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Fixed hundreds of documentation problems

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This commit is contained in:
Andrey Kamaev
2012-05-28 07:36:14 +00:00
parent 7e698726e4
commit eb2f1f81ed
65 changed files with 1708 additions and 1680 deletions
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12
modules/objdetect/doc/cascade_classification.rst
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@@ -131,7 +131,7 @@ FeatureEvaluator::create
----------------------------
Constructs the feature evaluator.
.. ocv:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
.. ocv:function:: Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
:param type: Type of features evaluated by cascade (``HAAR`` or ``LBP`` for now).
@@ -148,7 +148,7 @@ Loads a classifier from a file.
.. ocv:function:: CascadeClassifier::CascadeClassifier(const string& filename)
.. ocv:pyfunction:: cv2.CascadeClassifier(filename) -> <CascadeClassifier object>
.. ocv:pyfunction:: cv2.CascadeClassifier([filename]) -> <CascadeClassifier object>
:param filename: Name of the file from which the classifier is loaded.
@@ -193,9 +193,9 @@ Detects objects of different sizes in the input image. The detected objects are
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) -> objects
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image, rejectLevels, levelWeights[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) -> objects
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor=1.1, int minNeighbors=3, int flags=0, CvSize minSize=cvSize(0, 0), CvSize maxSize=cvSize(0, 0) )
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scale_factor=1.1, int min_neighbors=3, int flags=0, CvSize min_size=cvSize(0,0), CvSize max_size=cvSize(0,0) )
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scaleFactor=1.1, minNeighbors=3, flags=0, minSize=(0, 0))-> detectedObjects
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, min_size=(0, 0)) -> detectedObjects
:param cascade: Haar classifier cascade (OpenCV 1.x API only). It can be loaded from XML or YAML file using :ocv:cfunc:`Load`. When the cascade is not needed anymore, release it using ``cvReleaseHaarClassifierCascade(&cascade)``.
@@ -222,7 +222,7 @@ Sets an image for detection.
.. ocv:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& feval, const Mat& image )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tiltedSum, double scale )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tilted_sum, double scale )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.
@@ -241,7 +241,7 @@ Runs the detector at the specified point.
.. ocv:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& feval, Point pt )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( CvHaarClassifierCascade* cascade, CvPoint pt, int startStage=0 )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, CvPoint pt, int start_stage=0 )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.

140
modules/objdetect/doc/latent_svm.rst
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@@ -27,13 +27,13 @@ model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
:ocv:func:`cvReleaseLatentSvmDetector`, :ocv:func:`cvLatentSvmDetectObjects`).
C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
functionality in contrast with C version - it supports loading and detection
of several models.
C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
functionality in contrast with C version - it supports loading and detection
of several models.
There are two examples of Latent SVM usage: ``samples/c/latentsvmdetect.cpp``
and ``samples/cpp/latentsvm_multidetect.cpp``.
@@ -48,18 +48,18 @@ CvLSVMFilterPosition
Structure describes the position of the filter in the feature pyramid.
.. ocv:member:: unsigned int l
level in the feature pyramid
.. ocv:member:: unsigned int x
x-coordinate in level l
.. ocv:member:: unsigned int y
y-coordinate in level l
CvLSVMFilterObject
------------------
.. ocv:struct:: CvLSVMFilterObject
@@ -67,31 +67,31 @@ CvLSVMFilterObject
Description of the filter, which corresponds to the part of the object.
.. ocv:member:: CvLSVMFilterPosition V
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
.. 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
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
.. ocv:member:: int numFeatures
number of features
.. ocv:member:: float *H
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
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
@@ -99,30 +99,30 @@ CvLatentSvmDetector
Structure contains internal representation of trained Latent SVM detector.
.. ocv:member:: int num_filters
total number of filters (root plus part) in model
.. ocv:member:: int num_components
number of components in model
.. ocv:member:: int* num_part_filters
array containing number of part filters for each component
.. ocv:member:: CvLSVMFilterObject** filters
root and part filters for all model components
.. ocv:member:: float* b
biases for all model components
.. ocv:member:: float score_threshold
confidence level threshold
CvObjectDetection
-----------------
.. ocv:struct:: CvObjectDetection
@@ -130,11 +130,11 @@ CvObjectDetection
Structure contains the bounding box and confidence level for detected object.
.. ocv:member:: CvRect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
@@ -145,7 +145,7 @@ Loads trained detector from a file.
.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
:param filename: Name of the file containing the description of a trained detector
cvReleaseLatentSvmDetector
--------------------------
@@ -158,46 +158,46 @@ Release memory allocated for CvLatentSvmDetector structure.
cvLatentSvmDetectObjects
------------------------
Find rectangular regions in the given image that are likely to contain objects
Find rectangular regions in the given image that are likely to contain objects
and corresponding confidence levels.
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold, int numThreads)
:param image: image
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects( IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold=0.5f, int numThreads=-1 )
:param image: image
:param detector: LatentSVM detector in internal representation
:param storage: Memory storage to store the resultant sequence of the object candidate rectangles
:param overlap_threshold: Threshold for the non-maximum suppression algorithm
:param numThreads: Number of threads used in parallel version of the algorithm
.. highlight:: cpp
LatentSvmDetector
-----------------
.. ocv:class:: LatentSvmDetector
This is a C++ wrapping class of Latent SVM. It contains internal representation of several
trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
This is a C++ wrapping class of Latent SVM. It contains internal representation of several
trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
using them.
LatentSvmDetector::ObjectDetection
----------------------------------
.. ocv:class:: LatentSvmDetector::ObjectDetection
.. ocv:struct:: LatentSvmDetector::ObjectDetection
Structure contains the detection information.
.. ocv:member:: Rect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
.. ocv:member:: int classID
class (model or detector) ID that detect an object
class (model or detector) ID that detect an object
LatentSvmDetector::LatentSvmDetector
------------------------------------
Two types of constructors.
@@ -208,8 +208,8 @@ Two types of constructors.
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::~LatentSvmDetector
@@ -228,10 +228,10 @@ LatentSvmDetector::load
-----------------------
Load the trained models from given ``.xml`` files and return ``true`` if at least one model was loaded.
.. ocv:function:: bool LatentSvmDetector::load(const vector<string>& filenames, const vector<string>& classNames)
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
.. ocv:function:: bool LatentSvmDetector::load( const vector<string>& filenames, const vector<string>& classNames=vector<string>() )
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::detect
@@ -239,13 +239,13 @@ LatentSvmDetector::detect
Find rectangular regions in the given image that are likely to contain objects of loaded classes (models)
and corresponding confidence levels.
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5, int numThreads=-1 )
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 )
:param image: An image.
:param objectDetections: The detections: rectangulars, scores and class IDs.
:param overlapThreshold: Threshold for the non-maximum suppression algorithm.
:param numThreads: Number of threads used in parallel version of the algorithm.
LatentSvmDetector::getClassNames
--------------------------------
Return the class (model) names that were passed in constructor or method ``load`` or extracted from models filenames in those methods.
@@ -257,8 +257,8 @@ LatentSvmDetector::getClassCount
Return a count of loaded models (classes).
.. ocv:function:: size_t getClassCount() const
.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010
.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010

2
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -346,7 +346,7 @@ public:
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const;
virtual bool setImage(const Mat&, Size origWinSize);
virtual bool setImage(const Mat& img, Size origWinSize);
virtual bool setWindow(Point p);
virtual double calcOrd(int featureIdx) const;