More fixes for documentation.
This commit is contained in:
Andrey Kamaev
@@ -75,8 +75,6 @@ Estimates the Gaussian mixture parameters from a sample set.
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.. ocv:function:: bool CvEM::train( const CvMat* samples, const CvMat* sampleIdx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
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.. ocv:pyfunction:: cv2.EM.train(samples[, sampleIdx[, params]]) -> retval, labels
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:param samples: Samples from which the Gaussian mixture model will be estimated.
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:param sample_idx: Mask of samples to use. All samples are used by default.
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@@ -107,8 +105,6 @@ Returns a mixture component index of a sample.
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.. ocv:function:: float CvEM::predict( const CvMat* sample, CvMat* probs ) const
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.. ocv:pyfunction:: cv2.EM.predict(sample) -> retval, probs
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:param sample: A sample for classification.
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:param probs: If it is not null then the method will write posterior probabilities of each component given the sample data to this parameter.
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@@ -122,8 +118,6 @@ Returns the number of mixture components :math:`M` in the Gaussian mixture model
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.. ocv:function:: int CvEM::get_nclusters() const
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.. ocv:pyfunction:: cv2.EM.getNClusters() -> retval
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CvEM::getMeans
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------------------
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@@ -133,8 +127,6 @@ Returns mixture means :math:`a_k`.
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.. ocv:function:: const CvMat* CvEM::get_means() const
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.. ocv:pyfunction:: cv2.EM.getMeans() -> means
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CvEM::getCovs
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-------------
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@@ -144,8 +136,6 @@ Returns mixture covariance matrices :math:`S_k`.
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.. ocv:function:: const CvMat** CvEM::get_covs() const
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.. ocv:pyfunction:: cv2.EM.getCovs([covs]) -> covs
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CvEM::getWeights
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----------------
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@@ -155,8 +145,6 @@ Returns mixture weights :math:`\pi_k`.
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.. ocv:function:: const CvMat* CvEM::get_weights() const
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.. ocv:pyfunction:: cv2.EM.getWeights() -> weights
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CvEM::getProbs
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--------------
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@@ -166,8 +154,6 @@ Returns vectors of probabilities for each training sample.
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.. ocv:function:: const CvMat* CvEM::get_probs() const
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.. ocv:pyfunction:: cv2.EM.getProbs() -> probs
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For each training sample :math:`i` (that have been passed to the constructor or to :ocv:func:`CvEM::train`) returns probabilities :math:`p_{i,k}` to belong to a mixture component :math:`k`.
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@@ -179,8 +165,6 @@ Returns logarithm of likelihood.
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.. ocv:function:: double CvEM::get_log_likelihood() const
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.. ocv:pyfunction:: cv2.EM.getLikelihood() -> likelihood
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CvEM::write
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-----------
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@@ -81,22 +81,22 @@ RandomizedTree::train
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-------------------------
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Trains a randomized tree using an input set of keypoints.
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.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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.. ocv:function:: void RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits )
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.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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.. ocv:function:: void RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, PatchGenerator & make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits )
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:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
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:param rng: Random-number generator used for training.
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:param make_patch: Patch generator used for training.
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:param depth: Maximum tree depth.
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:param views: Number of random views of each keypoint neighborhood to generate.
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:param reduced_num_dim: Number of dimensions used in the compressed signature.
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:param num_quant_bits: Number of bits used for quantization.
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@@ -105,9 +105,9 @@ RandomizedTree::read
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------------------------
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Reads a pre-saved randomized tree from a file or stream.
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.. ocv:function:: read(const char* file_name, int num_quant_bits)
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.. ocv:function:: RandomizedTree::read(const char* file_name, int num_quant_bits)
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.. ocv:function:: read(std::istream &is, int num_quant_bits)
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.. ocv:function:: RandomizedTree::read(std::istream &is, int num_quant_bits)
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:param file_name: Name of the file that contains randomized tree data.
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@@ -121,9 +121,9 @@ RandomizedTree::write
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-------------------------
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Writes the current randomized tree to a file or stream.
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.. ocv:function:: void write(const char* file_name) const
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.. ocv:function:: void RandomizedTree::write(const char* file_name) const
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.. ocv:function:: void write(std::ostream &os) const
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.. ocv:function:: void RandomizedTree::write(std::ostream &os) const
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:param file_name: Name of the file where randomized tree data is stored.
