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Commit a2024897 authored by Andrey Kamaev's avatar Andrey Kamaev
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Documentation: fixed class/struc members documentation; added warning on… 

Documentation: fixed class/struc members documentation; added warning on incorrectly documented member
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doc/ocv.py
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......@@ -1301,6 +1301,9 @@ class OCVMemberObject(OCVObject):
return ''
def parse_definition(self, parser):
parent_class = self.env.temp_data.get('ocv:parent')
if parent_class is None:
parser.fail("missing parent structure/class")
return parser.parse_member_object()
def describe_signature(self, signode, obj):
......
modules/core/doc/dynamic_structures.rst
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......@@ -10,27 +10,27 @@ CvMemStorage
.. ocv:struct:: CvMemStorage
A storage for various OpenCV dynamic data structures, such as ``CvSeq``, ``CvSet`` etc.
A storage for various OpenCV dynamic data structures, such as ``CvSeq``, ``CvSet`` etc.
.. ocv:member:: CvMemBlock* bottom
.. ocv:member:: CvMemBlock* bottom
the first memory block in the double-linked list of blocks
the first memory block in the double-linked list of blocks
.. ocv:member:: CvMemBlock* top
.. ocv:member:: CvMemBlock* top
the current partially allocated memory block in the list of blocks
the current partially allocated memory block in the list of blocks
.. ocv:member:: CvMemStorage* parent
.. ocv:member:: CvMemStorage* parent
the parent storage (if any) from which the new memory blocks are borrowed.
the parent storage (if any) from which the new memory blocks are borrowed.
.. ocv:member:: int free_space
.. ocv:member:: int free_space
number of free bytes in the ``top`` block
number of free bytes in the ``top`` block
.. ocv:member:: int block_size
.. ocv:member:: int block_size
the total size of the memory blocks
the total size of the memory blocks
Memory storage is a low-level structure used to store dynamically growing data structures such as sequences, contours, graphs, subdivisions, etc. It is organized as a list of memory blocks of equal size -
``bottom`` field is the beginning of the list of blocks and ``top`` is the currently used block, but not necessarily the last block of the list. All blocks between ``bottom`` and ``top``, not including the
......@@ -64,38 +64,38 @@ CvSeq
.. ocv:struct:: CvSeq
Dynamically growing sequence.
Dynamically growing sequence.
.. ocv:member:: int flags
.. ocv:member:: int flags
sequence flags, including the sequence signature (CV_SEQ_MAGIC_VAL or CV_SET_MAGIC_VAL), type of the elements and some other information about the sequence.
sequence flags, including the sequence signature (CV_SEQ_MAGIC_VAL or CV_SET_MAGIC_VAL), type of the elements and some other information about the sequence.
.. ocv:member:: int header_size
.. ocv:member:: int header_size
size of the sequence header. It should be sizeof(CvSeq) at minimum. See :ocv:cfunc:`CreateSeq`.
size of the sequence header. It should be sizeof(CvSeq) at minimum. See :ocv:cfunc:`CreateSeq`.
.. ocv:member:: CvSeq* h_prev
.. ocv:member:: CvSeq* h_next
.. ocv:member:: CvSeq* v_prev
.. ocv:member:: CvSeq* v_next
.. ocv:member:: CvSeq* h_prev
.. ocv:member:: CvSeq* h_next
.. ocv:member:: CvSeq* v_prev
.. ocv:member:: CvSeq* v_next
pointers to another sequences in a sequence tree. Sequence trees are used to store hierarchical contour structures, retrieved by :ocv:cfunc:`FindContours`
pointers to another sequences in a sequence tree. Sequence trees are used to store hierarchical contour structures, retrieved by :ocv:cfunc:`FindContours`
.. ocv:member:: int total
.. ocv:member:: int total
the number of sequence elements
the number of sequence elements
.. ocv:member:: int elem_size
.. ocv:member:: int elem_size
size of each sequence element in bytes
size of each sequence element in bytes
.. ocv:member:: CvMemStorage* storage
.. ocv:member:: CvMemStorage* storage
memory storage where the sequence resides. It can be a NULL pointer.
memory storage where the sequence resides. It can be a NULL pointer.
