@@ -413,10 +413,8 @@ value if all of the corners are found and they are placed
in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder
them, it returns 0. For example, a regular chessboard has 8 x 8
squares and 7 x 7 internal corners, that is, points where the black
squares touch each other. The detected coordinates are approximate,
and to determine their position more accurately, you may use
the function
:ref:`cornerSubPix`.
squares touch each other. The detected coordinates are approximate so the function calls :ref:`cornerSubPix` internally to determine their position more accurately.
You also may use the function :ref:`cornerSubPix` with different parameters if returned coordinates are not accurate enough.
Sample usage of detecting and drawing chessboard corners: ::
then the point :math:`i` is considered an outlier. If ``srcPoints`` and ``dstPoints`` are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
@@ -5,9 +5,9 @@ Common Interfaces of Descriptor Extractors
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to computing descriptors
that are represented as vectors in a multidimensional space. All objects that implement the ``vector``
represented as vectors in a multidimensional space. All objects that implement the ``vector``
descriptor extractors inherit the
:ref:`DescriptorExtractor` interface.
:ocv:class:`DescriptorExtractor` interface.
.. index:: DescriptorExtractor
...
...
@@ -15,7 +15,7 @@ DescriptorExtractor
-------------------
.. ocv:class:: DescriptorExtractor
Abstract base class for computing descriptors for image keypoints ::
Abstract base class for computing descriptors for image keypoints. ::
class CV_EXPORTS DescriptorExtractor
{
...
...
@@ -45,7 +45,7 @@ dense, fixed-dimension vector of a basic type. Most descriptors
follow this pattern as it simplifies computing
distances between descriptors. Therefore, a collection of
descriptors is represented as
:ref:`Mat` , where each row is a keypoint descriptor.
:ocv:class:`Mat` , where each row is a keypoint descriptor.
.. index:: DescriptorExtractor::compute
...
...
@@ -57,9 +57,9 @@ DescriptorExtractor::compute
:param image: Image.
:param keypoints: Keypoints. Keypoints for which a descriptor cannot be computed are removed. Somtimes new keypoints can be added, eg SIFT duplicates keypoint with several dominant orientations (for each orientation).
:param keypoints: Keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: ``SIFT`` duplicates keypoint with several dominant orientations (for each orientation).
:param descriptors: Descriptors. Row i is the descriptor for keypoint i.
:param descriptors: Descriptors. Row ``i`` is the descriptor for keypoint ``i``.
@@ -6,8 +6,8 @@ Common Interfaces of Descriptor Matchers
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to matching descriptors
that are represented as vectors in a multidimensional space. All objects that implement ``vector``
descriptor matchers inherit
:ref:`DescriptorMatcher` interface.
descriptor matchers inherit the
:ocv:class:`DescriptorMatcher` interface.
.. index:: DMatch
...
...
@@ -18,7 +18,7 @@ DMatch
.. ocv:class:: DMatch
Class for matching keypoint descriptors: query descriptor index,
train descriptor index, train image index, and distance between descriptors ::
train descriptor index, train image index, and distance between descriptors. ::
struct DMatch
{
...
...
@@ -48,7 +48,7 @@ train descriptor index, train image index, and distance between descriptors ::
DescriptorMatcher
-----------------
.. c:type:: DescriptorMatcher
.. ocv:class:: DescriptorMatcher
Abstract base class for matching keypoint descriptors. It has two groups
of match methods: for matching descriptors of an image with another image or
...
...
@@ -198,7 +198,7 @@ DescriptorMatcher::knnMatch
:param k: Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.
:param compactResult: Parameter that is used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
These extended variants of :ocv:func:`DescriptorMatcher::match` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See :ocv:func:`DescriptorMatcher::match` for the details about query and train descriptors.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image ``trainDescCollection[i]``.
:param matches: The found matches.
:param matches: Found matches.
:param compactResult: Parameter that is used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
:param maxDistance: Threshold for the distance between matched descriptors.
