:param flags:The operation flags; may be 0 or ``CV_CALIB_ZERO_DISPARITY`` . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in horizontal or vertical direction (depending on the orientation of epipolar lines) in order to maximize the useful image area.
@ -2320,7 +2320,7 @@ StereoRectify
The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, that makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. On input the function takes the matrices computed by
:func:`stereoCalibrate`
:cpp:func:`stereoCalibrate`
and on output it gives 2 rotation matrices and also 2 projection matrices in the new coordinates. The 2 cases are distinguished by the function are:
@ -2562,10 +2562,10 @@ UndistortPoints
:param distCoeffs:The input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5 or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R:The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param R:The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param P:The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
:param P:The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
@ -9,20 +9,20 @@ The boundaries of the shapes can be rendered with antialiasing (implemented only
All the functions include the parameter color that uses a rgb value (that may be constructed
with
``CV_RGB``
macro or the :func:`cvScalar` function
macro or the :cpp:func:`cvScalar` function
) for color
images and brightness for grayscale images. For color images the order channel
is normally
*Blue, Green, Red*
, this is what
:func:`imshow`
:cpp:func:`imshow`
,
:func:`imread`
:cpp:func:`imread`
and
:func:`imwrite`
:cpp:func:`imwrite`
expect
, so if you form a color using
:func:`cvScalar`
:cpp:func:`cvScalar`
, it should look like:
@ -32,7 +32,7 @@ expect
If you are using your own image rendering and I/O functions, you can use any channel ordering, the drawing functions process each channel independently and do not depend on the channel order or even on the color space used. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor`
:cpp:func:`cvtColor`
.
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy, that is, the coordinates can be passed as fixed-point numbers, encoded as integers. The number of fractional bits is specified by the
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`
:cpp:func:`Mat`
's), that is, for each pixel location
:math:`(x,y)`
in the source image some its (normally rectangular) neighborhood is considered and used to compute the response. In case of a linear filter it is a weighted sum of pixel values, in case of morphological operations it is the minimum or maximum etc. The computed response is stored to the destination image at the same location
The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, that makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. On input the function takes the matrices computed by
:func:`stereoCalibrate`
:cpp:func:`stereoCalibrate`
and on output it gives 2 rotation matrices and also 2 projection matrices in the new coordinates. The 2 cases are distinguished by the function are:
@ -2595,12 +2595,12 @@ UndistortPoints
:type distCoeffs::class:`CvMat`
:param R:The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param R:The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:type R::class:`CvMat`
:param P:The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
:param P:The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
@ -14,14 +14,14 @@ images and brightness for grayscale images. For color images the order channel
is normally
*Blue, Green, Red*
, this is what
:func:`imshow`
:cpp:func:`imshow`
,
:func:`imread`
:cpp:func:`imread`
and
:func:`imwrite`
:cpp:func:`imwrite`
expect
If you are using your own image rendering and I/O functions, you can use any channel ordering, the drawing functions process each channel independently and do not depend on the channel order or even on the color space used. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor`
:cpp:func:`cvtColor`
.
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy, that is, the coordinates can be passed as fixed-point numbers, encoded as integers. The number of fractional bits is specified by the
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`
:cpp:func:`Mat`
's), that is, for each pixel location
:math:`(x,y)`
in the source image some its (normally rectangular) neighborhood is considered and used to compute the response. In case of a linear filter it is a weighted sum of pixel values, in case of morphological operations it is the minimum or maximum etc. The computed response is stored to the destination image at the same location
:param imageSize:Camera view image size in pixels.
:param centerPrincipalPoint:Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.
The function returns the camera matrix that is either an exact copy of the input ``cameraMatrix`` (when ``centerPrinicipalPoint=false`` ), or the modified one (when ``centerPrincipalPoint`` =true).
In the latter case, the new camera matrix will be:
:math:`(1,1)` elements of ``cameraMatrix`` , respectively.
By default, the undistortion functions in OpenCV (see
:ref:`initUndistortRectifyMap`,
:ref:`undistort`) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
..index:: getOptimalNewCameraMatrix
@ -730,7 +686,7 @@ By default, the undistortion functions in OpenCV (see
Computes the undistortion and rectification transformation map.
:param cameraMatrix:Input camera matrix :math:`A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R:Optional rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` , computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is assumed.
:param newCameraMatrix:New camera matrix :math:`A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}` .
:param size:Undistorted image size.
:param m1type:Type of the first output map that can be ``CV_32FC1`` or ``CV_16SC2`` . See :ref:`convertMaps` for details.
:param map1:The first output map.
:param map2:The second output map.
The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for
:ref:`Remap` . The undistorted image looks like original, as if it is captured with a camera using the camera matrix ``=newCameraMatrix`` and zero distortion. In case of a monocular camera, ``newCameraMatrix`` is usually equal to ``cameraMatrix`` , or it can be computed by
:ref:`GetOptimalNewCameraMatrix` for a better control over scaling. In case of a stereo camera, ``newCameraMatrix`` is normally set to ``P1`` or ``P2`` computed by
:ref:`StereoRectify` .
Also, this new camera is oriented differently in the coordinate space, according to ``R`` . That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by
:ref:`Remap` . That is, for each pixel
:math:`(u, v)` in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:
:math:`(k_1, k_2, p_1, p_2[, k_3])` are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
:ref:`StereoRectify` , which in its turn is called after
:ref:`StereoCalibrate` . But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using
:ref:`StereoRectifyUncalibrated` . For each camera, the function computes homography ``H`` as the rectification transformation in a pixel domain, not a rotation matrix ``R`` in 3D space. ``R`` can be computed from ``H`` as
@ -1113,9 +1009,9 @@ The class implements the modified H. Hirschmuller algorithm HH08 that differs fr
StereoSGBM::StereoSGBM
--------------------------
..c:function:: StereoSGBM::StereoSGBM()
..cpp:function:: StereoSGBM::StereoSGBM()
..c:function:: StereoSGBM::StereoSGBM( int minDisparity, int numDisparities, int SADWindowSize, int P1=0, int P2=0, int disp12MaxDiff=0, int preFilterCap=0, int uniquenessRatio=0, int speckleWindowSize=0, int speckleRange=0, bool fullDP=false)
..cpp:function:: StereoSGBM::StereoSGBM( int minDisparity, int numDisparities, int SADWindowSize, int P1=0, int P2=0, int disp12MaxDiff=0, int preFilterCap=0, int uniquenessRatio=0, int speckleWindowSize=0, int speckleRange=0, bool fullDP=false)
The constructor.
@ -1148,7 +1044,7 @@ The first constructor initializes ``StereoSGBM`` with all the default parameters
Transforms an image to compensate for lens distortion.
:param src:Input (distorted) image.
:param dst:Output (corrected) image that has the same size and type as ``src`` .
:param cameraMatrix:Input camera matrix :math:`A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param newCameraMatrix:Camera matrix of the distorted image. By default, it is the same as ``cameraMatrix`` but you may additionally scale and shift the result by using a different matrix.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of
:ref:`InitUndistortRectifyMap` (with unity ``R`` ) and
:ref:`Remap` (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by ``newCameraMatrix`` . You can use
:ref:`GetOptimalNewCameraMatrix` to compute the appropriate ``newCameraMatrix`` depending on your requirements.
The camera matrix and the distortion parameters can be determined using
:ref:`calibrateCamera` . If the resolution of images is different from the resolution used at the calibration stage,
:math:`f_x, f_y, c_x` and
:math:`c_y` need to be scaled accordingly, while the distortion coefficients remain the same.
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R:Rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used.
:param P:New camera matrix (3x3) or new projection matrix (3x4). ``P1`` or ``P2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used.
The function is similar to
:ref:`undistort` and
:ref:`initUndistortRectifyMap` but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to
:ref:`projectPoints` . In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper ``R`` is specified. ::
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u - cx)/fx
y" = (v - cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
u' = x*fx' + cx'
v' = y*fy' + cy',
where ``undistort()`` is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is
Template "trait" class for other OpenCV primitive data types ::
template<typename _Tp> class DataType
@ -36,7 +38,7 @@ Template "trait" class for other OpenCV primitive data types ::
};
};
The template class ``DataType`` is a descriptive class for OpenCV primitive data types and other types that comply with the following definition. A primitive OpenCV data type is one of ``unsigned char``, ``bool``, ``signed char``, ``unsigned short``, ``signed short``, ``int``, ``float``, ``double`` or a tuple of values of one of these types, where all the values in the tuple have the same type. Any primitive type from the list can be defined by an identifier in the form ``CV_<bit-depth>{U|S|F}C<number_of_channels>``, for example: ``uchar`` ~ ``CV_8UC1``, 3-element floating-point tuple ~ ``CV_32FC3``, and so on. A universal OpenCV structure that is able to store a single instance of such a primitive data type is
The template class ``DataType`` is a descriptive class for OpenCV primitive data types and other types that comply with the following definition. A primitive OpenCV data type is one of ``unsigned char``, ``bool``, ``signed char``, ``unsigned short``, ``signed short``, ``int``, ``float``, ``double`` or a tuple of values of one of these types, where all the values in the tuple have the same type. Any primitive type from the list can be defined by an identifier in the form ``CV_<bit-depth>{U|S|F}C(<number_of_channels>)``, for example: ``uchar`` ~ ``CV_8UC1``, 3-element floating-point tuple ~ ``CV_32FC3``, and so on. A universal OpenCV structure that is able to store a single instance of such a primitive data type is
:ref:`Vec`. Multiple instances of such a type can be stored in a ``std::vector``, ``Mat``, ``Mat_``, ``SparseMat``, ``SparseMat_``, or any other container that is able to store ``Vec`` instances.
The ``DataType`` class is basically used to provide a description of such primitive data types without adding any fields or methods to the corresponding classes (and it is actually impossible to add anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not ``DataType`` itself that is used but its specialized versions, such as: ::
@ -78,6 +80,8 @@ So, such traits are used to tell OpenCV which data type you are working with, ev
Point\_
-------
..cpp:class:: Point_
Template class for 2D points ::
template<typename _Tp> class Point_
@ -143,6 +147,8 @@ Example: ::
Point3\_
--------
..cpp:class:: Point3_
Template class for 3D points ::
template<typename _Tp> class Point3_
@ -174,7 +180,7 @@ The class represents a 3D point specified by its coordinates
:math:`z` .
An instance of the class is interchangeable with the C structure ``CvPoint2D32f`` . Similarly to ``Point_`` , the coordinates of 3D points can be converted to another type. The vector arithmetic and comparison operations are also supported.
The following types of?? aliases are available: ::
The following ``Point3_<>`` aliases are available: ::
typedef Point3_<int> Point3i;
typedef Point3_<float> Point3f;
@ -185,6 +191,8 @@ The following types of?? aliases are available: ::
Size\_
------
..cpp:class:: Size_
Template class for specfying an image or rectangle size ::
template<typename _Tp> class Size_
@ -214,7 +222,7 @@ Template class for specfying an image or rectangle size ::
The class ``Size_`` is similar to ``Point_`` except that the two members are called ``width`` and ``height`` instead of ``x`` and ``y`` . The structure can be converted to and from the old OpenCV structures
``CvSize`` and ``CvSize2D32f`` . The same set of arithmetic and comparison operations as for ``Point_`` is available.
OpenCV defines the following types of?? aliases: ::
OpenCV defines the following ``Size_<>`` aliases: ::
typedef Size_<int> Size2i;
typedef Size2i Size;
@ -225,6 +233,8 @@ OpenCV defines the following types of?? aliases: ::
Rect\_
------
..cpp:class:: Rect_
Template class for 2D rectangles ::
template<typename _Tp> class Rect_
@ -314,17 +324,19 @@ This is an example how the partial ordering on rectangles can be established (re
}
For your convenience, the following type of aliases?? is available: ::
For your convenience, the ``Rect_<>`` alias is available: ::
typedef Rect_<int> Rect;
..index:: _RotatedRect
.._RotatedRect:
RotatedRect
-----------
..cpp:class:: RotatedRect
Template class for rotated rectangles ::
class RotatedRect
@ -356,7 +368,7 @@ The class ``RotatedRect`` replaces the old ``CvBox2D`` and is fully compatible w
TermCriteria
------------
..c:type:: TermCriteria
..cpp:class:: TermCriteria
Template class defining termination criteria for iterative algorithms ::
@ -393,6 +405,8 @@ The class ``TermCriteria`` replaces the old ``CvTermCriteria`` and is fully comp
Matx
----
..cpp:class:: Matx
Template class for small matrices ::
template<typename T, int m, int n> class Matx
@ -428,9 +442,9 @@ Template class for small matrices ::
The class represents small matrices whose type and size are known at compilation time. If you need a more flexible type, use
:ref:`Mat` . The elements of the matrix ``M`` are accessible using the ``M(i,j)`` notation. Most of the common matrix operations (see also
:cpp:class:`Mat` . The elements of the matrix ``M`` are accessible using the ``M(i,j)`` notation. Most of the common matrix operations (see also
:ref:`MatrixExpressions` ) are available. To do an operation on ``Matx`` that is not implemented, you can easily convert the matrix to
:ref:`Mat` and backwards. ::
:cpp:class:`Mat` and backwards. ::
Matx33f m(1, 2, 3,
4, 5, 6,
@ -438,12 +452,14 @@ The class represents small matrices whose type and size are known at compilation
cout << sum(Mat(m*m.t())) << endl;
..index:: Vec
.._Vec:
Vec
---
..cpp:class:: Vec
Template class for short numerical vectors ::
template<typename T, int cn> class Vec : public Matx<T, cn, 1>
@ -487,7 +503,7 @@ Template class for short numerical vectors ::
The ``Vec`` class is commonly used to describe pixel types of multi-channel arrays. See
:ref:`Mat_`?? for details.
:ref:`Mat_` for details.
..index:: Scalar
@ -496,6 +512,8 @@ The ``Vec`` class is commonly used to describe pixel types of multi-channel arra
Scalar\_
--------
..cpp:class:: Scalar_
Template class for a 4-element vector ::
template<typename _Tp> class Scalar_ : public Vec<_Tp, 4>
@ -527,6 +545,8 @@ The template class ``Scalar_`` and its double-precision instantiation ``Scalar``
Range
-----
..cpp:class:: Range
Template class specifying a continuous subsequence (slice) of a sequence ::
class Range
@ -568,6 +588,8 @@ The static method ``Range::all()`` returns a special variable that means "the wh
Ptr
---
..cpp:class:: Ptr
Template class for smart reference-counting pointers ::
template<typename _Tp> class Ptr
@ -623,7 +645,7 @@ This class provides the following options:
Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or a C structure. For some objects, like files, windows, mutexes, sockets, and others, a copy constructor or an assignment operator are difficult to define. For some other objects, like complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, some of complex OpenCV and your own data structures may be written in C. However, copy constructors and default constructors can simplify programming a lot. Besides, they are often required (for example, by STL containers). By wrapping a pointer to such a complex object ``TObj`` to ``Ptr<TObj>`` , you automatically get all of the necessary constructors and the assignment operator.
*
Speed-up for the above-mentioned operations regardless of the data size, similar to "O(1)" operations.?? Indeed, while some structures, like ``std::vector``, provide a copy constructor and an assignment operator, the operations may take a considerable amount of time if the data structures are large. But if the structures are put into ``Ptr<>`` , the overhead is small and independent of the data size.