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@@ -133,7 +133,7 @@ Writes the current randomized tree to a file or stream.
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RandomizedTree::applyQuantization
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-------------------------------------
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.. ocv:function:: void applyQuantization(int num_quant_bits)
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.. ocv:function:: void RandomizedTree::applyQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree.
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@@ -240,26 +240,26 @@ RTreeClassifier::train
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--------------------------
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Trains a randomized tree classifier using an input set of keypoints.
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.. ocv:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS )
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.. ocv:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, PatchGenerator & make_patch, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS )
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:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
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:param rng: Random-number generator used for training.
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:param make_patch: Patch generator used for training.
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:param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
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:param depth: Maximum tree depth.
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:param views: Number of random views of each keypoint neighborhood to generate.
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:param reduced_num_dim: Number of dimensions used in the compressed signature.
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:param num_quant_bits: Number of bits used for quantization.
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:param print_status: Current status of training printed on the console.
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@@ -268,9 +268,9 @@ RTreeClassifier::getSignature
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---------------------------------
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Returns a signature for an image patch.
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.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
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.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, uchar *sig)
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.. ocv:function:: void getSignature(IplImage *patch, float *sig)
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.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, float *sig)
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:param patch: Image patch to calculate the signature for.
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:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
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@@ -278,15 +278,15 @@ Returns a signature for an image patch.
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RTreeClassifier::getSparseSignature
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---------------------------------------
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---------------------------------------
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Returns a sparse signature for an image patch
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.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
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.. ocv:function:: void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh)
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:param patch: Image patch to calculate the signature for.
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:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
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:param thresh: Threshold used for compressing the signature.
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Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
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@@ -296,7 +296,7 @@ RTreeClassifier::countNonZeroElements
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-----------------------------------------
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Returns the number of non-zero elements in an input array.
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.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
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.. ocv:function:: static int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol=1e-10)
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:param vec: Input vector containing float elements.
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@@ -310,9 +310,9 @@ RTreeClassifier::read
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-------------------------
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Reads a pre-saved ``RTreeClassifier`` from a file or stream.
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.. ocv:function:: read(const char* file_name)
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.. ocv:function:: void RTreeClassifier::read(const char* file_name)
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.. ocv:function:: read(std::istream& is)
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.. ocv:function:: void RTreeClassifier::read( std::istream & is )
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:param file_name: Name of the file that contains randomized tree data.
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@@ -324,9 +324,9 @@ RTreeClassifier::write
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--------------------------
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Writes the current ``RTreeClassifier`` to a file or stream.
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.. ocv:function:: void write(const char* file_name) const
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.. ocv:function:: void RTreeClassifier::write(const char* file_name) const
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.. ocv:function:: void write(std::ostream &os) const
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.. ocv:function:: void RTreeClassifier::write(std::ostream &os) const
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:param file_name: Name of the file where randomized tree data is stored.
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@@ -338,7 +338,7 @@ RTreeClassifier::setQuantization
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------------------------------------
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Applies quantization to the current randomized tree.
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.. ocv:function:: void setQuantization(int num_quant_bits)
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.. ocv:function:: void RTreeClassifier::setQuantization(int num_quant_bits)
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:param num_quant_bits: Number of bits used for quantization.
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95
modules/legacy/doc/histograms.rst
Normal file
95
modules/legacy/doc/histograms.rst
Normal file
@@ -0,0 +1,95 @@
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Histograms
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==========
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.. highlight:: cpp
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CalcPGH
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-------
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Calculates a pair-wise geometrical histogram for a contour.
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.. ocv:cfunction:: void cvCalcPGH( const CvSeq* contour, CvHistogram* hist )
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:param contour: Input contour. Currently, only integer point coordinates are allowed.
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:param hist: Calculated histogram. It must be two-dimensional.
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The function calculates a 2D pair-wise geometrical histogram (PGH), described in [Iivarinen97]_ for the contour. The algorithm considers every pair of contour
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edges. The angle between the edges and the minimum/maximum distances
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are determined for every pair. To do this, each of the edges in turn
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is taken as the base, while the function loops through all the other
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edges. When the base edge and any other edge are considered, the minimum
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and maximum distances from the points on the non-base edge and line of
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the base edge are selected. The angle between the edges defines the row
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of the histogram in which all the bins that correspond to the distance
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between the calculated minimum and maximum distances are incremented
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(that is, the histogram is transposed relatively to the definition in the original paper). The histogram can be used for contour matching.