...
...
@@ -265,7 +265,7 @@ DescriptorMatcher::create
BruteForceMatcher
-----------------
.. c:type:: BruteForceMatcher
.. ocv:class:: BruteForceMatcher
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. ::
...
...
@@ -351,9 +351,9 @@ For efficiency, ``BruteForceMatcher`` is used as a template parameterized with t
FlannBasedMatcher
-----------------
.. c:type:: FlannBasedMatcher
.. ocv:class:: FlannBasedMatcher
Flann-based descriptor matcher. This matcher trains :ref:`flann::Index` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. ``FlannBasedMatcher`` does not support masking permissible matches of descriptor sets because :ocv:func:`flann::Index` does not support this. ::
Flann-based descriptor matcher. This matcher trains :ocv:func:`flann::Index` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. ``FlannBasedMatcher`` does not support masking permissible matches of descriptor sets because ``flann::Index`` does not support this. ::
class FlannBasedMatcher : public DescriptorMatcher
Abstract interface for extracting and matching a keypoint descriptor. There are also :ref:`DescriptorExtractor` and :ref:`DescriptorMatcher` for these purposes but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. :ref:`DescriptorMatcher` and ``GenericDescriptorMatcher`` have two groups of match methods: for matching keypoints of an image with another image or with an image set. ::
Abstract interface for extracting and matching a keypoint descriptor. There are also :ocv:class:`DescriptorExtractor` and :ocv:class:`DescriptorMatcher` for these purposes but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. ``DescriptorMatcher`` and ``GenericDescriptorMatcher`` have two groups of match methods: for matching keypoints of an image with another image or with an image set. ::
:param trainKeypoints: Keypoints from a train image.
The method classifies each keypoint from a query set. The first variant of the method takes a train image and its keypoints as an input argument. The second variant uses the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method.
The method classifies each keypoint from a query set. The first variant of the method takes a train image and its keypoints as an input argument. The second variant uses the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method.
The methods do the following:
The methods do the following:
#.
Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set.
#.
Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set.
#.
Sey the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set.
#.
Set the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set.
:param matches: Matches. If a query descriptor (keypoint) is masked out in ``mask`` , match is added for this descriptor. So, ``matches`` size may be smaller than the query keypoints count.
:param mask: Mask specifying permissible matches between input query and train keypoints.
:param mask: Mask specifying permissible matches between an input query and train keypoints.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints and stored train keypoints from the i-th image.
The methods find the best match for each query keypoint. In the first variant of the method, a train image and its keypoints are the input arguments. In the second variant, query keypoints are matched to the internally stored training collection that can be built using ``GenericDescriptorMatcher::add`` method. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryKeypoints[i]`` can be matched with ``trainKeypoints[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
The methods find the best match for each query keypoint. In the first variant of the method, a train image and its keypoints are the input arguments. In the second variant, query keypoints are matched to the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryKeypoints[i]`` can be matched with ``trainKeypoints[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
Find the ``k`` best matches for each query keypoint.
Finds the ``k`` best matches for each query keypoint.
The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
Draw the found matches of keypoints from two images
Draws the found matches of keypoints from two images.
:param img1: The first source image.
:param img1: First source image.
:param keypoints1: Keypoints from the first source image.
:param img2: The second source image.
:param img2: Second source image.
:param keypoints2: Keypoints from the second source image.
...
...
@@ -75,5 +75,5 @@ drawKeypoints
:param color: Color of keypoints.
:param flags: Flags setting drawing features. Possible ``flags`` bit values are defined by ``DrawMatchesFlags``. See details above in :ref:`drawMatches` .
:param flags: Flags setting drawing features. Possible ``flags`` bit values are defined by ``DrawMatchesFlags``. See details above in :ocv:func:`drawMatches` .
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for usefull comments and parameters description.
.. index:: StarDetector
...
...