*O(1)* complexity of the above-mentioned operations. Indeed, while some structures, like ``std::vector``, provide a copy constructor and an assignment operator, the operations may take a considerable amount of time if the data structures are large. But if the structures are put into ``Ptr<>`` , the overhead is small and independent of the data size.
*
Automatic destruction, even for C structures. See the example below with ``FILE*`` .
@ -654,12 +676,10 @@ However, if the object is deallocated in a different way, the specialized method
..index:: Mat
.._Mat:
Mat
---
..c:type:: Mat
..cpp:class:: Mat
OpenCV C++ n-dimensional dense array class ::
@ -818,9 +838,9 @@ There are many different ways to create a ``Mat`` object. The most popular optio
..
??is the indent required here? does it apply to step 2 but not to the whole bulleted item??Partial yet very common cases of this *user-allocated data* case are conversions from ``CvMat`` and ``IplImage`` to ``Mat``. For this purpose, there are special constructors taking pointers to ``CvMat`` or ``IplImage`` and the optional flag indicating whether to copy the data or not.
Partial yet very common cases of this *user-allocated data* case are conversions from ``CvMat`` and ``IplImage`` to ``Mat``. For this purpose, there are special constructors taking pointers to ``CvMat`` or ``IplImage`` and the optional flag indicating whether to copy the data or not.
Backward conversion from ``Mat`` to ``CvMat`` or ``IplImage`` is provided via cast operators ``Mat::operator CvMat() const`` and ``Mat::operator IplImage()``. The operators do NOT copy the data.
Backward conversion from ``Mat`` to ``CvMat`` or ``IplImage`` is provided via cast operators ``Mat::operator CvMat() const`` and ``Mat::operator IplImage()``. The operators do NOT copy the data.
::
@ -967,8 +987,6 @@ Below is the formal description of the ``Mat`` methods.
..index:: Mat::Mat
.._Mat::Mat:
Mat::Mat
------------
..cpp:function:: Mat::Mat()
@ -1078,7 +1096,7 @@ Mat::operator =
:param m:The assigned, right-hand-side matrix. Matrix assignment is O(1) operation, that is, no data is copied. Instead, the data is shared and the reference counter, if any, is incremented. Before assigning new data, the old data is de-referenced via :ref:`Mat::release` .
:param expr:The assigned matrix expression object. As opposite to the first form of assignment operation, the second form can reuse already allocated matrix if it has the right size and type to fit the matrix expression result. It is automatically handled by the real function that the matrix expressions is expanded to. For example, ``C=A+B`` is expanded to ``add(A, B, C)`` , and :func:`add` takes care of automatic ``C`` reallocation.
:param expr:The assigned matrix expression object. As opposite to the first form of assignment operation, the second form can reuse already allocated matrix if it has the right size and type to fit the matrix expression result. It is automatically handled by the real function that the matrix expressions is expanded to. For example, ``C=A+B`` is expanded to ``add(A, B, C)`` , and :cpp:func:`add` takes care of automatic ``C`` reallocation.
:param s:The scalar assigned to each matrix element. The matrix size or type is not changed.
@ -1097,8 +1115,6 @@ The cast operator should not be called explicitly. It is used internally by the
..index:: Mat::row
.._Mat::row:
Mat::row
------------
..cpp:function:: Mat Mat::row(int i) const
@ -1138,8 +1154,6 @@ This is because ``A.row(i)`` forms a temporary header, which is further assigned
..index:: Mat::col
.._Mat::col:
Mat::col
------------
..cpp:function:: Mat Mat::col(int j) const
@ -1153,8 +1167,6 @@ The method makes a new header for the specified matrix column and returns it. Th
..index:: Mat::rowRange
.._Mat::rowRange:
Mat::rowRange
-----------------
..cpp:function:: Mat Mat::rowRange(int startrow, int endrow) const
@ -1167,16 +1179,14 @@ Mat::rowRange
:param endrow:A 0-based ending index of the row span.
:param r:The :func:`Range` structure containing both the start and the end indices.
:param r:The :cpp:func:`Range` structure containing both the start and the end indices.
The method makes a new header for the specified row span of the matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
..index:: Mat::colRange
.._Mat::colRange:
Mat::colRange
-----------------
..cpp:function:: Mat Mat::colRange(int startcol, int endcol) const
@ -1189,16 +1199,14 @@ Mat::colRange
:param endcol:A 0-based ending index of the column span.
:param r:The :func:`Range` structure containing both the start and the end indices.
:param r:The :cpp:func:`Range` structure containing both the start and the end indices.
The method makes a new header for the specified column span of the matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
..index:: Mat::diag
.._Mat::diag:
Mat::diag
-------------
..cpp:function:: Mat Mat::diag(int d) const
@ -1218,13 +1226,11 @@ Mat::diag
:param matD:A single-column matrix that forms the diagonal matrix.
The method makes a new header for the specified matrix diagonal. The new matrix is represented as a single-column matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
..index:: Mat::clone
.._Mat::clone:
Mat::clone
--------------
..cpp:function:: Mat Mat::clone() const
@ -1259,8 +1265,6 @@ When the operation mask is specified, and the ``Mat::create`` call shown above r
..index:: Mat::convertTo
.._Mat::convertTo:
Mat::convertTo
------------------
..cpp:function:: void Mat::convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const
@ -1300,7 +1304,7 @@ This is an internally used method called by the
Sets all or some of the array elements to the specified value.
@ -1327,7 +1331,7 @@ The method makes a new matrix header for ``*this`` elements. The new matrix may
*
No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of rows, or the operation changes the indices of elements' row in some other way, the matrix must be continuous. See
:func:`Mat::isContinuous` .
:cpp:func:`Mat::isContinuous` .
For example, if there is a set of 3D points stored as an STL vector, and you want to represent the points as a ``3xN`` matrix, do the following: ::
@ -1471,7 +1475,7 @@ Mat::ones
:param type:Created matrix type.
The method returns a Matlab-style 1's array initializer, similarly to
:func:`Mat::zeros` . Note that using this method you can initialize an array with an arbitrary value, using the following Matlab idiom:::
:cpp:func:`Mat::zeros` . Note that using this method you can initialize an array with an arbitrary value, using the following Matlab idiom:::
Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
@ -1496,7 +1500,7 @@ Mat::eye
:param type: Created matrix type.
The method returns a Matlab-style identity matrix initializer, similarly to
:func:`Mat::zeros` . Similarly to ``Mat::ones`` , you can use a scale operation to create a scaled identity matrix efficiently:::
:cpp:func:`Mat::zeros` . Similarly to ``Mat::ones`` , you can use a scale operation to create a scaled identity matrix efficiently:::
// make a 4x4 diagonal matrix with 0.1's on the diagonal.
Mat A = Mat::eye(4, 4, CV_32F)*0.1;
@ -1535,7 +1539,7 @@ This is one of the key ``Mat`` methods. Most new-style OpenCV functions and meth
#.
Otherwise, de-reference the previous data by calling
:func:`Mat::release` #.
:cpp:func:`Mat::release` #.
initialize the new header
#.
@ -1564,8 +1568,6 @@ because ``cvtColor`` , as well as the most of OpenCV functions, calls ``Mat::cre
..index:: Mat::addref
.._Mat::addref:
Mat::addref
---------------
..cpp:function:: void Mat::addref()
@ -1573,12 +1575,10 @@ Mat::addref
Increments the reference counter.
The method increments the reference counter associated with the matrix data. If the matrix header points to an external data set (see
:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It is called implicitly by the matrix assignment operator. The reference counter increment is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
:cpp:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It is called implicitly by the matrix assignment operator. The reference counter increment is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
..index:: Mat::release
.._Mat::release:
Mat::release
----------------
..cpp:function:: void Mat::release()
@ -1586,14 +1586,12 @@ Mat::release
Decrements the reference counter and deallocates the matrix if needed.
The method decrements the reference counter associated with the matrix data. When the reference counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers are set to NULL's. If the matrix header points to an external data set (see
:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case.
:cpp:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case.
This method can be called manually to force the matrix data deallocation. But since this method is automatically called in the destructor, or by any other method that changes the data pointer, it is usually not needed. The reference counter decrement and check for 0 is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
:param ofs:An output parameter that contains an offset of ``*this`` inside the whole matrix.
After you extracted a submatrix from a matrix using
:func:`Mat::row`,:func:`Mat::col`,:func:`Mat::rowRange`,:func:`Mat::colRange` , and others, the resultant submatrix will point just to the part of the original big matrix. However, each submatrix contains some information (represented by ``datastart`` and ``dataend`` fields) that helps reconstruct the original matrix size and the position of the extracted submatrix within the original matrix. The method ``locateROI`` does exactly that.
:cpp:func:`Mat::row`,:cpp:func:`Mat::col`,:cpp:func:`Mat::rowRange`,:cpp:func:`Mat::colRange` , and others, the resultant submatrix will point just to the part of the original big matrix. However, each submatrix contains some information (represented by ``datastart`` and ``dataend`` fields) that helps reconstruct the original matrix size and the position of the extracted submatrix within the original matrix. The method ``locateROI`` does exactly that.
..index:: Mat::adjustROI
@ -1669,7 +1665,7 @@ Mat::adjustROI
:param dright:The shift of the right submatrix boundary to the right.
The method is complimentary to
:func:`Mat::locateROI` . Indeed, the typical use of these functions is to determine the submatrix position within the parent matrix and then shift the position somehow. Typically, it can be required for filtering operations when pixels outside of the ROI should be taken into account. When all the method parameters are positive, the ROI needs to grow in all directions by the specified amount, for example:::
:cpp:func:`Mat::locateROI` . Indeed, the typical use of these functions is to determine the submatrix position within the parent matrix and then shift the position somehow. Typically, it can be required for filtering operations when pixels outside of the ROI should be taken into account. When all the method parameters are positive, the ROI needs to grow in all directions by the specified amount, for example:::
A.adjustROI(2, 2, 2, 2);
@ -1679,10 +1675,10 @@ In this example, the matrix size is increased by 4 elements in each direction. T
It is your responsibility to make sure ``adjustROI`` does not cross the parent matrix boundary. If it does, the function signals an error.
The function is used internally by the OpenCV filtering functions, like
:func:`filter2D` , morphological operations, and so on.
:cpp:func:`filter2D` , morphological operations, and so on.
See Also
:func:`copyMakeBorder`
:cpp:func:`copyMakeBorder`
..index:: Mat::operator()
@ -1707,14 +1703,14 @@ Mat::operator()
:param ranges:The array of selected ranges along each array dimension.
The operators make a new header for the specified sub-array of ``*this`` . They are the most generalized forms of
:func:`Mat::row`,:func:`Mat::col`,:func:`Mat::rowRange`, and
:func:`Mat::colRange` . For example, ``A(Range(0, 10), Range::all())`` is equivalent to ``A.rowRange(0, 10)`` . Similarly to all of the above, the operators are O(1) operations, that is, no matrix data is copied.
:cpp:func:`Mat::row`,:cpp:func:`Mat::col`,:cpp:func:`Mat::rowRange`, and
:cpp:func:`Mat::colRange` . For example, ``A(Range(0, 10), Range::all())`` is equivalent to ``A.rowRange(0, 10)`` . Similarly to all of the above, the operators are O(1) operations, that is, no matrix data is copied.
..index:: Mat::operator CvMat
Mat::operator CvMat
-----------------------
..cpp:function:: Mat::operator CvMat(void) const
..cpp:function:: Mat::operator CvMat() const
Creates the ``CvMat`` header for the matrix.
@ -1733,7 +1729,7 @@ where ``mycvOldFunc`` is a function written to work with OpenCV 1.x data structu
The method returns ``true`` if the matrix elements are stored continuously - without gaps in the end of each row. Otherwise, it returns ``false``. Obviously, ``1x1`` or ``1xN`` matrices are always continuous. Matrices created with
:func:`Mat::create` are always continuous. But if you extract a part of the matrix using
:func:`Mat::col`,:func:`Mat::diag` , and so on, or constructed a matrix header for externally allocated data, such matrices may no longer have this property.
:cpp:func:`Mat::create` are always continuous. But if you extract a part of the matrix using
:cpp:func:`Mat::col`,:cpp:func:`Mat::diag` , and so on, or constructed a matrix header for externally allocated data, such matrices may no longer have this property.
The continuity flag is stored as a bit in the ``Mat::flags`` field and is computed automatically when you construct a matrix header. Thus, the continuity check is a very fast operation, though it could be, in theory, done as following: ::
@ -1822,8 +1818,8 @@ The method is used in quite a few of OpenCV functions. The point is that element
This trick, while being very simple, can boost performance of a simple element-operation by 10-20 percents, especially if the image is rather small and the operation is quite simple.
Also, note that there is another OpenCV idiom in this function: a call of
:func:`Mat::create` for the destination array instead of checking that it already has the proper size and type. And while the newly allocated arrays are always continuous, we still check the destination array, because
:func:`create` does not always allocate a new matrix.
:cpp:func:`Mat::create` for the destination array instead of checking that it already has the proper size and type. And while the newly allocated arrays are always continuous, we still check the destination array, because
:cpp:func:`create` does not always allocate a new matrix.
..index:: Mat::elemSize
@ -1831,7 +1827,7 @@ Also, note that there is another OpenCV idiom in this function: a call of
Mat::elemSize
-----------------
..cpp:function:: size_t Mat::elemSize(void) const
..cpp:function:: size_t Mat::elemSize() const
Returns the matrix element size in bytes.
@ -1843,7 +1839,7 @@ The method returns the matrix element size in bytes. For example, if the matrix
@ -10,7 +10,7 @@ with ``CV_RGB`` or the :ref:`Scalar` constructor
) for color
images and brightness for grayscale images. For color images, the channel ordering
is normally *Blue, Green, Red*.
This is what :func:`imshow`, :func:`imread`, and :func:`imwrite` expect.
This is what :cpp:func:`imshow`, :cpp:func:`imread`, and :cpp:func:`imwrite` expect.
So, if you form a color using the
:ref:`Scalar` constructor, it should look like:
@ -19,7 +19,7 @@ So, if you form a color using the
\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])
If you are using your own image rendering and I/O functions, you can use any channel ordering. The drawing functions process each channel independently and do not depend on the channel order or even on the used color space. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor` .
:cpp:func:`cvtColor` .
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means that the coordinates can be passed as fixed-point numbers encoded as integers. The number of fractional bits is specified by the ``shift`` parameter and the real point coordinates are calculated as
:math:`\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})` . This feature is especially effective when rendering antialiased shapes.
@ -32,7 +32,7 @@ The functions do not support alpha-transparency when the target image is 4-chann
circle
----------
..c:function:: void circle(Mat& img, Point center, int radius, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..cpp:function:: void circle(Mat& img, Point center, int radius, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a circle.
@ -46,7 +46,7 @@ circle
:param thickness:Thickness of the circle outline if positive. Negative thickness means that a filled circle is to be drawn.
:param lineType:Type of the circle boundary. See :func:`line` description.
:param lineType:Type of the circle boundary. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the center's coordinates and in the radius value.
