:: cv2.Boost.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval
:param update: Specifies whether the classifier needs to be updated (``true``, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (``false``). :param update: Specifies whether the classifier needs to be updated (``true``, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (``false``).
...@@ -171,7 +171,7 @@ Predicts a response for an input sample. ...@@ -171,7 +171,7 @@ Predicts a response for an input sample.
.. ocv:function:: float CvBoost::predict( const Mat& sample, const Mat& missing=Mat(), const Range& slice=Range::all(), bool rawMode=false, bool returnSum=false ) const .. ocv:function:: float CvBoost::predict( const Mat& sample, const Mat& missing=Mat(), const Range& slice=Range::all(), bool rawMode=false, bool returnSum=false ) const
.. ocv:cfunction:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false, bool return_sum=false ) const .. ocv:function::float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false, bool return_sum=false ) const
.. ocv:pyfunction:: cv2.Boost.predict(sample[, missing[, slice[, rawMode[, returnSum]]]]) -> retval .. ocv:pyfunction:: cv2.Boost.predict(sample[, missing[, slice[, rawMode[, returnSum]]]]) -> retval
...@@ -193,7 +193,7 @@ CvBoost::prune ...@@ -193,7 +193,7 @@ CvBoost::prune
-------------- --------------
Removes the specified weak classifiers. Removes the specified weak classifiers.
.. ocv:cfunction:: void CvBoost::prune( CvSlice slice ) .. ocv:function::void CvBoost::prune( CvSlice slice )
.. ocv:pyfunction:: cv2.Boost.prune(slice) -> None .. ocv:pyfunction:: cv2.Boost.prune(slice) -> None
...@@ -208,7 +208,7 @@ CvBoost::calc_error ...@@ -208,7 +208,7 @@ CvBoost::calc_error
------------------- -------------------
Returns error of the boosted tree classifier. Returns error of the boosted tree classifier.
.. ocv:cfunction:: float CvBoost::calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ) .. ocv:function::float CvBoost::calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 )
The method is identical to :ocv:func:`CvDTree::calc_error` but uses the boosted tree classifier as predictor. The method is identical to :ocv:func:`CvDTree::calc_error` but uses the boosted tree classifier as predictor.
...@@ -217,7 +217,7 @@ CvBoost::get_weak_predictors ...@@ -217,7 +217,7 @@ CvBoost::get_weak_predictors
---------------------------- ----------------------------
Returns the sequence of weak tree classifiers. Returns the sequence of weak tree classifiers.
.. ocv:cfunction:: CvSeq* CvBoost::get_weak_predictors() .. ocv:function::CvSeq* CvBoost::get_weak_predictors()
The method returns the sequence of weak classifiers. Each element of the sequence is a pointer to the :ocv:class:`CvBoostTree` class or to some of its derivatives. The method returns the sequence of weak classifiers. Each element of the sequence is a pointer to the :ocv:class:`CvBoostTree` class or to some of its derivatives.
...@@ -232,5 +232,5 @@ CvBoost::get_data ...@@ -232,5 +232,5 @@ CvBoost::get_data
----------------- -----------------
Returns used train data of the boosted tree classifier. Returns used train data of the boosted tree classifier.
.. ocv:cfunction:: const CvDTreeTrainData* CvBoost::get_data() const .. ocv:function::const CvDTreeTrainData* CvBoost::get_data() const
modules/ml/doc/decision_trees.rst
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...@@ -227,11 +227,11 @@ Trains a decision tree. ...@@ -227,11 +227,11 @@ Trains a decision tree.
.. ocv:function:: bool CvDTree::train( const Mat& train_data, int tflag, const Mat& responses, const Mat& var_idx=Mat(), const Mat& sample_idx=Mat(), const Mat& var_type=Mat(), const Mat& missing_mask=Mat(), CvDTreeParams params=CvDTreeParams() ) .. ocv:function:: bool CvDTree::train( const Mat& train_data, int tflag, const Mat& responses, const Mat& var_idx=Mat(), const Mat& sample_idx=Mat(), const Mat& var_type=Mat(), const Mat& missing_mask=Mat(), CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvDTreeParams params=CvDTreeParams() ) .. ocv:function::bool CvDTree::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() ) .. ocv:function::bool CvDTree::train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx ) .. ocv:function::bool CvDTree::train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx )
.. ocv:pyfunction:: cv2.DTree.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval .. ocv:pyfunction:: cv2.DTree.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval
...@@ -251,7 +251,7 @@ Returns the leaf node of a decision tree corresponding to the input vector. ...@@ -251,7 +251,7 @@ Returns the leaf node of a decision tree corresponding to the input vector.