.. ocv:member:: CvSeqBlock* first
.. ocv:member:: CvSeqBlock* first
pointer to the first data block
pointer to the first data block
The structure ``CvSeq`` is a base for all of OpenCV dynamic data structures.
There are two types of sequences - dense and sparse. The base type for dense
......
modules/core/doc/old_basic_structures.rst
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modules/core/doc/old_xml_yaml_persistence.rst
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......@@ -48,41 +48,41 @@ CvFileNode
.. ocv:struct:: CvFileNode
File storage node. When XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of nodes. Each node can be a "leaf", that is, contain a single number or a string, or be a collection of other nodes. Collections are also referenced to as "structures" in the data writing functions. There can be named collections (mappings), where each element has a name and is accessed by a name, and ordered collections (sequences), where elements do not have names, but rather accessed by index.
File storage node. When XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of nodes. Each node can be a "leaf", that is, contain a single number or a string, or be a collection of other nodes. Collections are also referenced to as "structures" in the data writing functions. There can be named collections (mappings), where each element has a name and is accessed by a name, and ordered collections (sequences), where elements do not have names, but rather accessed by index.
.. ocv:member:: int tag
.. ocv:member:: int tag
type of the file node:
type of the file node:
* CV_NODE_NONE - empty node
* CV_NODE_INT - an integer
* CV_NODE_REAL - a floating-point number
* CV_NODE_STR - text string
* CV_NODE_SEQ - a sequence
* CV_NODE_MAP - a mapping
* CV_NODE_NONE - empty node
* CV_NODE_INT - an integer
* CV_NODE_REAL - a floating-point number
* CV_NODE_STR - text string
* CV_NODE_SEQ - a sequence
* CV_NODE_MAP - a mapping
type of the node can be retrieved using ``CV_NODE_TYPE(node->tag)`` macro.
type of the node can be retrieved using ``CV_NODE_TYPE(node->tag)`` macro.
.. ocv:member:: CvTypeInfo* info
.. ocv:member:: CvTypeInfo* info
optional pointer to the user type information. If you look at the matrix representation in XML and YAML, shown above, you may notice ``type_id="opencv-matrix"`` or ``!!opencv-matrix`` strings. They are used to specify that the certain element of a file is a representation of a data structure of certain type ("opencv-matrix" corresponds to :ocv:struct:`CvMat`). When a file is parsed, such type identifiers are passed to :ocv:cfunc:`FindType` to find type information and the pointer to it is stored in the file node. See :ocv:struct:`CvTypeInfo` for more details.
optional pointer to the user type information. If you look at the matrix representation in XML and YAML, shown above, you may notice ``type_id="opencv-matrix"`` or ``!!opencv-matrix`` strings. They are used to specify that the certain element of a file is a representation of a data structure of certain type ("opencv-matrix" corresponds to :ocv:struct:`CvMat`). When a file is parsed, such type identifiers are passed to :ocv:cfunc:`FindType` to find type information and the pointer to it is stored in the file node. See :ocv:struct:`CvTypeInfo` for more details.
.. ocv:member:: union data
.. ocv:member:: union data
the node data, declared as: ::
the node data, declared as: ::
union
{
double f; /* scalar floating-point number */
int i; /* scalar integer number */
CvString str; /* text string */
CvSeq* seq; /* sequence (ordered collection of file nodes) */
struct CvMap* map; /* map (collection of named file nodes) */
} data;
union
{
double f; /* scalar floating-point number */
int i; /* scalar integer number */
CvString str; /* text string */
CvSeq* seq; /* sequence (ordered collection of file nodes) */
struct CvMap* map; /* map (collection of named file nodes) */
} data;
..