@ -56,9 +56,9 @@ The function ``circle`` draws a simple or filled circle with a given center and
@ -77,9 +77,9 @@ They return ``false`` if the line segment is completely outside the rectangle. O
ellipse
-----------
..c:function:: void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..cpp:function:: void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..c:function:: void ellipse(Mat& img, const RotatedRect& box, const Scalar& color, int thickness=1, int lineType=8)
..cpp:function:: void ellipse(Mat& img, const RotatedRect& box, const Scalar& color, int thickness=1, int lineType=8)
Draws a simple or thick elliptic arc or fills an ellipse sector.
@ -101,15 +101,15 @@ ellipse
:param thickness:Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
:param lineType:Type of the ellipse boundary. See :func:`line` description.
:param lineType:Type of the ellipse boundary. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the center's coordinates and axes' values.
The functions ``ellipse`` with less parameters draw an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector.
A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
:func:`ellipse2Poly` and then render it with
:func:`polylines` or fill it with
:func:`fillPoly` . If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass ``startAngle=0`` and ``endAngle=360`` . The picture below explains the meaning of the parameters.
:cpp:func:`ellipse2Poly` and then render it with
:cpp:func:`polylines` or fill it with
:cpp:func:`fillPoly` . If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass ``startAngle=0`` and ``endAngle=360`` . The picture below explains the meaning of the parameters.
**Figure 1. Parameters of Elliptic Arc**
@ -119,15 +119,15 @@ A piecewise-linear curve is used to approximate the elliptic arc boundary. If yo
ellipse2Poly
----------------
..c:function:: void ellipse2Poly( Point center, Size axes, int angle, int startAngle, int endAngle, int delta, vector<Point>& pts )
..cpp:function:: void ellipse2Poly( Point center, Size axes, int angle, int startAngle, int endAngle, int delta, vector<Point>& pts )
Approximates an elliptic arc with a polyline.
:param center:Center of the arc.
:param axes:Half-sizes of the arc. See :func:`ellipse` for details.
:param axes:Half-sizes of the arc. See :cpp:func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See :func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See :cpp:func:`ellipse` for details.
:param startAngle: Starting angle of the elliptic arc in degrees.
@ -138,13 +138,13 @@ ellipse2Poly
:param pts:Output vector of polyline vertices.
The function ``ellipse2Poly`` computes the vertices of a polyline that approximates the specified elliptic arc. It is used by
:func:`ellipse` .
:cpp:func:`ellipse` .
..index:: fillConvexPoly
fillConvexPoly
------------------
..c:function:: void fillConvexPoly(Mat& img, const Point* pts, int npts, const Scalar& color, int lineType=8, int shift=0)
..cpp:function:: void fillConvexPoly(Mat& img, const Point* pts, int npts, const Scalar& color, int lineType=8, int shift=0)
Fills a convex polygon.
@ -156,7 +156,7 @@ fillConvexPoly
:param color:Polygon color.
:param lineType:Type of the polygon boundaries. See :func:`line` description.
:param lineType:Type of the polygon boundaries. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the vertex coordinates.
@ -168,7 +168,7 @@ that is, a polygon whose contour intersects every horizontal line (scan line) tw
fillPoly
------------
..c:function:: void fillPoly(Mat& img, const Point** pts, const int* npts, int ncontours, const Scalar& color, int lineType=8, int shift=0, Point offset=Point() )
..cpp:function:: void fillPoly(Mat& img, const Point** pts, const int* npts, int ncontours, const Scalar& color, int lineType=8, int shift=0, Point offset=Point() )
Fills the area bounded by one or more polygons.
@ -182,7 +182,7 @@ fillPoly
:param color:Polygon color.
:param lineType:Type of the polygon boundaries. See :func:`line` description.
:param lineType:Type of the polygon boundaries. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the vertex coordinates.
@ -193,17 +193,17 @@ areas with holes, contours with self-intersections (some of thier parts), and so
getTextSize
---------------
..c:function:: Size getTextSize(const string& text, int fontFace, double fontScale, int thickness, int* baseLine)
..cpp:function:: Size getTextSize(const string& text, int fontFace, double fontScale, int thickness, int* baseLine)
Calculates the width and height of a text string.
:param text:Input text string.
:param fontFace:Font to use. See :func:`putText` for details.
:param fontFace:Font to use. See :cpp:func:`putText` for details.
:param fontScale: Font scale. See :func:`putText` for details.
:param fontScale: Font scale. See :cpp:func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :cpp:func:`putText` for details.
:param baseLine: Output parameter - y-coordinate of the baseline relative to the bottom-most text point.
@ -244,7 +244,7 @@ That is, the following code renders some text, the tight box surrounding it, and
line
--------
..c:function:: void line(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..cpp:function:: void line(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a line segment connecting two points.
@ -321,7 +321,9 @@ The number of pixels along the line is stored in ``LineIterator::count`` . ::
rectangle
-------------
..c:function:: void rectangle(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..cpp:function:: void rectangle(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
..cpp:function:: void rectangle(Mat& img, Rect r, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a simple, thick, or filled up-right rectangle.
@ -330,22 +332,24 @@ rectangle
:param pt1:One of the rectangle's vertices.
:param pt2:Opposite to ``pt1`` rectangle vertex.
:param r:Alternative specification of the drawn rectangle
:param color:Rectangle color or brightness (grayscale image).
:param thickness:Thickness of lines that make up the rectangle. Negative values, like ``CV_FILLED`` , mean that the function has to draw a filled rectangle.
:param lineType:Type of the line. See :func:`line` description.
:param lineType:Type of the line. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the point coordinates.
The function ``rectangle`` draws a rectangle outline or a filled rectangle whose two opposite corners are ``pt1`` and ``pt2`` .
The function ``rectangle`` draws a rectangle outline or a filled rectangle whose two opposite corners are ``pt1`` and ``pt2``,or ``r.tl()`` and ``r.br()-Point(1,1)``.
..index:: polylines
polylines
-------------
..c:function:: void polylines(Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
..cpp:function:: void polylines(Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
Draws several polygonal curves.
@ -363,7 +367,7 @@ polylines
:param thickness:Thickness of the polyline edges.
:param lineType:Type of the line segments. See :func:`line` description.
:param lineType:Type of the line segments. See :cpp:func:`line` description.
:param shift:Number of fractional bits in the vertex coordinates.
@ -373,7 +377,7 @@ The function ``polylines`` draws one or more polygonal curves.
putText
-----------
..c:function:: void putText( Mat& img, const string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=8, bool bottomLeftOrigin=false )
..cpp:function:: void putText( Mat& img, const string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=8, bool bottomLeftOrigin=false )
Draws a text string.
@ -399,5 +403,5 @@ putText
The function ``putText`` renders the specified text string in the image.
Symbols that cannot be rendered using the specified font are
replaced by question marks. See
:func:`getTextSize` for a text rendering code example.
:cpp:func:`getTextSize` for a text rendering code example.
OpenCV has a modular structure, which means that the package includes several shared or static libraries. The following modules are available:
* **core** - a compact module defining basic data structures, including the dense multi-dimensional array ``Mat`` and basic functions used by all other modules.
* **imgproc** - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective wraping, generic table-based remapping), color space conversion, histograms, and so on.
* **imgproc** - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective warping, generic table-based remapping), color space conversion, histograms, and so on.
* **video** - a video analysis module that includes motion estimation, background subtraction, and object tracking algorithms.
* **calib3d** - basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction.
* **features2d** - salient feature detectors, descriptors, and descriptor matchers.
* **objdetect** - detection of objects and instances of the predefined classes (for example, faces, eyes, mugs, people, cars, and so on).
* **highgui** - an easy-to-use interface to video capturing, image and video codecs APIs, as well as simple UI capabilities.
* **highgui** - an easy-to-use interface to video capturing, image and video codecs, as well as simple UI capabilities.
* **gpu** - GPU-accelerated algorithms from different OpenCV modules.
* ... some other helper modules, such as FLANN and Google test wrappers, Python bindings, and others.
@ -56,7 +56,7 @@ Automatic Memory Management
OpenCV handles all the memory automatically.
First of all, ``std::vector``, ``Mat``, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of ``Mat``. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a ``Mat`` instance is copied, no actual data is really copied. Instead, the counter associated with its reference is incremented to memorize that there is another owner of the same data. There is also the ``Mat::clone`` method that creates a full copy of the matrix data. See the example below: ::
First of all, ``std::vector``, ``Mat``, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of ``Mat``. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a ``Mat`` instance is copied, no actual data is really copied. Instead, the reference counter is incremented to memorize that there is another owner of the same data. There is also the ``Mat::clone`` method that creates a full copy of the matrix data. See the example below: ::
// create a big 8Mb matrix
Mat A(1000, 1000, CV_64F);
@ -100,7 +100,7 @@ description for details.
Automatic Allocation of the Output Data
---------------------------------------
OpenCV deallocates the memory automatically, as well as automatically allocates the memory for output function parameters most of the time. So, if a function has one or more input arrays (``cv::Mat`` instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined from the size and type of input arrays. If needed, the functions take extra parameters that help to figure out the output array properties.
OpenCV deallocates the memory automatically, as well as automatically allocates the memory for output function parameters most of the time. So, if a function has one or more input arrays (``cv::Mat`` instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined from the size and type of input arrays. If needed, the functions take extra parameters that help to figure out the output array properties.
Example: ::
@ -152,9 +152,9 @@ where ``cv::uchar`` is an OpenCV 8-bit unsigned integer type. In the optimized S
Fixed Pixel Types. Limited Use of Templates
-------------------------------------------
Templates is a great feature of C++ that enables implementation of very powerful, efficient and yet safe data structures and algorithms. However, the extensive use of templates may dramatically increase compilation time and code size. Besides, it is difficult to separate an interface and implementation when templates are used exclusively. This could be fine for basic algorithms but not good for computer vision libraries where a single algorithm may span a thousand lines of code. Because of this and also to simplify development of bindings for other languages, like Python*, Java*, Matlab* that do not have templates at all or have limited template capabilities, the current OpenCV implementation is based on polymorphism and runtime dispatching over templates. In those places where runtime dispatching would be too slow (like pixel access operators), impossible (generic ``Ptr<>`` implementation), or just very inconvenient (``saturate_cast<>()``) the current implementation introduces small template classes, methods, and functions. Anywhere else in this implementation templates are not used.
Templates is a great feature of C++ that enables implementation of very powerful, efficient and yet safe data structures and algorithms. However, the extensive use of templates may dramatically increase compilation time and code size. Besides, it is difficult to separate an interface and implementation when templates are used exclusively. This could be fine for basic algorithms but not good for computer vision libraries where a single algorithm may span thousands lines of code. Because of this and also to simplify development of bindings for other languages, like Python, Java, Matlab that do not have templates at all or have limited template capabilities, the current OpenCV implementation is based on polymorphism and runtime dispatching over templates. In those places where runtime dispatching would be too slow (like pixel access operators), impossible (generic ``Ptr<>`` implementation), or just very inconvenient (``saturate_cast<>()``) the current implementation introduces small template classes, methods, and functions. Anywhere else in the current OpenCV version the use of templates is limited.
There is a limited fixed set of primitive data types the library can operate on. That is, array elements should have one of the following types:
Consequently, there is a limited fixed set of primitive data types the library can operate on. That is, array elements should have one of the following types:
* 8-bit unsigned integer (uchar)
* 8-bit signed integer (schar)
@ -163,7 +163,7 @@ There is a limited fixed set of primitive data types the library can operate on.
* 32-bit signed integer (int)
* 32-bit floating-point number (float)
* 64-bit floating-point number (double)
* a tuple of several elements where all elements have the same type (one of the above). An array whose elements are such tuples, are called multi-channel arrays, as opposite to the single-channel arrays, whose elements are scalar values. The maximum possible number of channels is defined by the ``CV_CN_MAX`` constant, which is not smaller than 32.
* a tuple of several elements where all elements have the same type (one of the above). An array whose elements are such tuples, are called multi-channel arrays, as opposite to the single-channel arrays, whose elements are scalar values. The maximum possible number of channels is defined by the ``CV_CN_MAX`` constant, which is currently set to 512.
For these basic types, the following enumeration is applied::
@ -190,12 +190,17 @@ Arrays with more complex elements cannot be constructed or processed using OpenC
* The face detection algorithm only works with 8-bit grayscale or color images.
* Linear algebra functions and most of the machine learning algorithms work with floating-point arrays only.
* Basic functions, such as ``cv::add``, support all types, except for ``CV_8SC(n)``.
* Basic functions, such as ``cv::add``, support all types.
* Color space conversion functions support 8-bit unsigned, 16-bit unsigned, and 32-bit floating-point types.
The subset of supported types for each functions has been defined from practical needs. All this information about supported types can be put together into a special table. In different implementations of the standard, the tables may look differently. For example, on embedded platforms the double-precision floating-point type (``CV_64F``) may be unavailable.
The subset of supported types for each function has been defined from practical needs and could be extended in future based on user requests.
InputArray and OutputArray
--------------------------
Many OpenCV functions process dense 2-dimensional or multi-dimensional numerical arrays. Usually, such functions take cpp:class:`Mat` as parameters, but in some cases it's more convenient to use ``std::vector<>`` (for a point set, for example) or ``Matx<>`` (for 3x3 homography matrix and such). To avoid many duplicates in the API, special "proxy" classes have been introduced. The base "proxy" class is ``InputArray``. It is used for passing read-only arrays on a function input. The derived from ``InputArray`` class ``OutputArray`` is used to specify an output array for a function. Normally, you should not care of those intermediate types (and you should not declare variables of those types explicitly) - it will all just work automatically. You can assume that instead of ``InputArray``/``OutputArray`` you can always use ``Mat``, ``std::vector<>``, ``Matx<>``, ``Vec<>`` or ``Scalar``. When a function has an optional input or output array, and you do not have or do not want one, pass ``cv::None()``.
The generic function ``deallocate`` deallocates the buffer allocated with
:func:`allocate` . The number of elements must match the number passed to
:func:`allocate` .
:cpp:func:`allocate` . The number of elements must match the number passed to
:cpp:func:`allocate` .
..index:: CV_Assert
@ -73,7 +73,7 @@ The generic function ``deallocate`` deallocates the buffer allocated with
CV_Assert
---------
..c:function:: CV_Assert(expr)
..cpp:function:: CV_Assert(expr)
Checks a condition at runtime. ::
@ -84,17 +84,17 @@ CV_Assert
:param expr:Expression to check.
The macros ``CV_Assert`` and ``CV_DbgAssert`` evaluate the specified expression. If it is 0, the macros raise an error (see
:func:`error` ). The macro ``CV_Assert`` checks the condition in both Debug and Release configurations, while ``CV_DbgAssert`` is only retained in the Debug configuration.
:cpp:func:`error` ). The macro ``CV_Assert`` checks the condition in both Debug and Release configurations, while ``CV_DbgAssert`` is only retained in the Debug configuration.
@ -148,13 +148,13 @@ Exception class passed to error ::
};
The class ``Exception`` encapsulates all or almost all the necessary information about the error happened in the program. The exception is usually constructed and thrown implicitly via ``CV_Error`` and ``CV_Error_`` macros. See
:func:`error` .
:cpp:func:`error` .
..index:: fastMalloc
fastMalloc
--------------
..c:function:: void* fastMalloc(size_t size)
..cpp:function:: void* fastMalloc(size_t size)
Allocates an aligned memory buffer.