.. ocv:function:: CvDTreeNode* CvDTree::predict( const Mat& sample, const Mat& missingDataMask=Mat(), bool preprocessedInput=false ) const .. ocv:function:: CvDTreeNode* CvDTree::predict( const Mat& sample, const Mat& missingDataMask=Mat(), bool preprocessedInput=false ) const
.. ocv:cfunction:: CvDTreeNode* CvDTree::predict( const CvMat* sample, const CvMat* missingDataMask=0, bool preprocessedInput=false ) const .. ocv:function::CvDTreeNode* CvDTree::predict( const CvMat* sample, const CvMat* missingDataMask=0, bool preprocessedInput=false ) const
.. ocv:pyfunction:: cv2.DTree.predict(sample[, missingDataMask[, preprocessedInput]]) -> retval .. ocv:pyfunction:: cv2.DTree.predict(sample[, missingDataMask[, preprocessedInput]]) -> retval
...@@ -269,7 +269,7 @@ CvDTree::calc_error ...@@ -269,7 +269,7 @@ CvDTree::calc_error
------------------- -------------------
Returns error of the decision tree. Returns error of the decision tree.
.. ocv:cfunction:: float CvDTree::calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 ) .. ocv:function::float CvDTree::calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 )
:param data: Data for the decision tree. :param data: Data for the decision tree.
...@@ -290,7 +290,7 @@ Returns the variable importance array. ...@@ -290,7 +290,7 @@ Returns the variable importance array.
.. ocv:function:: Mat CvDTree::getVarImportance() .. ocv:function:: Mat CvDTree::getVarImportance()
.. ocv:cfunction:: const CvMat* CvDTree::get_var_importance() .. ocv:function::const CvMat* CvDTree::get_var_importance()
.. ocv:pyfunction:: cv2.DTree.getVarImportance() -> importanceVector .. ocv:pyfunction:: cv2.DTree.getVarImportance() -> importanceVector
...@@ -313,7 +313,7 @@ CvDTree::get_data ...@@ -313,7 +313,7 @@ CvDTree::get_data
----------------- -----------------
Returns used train data of the decision tree. Returns used train data of the decision tree.
.. ocv:cfunction:: const CvDTreeTrainData* CvDTree::get_data() const .. ocv:function::const CvDTreeTrainData* CvDTree::get_data() const
Example: building a tree for classifying mushrooms. See the ``mushroom.cpp`` sample that demonstrates how to build and use the Example: building a tree for classifying mushrooms. See the ``mushroom.cpp`` sample that demonstrates how to build and use the
decision tree. decision tree.
......
modules/ml/doc/gradient_boosted_trees.rst
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...@@ -159,7 +159,7 @@ Default and training constructors. ...@@ -159,7 +159,7 @@ Default and training constructors.
.. ocv:function:: CvGBTrees::CvGBTrees( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams() ) .. ocv:function:: CvGBTrees::CvGBTrees( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams() )
.. ocv:cfunction:: CvGBTrees::CvGBTrees( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() ) .. ocv:function::CvGBTrees::CvGBTrees( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() )
.. ocv:pyfunction:: cv2.GBTrees([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <GBTrees object> .. ocv:pyfunction:: cv2.GBTrees([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <GBTrees object>
...@@ -171,11 +171,11 @@ Trains a Gradient boosted tree model. ...@@ -171,11 +171,11 @@ Trains a Gradient boosted tree model.
.. ocv:function:: bool CvGBTrees::train(const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false) .. ocv:function:: bool CvGBTrees::train(const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false)
.. ocv:pyfunction:: cv2.GBTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval .. ocv:function::bool CvGBTrees::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false )
.. ocv:cfunction:: bool CvGBTrees::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false ) .. ocv:function::bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
.. ocv:cfunction:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false) .. ocv:pyfunction:: cv2.GBTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval
The first train method follows the common template (see :ocv:func:`CvStatModel::train`). The first train method follows the common template (see :ocv:func:`CvStatModel::train`).
Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported. Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported.
...@@ -198,9 +198,9 @@ Predicts a response for an input sample. ...@@ -198,9 +198,9 @@ Predicts a response for an input sample.
.. ocv:function:: float CvGBTrees::predict(const Mat& sample, const Mat& missing=Mat(), const Range& slice = Range::all(), int k=-1) const .. ocv:function:: float CvGBTrees::predict(const Mat& sample, const Mat& missing=Mat(), const Range& slice = Range::all(), int k=-1) const
.. ocv:pyfunction:: cv2.GBTrees.predict(sample[, missing[, slice[, k]]]) -> retval .. ocv:function::float CvGBTrees::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const
.. ocv:cfunction:: float CvGBTrees::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const .. ocv:pyfunction:: cv2.GBTrees.predict(sample[, missing[, slice[, k]]]) -> retval
:param sample: Input feature vector that has the same format as every training set :param sample: Input feature vector that has the same format as every training set
element. If not all the variables were actualy used during training, element. If not all the variables were actualy used during training,
......
modules/ml/doc/k_nearest_neighbors.rst
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...@@ -19,7 +19,7 @@ Default and training constructors. ...@@ -19,7 +19,7 @@ Default and training constructors.
.. ocv:function:: CvKNearest::CvKNearest( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int max_k=32 ) .. ocv:function:: CvKNearest::CvKNearest( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int max_k=32 )
.. ocv:cfunction:: CvKNearest::CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 ) .. ocv:function::CvKNearest::CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 )
See :ocv:func:`CvKNearest::train` for additional parameters descriptions. See :ocv:func:`CvKNearest::train` for additional parameters descriptions.
...@@ -29,9 +29,9 @@ Trains the model. ...@@ -29,9 +29,9 @@ Trains the model.
.. ocv:function:: bool CvKNearest::train( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int maxK=32, bool updateBase=false ) .. ocv:function:: bool CvKNearest::train( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int maxK=32, bool updateBase=false )
.. ocv:pyfunction:: cv2.KNearest.train(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retval .. ocv:function::bool CvKNearest::train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false )
.. ocv:cfunction:: bool CvKNearest::train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false ) .. ocv:pyfunction:: cv2.KNearest.train(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retval
:param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification. :param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification.
...@@ -54,9 +54,10 @@ Finds the neighbors and predicts responses for input vectors. ...@@ -54,9 +54,10 @@ Finds the neighbors and predicts responses for input vectors.
.. ocv:function:: float CvKNearest::find_nearest( const Mat& samples, int k, Mat& results, Mat& neighborResponses, Mat& dists) const .. ocv:function:: float CvKNearest::find_nearest( const Mat& samples, int k, Mat& results, Mat& neighborResponses, Mat& dists) const
.. ocv:function::float CvKNearest::find_nearest( const CvMat* samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighborResponses=0, CvMat* dist=0 ) const
.. ocv:pyfunction:: cv2.KNearest.find_nearest(samples, k[, results[, neighborResponses[, dists]]]) -> retval, results, neighborResponses, dists .. ocv:pyfunction:: cv2.KNearest.find_nearest(samples, k[, results[, neighborResponses[, dists]]]) -> retval, results, neighborResponses, dists
.. ocv:cfunction:: float CvKNearest::find_nearest( const CvMat* samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighborResponses=0, CvMat* dist=0 ) const
:param samples: Input samples stored by rows. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times number\_of\_features` size. :param samples: Input samples stored by rows. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times number\_of\_features` size.
......
modules/ml/doc/neural_networks.rst
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...@@ -182,7 +182,7 @@ The constructors. ...@@ -182,7 +182,7 @@ The constructors.
.. ocv:function:: CvANN_MLP::CvANN_MLP() .. ocv:function:: CvANN_MLP::CvANN_MLP()
.. ocv:cfunction:: CvANN_MLP::CvANN_MLP( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ) .. ocv:function::CvANN_MLP::CvANN_MLP( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:pyfunction:: cv2.ANN_MLP(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> <ANN_MLP object> .. ocv:pyfunction:: cv2.ANN_MLP(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> <ANN_MLP object>
...@@ -194,7 +194,7 @@ Constructs MLP with the specified topology. ...@@ -194,7 +194,7 @@ Constructs MLP with the specified topology.