..
Primitive nodes are read using :ocv:cfunc:`ReadInt`, :ocv:cfunc:`ReadReal` and :ocv:cfunc:`ReadString`. Sequences are read by iterating through ``node->data.seq`` (see "Dynamic Data Structures" section). Mappings are read using :ocv:cfunc:`GetFileNodeByName`. Nodes with the specified type (so that ``node->info != NULL``) can be read using :ocv:cfunc:`Read`.
Primitive nodes are read using :ocv:cfunc:`ReadInt`, :ocv:cfunc:`ReadReal` and :ocv:cfunc:`ReadString`. Sequences are read by iterating through ``node->data.seq`` (see "Dynamic Data Structures" section). Mappings are read using :ocv:cfunc:`GetFileNodeByName`. Nodes with the specified type (so that ``node->info != NULL``) can be read using :ocv:cfunc:`Read`.
CvAttrList
----------
......
modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst
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......@@ -258,7 +258,7 @@ Creates a descriptor matcher of a given type with the default parameters (using
BFMatcher
-----------------
.. ocv:class::BFMatcher : public DescriptorMatcher
.. ocv:class:: BFMatcher : public DescriptorMatcher
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets.
......
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
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......@@ -12,31 +12,31 @@ KeyPoint
--------
.. ocv:class:: KeyPoint
Data structure for salient point detectors.
Data structure for salient point detectors.
.. ocv:member:: Point2f pt
.. ocv:member:: Point2f pt
coordinates of the keypoint
coordinates of the keypoint
.. ocv:member:: float size
.. ocv:member:: float size
diameter of the meaningful keypoint neighborhood
diameter of the meaningful keypoint neighborhood
.. ocv:member:: float angle
.. ocv:member:: float angle
computed orientation of the keypoint (-1 if not applicable)
computed orientation of the keypoint (-1 if not applicable)
.. ocv:member:: float response
.. ocv:member:: float response
the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling
the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling
.. ocv:member:: int octave
.. ocv:member:: int octave
octave (pyramid layer) from which the keypoint has been extracted
octave (pyramid layer) from which the keypoint has been extracted
.. ocv:member:: int class_id
.. ocv:member:: int class_id
object id that can be used to clustered keypoints by an object they belong to
object id that can be used to clustered keypoints by an object they belong to
KeyPoint::KeyPoint
------------------
......
modules/gpu/doc/image_processing.rst
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......@@ -282,7 +282,7 @@ Computes a convolution (or cross-correlation) of two images.
:param ccorr: Flags to evaluate cross-correlation instead of convolution.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::ConvolveBuf`.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:struct:`gpu::ConvolveBuf`.
:param stream: Stream for the asynchronous version.
......@@ -321,7 +321,7 @@ Computes a proximity map for a raster template and an image where the template i
:param method: Specifies the way to compare the template with the image.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::MatchTemplateBuf`.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:struct:`gpu::MatchTemplateBuf`.
:param stream: Stream for the asynchronous version.
......
modules/ml/doc/boosting.rst
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......@@ -69,6 +69,17 @@ CvBoostParams
Boosting training parameters.
There is one structure member that you can set directly:
.. ocv:member:: int split_criteria
Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
The structure is derived from :ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
......@@ -96,17 +107,6 @@ The constructors.
See :ocv:func:`CvDTreeParams::CvDTreeParams` for description of other parameters.
Also there is one structure member that you can set directly:
.. ocv:member:: int split_criteria
Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
Default parameters are:
::
......
modules/ml/doc/decision_trees.rst
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......@@ -60,84 +60,84 @@ CvDTreeSplit
.. ocv:struct:: CvDTreeSplit
The structure represents a possible decision tree node split. It has public members:
The structure represents a possible decision tree node split. It has public members:
.. ocv:member:: int var_idx
.. ocv:member:: int var_idx
Index of variable on which the split is created.