@ -166,74 +166,74 @@ The function allocates the buffer of the specified size and returns it. When the
fastFree
------------
..c:function:: void fastFree(void* ptr)
..cpp:function:: void fastFree(void* ptr)
Deallocates a memory buffer.
:param ptr:Pointer to the allocated buffer.
The function deallocates the buffer allocated with
:func:`fastMalloc` .
:cpp:func:`fastMalloc` .
If NULL pointer is passed, the function does nothing.
The function acts like ``sprintf`` but forms and returns an STL string. It can be used to form an error message in the
:func:`Exception` constructor.
:cpp:func:`Exception` constructor.
..index:: getNumThreads
getNumThreads
-----------------
..c:function:: int getNumThreads()
..cpp:function:: int getNumThreads()
Returns the number of threads used by OpenCV.
The function returns the number of threads that is used by OpenCV.
See Also:
:func:`setNumThreads`,
:func:`getThreadNum`
:cpp:func:`setNumThreads`,
:cpp:func:`getThreadNum`
..index:: getThreadNum
getThreadNum
----------------
..c:function:: int getThreadNum()
..cpp:function:: int getThreadNum()
Returns the index of the currently executed thread.
The function returns a 0-based index of the currently executed thread. The function is only valid inside a parallel OpenMP region. When OpenCV is built without OpenMP support, the function always returns 0.
See Also:
:func:`setNumThreads`,
:func:`getNumThreads` .
:cpp:func:`setNumThreads`,
:cpp:func:`getNumThreads` .
..index:: getTickCount
getTickCount
----------------
..c:function:: int64 getTickCount()
..cpp:function:: int64 getTickCount()
Returns the number of ticks.
The function returns the number of ticks after the certain event (for example, when the machine was turned on).
It can be used to initialize
:func:`RNG` or to measure a function execution time by reading the tick count before and after the function call. See also the tick frequency.
:cpp:func:`RNG` or to measure a function execution time by reading the tick count before and after the function call. See also the tick frequency.
..index:: getTickFrequency
getTickFrequency
--------------------
..c:function:: double getTickFrequency()
..cpp:function:: double getTickFrequency()
Returns the number of ticks per second.
@ -248,7 +248,7 @@ That is, the following code computes the execution time in seconds: ::
setNumThreads
-----------------
..c:function:: void setNumThreads(int nthreads)
..cpp:function:: void setNumThreads(int nthreads)
Sets the number of threads used by OpenCV.
@ -257,5 +257,5 @@ setNumThreads
The function sets the number of threads used by OpenCV in parallel OpenMP regions. If ``nthreads=0`` , the function uses the default number of threads that is usually equal to the number of the processing cores.
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
Returns the count of all descriptors stored in the training set.
@ -67,11 +67,11 @@ BOWTrainer::descripotorsCount
BOWTrainer::cluster
-----------------------
..c:function:: Mat BOWTrainer::cluster() const
..cpp:function:: Mat BOWTrainer::cluster() const
Clusters train descriptors. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.
..c:function:: Mat BOWTrainer::cluster( const Mat& descriptors ) const
..cpp:function:: Mat BOWTrainer::cluster( const Mat& descriptors ) const
:param descriptors:Descriptors to cluster. Each row of the ``descriptors`` matrix is a descriptor. Descriptors are not added to the inner train descriptor set.
@ -146,7 +146,7 @@ Here is the class declaration ::
@ -148,7 +148,7 @@ In contrast with :c:type:`Mat`, in most cases ``GpuMat::isContinuous() == false`
You are not recommended to leave static or global ``GpuMat`` variables allocated, that is to rely on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory release function returns error if the CUDA context has been destroyed before.
See Also:
:func:`Mat`
:cpp:func:`Mat`
..index:: gpu::CudaMem
@ -157,7 +157,7 @@ gpu::CudaMem
..cpp:class:: gpu::CudaMem
This class with reference counting wraps special memory type allocation functions from CUDA. Its interface is also
:func:`Mat`-like but with additional memory type parameters.
:cpp:func:`Mat`-like but with additional memory type parameters.
*
``ALLOC_PAGE_LOCKED``: Sets a page locked memory type, used commonly for fast and asynchronous uploading/downloading data from/to GPU.
Finds the k best matches for each descriptor from a query set with train descriptors. The function returns detected k (or less if not possible) matches in the increasing order by distance.
@ -93,7 +93,7 @@ This is a base class for Filter Engine. ::
The class can be used to apply an arbitrary filtering operation to an image. It contains all the necessary intermediate buffers. Pointers to the initialized ``FilterEngine_GPU`` instances are returned by various ``create*Filter_GPU`` functions (see below), and they are used inside high-level functions such as
:func:`gpu::filter2D`, :func:`gpu::erode`, :func:`gpu::Sobel` , and others.
:cpp:func:`gpu::filter2D`, :cpp:func:`gpu::erode`, :cpp:func:`gpu::Sobel` , and others.
By using ``FilterEngine_GPU`` instead of functions you can avoid unnecessary memory allocation for intermediate buffers and get much better performance:
::
@ -224,7 +224,7 @@ gpu::createBoxFilter_GPU
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`boxFilter`
See Also: :c:cpp:func:`boxFilter`
..index:: gpu::boxFilter
@ -248,7 +248,7 @@ gpu::boxFilter
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`boxFilter`
See Also: :c:cpp:func:`boxFilter`
..index:: gpu::blur
@ -270,7 +270,7 @@ gpu::blur
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`blur`, :cpp:func:`gpu::boxFilter`
See Also: :c:cpp:func:`blur`, :cpp:func:`gpu::boxFilter`
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`createMorphologyFilter`
See Also: :c:cpp:func:`createMorphologyFilter`
..index:: gpu::erode
@ -320,7 +320,7 @@ gpu::erode
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`erode`
See Also: :c:cpp:func:`erode`
..index:: gpu::dilate
@ -344,7 +344,7 @@ gpu::dilate
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`dilate`
See Also: :c:cpp:func:`dilate`
..index:: gpu::morphologyEx
@ -381,7 +381,7 @@ gpu::morphologyEx
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`morphologyEx`
See Also: :c:cpp:func:`morphologyEx`
..index:: gpu::createLinearFilter_GPU
@ -407,7 +407,7 @@ gpu::createLinearFilter_GPU
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`createLinearFilter`
See Also: :c:cpp:func:`createLinearFilter`
..index:: gpu::filter2D
@ -431,7 +431,7 @@ gpu::filter2D
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`filter2D`
See Also: :c:cpp:func:`filter2D`
..index:: gpu::Laplacian
@ -447,15 +447,15 @@ gpu::Laplacian
:param ddepth:Desired depth of the destination image. It supports only the same depth as the source image depth.
:param ksize:Aperture size used to compute the second-derivative filters (see :c:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param ksize:Aperture size used to compute the second-derivative filters (see :c:cpp:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param scale:Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :c:func:`getDerivKernels` ).
:param scale:Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :c:cpp:func:`getDerivKernels` ).
**Note:**
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`Laplacian`,:func:`gpu::filter2D` .
See Also: :c:cpp:func:`Laplacian`,:cpp:func:`gpu::filter2D` .
..index:: gpu::getLinearRowFilter_GPU
@ -473,13 +473,13 @@ gpu::getLinearRowFilter_GPU
:param anchor:Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType:Pixel extrapolation method. For details, see :c:func:`borderInterpolate`. For details on limitations, see below.
:param borderType:Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate`. For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``srcType == CV_8UC1`` or ``srcType == CV_8UC4`` and ``bufType == srcType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101``,``BORDER_REPLICATE``, and ``BORDER_CONSTANT`` border types. It checks indices outside the image.
See Also:,:func:`createSeparableLinearFilter` .
See Also:,:cpp:func:`createSeparableLinearFilter` .
:param anchor:Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType:Pixel extrapolation method. For details, see :c:func:`borderInterpolate` . For details on limitations, see below.
:param borderType:Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate` . For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``dstType == CV_8UC1`` or ``dstType == CV_8UC4`` and ``bufType == dstType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101``, ``BORDER_REPLICATE``, and ``BORDER_CONSTANT`` border types. It checks indices outside image.
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :c:func:`createSeparableLinearFilter`
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :c:cpp:func:`createSeparableLinearFilter`
:param anchor:Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
:param rowBorderType, columnBorderType:Pixel extrapolation method in the horizontal and vertical directions For details, see :c:func:`borderInterpolate`. For details on limitations, see :cpp:func:`gpu::getLinearRowFilter_GPU`, cpp:func:`gpu::getLinearColumnFilter_GPU`.
:param rowBorderType, columnBorderType:Pixel extrapolation method in the horizontal and vertical directions For details, see :c:cpp:func:`borderInterpolate`. For details on limitations, see :cpp:func:`gpu::getLinearRowFilter_GPU`, cpp:cpp:func:`gpu::getLinearColumnFilter_GPU`.
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :cpp:func:`gpu::getLinearColumnFilter_GPU`, :c:func:`createSeparableLinearFilter`
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :cpp:func:`gpu::getLinearColumnFilter_GPU`, :c:cpp:func:`createSeparableLinearFilter`
..index:: gpu::sepFilter2D
@ -544,9 +544,9 @@ gpu::sepFilter2D
:param anchor:Anchor position within the kernel. The default value ``(-1, 1)`` means that the anchor is at the kernel center.
:param rowBorderType, columnBorderType:Pixel extrapolation method. For details, see :c:func:`borderInterpolate`.
:param rowBorderType, columnBorderType:Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate`.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`sepFilter2D`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`sepFilter2D`
..index:: gpu::createDerivFilter_GPU
@ -564,11 +564,11 @@ gpu::createDerivFilter_GPU
:param dy:Derivative order in respect of y.
:param ksize:Aperture size. See :c:func:`getDerivKernels` for details.
:param ksize:Aperture size. See :c:cpp:func:`getDerivKernels` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`createDerivFilter`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`createDerivFilter`
..index:: gpu::Sobel
@ -590,11 +590,11 @@ gpu::Sobel
:param ksize:Size of the extended Sobel kernel. Possible valies are 1, 3, 5 or 7.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :c:func:`getDerivKernels` .
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :c:cpp:func:`getDerivKernels` .
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`Sobel`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`Sobel`
..index:: gpu::Scharr
@ -614,11 +614,11 @@ gpu::Scharr
:param yorder:Order of the derivative y.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :c:func:`getDerivKernels` for details.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :c:cpp:func:`getDerivKernels` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation method. For details, see :c:func:`borderInterpolate` and :c:func:`Scharr` .
:param rowBorderType, columnBorderType:Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate` and :c:cpp:func:`Scharr` .
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`Scharr`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`Scharr`
:param type:Source and destination image type. ``CV_8UC1``, ``CV_8UC4``, ``CV_16SC1``, ``CV_16SC2``, ``CV_32SC1``, ``CV_32FC1`` are supported.
:param ksize:Aperture size. See :c:func:`getGaussianKernel` for details.
:param ksize:Aperture size. See :c:cpp:func:`getGaussianKernel` for details.
:param sigmaX:Gaussian sigma in the horizontal direction. See :c:func:`getGaussianKernel` for details.
:param sigmaX:Gaussian sigma in the horizontal direction. See :c:cpp:func:`getGaussianKernel` for details.
:param sigmaY:Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigmaY}\leftarrow\texttt{sigmaX}` .
:param rowBorderType, columnBorderType:Border type to use. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Border type to use. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`createGaussianFilter`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`createGaussianFilter`
..index:: gpu::GaussianBlur
@ -654,11 +654,11 @@ gpu::GaussianBlur
:param ksize:Gaussian kernel size. ``ksize.width`` and ``ksize.height`` can differ but they both must be positive and odd. If they are zeros, they are computed from ``sigmaX`` and ``sigmaY`` .
:param sigmaX, sigmaY:Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height``, respectively. See :c:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modification of all this semantics, you are recommended to specify all of ``ksize``, ``sigmaX``, and ``sigmaY`` .
:param sigmaX, sigmaY:Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height``, respectively. See :c:cpp:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modification of all this semantics, you are recommended to specify all of ``ksize``, ``sigmaX``, and ``sigmaY`` .
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createGaussianFilter_GPU`, :c:func:`GaussianBlur`
See Also: :cpp:func:`gpu::createGaussianFilter_GPU`, :c:cpp:func:`GaussianBlur`
:param criteria:Termination criteria. See :cpp:class:`TermCriteria`.
See Also:
:c:func:`gpu::meanShiftFiltering`
:c:cpp:func:`gpu::meanShiftFiltering`
..index:: gpu::meanShiftSegmentation
@ -81,7 +81,7 @@ gpu::integral
:param sqsum:Squared integral image of the ``CV_32FC1`` type.
See Also:
:c:func:`integral`
:c:cpp:func:`integral`
..index:: gpu::sqrIntegral
@ -128,7 +128,7 @@ gpu::cornerHarris
:param borderType:Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
See Also:
:c:func:`cornerHarris`
:c:cpp:func:`cornerHarris`
..index:: gpu::cornerMinEigenVal
@ -150,7 +150,7 @@ gpu::cornerMinEigenVal
:param borderType:Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
See also: :c:func:`cornerMinEigenVal`
See also: :c:cpp:func:`cornerMinEigenVal`
..index:: gpu::mulSpectrums
@ -173,7 +173,7 @@ gpu::mulSpectrums
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
See Also:
:c:func:`mulSpectrums`
:c:cpp:func:`mulSpectrums`
..index:: gpu::mulAndScaleSpectrums
@ -198,7 +198,7 @@ gpu::mulAndScaleSpectrums
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
See Also:
:c:func:`mulSpectrums`
:c:cpp:func:`mulSpectrums`
..index:: gpu::dft
@ -237,7 +237,7 @@ gpu::dft
If the source matrix is real (its type is ``CV_32FC1`` ), forward DFT is performed. The result of the DFT is packed into complex ( ``CV_32FC2`` ) matrix. So, the width of the destination matrix is ``dft_size.width / 2 + 1`` . But if the source is a single column, the height is reduced instead of the width.
:c:func:`convolve` function with respective arguments.
:c:cpp:func:`convolve` function with respective arguments.
..index:: gpu::matchTemplate
@ -328,7 +328,7 @@ gpu::matchTemplate
* ``CV_TM_CCORR``
See Also:
:c:func:`matchTemplate`
:c:cpp:func:`matchTemplate`
..index:: gpu::remap
@ -354,7 +354,7 @@ gpu::remap
Values of pixels with non-integer coordinates are computed using bilinear the interpolation.
See Also: :c:func:`remap`
See Also: :c:cpp:func:`remap`
..index:: gpu::cvtColor
@ -370,7 +370,7 @@ gpu::cvtColor
:param dst:Destination image with the same size and depth as ``src`` .
:param code:Color space conversion code. For details, see :func:`cvtColor` . Conversion to/from Luv and Bayer color spaces is not supported.
:param code:Color space conversion code. For details, see :cpp:func:`cvtColor` . Conversion to/from Luv and Bayer color spaces is not supported.
:param dcn:Number of channels in the destination image. If the parameter is 0, the number of the channels is derived automatically from ``src`` and the ``code`` .
