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some more doc cleanup

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This commit is contained in:
Vadim Pisarevsky
2011-03-03 07:29:55 +00:00
parent 4e6572acd9
commit f025e4739a
39 changed files with 1531 additions and 889 deletions
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modules/features2d/doc/object_categorization.rst
+32 -42
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@@ -36,12 +36,12 @@ Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
protected:
...
};
..
.. index:: BOWTrainer::add
BOWTrainer::add
------------------- ````
-------------------
.. c:function:: void BOWTrainer::add( const Mat\& descriptors )
Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.
@@ -70,14 +70,11 @@ BOWTrainer::cluster
-----------------------
.. c:function:: 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
clustered, in second variant -- input descriptors will be clustered.
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.
.. c:function:: 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
@@ -103,7 +100,7 @@ BOWKMeansTrainer
protected:
...
};
..
To gain an understanding of constructor parameters see
:func:`kmeans` function
@@ -117,35 +114,32 @@ BOWImgDescriptorExtractor
-------------------------
.. c:type:: 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,
\
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. ::
Class to compute image descriptor using ''bad of visual words''. In few, such computing consists from the following steps:
class BOWImgDescriptorExtractor
{
public:
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
virtual ~BOWImgDescriptorExtractor(){}
#. Compute descriptors for given image and it's keypoints set
#. Find nearest visual words from vocabulary for each keypoint descriptor,
#. 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. ::
void setVocabulary( const Mat& vocabulary );
const Mat& getVocabulary() const;
void compute( const Mat& image, vector<KeyPoint>& keypoints,
Mat& imgDescriptor,
vector<vector<int> >* pointIdxsOfClusters=0,
Mat* descriptors=0 );
int descriptorSize() const;
int descriptorType() const;
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,
Mat* descriptors=0 );
int descriptorSize() const;
int descriptorType() const;
protected:
...
};
protected:
...
};
..
.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
@@ -155,11 +149,9 @@ BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
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.
:param dmatcher: Descriptor matcher that will be used to find nearest word of trained vocabulary to each keupoints descriptor of the image.
.. index:: BOWImgDescriptorExtractor::setVocabulary
@@ -169,8 +161,7 @@ BOWImgDescriptorExtractor::setVocabulary
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
@@ -194,8 +185,7 @@ BOWImgDescriptorExtractor::compute
: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 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.)