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.. [Iivarinen97] Jukka Iivarinen, Markus Peura, Jaakko Srel, and Ari Visa. *Comparison of Combined Shape Descriptors for Irregular Objects*, 8th British Machine Vision Conference, BMVC'97. http://www.cis.hut.fi/research/IA/paper/publications/bmvc97/bmvc97.html
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QueryHistValue*D
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----------------
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Queries the value of the histogram bin.
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.. ocv:cfunction:: float cvQueryHistValue_1D(CvHistogram hist, int idx0)
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.. ocv:cfunction:: float cvQueryHistValue_2D(CvHistogram hist, int idx0, int idx1)
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.. ocv:cfunction:: float cvQueryHistValue_3D(CvHistogram hist, int idx0, int idx1, int idx2)
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.. ocv:cfunction:: float cvQueryHistValue_nD(CvHistogram hist, const int* idx)
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.. ocv:pyoldfunction:: cv.QueryHistValue_1D(hist, idx0) -> float
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.. ocv:pyoldfunction:: cv.QueryHistValue_2D(hist, idx0, idx1) -> float
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.. ocv:pyoldfunction:: cv.QueryHistValue_3D(hist, idx0, idx1, idx2) -> float
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.. ocv:pyoldfunction:: cv.QueryHistValue_nD(hist, idx) -> float
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:param hist: Histogram.
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:param idx0: 0-th index.
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:param idx1: 1-st index.
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:param idx2: 2-nd index.
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:param idx: Array of indices.
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The macros return the value of the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function returns 0. If the bin is not present in the histogram, no new bin is created.
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GetHistValue\_?D
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----------------
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Returns a pointer to the histogram bin.
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.. ocv:cfunction:: float cvGetHistValue_1D(CvHistogram hist, int idx0)
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.. ocv:cfunction:: float cvGetHistValue_2D(CvHistogram hist, int idx0, int idx1)
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.. ocv:cfunction:: float cvGetHistValue_3D(CvHistogram hist, int idx0, int idx1, int idx2)
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.. ocv:cfunction:: float cvGetHistValue_nD(CvHistogram hist, int idx)
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:param hist: Histogram.
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:param idx0: 0-th index.
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:param idx1: 1-st index.
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:param idx2: 2-nd index.
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:param idx: Array of indices.
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::
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#define cvGetHistValue_1D( hist, idx0 )
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((float*)(cvPtr1D( (hist)->bins, (idx0), 0 ))
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#define cvGetHistValue_2D( hist, idx0, idx1 )
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((float*)(cvPtr2D( (hist)->bins, (idx0), (idx1), 0 )))
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#define cvGetHistValue_3D( hist, idx0, idx1, idx2 )
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((float*)(cvPtr3D( (hist)->bins, (idx0), (idx1), (idx2), 0 )))
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#define cvGetHistValue_nD( hist, idx )
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((float*)(cvPtrND( (hist)->bins, (idx), 0 )))
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..
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The macros ``GetHistValue`` return a pointer to the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function creates a new bin and sets it to 0, unless it exists already.
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@@ -9,6 +9,7 @@ legacy. Deprecated stuff
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motion_analysis
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expectation_maximization
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histograms
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planar_subdivisions
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feature_detection_and_description
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common_interfaces_of_descriptor_extractors
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@@ -19,7 +19,7 @@ Planar subdivision.
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CvSubdiv2DEdge recent_edge; \
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CvPoint2D32f topleft; \
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CvPoint2D32f bottomright;
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typedef struct CvSubdiv2D
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{
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CV_SUBDIV2D_FIELDS()
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@@ -64,13 +64,13 @@ Quad-edge of a planar subdivision.
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/* one of edges within quad-edge, lower 2 bits is index (0..3)
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and upper bits are quad-edge pointer */
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typedef long CvSubdiv2DEdge;
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/* quad-edge structure fields */
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#define CV_QUADEDGE2D_FIELDS() \
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int flags; \
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struct CvSubdiv2DPoint* pt[4]; \
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CvSubdiv2DEdge next[4];
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typedef struct CvQuadEdge2D
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{
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CV_QUADEDGE2D_FIELDS()
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@@ -97,9 +97,9 @@ Point of an original or dual subdivision.