@ -379,7 +379,7 @@ gpu::cvtColor
3-channel color spaces (like ``HSV``, ``XYZ``, and so on) can be stored in a 4-channel image for better perfomance.
See Also:
:func:`cvtColor`
:cpp:func:`cvtColor`
..index:: gpu::threshold
@ -399,12 +399,12 @@ gpu::threshold
:param maxVal:Maximum value to use with ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` threshold types.
:param thresholdType:Threshold type. For details, see :func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param thresholdType:Threshold type. For details, see :cpp:func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param stream:Stream for the asynchronous version.
See Also:
:func:`threshold`
:cpp:func:`threshold`
..index:: gpu::resize
@ -439,7 +439,7 @@ gpu::resize
:param interpolation:Interpolation method. Only ``INTER_NEAREST`` and ``INTER_LINEAR`` are supported.
See Also: :func:`resize`
See Also: :cpp:func:`resize`
..index:: gpu::warpAffine
@ -457,10 +457,10 @@ gpu::warpAffine
:param dsize:Size of the destination image.
:param flags:Combination of interpolation methods (see :func:`resize`) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is an inverse transformation (``dst=>src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
:param flags:Combination of interpolation methods (see :cpp:func:`resize`) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is an inverse transformation (``dst=>src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
See Also:
:func:`warpAffine`
:cpp:func:`warpAffine`
..index:: gpu::warpPerspective
@ -478,10 +478,10 @@ gpu::warpPerspective
:param dsize:Size of the destination image.
:param flags:Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is the inverse transformation (``dst => src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
:param flags:Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is the inverse transformation (``dst => src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
See Also:
:func:`warpPerspective`
:cpp:func:`warpPerspective`
..index:: gpu::rotate
@ -506,7 +506,7 @@ gpu::rotate
:param interpolation:Interpolation method. Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` are supported.
@ -15,9 +15,9 @@ The GPU module depends on the CUDA Toolkit and NVIDIA Performance Primitives lib
The OpenCV GPU module is designed for ease of use and does not require any knowledge of CUDA. Though, such a knowledge will certainly be useful to handle non-trivial cases or achieve the highest performance. It is helpful to understand the cost of various operations, what the GPU does, what the preferred data formats are, and so on. The GPU module is an effective instrument for quick implementation of GPU-accelerated computer vision algorithms. However, if your algorithm involves many simple operations, then, for the best possible performance, you may still need to write your own kernels to avoid extra write and read operations on the intermediate results.
To enable CUDA support, configure OpenCV using ``CMake`` with ``WITH_CUDA=ON`` . When the flag is set and if CUDA is installed, the full-featured OpenCV GPU module is built. Otherwise, the module is still built, but at runtime all functions from the module throw
:func:`Exception` with ``CV_GpuNotSupported`` error code, except for
:func:`gpu::getCudaEnabledDeviceCount()`. The latter function returns zero GPU count in this case. Building OpenCV without CUDA support does not perform device code compilation, so it does not require the CUDA Toolkit installed. Therefore, using the
:func:`gpu::getCudaEnabledDeviceCount()` function, you can implement a high-level algorithm that will detect GPU presence at runtime and choose an appropriate implementation (CPU or GPU) accordingly.
:cpp:func:`Exception` with ``CV_GpuNotSupported`` error code, except for
:cpp:func:`gpu::getCudaEnabledDeviceCount()`. The latter function returns zero GPU count in this case. Building OpenCV without CUDA support does not perform device code compilation, so it does not require the CUDA Toolkit installed. Therefore, using the
:cpp:func:`gpu::getCudaEnabledDeviceCount()` function, you can implement a high-level algorithm that will detect GPU presence at runtime and choose an appropriate implementation (CPU or GPU) accordingly.
Compilation for Different NVIDIA* Platforms
-------------------------------------------
@ -34,12 +34,12 @@ By default, the OpenCV GPU module includes:
PTX code for compute capabilities 1.1 and 1.3 (controlled by ``CUDA_ARCH_PTX`` in ``CMake``)
This means that for devices with CC 1.3 and 2.0 binary images are ready to run. For all newer platforms, the PTX code for 1.3 is JIT'ed to a binary image. For devices with CC 1.1 and 1.2, the PTX for 1.1 is JIT'ed. For devices with CC 1.0, no code is available and the functions throw
:func:`Exception`. For platforms where JIT compilation is performed first, the run is slow.
:cpp:func:`Exception`. For platforms where JIT compilation is performed first, the run is slow.
On a GPU with CC 1.0, you can still compile the GPU module and most of the functions will run flawlessly. To achieve this, add "1.0" to the list of binaries, for example, ``CUDA_ARCH_BIN="1.0 1.3 2.0"`` . The functions that cannot be run on CC 1.0 GPUs throw an exception.
You can always determine at runtime whether the OpenCV GPU-built binaries (or PTX code) are compatible with your GPU. The function
:func:`gpu::DeviceInfo::isCompatible` returns the compatibility status (true/false).
:cpp:func:`gpu::DeviceInfo::isCompatible` returns the compatibility status (true/false).
Threading and Multi-threading
------------------------------
@ -57,7 +57,7 @@ In the current version, each of the OpenCV GPU algorithms can use only a single
*
If you use only synchronous functions, create several CPU threads (one per each GPU) and from within each thread create a CUDA context for the corresponding GPU using
:func:`gpu::setDevice()` or Driver API. Each of the threads will use the associated GPU.
:cpp:func:`gpu::setDevice()` or Driver API. Each of the threads will use the associated GPU.
*
If you use asynchronous functions, you can use the Driver API to create several CUDA contexts associated with different GPUs but attached to one CPU thread. Within the thread you can switch from one GPU to another by making the corresponding context "current". With non-blocking GPU calls, managing algorithm is clear.
:param scale0:Coefficient of the detection window increase.
:param group_threshold:Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See :func:`groupRectangles` .
:param group_threshold:Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See :cpp:func:`groupRectangles` .
@ -62,7 +62,7 @@ The following code is an example used to generate the figure. ::
setWindowProperty
---------------------
..c:function:: void setWindowProperty(const string& name, int prop_id, double prop_value)
..cpp:function:: void setWindowProperty(const string& name, int prop_id, double prop_value)
Changes parameters of a window dynamically.
@ -96,7 +96,7 @@ The function ``setWindowProperty`` enables changing properties of a window.
getWindowProperty
---------------------
..c:function:: void getWindowProperty(const char* name, int prop_id)
..cpp:function:: void getWindowProperty(const string& name, int prop_id)
Provides parameters of a window.
@ -122,7 +122,7 @@ The function ``getWindowProperty`` returns properties of a window.
fontQt
----------
..c:function:: CvFont fontQt(const string& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), int weight = CV_FONT_NORMAL, int style = CV_STYLE_NORMAL, int spacing = 0)
..cpp:function:: CvFont fontQt(const string& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), int weight = CV_FONT_NORMAL, int style = CV_STYLE_NORMAL, int spacing = 0)
Creates the font to draw a text on an image.
@ -167,7 +167,7 @@ A basic usage of this function is the following: ::
Finds edges in an image using the Canny algorithm.
@ -21,7 +19,7 @@ Canny
:param threshold2:The second threshold for the hysteresis procedure.
:param apertureSize:Aperture size for the :func:`Sobel` operator.
:param apertureSize:Aperture size for the :cpp:func:`Sobel` operator.
:param L2gradient:Flag indicating whether a more accurate :math:`L_2` norm :math:`=\sqrt{(dI/dx)^2 + (dI/dy)^2}` should be used to compute the image gradient magnitude ( ``L2gradient=true`` ), or a faster default :math:`L_1` norm :math:`=|dI/dx|+|dI/dy|` is enough ( ``L2gradient=false`` ).
:param maxCorners:Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
:param qualityLevel:Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :func:`cornerMinEigenVal` ) or the Harris function response (see :func:`cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param qualityLevel:Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :cpp:func:`cornerMinEigenVal` ) or the Harris function response (see :cpp:func:`cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param minDistance:Minimum possible Euclidean distance between the returned corners.
:param mask:Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected.
:param blockSize:Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :func:`cornerEigenValsAndVecs` .
:param blockSize:Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :cpp:func:`cornerEigenValsAndVecs` .
:param useHarrisDetector:Parameter indicating whether to use a Harris detector (see :func:`cornerHarris`) or :func:`cornerMinEigenVal`.
:param useHarrisDetector:Parameter indicating whether to use a Harris detector (see :cpp:func:`cornerHarris`) or :cpp:func:`cornerMinEigenVal`.
:param k:Free parameter of the Harris detector.
@ -231,8 +219,8 @@ The function finds the most prominent corners in the image or in the specified i
#.
Function calculates the corner quality measure at every source image pixel using the
:func:`cornerMinEigenVal` or
:func:`cornerHarris` .
:cpp:func:`cornerMinEigenVal` or
:cpp:func:`cornerHarris` .
#.
Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
@ -251,21 +239,19 @@ The function can be used to initialize a point-based tracker of an object.
**Note**: If the function is called with different values ``A`` and ``B`` of the parameter ``qualityLevel`` , and ``A`` > {B}, the vector of returned corners with ``qualityLevel=A`` will be the prefix of the output vector with ``qualityLevel=B`` .
See Also: :func:`cornerMinEigenVal`,
:func:`cornerHarris`,
:func:`calcOpticalFlowPyrLK`,
:func:`estimateRigidMotion`,
:func:`PlanarObjectDetector`,
:func:`OneWayDescriptor`
See Also: :cpp:func:`cornerMinEigenVal`,
:cpp:func:`cornerHarris`,
:cpp:func:`calcOpticalFlowPyrLK`,
:cpp:func:`estimateRigidMotion`,
:cpp:func:`PlanarObjectDetector`,
:cpp:func:`OneWayDescriptor`
..index:: HoughCircles
.._HoughCircles:
HoughCircles
------------
..c:function:: void HoughCircles( Mat& image, vector<Vec3f>& circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
..cpp:function:: void HoughCircles( InputArray image, OutputArray circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
Finds circles in a grayscale image using the Hough transform.
@ -279,7 +265,7 @@ HoughCircles
:param minDist:Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
:param param1:The first method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the higher threshold of the two passed to the :func:`Canny` edge detector (the lower one is twice smaller).
:param param1:The first method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the higher threshold of the two passed to the :cpp:func:`Canny` edge detector (the lower one is twice smaller).
:param param2:The second method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first
@ -323,17 +309,15 @@ The function finds circles in a grayscale image using a modification of the Houg
**Note**: Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( ``minRadius`` and ``maxRadius`` ) if you know it. Or, you may ignore the returned radius, use only the center, and find the correct radius using an additional procedure.
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`'s). It means that for each pixel location
:cpp:func:`Mat`'s). It means that for each pixel location
:math:`(x,y)` in the source image (normally, rectangular), its neighborhood is considered and used to compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of morphological operations, it is the minimum or maximum values, and so on. The computed response is stored in the destination image at the same location
:math:`(x,y)` . It means that the output image will be of the same size as the input image. Normally, the functions support multi-channel arrays, in which case every channel is processed independently. Therefore, the output image will also have the same number of channels as the input one.
Another common feature of the functions and classes described in this section is that, unlike simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For example, if you want to smooth an image using a Gaussian
:math:`3 \times 3` filter, then, when processing the left-most pixels in each row, you need pixels to the left of them, that is, outside of the image. You can let these pixels be the same as the left-most image pixels ("replicated border" extrapolation method), or assume that all the non-existing pixels are zeros ("contant border" extrapolation method), and so on.
OpenCV enables you to specify the extrapolation method. For details, see the function :func:`borderInterpolate` and discussion of the ``borderType`` parameter in various functions below.
OpenCV enables you to specify the extrapolation method. For details, see the function :cpp:func:`borderInterpolate` and discussion of the ``borderType`` parameter in various functions below.
..index:: BaseColumnFilter
.._BaseColumnFilter:
BaseColumnFilter
----------------
..c:type:: BaseColumnFilter
..cpp:class:: BaseColumnFilter
Base class for filters with single-column kernels ::
@ -55,23 +53,21 @@ The class ``BaseColumnFilter`` is a base class for filtering data using single-c
where
:math:`F` is a filtering function but, as it is represented as a class, it can produce any side effects, memorize previously processed data, and so on. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseRowFilter`,
:func:`BaseFilter`,
:func:`FilterEngine`,
:func:`getColumnSumFilter`,
:func:`getLinearColumnFilter`,
:func:`getMorphologyColumnFilter`
:cpp:func:`BaseRowFilter`,
:cpp:func:`BaseFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getColumnSumFilter`,
:cpp:func:`getLinearColumnFilter`,
:cpp:func:`getMorphologyColumnFilter`
..index:: BaseFilter
.._BaseFilter:
BaseFilter
----------
..c:type:: BaseFilter
..cpp:class:: BaseFilter
Base class for 2D image filters ::
@ -107,22 +103,20 @@ The class ``BaseFilter`` is a base class for filtering data using 2D kernels. Fi
where
:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseColumnFilter`,
:func:`BaseRowFilter`,
:func:`FilterEngine`,
:func:`getLinearFilter`,
:func:`getMorphologyFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`BaseRowFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getLinearFilter`,
:cpp:func:`getMorphologyFilter`
..index:: BaseRowFilter
.._BaseRowFilter:
BaseRowFilter
-------------
..c:type:: BaseRowFilter
..cpp:class:: BaseRowFilter
Base class for filters with single-row kernels ::
@ -150,23 +144,21 @@ The class ``BaseRowFilter`` is a base class for filtering data using single-row
where
:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseColumnFilter`,
:func:`Filter`,
:func:`FilterEngine`,
:func:`getLinearRowFilter`,
:func:`getMorphologyRowFilter`,
:func:`getRowSumFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`Filter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getLinearRowFilter`,
:cpp:func:`getMorphologyRowFilter`,
:cpp:func:`getRowSumFilter`
..index:: FilterEngine
.._FilterEngine:
FilterEngine
------------
..c:type:: FilterEngine
..cpp:class:: FilterEngine
Generic image filtering class ::
@ -239,12 +231,12 @@ The class ``FilterEngine`` can be used to apply an arbitrary filtering operation
It contains all the necessary intermediate buffers, computes extrapolated values
of the "virtual" pixels outside of the image, and so on. Pointers to the initialized ``FilterEngine`` instances
are returned by various ``create*Filter`` functions (see below) and they are used inside high-level functions such as
:func:`filter2D`,
:func:`erode`,
:func:`dilate`, and others. Thus, the class plays a key role in many of OpenCV filtering functions.
:cpp:func:`filter2D`,
:cpp:func:`erode`,
:cpp:func:`dilate`, and others. Thus, the class plays a key role in many of OpenCV filtering functions.