Index of variable on which the split is created.
.. ocv:member:: int inversed
.. ocv:member:: int inversed
If it is not null then inverse split rule is used that is left and right branches are exchanged in the rule expressions below.
If it is not null then inverse split rule is used that is left and right branches are exchanged in the rule expressions below.
.. ocv:member:: float quality
.. ocv:member:: float quality
The split quality, a positive number. It is used to choose the best primary split, then to choose and sort the surrogate splits. After the tree is constructed, it is also used to compute variable importance.
The split quality, a positive number. It is used to choose the best primary split, then to choose and sort the surrogate splits. After the tree is constructed, it is also used to compute variable importance.
.. ocv:member:: CvDTreeSplit* next
.. ocv:member:: CvDTreeSplit* next
Pointer to the next split in the node list of splits.
Pointer to the next split in the node list of splits.
.. ocv:member:: int[] subset
.. ocv:member:: int[] subset
Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
if var_value in subset
then next_node <- left
else next_node <- right
.. ocv:member:: float ord::c
.. ocv:member:: float ord::c
The threshold value in case of split on an ordered variable. The rule is: ::
The threshold value in case of split on an ordered variable. The rule is: ::
if var_value < ord.c
then next_node<-left
else next_node<-right
.. ocv:member:: int ord::split_point
.. ocv:member:: int ord::split_point
Used internally by the training algorithm.
Used internally by the training algorithm.
CvDTreeNode
-----------
.. ocv:struct:: CvDTreeNode
The structure represents a node in a decision tree. It has public members:
The structure represents a node in a decision tree. It has public members:
.. ocv:member:: int class_idx
.. ocv:member:: int class_idx
Class index normalized to 0..class_count-1 range and assigned to the node. It is used internally in classification trees and tree ensembles.
.. ocv:member:: int Tn
.. ocv:member:: int Tn
Tree index in a ordered sequence of pruned trees. The indices are used during and after the pruning procedure. The root node has the maximum value ``Tn`` of the whole tree, child nodes have ``Tn`` less than or equal to the parent's ``Tn``, and nodes with :math:`Tn \leq CvDTree::pruned\_tree\_idx` are not used at prediction stage (the corresponding branches are considered as cut-off), even if they have not been physically deleted from the tree at the pruning stage.
.. ocv:member:: double value
.. ocv:member:: double value
Value at the node: a class label in case of classification or estimated function value in case of regression.
.. ocv:member:: CvDTreeNode* parent
.. ocv:member:: CvDTreeNode* parent
Pointer to the parent node.
.. ocv:member:: CvDTreeNode* left
.. ocv:member:: CvDTreeNode* left
Pointer to the left child node.
.. ocv:member:: CvDTreeNode* right
.. ocv:member:: CvDTreeNode* right
Pointer to the right child node.
.. ocv:member:: CvDTreeSplit* split
.. ocv:member:: CvDTreeSplit* split
Pointer to the first (primary) split in the node list of splits.
.. ocv:member:: int sample_count
.. ocv:member:: int sample_count
The number of samples that fall into the node at the training stage. It is used to resolve the difficult cases - when the variable for the primary split is missing and all the variables for other surrogate splits are missing too. In this case the sample is directed to the left if ``left->sample_count > right->sample_count`` and to the right otherwise.
.. ocv:member:: int depth
.. ocv:member:: int depth
Depth of the node. The root node depth is 0, the child nodes depth is the parent's depth + 1.
......
modules/ml/doc/ertrees.rst
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......@@ -12,4 +12,4 @@ CvERTrees
----------
.. ocv:class:: CvERTrees : public CvRTrees
The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:class:`CvRTParams` is used.
The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:struct:`CvRTParams` is used.
modules/ml/doc/neural_networks.rst
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......@@ -99,39 +99,39 @@ CvANN_MLP_TrainParams
---------------------
.. ocv:struct:: CvANN_MLP_TrainParams
Parameters of the MLP training algorithm. You can initialize the structure by a constructor or the individual parameters can be adjusted after the structure is created.