.. ocv:function:: void CvANN_MLP::create( const Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ) .. ocv:function:: void CvANN_MLP::create( const Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:cfunction:: void CvANN_MLP::create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ) .. ocv:function::void CvANN_MLP::create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:pyfunction:: cv2.ANN_MLP.create(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> None .. ocv:pyfunction:: cv2.ANN_MLP.create(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> None
...@@ -212,7 +212,7 @@ Trains/updates MLP. ...@@ -212,7 +212,7 @@ Trains/updates MLP.
.. ocv:function:: int CvANN_MLP::train( const Mat& inputs, const Mat& outputs, const Mat& sampleWeights, const Mat& sampleIdx=Mat(), CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 ) .. ocv:function:: int CvANN_MLP::train( const Mat& inputs, const Mat& outputs, const Mat& sampleWeights, const Mat& sampleIdx=Mat(), CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:cfunction:: int CvANN_MLP::train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 ) .. ocv:function::int CvANN_MLP::train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:pyfunction:: cv2.ANN_MLP.train(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> niterations .. ocv:pyfunction:: cv2.ANN_MLP.train(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> niterations
...@@ -242,7 +242,7 @@ Predicts responses for input samples. ...@@ -242,7 +242,7 @@ Predicts responses for input samples.
.. ocv:function:: float CvANN_MLP::predict( const Mat& inputs, Mat& outputs ) const .. ocv:function:: float CvANN_MLP::predict( const Mat& inputs, Mat& outputs ) const
.. ocv:cfunction:: float CvANN_MLP::predict( const CvMat* inputs, CvMat* outputs ) const .. ocv:function::float CvANN_MLP::predict( const CvMat* inputs, CvMat* outputs ) const
.. ocv:pyfunction:: cv2.ANN_MLP.predict(inputs, outputs) -> retval .. ocv:pyfunction:: cv2.ANN_MLP.predict(inputs, outputs) -> retval
...@@ -262,7 +262,7 @@ CvANN_MLP::get_layer_sizes ...@@ -262,7 +262,7 @@ CvANN_MLP::get_layer_sizes
-------------------------- --------------------------
Returns numbers of neurons in each layer of the MLP. Returns numbers of neurons in each layer of the MLP.
.. ocv:cfunction:: const CvMat* CvANN_MLP::get_layer_sizes() .. ocv:function::const CvMat* CvANN_MLP::get_layer_sizes()
The method returns the integer vector specifying the number of neurons in each layer including the input and output layers of the MLP. The method returns the integer vector specifying the number of neurons in each layer including the input and output layers of the MLP.
......
modules/ml/doc/normal_bayes_classifier.rst
View file @ 36af349a
...@@ -23,7 +23,7 @@ Default and training constructors. ...@@ -23,7 +23,7 @@ Default and training constructors.
.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() ) .. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() )
.. ocv:cfunction:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 ) .. ocv:function::CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
.. ocv:pyfunction:: cv2.NormalBayesClassifier(trainData, responses[, varIdx[, sampleIdx]]) -> <NormalBayesClassifier object> .. ocv:pyfunction:: cv2.NormalBayesClassifier(trainData, responses[, varIdx[, sampleIdx]]) -> <NormalBayesClassifier object>
...@@ -35,9 +35,9 @@ Trains the model. ...@@ -35,9 +35,9 @@ Trains the model.
.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false ) .. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false )
.. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval .. ocv:function::bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false )
.. ocv:cfunction:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false ) .. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval
:param update: Identifies whether the model should be trained from scratch (``update=false``) or should be updated using the new training data (``update=true``). :param update: Identifies whether the model should be trained from scratch (``update=false``) or should be updated using the new training data (``update=true``).
...@@ -54,9 +54,9 @@ Predicts the response for sample(s). ...@@ -54,9 +54,9 @@ Predicts the response for sample(s).
.. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const .. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const
.. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results .. ocv:function::float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
.. ocv:cfunction:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const .. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results
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 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.
modules/ml/doc/random_trees.rst
View file @ 36af349a
...@@ -110,9 +110,9 @@ Trains the Random Trees model. ...@@ -110,9 +110,9 @@ Trains the Random Trees model.