This class makes it easier to combine filtering operations with other operations, such as color space conversions, thresholding, arithmetic operations, and others. By combining several operations together you can get much better performance because your data will stay in cache. For example, see below the implementation of the Laplace operator for floating-point images, which is a simplified implementation of
:func:`Laplacian` :::
:cpp:func:`Laplacian` :::
void laplace_f(const Mat& src, Mat& dst)
{
@ -355,7 +347,7 @@ Unlike the earlier versions of OpenCV, now the filtering operations fully suppor
Explore the data types. As it was mentioned in the
:func:`BaseFilter` description, the specific filters can process data of any type, despite that ``Base*Filter::operator()`` only takes ``uchar`` pointers and no information about the actual types. To make it all work, the following rules are used:
:cpp:func:`BaseFilter` description, the specific filters can process data of any type, despite that ``Base*Filter::operator()`` only takes ``uchar`` pointers and no information about the actual types. To make it all work, the following rules are used:
*
In case of separable filtering, ``FilterEngine::rowFilter`` is applied first. It transforms the input image data (of type ``srcType`` ) to the intermediate results stored in the internal buffers (of type ``bufType`` ). Then, these intermediate results are processed as
@ -366,21 +358,21 @@ Explore the data types. As it was mentioned in the
In case of non-separable filtering, ``bufType`` must be the same as ``srcType`` . The source data is copied to the temporary buffer, if needed, and then just passed to ``FilterEngine::filter2D`` . That is, the input type for ``filter2D`` is ``srcType`` (= ``bufType`` ) and the output type is ``dstType`` .
See Also:
:func:`BaseColumnFilter`,
:func:`BaseFilter`,
:func:`BaseRowFilter`,
:func:`createBoxFilter`,
:func:`createDerivFilter`,
:func:`createGaussianFilter`,
:func:`createLinearFilter`,
:func:`createMorphologyFilter`,
:func:`createSeparableLinearFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`BaseFilter`,
:cpp:func:`BaseRowFilter`,
:cpp:func:`createBoxFilter`,
:cpp:func:`createDerivFilter`,
:cpp:func:`createGaussianFilter`,
:cpp:func:`createLinearFilter`,
:cpp:func:`createMorphologyFilter`,
:cpp:func:`createSeparableLinearFilter`
..index:: bilateralFilter
bilateralFilter
-------------------
..c:function:: void bilateralFilter( const Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT )
..cpp:function:: void bilateralFilter( InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT )
..c:function:: void blur( const Mat& src, Mat& dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT )
..cpp:function:: void blur( InputArray src, OutputArray dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT )
Smoothes an image using the normalized box filter.
@ -424,16 +416,16 @@ The function smoothes an image using the kernel:
The call ``blur(src, dst, ksize, anchor, borderType)`` is equivalent to ``boxFilter(src, dst, src.type(), anchor, true, borderType)`` .
See Also:
:func:`boxFilter`,
:func:`bilateralFilter`,
:func:`GaussianBlur`,
:func:`medianBlur`
:cpp:func:`boxFilter`,
:cpp:func:`bilateralFilter`,
:cpp:func:`GaussianBlur`,
:cpp:func:`medianBlur`
..index:: borderInterpolate
borderInterpolate
---------------------
..c:function:: int borderInterpolate( int p, int len, int borderType )
..cpp:function:: int borderInterpolate( int p, int len, int borderType )
Computes the source location of an extrapolated pixel.
@ -450,18 +442,18 @@ The function computes and returns the coordinate of the donor pixel, correspondi
Normally, the function is not called directly. It is used inside
:func:`FilterEngine` and
:func:`copyMakeBorder` to compute tables for quick extrapolation.
:cpp:func:`FilterEngine` and
:cpp:func:`copyMakeBorder` to compute tables for quick extrapolation.
See Also:
:func:`FilterEngine`,
:func:`copyMakeBorder`
:cpp:func:`FilterEngine`,
:cpp:func:`copyMakeBorder`
..index:: boxFilter
boxFilter
-------------
..c:function:: void boxFilter( const Mat& src, Mat& dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT )
..cpp:function:: void boxFilter( InputArray src, OutputArray dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT )
Smoothes an image using the box filter.
@ -491,24 +483,24 @@ where
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms,
and so on). If you need to compute pixel sums over variable-size windows, use
..cpp:function:: void buildPyramid( InputArray src, OutputArrayOfArrays dst, int maxlevel )
Constructs the Gaussian pyramid for an image.
:param src:Source image. Check :func:`pyrDown` for the list of supported types.
:param src:Source image. Check :cpp:func:`pyrDown` for the list of supported types.
:param dst:Destination vector of ``maxlevel+1`` images of the same type as ``src`` . ``dst[0]`` will be the same as ``src`` . ``dst[1]`` is the next pyramid layer,
a smoothed and down-sized ``src`` , and so on.
@ -516,13 +508,13 @@ buildPyramid
:param maxlevel:0-based index of the last (the smallest) pyramid layer. It must be non-negative.
The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
:func:`pyrDown` to the previously built pyramid layers, starting from ``dst[0]==src`` .
:cpp:func:`pyrDown` to the previously built pyramid layers, starting from ``dst[0]==src`` .
..index:: copyMakeBorder
copyMakeBorder
------------------
..c:function:: void copyMakeBorder( const Mat& src, Mat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() )
..cpp:function:: void copyMakeBorder( InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() )
Forms a border around an image.
@ -532,12 +524,12 @@ copyMakeBorder
:param top, bottom, left, right:Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, ``top=1, bottom=1, left=1, right=1`` mean that 1 pixel-wide border needs to be built.
:param borderType:Border type. See :func:`borderInterpolate` for details.
:param borderType:Border type. See :cpp:func:`borderInterpolate` for details.
:param value:Border value if ``borderType==BORDER_CONSTANT`` .
The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what
:func:`FilterEngine` or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling.
:cpp:func:`FilterEngine` or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling.
The function supports the mode when ``src`` is already in the middle of ``dst`` . In this case, the function does not copy ``src`` itself but simply constructs the border, for example: ::
@ -557,16 +549,16 @@ The function supports the mode when ``src`` is already in the middle of ``dst``
See Also:
:func:`borderInterpolate`
:cpp:func:`borderInterpolate`
..index:: createBoxFilter
createBoxFilter
-------------------
..c:function:: Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
..cpp:function:: Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
..c:function:: Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1)
..cpp:function:: Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1)
..c:function:: Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor=-1, double scale=1)
..cpp:function:: Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor=-1, double scale=1)
Returns a box filter engine.
@ -580,31 +572,31 @@ createBoxFilter
:param anchor:Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
:param normalize:Flag specifying whether the sums are normalized or not. See :func:`boxFilter` for details.
:param normalize:Flag specifying whether the sums are normalized or not. See :cpp:func:`boxFilter` for details.
:param scale:Another way to specify normalization in lower-level ``getColumnSumFilter`` .
:param borderType:Border type to use. See :func:`borderInterpolate` .
:param borderType:Border type to use. See :cpp:func:`borderInterpolate` .
The function is a convenience function that retrieves the horizontal sum primitive filter with
:func:`getRowSumFilter` , vertical sum filter with
:func:`getColumnSumFilter` , constructs new
:func:`FilterEngine` , and passes both of the primitive filters there. The constructed filter engine can be used for image filtering with normalized or unnormalized box filter.
:cpp:func:`getRowSumFilter` , vertical sum filter with
:cpp:func:`getColumnSumFilter` , constructs new
:cpp:func:`FilterEngine` , and passes both of the primitive filters there. The constructed filter engine can be used for image filtering with normalized or unnormalized box filter.
The function itself is used by
:func:`blur` and
:func:`boxFilter` .
:cpp:func:`blur` and
:cpp:func:`boxFilter` .
See Also:
:func:`FilterEngine`,
:func:`blur`,
:func:`boxFilter`
:cpp:func:`FilterEngine`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`
..index:: createDerivFilter
createDerivFilter
---------------------
..c:function:: Ptr<FilterEngine> createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT )
..cpp:function:: Ptr<FilterEngine> createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT )
Returns an engine for computing image derivatives.
@ -616,57 +608,57 @@ createDerivFilter
:param dy:Derivative order in respect of y.
:param ksize:Aperture size See :func:`getDerivKernels` .
:param ksize:Aperture size See :cpp:func:`getDerivKernels` .
:param borderType:Border type to use. See :func:`borderInterpolate` .
:param borderType:Border type to use. See :cpp:func:`borderInterpolate` .
The function :func:`createDerivFilter` is a small convenience function that retrieves linear filter coefficients for computing image derivatives using
:func:`getDerivKernels` and then creates a separable linear filter with
:func:`createSeparableLinearFilter` . The function is used by
:func:`Sobel` and
:func:`Scharr` .
The function :cpp:func:`createDerivFilter` is a small convenience function that retrieves linear filter coefficients for computing image derivatives using
:cpp:func:`getDerivKernels` and then creates a separable linear filter with
:cpp:func:`createSeparableLinearFilter` . The function is used by
:cpp:func:`Sobel` and
:cpp:func:`Scharr` .
See Also:
:func:`createSeparableLinearFilter`,
:func:`getDerivKernels`,
:func:`Scharr`,
:func:`Sobel`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getDerivKernels`,
:cpp:func:`Scharr`,
:cpp:func:`Sobel`
..index:: createGaussianFilter
createGaussianFilter
------------------------
..c:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
..cpp:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
Returns an engine for smoothing images with the Gaussian filter.
:param type:Source and destination image type.
:param ksize:Aperture size. See :func:`getGaussianKernel` .
:param ksize:Aperture size. See :cpp:func:`getGaussianKernel` .
:param sigmaX:Gaussian sigma in the horizontal direction. See :func:`getGaussianKernel` .
:param sigmaX:Gaussian sigma in the horizontal direction. See :cpp:func:`getGaussianKernel` .
:param sigmaY:Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigmaY}\leftarrow\texttt{sigmaX}` .
:param borderType:Border type to use. See :func:`borderInterpolate` .
:param borderType:Border type to use. See :cpp:func:`borderInterpolate` .
The function :func:`createGaussianFilter` computes Gaussian kernel coefficients and then returns a separable linear filter for that kernel. The function is used by
:func:`GaussianBlur` . Note that while the function takes just one data type, both for input and output, you can pass this limitation by calling
:func:`getGaussianKernel` and then
:func:`createSeparableFilter` directly.
The function :cpp:func:`createGaussianFilter` computes Gaussian kernel coefficients and then returns a separable linear filter for that kernel. The function is used by
:cpp:func:`GaussianBlur` . Note that while the function takes just one data type, both for input and output, you can pass this limitation by calling
:cpp:func:`getGaussianKernel` and then
:cpp:func:`createSeparableFilter` directly.
See Also:
:func:`createSeparableLinearFilter`,
:func:`getGaussianKernel`,
:func:`GaussianBlur`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getGaussianKernel`,
:cpp:func:`GaussianBlur`
..index:: createLinearFilter
createLinearFilter
----------------------
..c:function:: Ptr<FilterEngine> createLinearFilter(int srcType, int dstType, const Mat& kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
..cpp:function:: Ptr<FilterEngine> createLinearFilter(int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
..c:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, const Mat& kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
..cpp:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
Creates a non-separable linear filter engine.
@ -682,30 +674,30 @@ createLinearFilter
:param bits:Number of the fractional bits. the parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border vaule used in case of a constant border.
The function returns a pointer to a 2D linear filter for the specified kernel, the source array type, and the destination array type. The function is a higher-level function that calls ``getLinearFilter`` and passes the retrieved 2D filter to the
:func:`FilterEngine` constructor.
:cpp:func:`FilterEngine` constructor.
See Also:
:func:`createSeparableLinearFilter`,
:func:`FilterEngine`,
:func:`filter2D`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`filter2D`
..index:: createMorphologyFilter
createMorphologyFilter
--------------------------
..c:function:: Ptr<FilterEngine> createMorphologyFilter(int op, int type, const Mat& element, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue())
..cpp:function:: Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray element, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue())
..c:function:: Ptr<BaseFilter> getMorphologyFilter(int op, int type, const Mat& element, Point anchor=Point(-1,-1))
..cpp:function:: Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray element, Point anchor=Point(-1,-1))
..c:function:: Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int esize, int anchor=-1)
..cpp:function:: Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int esize, int anchor=-1)
..c:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int esize, int anchor=-1)
..cpp:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int esize, int anchor=-1)
Creates an engine for non-separable morphological operations.
@ -719,32 +711,32 @@ createMorphologyFilter
:param anchor:Anchor position within the structuring element. Negative values mean that the anchor is at the kernel center.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border value in case of a constant border. The default value, \ ``morphologyDefaultBorderValue`` , has a special meaning. It is transformed :math:`+\inf` for the erosion and to :math:`-\inf` for the dilation, which means that the minimum (maximum) is effectively computed only over the pixels that are inside the image.
The functions construct primitive morphological filtering operations or a filter engine based on them. Normally it is enough to use
:func:`createMorphologyFilter` or even higher-level
:func:`erode`,
:func:`dilate` , or
:func:`morphologyEx` .
:cpp:func:`createMorphologyFilter` or even higher-level
:cpp:func:`erode`,
:cpp:func:`dilate` , or
:cpp:func:`morphologyEx` .
Note that
:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.
:cpp:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.
See Also:
:func:`erode`,
:func:`dilate`,
:func:`morphologyEx`,
:func:`FilterEngine`
:cpp:func:`erode`,
:cpp:func:`dilate`,
:cpp:func:`morphologyEx`,
:cpp:func:`FilterEngine`
..index:: createSeparableLinearFilter
createSeparableLinearFilter
-------------------------------
..c:function:: Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType, const Mat& rowKernel, const Mat& columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
..cpp:function:: Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
..c:function:: Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType, const Mat& columnKernel, int anchor, int symmetryType, double delta=0, int bits=0)
..cpp:function:: Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType, InputArray columnKernel, int anchor, int symmetryType, double delta=0, int bits=0)
..c:function:: Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType, const Mat& rowKernel, int anchor, int symmetryType)
..cpp:function:: Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType, InputArray rowKernel, int anchor, int symmetryType)
Creates an engine for a separable linear filter.
@ -764,28 +756,28 @@ createSeparableLinearFilter
:param bits:Number of the fractional bits. The parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType:Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border value used in case of a constant border.
:param symmetryType:Type of each row and column kernel. See :func:`getKernelType` .
:param symmetryType:Type of each row and column kernel. See :cpp:func:`getKernelType` .
The functions construct primitive separable linear filtering operations or a filter engine based on them. Normally it is enough to use
:func:`createSeparableLinearFilter` or even higher-level
:func:`sepFilter2D` . The function
:func:`createMorphologyFilter` is smart enough to figure out the ``symmetryType`` for each of the two kernels, the intermediate ``bufType`` and, if filtering can be done in integer arithmetics, the number of ``bits`` to encode the filter coefficients. If it does not work for you, it is possible to call ``getLinearColumnFilter``,``getLinearRowFilter`` directly and then pass them to the
:func:`FilterEngine` constructor.
:cpp:func:`createSeparableLinearFilter` or even higher-level
:cpp:func:`sepFilter2D` . The function
:cpp:func:`createMorphologyFilter` is smart enough to figure out the ``symmetryType`` for each of the two kernels, the intermediate ``bufType`` and, if filtering can be done in integer arithmetics, the number of ``bits`` to encode the filter coefficients. If it does not work for you, it is possible to call ``getLinearColumnFilter``,``getLinearRowFilter`` directly and then pass them to the
:cpp:func:`FilterEngine` constructor.
See Also:
:func:`sepFilter2D`,
:func:`createLinearFilter`,
:func:`FilterEngine`,
:func:`getKernelType`
:cpp:func:`sepFilter2D`,
:cpp:func:`createLinearFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getKernelType`
..index:: dilate
dilate
----------
..c:function:: void dilate( const Mat& src, Mat& dst, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
..cpp:function:: void dilate( InputArray src, OutputArray dst, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
Dilates an image by using a specific structuring element.