Parameters of the MLP training algorithm. You can initialize the structure by a constructor or the individual parameters can be adjusted after the structure is created.
The back-propagation algorithm parameters:
The back-propagation algorithm parameters:
.. ocv:member:: double bp_dw_scale
.. ocv:member:: double bp_dw_scale
Strength of the weight gradient term. The recommended value is about 0.1.
Strength of the weight gradient term. The recommended value is about 0.1.
.. ocv:member:: double bp_moment_scale
.. ocv:member:: double bp_moment_scale
Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough
Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough
The RPROP algorithm parameters (see [RPROP93]_ for details):
The RPROP algorithm parameters (see [RPROP93]_ for details):
.. ocv:member:: double rp_dw0
.. ocv:member:: double rp_dw0
Initial value :math:`\Delta_0` of update-values :math:`\Delta_{ij}`.
Initial value :math:`\Delta_0` of update-values :math:`\Delta_{ij}`.
.. ocv:member:: double rp_dw_plus
.. ocv:member:: double rp_dw_plus
Increase factor :math:`\eta^+`. It must be >1.
Increase factor :math:`\eta^+`. It must be >1.
.. ocv:member:: double rp_dw_minus
.. ocv:member:: double rp_dw_minus
Decrease factor :math:`\eta^-`. It must be <1.
Decrease factor :math:`\eta^-`. It must be <1.
.. ocv:member:: double rp_dw_min
.. ocv:member:: double rp_dw_min
Update-values lower limit :math:`\Delta_{min}`. It must be positive.
Update-values lower limit :math:`\Delta_{min}`. It must be positive.
.. ocv:member:: double rp_dw_max
.. ocv:member:: double rp_dw_max
Update-values upper limit :math:`\Delta_{max}`. It must be >1.
Update-values upper limit :math:`\Delta_{max}`. It must be >1.
CvANN_MLP_TrainParams::CvANN_MLP_TrainParams
......
modules/ml/doc/support_vector_machines.rst
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......@@ -18,19 +18,19 @@ CvParamGrid
-----------
.. ocv:struct:: CvParamGrid
The structure represents the logarithmic grid range of statmodel parameters. It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate being computed by cross-validation.
The structure represents the logarithmic grid range of statmodel parameters. It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate being computed by cross-validation.
.. ocv:member:: double CvParamGrid::min_val
.. ocv:member:: double CvParamGrid::min_val
Minimum value of the statmodel parameter.
Minimum value of the statmodel parameter.
.. ocv:member:: double CvParamGrid::max_val
.. ocv:member:: double CvParamGrid::max_val
Maximum value of the statmodel parameter.
Maximum value of the statmodel parameter.
.. ocv:member:: double CvParamGrid::step
.. ocv:member:: double CvParamGrid::step
Logarithmic step for iterating the statmodel parameter.
Logarithmic step for iterating the statmodel parameter.
The grid determines the following iteration sequence of the statmodel parameter values:
......
modules/nonfree/doc/feature_detection.rst
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......@@ -48,29 +48,29 @@ SURF
----
.. ocv:class:: SURF : public Feature2D
Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters:
Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters:
.. ocv:member:: int extended
.. ocv:member:: int extended
* 0 means that the basic descriptors (64 elements each) shall be computed
* 1 means that the extended descriptors (128 elements each) shall be computed
* 0 means that the basic descriptors (64 elements each) shall be computed
* 1 means that the extended descriptors (128 elements each) shall be computed
.. ocv:member:: int upright
.. ocv:member:: int upright
* 0 means that detector computes orientation of each feature.
* 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``.
* 0 means that detector computes orientation of each feature.
* 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``.
.. ocv:member:: double hessianThreshold
.. ocv:member:: double hessianThreshold
Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast.
Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast.