@@ -56,7 +56,7 @@ StarDetector
------------
.. ocv:class:: StarDetector
Class implementing the Star keypoint detector ::
Class implementing the ``Star`` keypoint detector. ::
class StarDetector : CvStarDetectorParams
{
...
...
@@ -89,13 +89,11 @@ The class implements a modified version of the ``CenSurE`` keypoint detector des
.. index:: SIFT
.. _SIFT:
SIFT
----
.. ocv:class:: SIFT
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach ::
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach. ::
class CV_EXPORTS SIFT
{
...
...
@@ -179,13 +177,11 @@ Class for extracting keypoints and computing descriptors using the Scale Invaria
.. index:: SURF
.. _SURF:
SURF
----
.. ocv:class:: SURF
Class for extracting Speeded Up Robust Features from an image ::
Class for extracting Speeded Up Robust Features from an image. ::
class SURF : public CvSURFParams
{
...
...
@@ -214,18 +210,16 @@ The class implements the Speeded Up Robust Features descriptor
[Bay06].
There is a fast multi-scale Hessian keypoint detector that can be used to find keypoints
(default option). But the descriptors can be also computed for the user-specified keypoints.
The algorithm can be used for object tracking and localization, image stitching, and so on. See the ``find_obj.cpp`` demo in OpenCV samples directory.
The algorithm can be used for object tracking and localization, image stitching, and so on. See the ``find_obj.cpp`` demo in the OpenCV samples directory.
.. index:: ORB
.. _ORB:
ORB
----
.. ocv:class:: ORB
Class for extracting ORB features and descriptors from an image ::
Class for extracting ORB features and descriptors from an image. ::
class ORB
{
...
...
@@ -272,18 +266,17 @@ Class for extracting ORB features and descriptors from an image ::
bool useProvidedKeypoints=false) const;
};
The class implements ORB
The class implements ORB.
.. index:: RandomizedTree
.. _RandomizedTree:
RandomizedTree
--------------
.. ocv:class:: RandomizedTree
Class containing a base structure for ``RTreeClassifier`` ::
Class containing a base structure for ``RTreeClassifier``. ::
class CV_EXPORTS RandomizedTree
{
...
...
@@ -423,7 +416,7 @@ RTreeNode
---------
.. ocv:class:: RTreeNode
Class containing a base structure for ``RandomizedTree`` ::
Class containing a base structure for ``RandomizedTree``. ::
struct RTreeNode
{
...
...
@@ -451,7 +444,7 @@ RTreeClassifier
---------------
.. ocv:class:: RTreeClassifier
Class containing ``RTreeClassifier``. It represents the Calonder descriptor that was originally introduced by Michael Calonder. ::
Class containing ``RTreeClassifier``. It represents the Calonder descriptor originally introduced by Michael Calonder. ::
.. ocv:function:: BOWKMeansTrainer::BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), int attempts=3, int flags=KMEANS_PP_CENTERS );
To understand constructor parameters, see :ref:`kmeans` function arguments.
See :ocv:func:`kmeans` function parameters.
BOWImgDescriptorExtractor
-------------------------
.. ocv:class:: BOWImgDescriptorExtractor
Class to compute an image descriptor using the ''bag of visual words''. Such a computation consists of the following steps:
Class to compute an image descriptor using the *bag of visual words*. Such a computation consists of the following steps:
#. Compute descriptors for a given image and its keypoints set.
#. Find the nearest visual words from the vocabulary for each keypoint descriptor.
#. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The ``i``-th bin of the histogram is a frequency of ``i``-th word of the vocabulary in the given image.
:param vocabulary: Vocabulary (can be trained using the inheritor of :ref:`BOWTrainer` ). Each row of the vocabulary is a visual word (cluster center).
:param vocabulary: Vocabulary (can be trained using the inheritor of :ocv:class:`BOWTrainer` ). Each row of the vocabulary is a visual word (cluster center).
:param delay: Delay in milliseconds. 0 is the special value that means "forever".