@ -799,9 +791,9 @@ dilate
:param iterations:Number of times dilation is applied.
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphologyFilter` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphologyFilter` for details.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
@ -812,14 +804,14 @@ The function dilates the source image using the specified structuring element th
The function supports the in-place mode. Dilation can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
See Also:
:func:`erode`,
:func:`morphologyEx`,
:func:`createMorphologyFilter`
:cpp:func:`erode`,
:cpp:func:`morphologyEx`,
:cpp:func:`createMorphologyFilter`
..index:: erode
erode
---------
..c:function:: void erode( const Mat& src, Mat& dst, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
..cpp:function:: void erode( InputArray src, OutputArray dst, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
Erodes an image by using a specific structuring element.
@ -833,9 +825,9 @@ erode
:param iterations:Number of times erosion is applied.
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphoogyFilter` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphoogyFilter` for details.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
@ -846,15 +838,15 @@ The function erodes the source image using the specified structuring element tha
The function supports the in-place mode. Erosion can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
See Also:
:func:`dilate`,
:func:`morphologyEx`,
:func:`createMorphologyFilter`
:cpp:func:`dilate`,
:cpp:func:`morphologyEx`,
:cpp:func:`createMorphologyFilter`
..index:: filter2D
filter2D
------------
..c:function:: void filter2D( const Mat& src, Mat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
..cpp:function:: void filter2D( InputArray src, OutputArray dst, int ddepth, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
Convolves an image with the kernel.
@ -864,13 +856,13 @@ filter2D
:param ddepth:Desired depth of the destination image. If it is negative, it will be the same as ``src.depth()`` .
:param kernel:Convolution kernel (or rather a correlation kernel), a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using :func:`split` and process them individually.
:param kernel:Convolution kernel (or rather a correlation kernel), a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using :cpp:func:`split` and process them individually.
:param anchor:Anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor should lie within the kernel. The special default value (-1,-1) means that the anchor is at the kernel center.
:param delta:Optional value added to the filtered pixels before storing them in ``dst`` .
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
@ -881,21 +873,21 @@ The function does actually compute correlation, not the convolution:
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using
:func:`flip` and set the new anchor to ``(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`` .
:cpp:func:`flip` and set the new anchor to ``(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`` .
The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm (that uses the engine retrieved by :func:`createLinearFilter` ) for small kernels.
The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm (that uses the engine retrieved by :cpp:func:`createLinearFilter` ) for small kernels.
:param ksize:Gaussian kernel size. ``ksize.width`` and ``ksize.height`` can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from ``sigma*`` .
:param sigmaX, sigmaY:Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height`` , respectively. See :func:`getGaussianKernel` for details. To fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ``ksize`` , ``sigmaX`` , and ``sigmaY`` .
:param sigmaX, sigmaY:Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height`` , respectively. See :cpp:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ``ksize`` , ``sigmaX`` , and ``sigmaY`` .
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
See Also:
:func:`sepFilter2D`,
:func:`filter2D`,
:func:`blur`,
:func:`boxFilter`,
:func:`bilateralFilter`,
:func:`medianBlur`
:cpp:func:`sepFilter2D`,
:cpp:func:`filter2D`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`,
:cpp:func:`bilateralFilter`,
:cpp:func:`medianBlur`
..index:: getDerivKernels
getDerivKernels
-------------------
..c:function:: void getDerivKernels( Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize=false, int ktype=CV_32F )
..cpp:function:: void getDerivKernels( OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize=false, int ktype=CV_32F )
Returns filter coefficients for computing spatial image derivatives.
@ -942,16 +934,16 @@ getDerivKernels
The function computes and returns the filter coefficients for spatial image derivatives. When ``ksize=CV_SCHARR`` , the Scharr
:math:`3 \times 3` kernels are generated (see
:func:`Scharr` ). Otherwise, Sobel kernels are generated (see
:func:`Sobel` ). The filters are normally passed to
:func:`sepFilter2D` or to
:func:`createSeparableLinearFilter` .
:cpp:func:`Scharr` ). Otherwise, Sobel kernels are generated (see
:cpp:func:`Sobel` ). The filters are normally passed to
:cpp:func:`sepFilter2D` or to
:cpp:func:`createSeparableLinearFilter` .
..index:: getGaussianKernel
getGaussianKernel
---------------------
..c:function:: Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F )
..cpp:function:: Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F )
Returns Gaussian filter coefficients.
@ -973,22 +965,22 @@ where
:math:`\sum_i G_i=1`.
Two of such generated kernels can be passed to
:func:`sepFilter2D` or to
:func:`createSeparableLinearFilter`. Those functions automatically recognize smoothing kernels (i.e. symmetrical kernel with sum of weights = 1) and handle them accordingly. You may also use the higher-level
:func:`GaussianBlur`.
:cpp:func:`sepFilter2D` or to
:cpp:func:`createSeparableLinearFilter`. Those functions automatically recognize smoothing kernels (i.e. symmetrical kernel with sum of weights = 1) and handle them accordingly. You may also use the higher-level
:cpp:func:`GaussianBlur`.
See Also:
:func:`sepFilter2D`,
:func:`createSeparableLinearFilter`,
:func:`getDerivKernels`,
:func:`getStructuringElement`,
:func:`GaussianBlur`
:cpp:func:`sepFilter2D`,
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getDerivKernels`,
:cpp:func:`getStructuringElement`,
:cpp:func:`GaussianBlur`
..index:: getKernelType
getKernelType
-----------------
..c:function:: int getKernelType(const Mat& kernel, Point anchor)
..cpp:function:: int getKernelType(InputArray kernel, Point anchor)
Returns the kernel type.
@ -1011,7 +1003,7 @@ The function analyzes the kernel coefficients and returns the corresponding kern
getStructuringElement
-------------------------
..c:function:: Mat getStructuringElement(int shape, Size esize, Point anchor=Point(-1,-1))
..cpp:function:: Mat getStructuringElement(int shape, Size esize, Point anchor=Point(-1,-1))
Returns a structuring element of the specified size and shape for morphological operations.
@ -1036,16 +1028,16 @@ getStructuringElement
:param anchor:Anchor position within the element. The default value :math:`(-1, -1)` means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.
The function constructs and returns the structuring element that can be then passed to
:func:`createMorphologyFilter`,
:func:`erode`,
:func:`dilate` or
:func:`morphologyEx` . But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
:cpp:func:`createMorphologyFilter`,
:cpp:func:`erode`,
:cpp:func:`dilate` or
:cpp:func:`morphologyEx` . But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
..cpp:function:: void medianBlur( InputArray src, OutputArray dst, int ksize )
Smoothes an image using the median filter.
@ -1059,16 +1051,16 @@ The function smoothes an image using the median filter with the
:math:`\texttt{ksize} \times \texttt{ksize}` aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.
See Also:
:func:`bilateralFilter`,
:func:`blur`,
:func:`boxFilter`,
:func:`GaussianBlur`
:cpp:func:`bilateralFilter`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`,
:cpp:func:`GaussianBlur`
..index:: morphologyEx
morphologyEx
----------------
..c:function:: void morphologyEx( const Mat& src, Mat& dst, int op, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
..cpp:function:: void morphologyEx( InputArray src, OutputArray dst, int op, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
Performs advanced morphological transformations.
@ -1092,9 +1084,9 @@ morphologyEx
:param iterations:Number of times erosion and dilation are applied.
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphoogyFilter` for details.
:param borderValue:Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphoogyFilter` for details.
The function can perform advanced morphological transformations using an erosion and dilation as basic operations.
@ -1131,15 +1123,15 @@ Morphological gradient:
Any of the operations can be done in-place.
See Also:
:func:`dilate`,
:func:`erode`,
:func:`createMorphologyFilter`
:cpp:func:`dilate`,
:cpp:func:`erode`,
:cpp:func:`createMorphologyFilter`
..index:: Laplacian
Laplacian
-------------
..c:function:: void Laplacian( const Mat& src, Mat& dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
..cpp:function:: void Laplacian( InputArray src, OutputArray dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
Calculates the Laplacian of an image.
@ -1149,13 +1141,13 @@ Laplacian
:param ddepth:Desired depth of the destination image.
:param ksize:Aperture size used to compute the second-derivative filters. See :func:`getDerivKernels` for details. The size must be positive and odd.
:param ksize:Aperture size used to compute the second-derivative filters. See :cpp:func:`getDerivKernels` for details. The size must be positive and odd.
:param scale:Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale:Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta:Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
@ -1171,14 +1163,14 @@ This is done when ``ksize > 1`` . When ``ksize == 1`` , the Laplacian is compute
The function performs the upsampling step of the Gaussian pyramid construction though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in
:func:`pyrDown` multiplied by 4.
:cpp:func:`pyrDown` multiplied by 4.
..index:: sepFilter2D
sepFilter2D
---------------
..c:function:: void sepFilter2D( const Mat& src, Mat& dst, int ddepth, const Mat& rowKernel, const Mat& columnKernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
..cpp:function:: void sepFilter2D( InputArray src, OutputArray dst, int ddepth, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
Applies a separable linear filter to an image.
@ -1245,23 +1237,23 @@ sepFilter2D
:param delta:Value added to the filtered results before storing them.
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function applies a separable linear filter to the image. That is, first, every row of ``src`` is filtered with the 1D kernel ``rowKernel`` . Then, every column of the result is filtered with the 1D kernel ``columnKernel`` . The final result shifted by ``delta`` is stored in ``dst`` .
See Also:
:func:`createSeparableLinearFilter`,
:func:`filter2D`,
:func:`Sobel`,
:func:`GaussianBlur`,
:func:`boxFilter`,
:func:`blur`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`filter2D`,
:cpp:func:`Sobel`,
:cpp:func:`GaussianBlur`,
:cpp:func:`boxFilter`,
:cpp:func:`blur`
..index:: Sobel
Sobel
---------
..c:function:: void Sobel( const Mat& src, Mat& dst, int ddepth, int xorder, int yorder, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
..cpp:function:: void Sobel( InputArray src, OutputArray dst, int ddepth, int xorder, int yorder, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
@ -1277,11 +1269,11 @@ Sobel
:param ksize:Size of the extended Sobel kernel. It must be 1, 3, 5, or 7.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta:Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
In all cases except one, the
:math:`\texttt{ksize} \times
@ -1324,17 +1316,17 @@ The second case corresponds to a kernel of:
\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}
See Also:
:func:`Scharr`,
:func:`Lapacian`,
:func:`sepFilter2D`,
:func:`filter2D`,
:func:`GaussianBlur`
:cpp:func:`Scharr`,
:cpp:func:`Lapacian`,
:cpp:func:`sepFilter2D`,
:cpp:func:`filter2D`,
:cpp:func:`GaussianBlur`
..index:: Scharr
Scharr
----------
..c:function:: void Scharr( const Mat& src, Mat& dst, int ddepth, int xorder, int yorder, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
..cpp:function:: void Scharr( InputArray src, OutputArray dst, int ddepth, int xorder, int yorder, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
Calculates the first x- or y- image derivative using Scharr operator.
@ -1348,11 +1340,11 @@ Scharr
:param yorder:Order of the derivative y.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale:Optional scale factor for the computed derivative values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta:Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType:Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType:Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
Converts image transformation maps from one representation to another.
@ -56,11 +56,11 @@ convertMaps
:param nninterpolation:Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.
The function converts a pair of maps for
:func:`remap` from one representation to another. The following options ( ``(map1.type(), map2.type())``:math:`\rightarrow```(dstmap1.type(), dstmap2.type())`` ) are supported:
:cpp:func:`remap` from one representation to another. The following options ( ``(map1.type(), map2.type())``:math:`\rightarrow```(dstmap1.type(), dstmap2.type())`` ) are supported:
*
:math:`\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}` . This is the most frequently used conversion operation, in which the original floating-point maps (see
:func:`remap` ) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when ``nninterpolation=false`` ) contains indices in the interpolation tables.
:cpp:func:`remap` ) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when ``nninterpolation=false`` ) contains indices in the interpolation tables.
*
:math:`\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}` . The same as above but the original maps are stored in one 2-channel matrix.
@ -69,17 +69,15 @@ The function converts a pair of maps for
Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
See Also:
:func:`remap`,
:func:`undisort`,
:func:`initUndistortRectifyMap`
:cpp:func:`remap`,
:cpp:func:`undisort`,
:cpp:func:`initUndistortRectifyMap`
..index:: getAffineTransform
.._getAffineTransform:
getAffineTransform
----------------------
..c:function:: Mat getAffineTransform( const Point2f src[], const Point2f dst[] )
..cpp:function:: Mat getAffineTransform( const Point2f src[], const Point2f dst[] )
Calculates an affine transform from three pairs of the corresponding points.
@ -102,8 +100,8 @@ where
i=0,1,2
See Also:
:func:`warpAffine`,
:func:`transform`
:cpp:func:`warpAffine`,
:cpp:func:`transform`
..index:: getPerspectiveTransform
@ -112,7 +110,7 @@ See Also:
getPerspectiveTransform
---------------------------
..c:function:: Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
..cpp:function:: Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
Calculates a perspective transform from four pairs of the corresponding points.
@ -135,9 +133,9 @@ where
i=0,1,2
See Also:
:func:`findHomography`,
:func:`warpPerspective`,
:func:`perspectiveTransform`
:cpp:func:`findHomography`,
:cpp:func:`warpPerspective`,
:cpp:func:`perspectiveTransform`
..index:: getRectSubPix
@ -145,7 +143,7 @@ See Also:
getRectSubPix
-----------------
..c:function:: void getRectSubPix( const Mat& image, Size patchSize, Point2f center, Mat& dst, int patchType=-1 )
..cpp:function:: void getRectSubPix( InputArray image, Size patchSize, Point2f center, OutputArray dst, int patchType=-1 )
Retrieves a pixel rectangle from an image with sub-pixel accuracy.
@ -170,12 +168,12 @@ using bilinear interpolation. Every channel of multi-channel
images is processed independently. While the center of the rectangle
must be inside the image, parts of the rectangle may be
outside. In this case, the replication border mode (see
:func:`borderInterpolate` ) is used to extrapolate
:cpp:func:`borderInterpolate` ) is used to extrapolate
the pixel values outside of the image.
See Also:
:func:`warpAffine`,
:func:`warpPerspective`
:cpp:func:`warpAffine`,
:cpp:func:`warpPerspective`
..index:: getRotationMatrix2D
@ -183,7 +181,7 @@ See Also:
getRotationMatrix2D
-----------------------
..c:function:: Mat getRotationMatrix2D( Point2f center, double angle, double scale )
..cpp:function:: Mat getRotationMatrix2D( Point2f center, double angle, double scale )
Calculates an affine matrix of 2D rotation.
@ -208,9 +206,9 @@ where
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
See Also:
:func:`getAffineTransform`,
:func:`warpAffine`,
:func:`transform`
:cpp:func:`getAffineTransform`,
:cpp:func:`warpAffine`,
:cpp:func:`transform`
..index:: invertAffineTransform
@ -218,7 +216,7 @@ See Also:
invertAffineTransform
-------------------------
..c:function:: void invertAffineTransform(const Mat& M, Mat& iM)
..cpp:function:: void invertAffineTransform(InputArray M, OutputArray iM)
..cpp:function:: void remap( InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies a generic geometrical transformation to an image.