.. ocv:function:: bool CvRTrees::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvRTParams params=CvRTParams() ) .. ocv:function:: bool CvRTrees::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvRTParams params=CvRTParams() )
.. ocv:cfunction:: bool CvRTrees::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() ) .. ocv:function::bool CvRTrees::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() )
.. ocv:cfunction:: bool CvRTrees::train( CvMLData* data, CvRTParams params=CvRTParams() ) .. ocv:function::bool CvRTrees::train( CvMLData* data, CvRTParams params=CvRTParams() )
.. ocv:pyfunction:: cv2.RTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval .. ocv:pyfunction:: cv2.RTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval
...@@ -124,7 +124,7 @@ Predicts the output for an input sample. ...@@ -124,7 +124,7 @@ Predicts the output for an input sample.
.. ocv:function:: double CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const .. ocv:function:: double CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const
.. ocv:cfunction:: float CvRTrees::predict( const CvMat* sample, const CvMat* missing = 0 ) const .. ocv:function::float CvRTrees::predict( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:pyfunction:: cv2.RTrees.predict(sample[, missing]) -> retval .. ocv:pyfunction:: cv2.RTrees.predict(sample[, missing]) -> retval
...@@ -141,7 +141,7 @@ Returns a fuzzy-predicted class label. ...@@ -141,7 +141,7 @@ Returns a fuzzy-predicted class label.
.. ocv:function:: float CvRTrees::predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const .. ocv:function:: float CvRTrees::predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const
.. ocv:cfunction:: float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const .. ocv:function::float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:pyfunction:: cv2.RTrees.predict_prob(sample[, missing]) -> retval .. ocv:pyfunction:: cv2.RTrees.predict_prob(sample[, missing]) -> retval
...@@ -158,9 +158,9 @@ Returns the variable importance array. ...@@ -158,9 +158,9 @@ Returns the variable importance array.
.. ocv:function:: Mat CvRTrees::getVarImportance() .. ocv:function:: Mat CvRTrees::getVarImportance()
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector .. ocv:function::const CvMat* CvRTrees::get_var_importance()
.. ocv:cfunction:: const CvMat* CvRTrees::get_var_importance() .. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector
The method returns the variable importance vector, computed at the training stage when ``CvRTParams::calc_var_importance`` is set to true. If this flag was set to false, the ``NULL`` pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training. The method returns the variable importance vector, computed at the training stage when ``CvRTParams::calc_var_importance`` is set to true. If this flag was set to false, the ``NULL`` pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training.
...@@ -169,7 +169,7 @@ CvRTrees::get_proximity ...@@ -169,7 +169,7 @@ CvRTrees::get_proximity
----------------------- -----------------------
Retrieves the proximity measure between two training samples. Retrieves the proximity measure between two training samples.
.. ocv:cfunction:: float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const .. ocv:function::float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const
:param sample_1: The first sample. :param sample_1: The first sample.
...@@ -185,7 +185,7 @@ CvRTrees::calc_error ...@@ -185,7 +185,7 @@ CvRTrees::calc_error
-------------------- --------------------
Returns error of the random forest. Returns error of the random forest.
.. ocv:cfunction:: float CvRTrees::calc_error( CvMLData* data, int type, std::vector<float> *resp = 0 ) .. ocv:function::float CvRTrees::calc_error( CvMLData* data, int type, std::vector<float> *resp = 0 )
The method is identical to :ocv:func:`CvDTree::calc_error` but uses the random forest as predictor. The method is identical to :ocv:func:`CvDTree::calc_error` but uses the random forest as predictor.
...@@ -203,7 +203,7 @@ CvRTrees::get_rng ...@@ -203,7 +203,7 @@ CvRTrees::get_rng
----------------- -----------------
Returns the state of the used random number generator. Returns the state of the used random number generator.
.. ocv:cfunction:: CvRNG* CvRTrees::get_rng() .. ocv:function::CvRNG* CvRTrees::get_rng()
CvRTrees::get_tree_count CvRTrees::get_tree_count
......
modules/ml/doc/support_vector_machines.rst
View file @ 36af349a
...@@ -158,7 +158,7 @@ Default and training constructors. ...@@ -158,7 +158,7 @@ Default and training constructors.