.. ocv:member:: int nOctaves
.. ocv:member:: int nOctaves
The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it.
The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it.
.. ocv:member:: int nOctaveLayers
.. ocv:member:: int nOctaveLayers
The number of images within each octave of a gaussian pyramid. It is set to 2 by default.
The number of images within each octave of a gaussian pyramid. It is set to 2 by default.
.. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006
......
modules/objdetect/doc/latent_svm.rst
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......@@ -45,97 +45,97 @@ CvLSVMFilterPosition
--------------------
.. ocv:struct:: CvLSVMFilterPosition
Structure describes the position of the filter in the feature pyramid.
Structure describes the position of the filter in the feature pyramid.
.. ocv:member:: unsigned int l
.. ocv:member:: unsigned int l
level in the feature pyramid
level in the feature pyramid
.. ocv:member:: unsigned int x
.. ocv:member:: unsigned int x
x-coordinate in level l
x-coordinate in level l
.. ocv:member:: unsigned int y
.. ocv:member:: unsigned int y
y-coordinate in level l
y-coordinate in level l
CvLSVMFilterObject
------------------
.. ocv:struct:: CvLSVMFilterObject
Description of the filter, which corresponds to the part of the object.
Description of the filter, which corresponds to the part of the object.
.. ocv:member:: CvLSVMFilterPosition V
.. ocv:member:: CvLSVMFilterPosition V
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
.. ocv:member:: float fineFunction[4]
.. ocv:member:: float fineFunction[4]
vector describes penalty function (d_i in the paper)
pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
vector describes penalty function (d_i in the paper)
pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
.. ocv:member:: int numFeatures
.. ocv:member:: int numFeatures
number of features
number of features
.. ocv:member:: float *H
.. ocv:member:: float *H
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
where k - component of feature vector in cell (i, j)
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
where k - component of feature vector in cell (i, j)
CvLatentSvmDetector
-------------------
.. ocv:struct:: CvLatentSvmDetector
Structure contains internal representation of trained Latent SVM detector.
Structure contains internal representation of trained Latent SVM detector.
.. ocv:member:: int num_filters
.. ocv:member:: int num_filters
total number of filters (root plus part) in model
total number of filters (root plus part) in model
.. ocv:member:: int num_components
.. ocv:member:: int num_components
number of components in model
number of components in model
.. ocv:member:: int* num_part_filters
.. ocv:member:: int* num_part_filters
array containing number of part filters for each component
array containing number of part filters for each component
.. ocv:member:: CvLSVMFilterObject** filters
.. ocv:member:: CvLSVMFilterObject** filters
root and part filters for all model components
root and part filters for all model components
.. ocv:member:: float* b
.. ocv:member:: float* b
biases for all model components
biases for all model components
.. ocv:member:: float score_threshold
.. ocv:member:: float score_threshold
confidence level threshold
confidence level threshold
CvObjectDetection
-----------------
.. ocv:struct:: CvObjectDetection
Structure contains the bounding box and confidence level for detected object.
Structure contains the bounding box and confidence level for detected object.
.. ocv:member:: CvRect rect
.. ocv:member:: CvRect rect
bounding box for a detected object
bounding box for a detected object
.. ocv:member:: float score
.. ocv:member:: float score
confidence level
confidence level
cvLoadLatentSvmDetector
......@@ -183,19 +183,19 @@ LatentSvmDetector::ObjectDetection
----------------------------------
.. ocv:struct:: LatentSvmDetector::ObjectDetection
Structure contains the detection information.
Structure contains the detection information.
.. ocv:member:: Rect rect
.. ocv:member:: Rect rect
bounding box for a detected object
bounding box for a detected object
.. ocv:member:: float score
.. ocv:member:: float score
confidence level
confidence level
.. ocv:member:: int classID
.. ocv:member:: int classID
class (model or detector) ID that detect an object
class (model or detector) ID that detect an object
LatentSvmDetector::LatentSvmDetector
......
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