The function ``waitKey`` waits for a key event infinitely (when
:math:`\texttt{delay}\leq 0` ) or for ``delay`` milliseconds, when it is positive. It returns the code of the pressed key or -1 if no key was pressed before the specified time had elapsed.
:math:`\texttt{delay}\leq 0` ) or for ``delay`` milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the function will not wait exactly ``delay`` ms, it will wait at least ``delay`` ms, depending on what else is running on your computer at that time. It returns the code of the pressed key or -1 if no key was pressed before the specified time had elapsed.
``bilateralFilter`` can do a very good job of reducing unwanted noise while keep edges fairly sharp. However it is very slow compared to most filters.
*Sigma values*: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10) then the filter will not have much effect, whereas if they are large (> 150) then they will have a very strong effect, making the image look "cartoonish".
*Filter size*: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.
To shrink an image, it will generally look best with CV_INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with CV_INTER_CUBIC (slow) or CV_INTER_LINEAR (faster but still looks OK).
The function converts an input image from one color
space to another. In case of transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR).
Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green and the third byte will be Red. The fourth, fifth and sixth bytes would then be the 2nd pixel (Blue then Green then Red) and so on.
The conventional ranges for R, G, and B channel values are:
...
...
@@ -103,6 +104,8 @@ But in case of a non-linear transformation, an input RGB image should be normali
img *= 1./255;
cvtColor(img, img, CV_BGR2Luv);
If you use ``cvtColor`` with 8-bit images then conversion will have lost some information. For many applications this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
The function can do the following transformations:
CvDTreeParams( int _max_depth, int _min_sample_count,
float _regression_accuracy, bool _use_surrogates,
int _max_categories, int _cv_folds,
bool _use_1se_rule, bool _truncate_pruned_tree,
const float* _priors );
};
The structure contains all the decision tree training parameters. You can initialize it by default constructor and then override any parameters directly before training, or the structure may be fully initialized using the advanced variant of the constructor.
.. index:: CvDTreeParams::CvDTreeParams
.. _CvDTreeParams::CvDTreeParams
CvDTreeParams::CvDTreeParams
----------------------------
.. ocv:function:: CvDTreeParams::CvDTreeParams()
.. ocv:function:: CvDTreeParams( int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, int cv_folds, bool use_1se_rule, bool truncate_pruned_tree, const float* priors )
:param max_depth: The maximum number of levels in a tree. The depth of a constructed tree may be smaller due to other termination criterias or pruning of the tree.
:param min_sample_count: If the number of samples in a node is less than this parameter then the node will not be splitted.
:param regression_accuracy: Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be splitted.
:param use_surrogates: If true then surrogate splits will be built. These splits allow to work with missing data.
:param max_categories: Cluster possible values of a categorical variable into ``K`` :math:`\leq` ``max_categories`` clusters to find a suboptimal split. The clustering is applied only in n>2-class classification problems for categorical variables with ``N > max_categories`` possible values. See the Learning OpenCV book (page 489) for more detailed explanation.
:param cv_folds: If ``cv_folds > 1`` then prune a tree with ``K``-fold cross-validation where ``K`` is equal to ``cv_folds``.
:param use_1se_rule: If true then a pruning will be harsher. This will make a tree more compact but a bit less accurate.
:param truncate_pruned_tree: If true then pruned branches are removed completely from the tree. Otherwise they are retained and it is possible to get the unpruned tree or prune the tree differently by changing ``CvDTree::pruned_tree_idx`` parameter.
:param priors: Weights of prediction categories which determine relative weights that you give to misclassification. That is, if the weight of the first category is 1 and the weight of the second category is 10, then each mistake in predicting the second category is equivalent to making 10 mistakes in predicting the first category.
The default constructor initializes all the parameters with the default values tuned for the standalone classification tree:
::
The structure contains all the decision tree training parameters. There is a default constructor that initializes all the parameters with the default values tuned for the standalone classification tree. Any parameters can be overridden then, or the structure may be fully initialized using the advanced variant of the constructor.
