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Vadim Pisarevsky
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modules/features2d/doc/object_categorization.rst
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@@ -3,155 +3,81 @@ Object Categorization
.. highlight:: cpp
Some approaches based on local 2D features and used to object categorization
Some approaches based on local 2D features and used to object categorization
are described in this section.
.. index:: BOWTrainer
.. _BOWTrainer:
BOWTrainer
----------
`id=0.926370937775 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer>`__
.. ctype:: BOWTrainer
Abstract base class for training ''bag of visual words'' vocabulary from a set of descriptors.
See e.g. ''Visual Categorization with Bags of Keypoints'' of Gabriella Csurka, Christopher R. Dance,
Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
Abstract base class for training ''bag of visual words'' vocabulary from a set of descriptors.
See e.g. ''Visual Categorization with Bags of Keypoints'' of Gabriella Csurka, Christopher R. Dance,
Lixin Fan, Jutta Willamowski, Cedric Bray, 2004.
::
class BOWTrainer
{
public:
BOWTrainer(){}
virtual ~BOWTrainer(){}
void add( const Mat& descriptors );
const vector<Mat>& getDescriptors() const;
int descripotorsCount() const;
virtual void clear();
virtual Mat cluster() const = 0;
virtual Mat cluster( const Mat& descriptors ) const = 0;
protected:
...
};
..
.. index:: BOWTrainer::add
cv::BOWTrainer::add
-------------------
`id=0.849162389183 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Aadd>`__
````
------------------- ````
.. cfunction:: void BOWTrainer::add( const Mat\& descriptors )
Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.
:param descriptors: Descriptors to add to training set. Each row of ``descriptors``
matrix is a one descriptor.
:param descriptors: Descriptors to add to training set. Each row of ``descriptors`` matrix is a one descriptor.
.. index:: BOWTrainer::getDescriptors
cv::BOWTrainer::getDescriptors
------------------------------
`id=0.999824242082 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AgetDescriptors>`__
.. cfunction:: const vector<Mat>\& BOWTrainer::getDescriptors() const
Returns training set of descriptors.
.. index:: BOWTrainer::descripotorsCount
cv::BOWTrainer::descripotorsCount
---------------------------------
`id=0.497913292449 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AdescripotorsCount>`__
.. cfunction:: const vector<Mat>\& BOWTrainer::descripotorsCount() const
Returns count of all descriptors stored in the training set.
.. index:: BOWTrainer::cluster
cv::BOWTrainer::cluster
-----------------------
`id=0.560094315089 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Acluster>`__
.. cfunction:: Mat BOWTrainer::cluster() const
Cluster train descriptors. Vocabulary consists from cluster centers. So this method
returns vocabulary. In first method variant the stored in object train descriptors will be
Cluster train descriptors. Vocabulary consists from cluster centers. So this method
returns vocabulary. In first method variant the stored in object train descriptors will be
clustered, in second variant -- input descriptors will be clustered.
.. cfunction:: Mat BOWTrainer::cluster( const Mat\& descriptors ) const
:param descriptors: Descriptors to cluster. Each row of ``descriptors``
matrix is a one descriptor. Descriptors will not be added
to the inner train descriptor set.
:param descriptors: Descriptors to cluster. Each row of ``descriptors`` matrix is a one descriptor. Descriptors will not be added
to the inner train descriptor set.
.. index:: BOWKMeansTrainer
@@ -159,250 +85,133 @@ clustered, in second variant -- input descriptors will be clustered.
BOWKMeansTrainer
----------------
`id=0.588500098443 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWKMeansTrainer>`__
.. ctype:: BOWKMeansTrainer
:func:`kmeans` based class to train visual vocabulary using the ''bag of visual words'' approach. ::
:func:`kmeans`
based class to train visual vocabulary using the ''bag of visual words'' approach.
::
class BOWKMeansTrainer : public BOWTrainer
{
public:
BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
int attempts=3, int flags=KMEANS_PP_CENTERS );
virtual ~BOWKMeansTrainer(){}
// Returns trained vocabulary (i.e. cluster centers).
virtual Mat cluster() const;
virtual Mat cluster( const Mat& descriptors ) const;
protected:
...
};
..
To gain an understanding of constructor parameters see
:func:`kmeans`
function
To gain an understanding of constructor parameters see
:func:`kmeans` function
arguments.