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CvSubdiv2DEdge first; \
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CvPoint2D32f pt; \
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int id;
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#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30)
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typedef struct CvSubdiv2DPoint
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{
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CV_SUBDIV2D_POINT_FIELDS()
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@@ -135,7 +135,7 @@ Removes all virtual points.
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:param subdiv: Delaunay subdivision.
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The function removes all of the virtual points. It
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is called internally in
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is called internally in
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:ocv:cfunc:`CalcSubdivVoronoi2D`
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if the subdivision
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was modified after the previous call to the function.
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@@ -145,7 +145,7 @@ CreateSubdivDelaunay2D
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Creates an empty Delaunay triangulation.
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.. ocv:cfunction:: CvSubdiv2D* cvCreateSubdivDelaunay2D( CvRect rect, CvMemStorage* storage )
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.. ocv:pyoldfunction:: cv.CreateSubdivDelaunay2D(rect, storage)-> emptyDelaunayTriangulation
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.. ocv:pyoldfunction:: cv.CreateSubdivDelaunay2D(rect, storage) -> CvSubdiv2D
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:param rect: Rectangle that includes all of the 2D points that are to be added to the subdivision.
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@@ -157,7 +157,7 @@ subdivision where 2D points can be added using the function
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. All of the points to be added must be within
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the specified rectangle, otherwise a runtime error is raised.
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Note that the triangulation is a single large triangle that covers the given rectangle. Hence the three vertices of this triangle are outside the rectangle
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Note that the triangulation is a single large triangle that covers the given rectangle. Hence the three vertices of this triangle are outside the rectangle
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``rect``
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.
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@@ -192,7 +192,7 @@ Returns the edge destination.
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The function returns the edge destination. The
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returned pointer may be NULL if the edge is from a dual subdivision and
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the virtual point coordinates are not calculated yet. The virtual points
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can be calculated using the function
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can be calculated using the function
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:ocv:cfunc:`CalcSubdivVoronoi2D`.
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Subdiv2DGetEdge
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@@ -235,9 +235,9 @@ Returns next edge around the edge origin.
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|
||||
:param edge: Subdivision edge (not a quad-edge).
|
||||
|
||||
The function returns the next edge around the edge origin:
|
||||
The function returns the next edge around the edge origin:
|
||||
``eOnext``
|
||||
on the picture above if
|
||||
on the picture above if
|
||||
``e``
|
||||
is the input edge).
|
||||
|
||||
@@ -259,33 +259,33 @@ Returns the location of a point within a Delaunay triangulation.
|
||||
The function locates the input point within the subdivision. There are five cases:
|
||||
|
||||
*
|
||||
The point falls into some facet. The function returns
|
||||
The point falls into some facet. The function returns
|
||||
``CV_PTLOC_INSIDE``
|
||||
and
|
||||
and
|
||||
``*edge``
|
||||
will contain one of edges of the facet.
|
||||
|
||||
*
|
||||
The point falls onto the edge. The function returns
|
||||
The point falls onto the edge. The function returns
|
||||
``CV_PTLOC_ON_EDGE``
|
||||
and
|
||||
and
|
||||
``*edge``
|
||||
will contain this edge.
|
||||
|
||||
*
|
||||
The point coincides with one of the subdivision vertices. The function returns
|
||||
The point coincides with one of the subdivision vertices. The function returns
|
||||
``CV_PTLOC_VERTEX``
|
||||
and
|
||||
and
|
||||
``*vertex``
|
||||
will contain a pointer to the vertex.
|
||||
|
||||
*
|
||||
The point is outside the subdivision reference rectangle. The function returns
|
||||
The point is outside the subdivision reference rectangle. The function returns
|
||||
``CV_PTLOC_OUTSIDE_RECT``
|
||||
and no pointers are filled.
|
||||
|
||||
*
|
||||
One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error processing mode is selected,
|
||||
One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error processing mode is selected,
|
||||
``CV_PTLOC_ERROR``
|
||||
is returnd.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user