.. ocv:function:: CvSVM::CvSVM( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() ) .. ocv:function:: CvSVM::CvSVM( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() )
.. ocv:cfunction:: CvSVM::CvSVM( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() ) .. ocv:function::CvSVM::CvSVM( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:pyfunction:: cv2.SVM(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> <SVM object> .. ocv:pyfunction:: cv2.SVM(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> <SVM object>
...@@ -170,7 +170,7 @@ Trains an SVM. ...@@ -170,7 +170,7 @@ Trains an SVM.
.. ocv:function:: bool CvSVM::train( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() ) .. ocv:function:: bool CvSVM::train( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() )
.. ocv:cfunction:: bool CvSVM::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() ) .. ocv:function::bool CvSVM::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:pyfunction:: cv2.SVM.train(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> retval .. ocv:pyfunction:: cv2.SVM.train(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> retval
...@@ -194,7 +194,7 @@ Trains an SVM with optimal parameters. ...@@ -194,7 +194,7 @@ Trains an SVM with optimal parameters.
.. ocv:function:: bool CvSVM::train_auto( const Mat& trainData, const Mat& responses, const Mat& varIdx, const Mat& sampleIdx, CvSVMParams params, int k_fold = 10, CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced=false) .. ocv:function:: bool CvSVM::train_auto( const Mat& trainData, const Mat& responses, const Mat& varIdx, const Mat& sampleIdx, CvSVMParams params, int k_fold = 10, CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced=false)
.. ocv:cfunction:: bool CvSVM::train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, CvParamGrid Cgrid = get_default_grid(CvSVM::C), CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = get_default_grid(CvSVM::P), CvParamGrid nuGrid = get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE), bool balanced=false ) .. ocv:function::bool CvSVM::train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, CvParamGrid Cgrid = get_default_grid(CvSVM::C), CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = get_default_grid(CvSVM::P), CvParamGrid nuGrid = get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE), bool balanced=false )
.. ocv:pyfunction:: cv2.SVM.train_auto(trainData, responses, varIdx, sampleIdx, params[, k_fold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval .. ocv:pyfunction:: cv2.SVM.train_auto(trainData, responses, varIdx, sampleIdx, params[, k_fold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval
...@@ -226,9 +226,9 @@ Predicts the response for input sample(s). ...@@ -226,9 +226,9 @@ Predicts the response for input sample(s).
.. ocv:function:: float CvSVM::predict( const Mat& sample, bool returnDFVal=false ) const .. ocv:function:: float CvSVM::predict( const Mat& sample, bool returnDFVal=false ) const
.. ocv:cfunction:: float CvSVM::predict( const CvMat* sample, bool returnDFVal=false ) const .. ocv:function::float CvSVM::predict( const CvMat* sample, bool returnDFVal=false ) const
.. ocv:cfunction:: float CvSVM::predict( const CvMat* samples, CvMat* results ) const .. ocv:function::float CvSVM::predict( const CvMat* samples, CvMat* results ) const
.. ocv:pyfunction:: cv2.SVM.predict(sample[, returnDFVal]) -> retval .. ocv:pyfunction:: cv2.SVM.predict(sample[, returnDFVal]) -> retval
......
modules/video/doc/motion_analysis_and_object_tracking.rst
View file @ 36af349a
...@@ -81,7 +81,7 @@ The function finds an optical flow for each ``prevImg`` pixel using the [Farneba ...@@ -81,7 +81,7 @@ The function finds an optical flow for each ``prevImg`` pixel using the [Farneba
.. math:: .. math::
\texttt{prevImg} (x,y) \sim \texttt{nextImg} ( \texttt{flow} (x,y)[0], \texttt{flow} (x,y)[1]) \texttt{prevImg} (y,x) \sim \texttt{nextImg} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])
...@@ -441,7 +441,7 @@ Re-initializes Kalman filter. The previous content is destroyed. ...@@ -441,7 +441,7 @@ Re-initializes Kalman filter. The previous content is destroyed.
.. ocv:function:: void KalmanFilter::init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F) .. ocv:function:: void KalmanFilter::init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
:param dynamParams: Dimensionality of the state. :param dynamParams: Dimensionalityensionality of the state.
:param measureParams: Dimensionality of the measurement. :param measureParams: Dimensionality of the measurement.
......
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