.. index:: BOWImgDescriptorExtractor
.. _BOWImgDescriptorExtractor:
BOWImgDescriptorExtractor
-------------------------
`id=0.166378792557 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor>`__
.. ctype:: BOWImgDescriptorExtractor
Class to compute image descriptor using ''bad of visual words''. In few,
such computing consists from the following steps:
1. Compute descriptors for given image and it's keypoints set,
Class to compute image descriptor using ''bad of visual words''. In few,
such computing consists from the following steps:
1. Compute descriptors for given image and it's keypoints set,
\
2. Find nearest visual words from vocabulary for each keypoint descriptor,
2. Find nearest visual words from vocabulary for each keypoint descriptor,
\
3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e.
``i``
-bin of the histogram is a frequency of
``i``
-word of vocabulary in the given image.
3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e.
``i`` -bin of the histogram is a frequency of ``i`` -word of vocabulary in the given image. ::
::
class BOWImgDescriptorExtractor
{
public:
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
virtual ~BOWImgDescriptorExtractor(){}
void setVocabulary( const Mat& vocabulary );
const Mat& getVocabulary() const;
void compute( const Mat& image, vector<KeyPoint>& keypoints,
Mat& imgDescriptor,
vector<vector<int> >* pointIdxsOfClusters=0,
void compute( const Mat& image, vector<KeyPoint>& keypoints,
Mat& imgDescriptor,
vector<vector<int> >* pointIdxsOfClusters=0,
Mat* descriptors=0 );
int descriptorSize() const;
int descriptorType() const;
protected:
...
};
..
.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
--------------------------------------------------------
`id=0.355574799377 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3ABOWImgDescriptorExtractor>`__
.. cfunction:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>\& dextractor, const Ptr<DescriptorMatcher>\& dmatcher )
Constructor.
:param dextractor: Descriptor extractor that will be used to compute descriptors
for input image and it's keypoints.
:param dextractor: Descriptor extractor that will be used to compute descriptors
for input image and it's keypoints.
:param dmatcher: Descriptor matcher that will be used to find nearest word of trained vocabulary to
each keupoints descriptor of the image.
each keupoints descriptor of the image.
.. index:: BOWImgDescriptorExtractor::setVocabulary
cv::BOWImgDescriptorExtractor::setVocabulary
--------------------------------------------
`id=0.592484692408 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AsetVocabulary>`__
.. cfunction:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )
Method to set visual vocabulary.
:param vocabulary: Vocabulary (can be trained using inheritor of :func:`BOWTrainer` ).
Each row of vocabulary is a one visual word (cluster center).
:param vocabulary: Vocabulary (can be trained using inheritor of :func:`BOWTrainer` ).
Each row of vocabulary is a one visual word (cluster center).
.. index:: BOWImgDescriptorExtractor::getVocabulary
cv::BOWImgDescriptorExtractor::getVocabulary
--------------------------------------------
`id=0.0185667539631 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AgetVocabulary>`__
.. cfunction:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const
Returns set vocabulary.
.. index:: BOWImgDescriptorExtractor::compute
cv::BOWImgDescriptorExtractor::compute
--------------------------------------
`id=0.558308680471 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3Acompute>`__
.. cfunction:: void BOWImgDescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
Compute image descriptor using set visual vocabulary.
:param image: The image. Image descriptor will be computed for this.
:param keypoints: Keypoints detected in the input image.
:param imgDescriptor: This is output, i.e. computed image descriptor.
:param pointIdxsOfClusters: Indices of keypoints which belong to the cluster, i.e. ``pointIdxsOfClusters[i]`` is keypoint indices which belong
to the ``i-`` cluster (word of vocabulary) (returned if it is not 0.)
:param image: The image. Image descriptor will be computed for this.
:param keypoints: Keypoints detected in the input image.
:param imgDescriptor: This is output, i.e. computed image descriptor.
:param pointIdxsOfClusters: Indices of keypoints which belong to the cluster, i.e.
``pointIdxsOfClusters[i]`` is keypoint indices which belong
to the ``i-`` cluster (word of vocabulary) (returned if it is not 0.)
:param descriptors: Descriptors of the image keypoints (returned if it is not 0.)
:param descriptors: Descriptors of the image keypoints (returned if it is not 0.)
.. index:: BOWImgDescriptorExtractor::descriptorSize
cv::BOWImgDescriptorExtractor::descriptorSize
---------------------------------------------
`id=0.758326749957 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorSize>`__
.. cfunction:: int BOWImgDescriptorExtractor::descriptorSize() const
Returns image discriptor size, if vocabulary was set, and 0 otherwise.
.. index:: BOWImgDescriptorExtractor::descriptorType
cv::BOWImgDescriptorExtractor::descriptorType
---------------------------------------------
`id=0.940227909801 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorType>`__
.. cfunction:: int BOWImgDescriptorExtractor::descriptorType() const
Returns image descriptor type.