:param src:Source image.
:param dst:Destination image. It has the same size as ``map1`` and the same type as ``src`` .
:param map1:The first map of either ``(x,y)`` points or just ``x`` values having the type ``CV_16SC2`` , ``CV_32FC1`` , or ``CV_32FC2`` . See :func:`convertMaps` for details on converting a floating point representation to fixed-point for speed.
:param map1:The first map of either ``(x,y)`` points or just ``x`` values having the type ``CV_16SC2`` , ``CV_32FC1`` , or ``CV_32FC2`` . See :cpp:func:`convertMaps` for details on converting a floating point representation to fixed-point for speed.
:param map2:The second map of ``y`` values having the type ``CV_16UC1`` , ``CV_32FC1`` , or none (empty map if ``map1`` is ``(x,y)`` points), respectively.
:param interpolation:Interpolation method (see :func:`resize` ). The method ``INTER_AREA`` is not supported by this function.
:param interpolation:Interpolation method (see :cpp:func:`resize` ). The method ``INTER_AREA`` is not supported by this function.
:param borderMode:Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderMode:Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderValue:Value used in case of a constant border. By default, it is 0.
@ -274,7 +272,7 @@ where values of pixels with non-integer coordinates are computed using one of av
:math:`(x,y)` in
:math:`map_1` , or
fixed-point maps created by using
:func:`convertMaps` . The reason you might want to convert from floating to fixed-point
:cpp:func:`convertMaps` . The reason you might want to convert from floating to fixed-point
representations of a map is that they can yield much faster (~2x) remapping operations. In the converted case,
:math:`map_1` contains pairs ``(cvFloor(x), cvFloor(y))`` and
:math:`map_2` contains indices in a table of interpolation coefficients.
@ -288,7 +286,7 @@ This function cannot operate in-place.
@ -343,9 +341,9 @@ If you want to decimate the image by factor of 2 in each direction, you can call
See Also:
:func:`warpAffine`,
:func:`warpPerspective`,
:func:`remap`
:cpp:func:`warpAffine`,
:cpp:func:`warpPerspective`,
:cpp:func:`remap`
..index:: warpAffine
@ -353,7 +351,7 @@ See Also:
warpAffine
--------------
..c:function:: void warpAffine( const Mat& src, Mat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
..cpp:function:: void warpAffine( InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies an affine transformation to an image.
@ -365,9 +363,9 @@ warpAffine
:param dsize:Size of the destination image.
:param flags:Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param flags:Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param borderMode:Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function.
:param borderMode:Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function.
:param borderValue:Value used in case of a constant border. By default, it is 0.
@ -378,23 +376,21 @@ The function ``warpAffine`` transforms the source image using the specified matr
\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})
when the flag ``WARP_INVERSE_MAP`` is set. Otherwise, the transformation is first inverted with
:func:`invertAffineTransform` and then put in the formula above instead of ``M`` .
:cpp:func:`invertAffineTransform` and then put in the formula above instead of ``M`` .
The function cannot operate in-place.
See Also:
:func:`warpPerspective`,
:func:`resize`,
:func:`remap`,
:func:`getRectSubPix`,
:func:`transform`
:cpp:func:`warpPerspective`,
:cpp:func:`resize`,
:cpp:func:`remap`,
:cpp:func:`getRectSubPix`,
:cpp:func:`transform`
..index:: warpPerspective
.._warpPerspective:
warpPerspective
-------------------
..c:function:: void warpPerspective( const Mat& src, Mat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
..cpp:function:: void warpPerspective( InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies a perspective transformation to an image.
@ -406,9 +402,9 @@ warpPerspective
:param dsize:Size of the destination image.
:param flags:Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param flags:Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param borderMode:Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderMode:Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderValue:Value used in case of a constant border. By default, it is 0.
@ -420,13 +416,178 @@ The function ``warpPerspective`` transforms the source image using the specified
\frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )
when the flag ``WARP_INVERSE_MAP`` is set. Otherwise, the transformation is first inverted with
:func:`invert` and then put in the formula above instead of ``M`` .
:cpp:func:`invert` and then put in the formula above instead of ``M`` .
Computes the undistortion and rectification transformation map.
:param cameraMatrix:Input camera matrix :math:`A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R:Optional rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` , computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is assumed.
:param newCameraMatrix:New camera matrix :math:`A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}` .
:param size:Undistorted image size.
:param m1type:Type of the first output map that can be ``CV_32FC1`` or ``CV_16SC2`` . See :ref:`convertMaps` for details.
:param map1:The first output map.
:param map2:The second output map.
The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for
:ref:`Remap` . The undistorted image looks like original, as if it is captured with a camera using the camera matrix ``=newCameraMatrix`` and zero distortion. In case of a monocular camera, ``newCameraMatrix`` is usually equal to ``cameraMatrix`` , or it can be computed by
:ref:`GetOptimalNewCameraMatrix` for a better control over scaling. In case of a stereo camera, ``newCameraMatrix`` is normally set to ``P1`` or ``P2`` computed by
:ref:`StereoRectify` .
Also, this new camera is oriented differently in the coordinate space, according to ``R`` . That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by
:ref:`Remap` . That is, for each pixel
:math:`(u, v)` in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:
:math:`(k_1, k_2, p_1, p_2[, k_3])` are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
:ref:`StereoRectify` , which in its turn is called after
:ref:`StereoCalibrate` . But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using
:ref:`StereoRectifyUncalibrated` . For each camera, the function computes homography ``H`` as the rectification transformation in a pixel domain, not a rotation matrix ``R`` in 3D space. ``R`` can be computed from ``H`` as
..cpp:function:: Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgSize=Size(), bool centerPrincipalPoint=false )
Returns the default new camera matrix.
:param cameraMatrix:Input camera matrix.
:param imageSize:Camera view image size in pixels.
:param centerPrincipalPoint:Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.
The function returns the camera matrix that is either an exact copy of the input ``cameraMatrix`` (when ``centerPrinicipalPoint=false`` ), or the modified one (when ``centerPrincipalPoint`` =true).
In the latter case, the new camera matrix will be:
:math:`(1,1)` elements of ``cameraMatrix`` , respectively.
By default, the undistortion functions in OpenCV (see
:ref:`initUndistortRectifyMap`,
:ref:`undistort`) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
Transforms an image to compensate for lens distortion.
:param src:Input (distorted) image.
:param dst:Output (corrected) image that has the same size and type as ``src`` .
:param cameraMatrix:Input camera matrix :math:`A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param newCameraMatrix:Camera matrix of the distorted image. By default, it is the same as ``cameraMatrix`` but you may additionally scale and shift the result by using a different matrix.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of
:ref:`InitUndistortRectifyMap` (with unity ``R`` ) and
:ref:`Remap` (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by ``newCameraMatrix`` . You can use
:ref:`GetOptimalNewCameraMatrix` to compute the appropriate ``newCameraMatrix`` depending on your requirements.
The camera matrix and the distortion parameters can be determined using
:ref:`calibrateCamera` . If the resolution of images is different from the resolution used at the calibration stage,
:math:`f_x, f_y, c_x` and
:math:`c_y` need to be scaled accordingly, while the distortion coefficients remain the same.
:param distCoeffs:Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R:Rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used.
:param P:New camera matrix (3x3) or new projection matrix (3x4). ``P1`` or ``P2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used.
The function is similar to
:ref:`undistort` and
:ref:`initUndistortRectifyMap` but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to
:ref:`projectPoints` . In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper ``R`` is specified. ::
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u - cx)/fx
y" = (v - cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
u' = x*fx' + cx'
v' = y*fy' + cy',
where ``undistort()`` is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the
..c:function:: void adaptiveThreshold( const Mat& src, Mat& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )
..cpp:function:: void adaptiveThreshold( InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )
Applies an adaptive threshold to an array.
@ -54,14 +54,14 @@ where
:math:`T(x, y)` is a weighted sum (cross-correlation with a Gaussian window) of the
:math:`\texttt{blockSize} \times \texttt{blockSize}` neighborhood of
:math:`(x, y)` minus ``C`` . The default sigma (standard deviation) is used for the specified ``blockSize`` . See
:func:`getGaussianKernel` .
:cpp:func:`getGaussianKernel` .
The function can process the image in-place.
See Also:
:func:`threshold`,
:func:`blur`,
:func:`GaussianBlur`
:cpp:func:`threshold`,
:cpp:func:`blur`,
:cpp:func:`GaussianBlur`
..index:: cvtColor
@ -70,7 +70,7 @@ See Also:
cvtColor
------------
..c:function:: void cvtColor( const Mat& src, Mat& dst, int code, int dstCn=0 )
..cpp:function:: void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 )
Converts an image from one color space to another.
@ -127,7 +127,7 @@ The function can do the following transformations:
..
More advanced channel reordering can also be done with
:func:`mixChannels` .
:cpp:func:`mixChannels` .
*
RGB
@ -404,9 +404,9 @@ The function can do the following transformations:
distanceTransform
---------------------
..c:function:: void distanceTransform( const Mat& src, Mat& dst, int distanceType, int maskSize )
..cpp:function:: void distanceTransform( InputArray src, OutputArray dst, int distanceType, int maskSize )
..c:function:: void distanceTransform( const Mat& src, Mat& dst, Mat& labels, int distanceType, int maskSize )
..cpp:function:: void distanceTransform( InputArray src, OutputArray dst, OutputArray labels, int distanceType, int maskSize )
Calculates the distance to the closest zero pixel for each pixel of the source image.
@ -472,9 +472,9 @@ Currently, the second variant can use only the approximate distance transform al
floodFill
-------------
..c:function:: int floodFill( Mat& image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
..cpp:function:: int floodFill( InputOutputArray image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
..c:function:: int floodFill( Mat& image, Mat& mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
..cpp:function:: int floodFill( InputOutputArray image, InputOutputArray mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
Fills a connected component with the given color.
@ -566,7 +566,7 @@ where
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on. Various modes of the function are demonstrated in the ``floodfill.cpp`` sample.
@ -28,15 +28,15 @@ The function supports multi-channel images. Each channel is processed independen
The functions ``accumulate*`` can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.
After the function finishes the comparison, the best matches can be found as global minimums (when ``CV_TM_SQDIFF`` was used) or maximums (when ``CV_TM_CCORR`` or ``CV_TM_CCOEFF`` was used) using the
:func:`minMaxLoc` function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
:cpp:func:`minMaxLoc` function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.
:param moments:Input moments computed with :func:`moments` .
:param moments:Input moments computed with :cpp:func:`moments` .
:param h:Output Hu invariants.
The function calculates the seven Hu invariants (see
@ -101,19 +101,19 @@ where
These values are proved to be invariants to the image scale, rotation, and reflection except the seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of infinite image resolution. In case of raster images, the computed Hu invariants for the original and transformed images are a bit different.
See Also:
:func:`matchShapes`
:cpp:func:`matchShapes`
..index:: findContours
findContours
----------------
..c:function:: void findContours( const Mat& image, vector<vector<Point> >& contours, vector<Vec4i>& hierarchy, int mode, int method, Point offset=Point())
..cpp:function:: void findContours( InputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
..c:function:: void findContours( const Mat& image, vector<vector<Point> >& contours, int mode, int method, Point offset=Point())
..cpp:function:: void findContours( InputArray image, OutputArrayOfArrays contours, int mode, int method, Point offset=Point())
Finds contours in a binary image.
:param image:Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as ``binary`` . You can use :func:`compare` , :func:`inRange` , :func:`threshold` , :func:`adaptiveThreshold` , :func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the ``image`` while extracting the contours.
:param image:Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as ``binary`` . You can use :cpp:func:`compare` , :cpp:func:`inRange` , :cpp:func:`threshold` , :cpp:func:`adaptiveThreshold` , :cpp:func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the ``image`` while extracting the contours.
:param contours:Detected contours. Each contour is stored as a vector of points.
@ -150,7 +150,7 @@ Source ``image`` is modified by this function.
drawContours
----------------
..c:function:: void drawContours( Mat& image, const vector<vector<Point> >& contours, int contourIdx, const Scalar& color, int thickness=1, int lineType=8, const vector<Vec4i>& hierarchy=vector<Vec4i>(), int maxLevel=INT_MAX, Point offset=Point() )
..cpp:function:: void drawContours( InputOutputArray image, InputArrayOfArrays contours, int contourIdx, const Scalar& color, int thickness=1, int lineType=8, InputArray hierarchy=None(), int maxLevel=INT_MAX, Point offset=Point() )
Draws contours outlines or filled contours.
@ -165,7 +165,7 @@ drawContours
:param thickness:Thickness of lines the contours are drawn with. If it is negative (for example, ``thickness=CV_FILLED`` ), the contour interiors are
drawn.
:param lineType:Line connectivity. See :func:`line` for details.
:param lineType:Line connectivity. See :cpp:func:`line` for details.
:param hierarchy:Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see ``maxLevel`` ).
@ -221,13 +221,11 @@ The function draws contour outlines in the image if
Approximates a polygonal curve(s) with the specified precision.
:param curve:Polygon or curve to approximate. It must be :math:`1 \times N` or :math:`N \times 1` matrix of type ``CV_32SC2`` or ``CV_32FC2`` . You can also convert ``vector<Point>`` or ``vector<Point2f>`` to the matrix by calling the ``Mat(const vector<T>&)`` constructor.
:param curve:Input vector of 2d point, stored in ``std::vector`` or ``Mat``.
:param approxCurve:Result of the approximation. The type should match the type of the input curve.
@ -238,15 +236,17 @@ approxPolyDP
The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices, so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
:param curve:Input vector of 2D points represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix with the ``Mat(const vector<T>&)`` constructor.
:param curve:Input vector of 2D points, stored in ``std::vector`` or ``Mat``.
:param closed:Flag indicating whether the curve is closed or not.
@ -256,11 +256,11 @@ The function computes a curve length or a closed contour perimeter.
boundingRect
----------------
..c:function:: Rect boundingRect( const Mat& points )
..cpp:function:: Rect boundingRect( InputArray points )
Calculates the up-right bounding rectangle of a point set.
:param points:Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param points:Input 2D point set, stored in ``std::vector`` or ``Mat``.
The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
@ -268,14 +268,14 @@ The function calculates and returns the minimal up-right bounding rectangle for
..cpp:function:: Mat estimateRigidTransform( InputArray srcpt, InputArray dstpt, bool fullAffine )
Computes an optimal affine transformation between two 2D point sets.
:param srcpt:The first input 2D point set.
:param srcpt:The first input 2D point set, stored in ``std::vector`` or ``Mat``.
:param dst:The second input 2D point set of the same size and the same type as ``A`` .
:param fullAffine:If true, the function finds an optimal affine transformation with no additional resrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom).
:param contour:Contour vertices represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param contour:Input vector of 2d points (contour vertices), stored in ``std::vector`` or ``Mat``.
The function computes a contour area. Similarly to
:func:`moments` , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
:func:`drawContours` or
:func:`fillPoly` , can be different.
:cpp:func:`moments` , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
:param points:Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param points:Input 2D point set, stored in ``std::vector`` or ``Mat``.
:param hull:Output convex hull. It is either a vector of points that form the hull (must have the same type as the input points), or a vector of 0-based point indices of the hull points in the original array (since the set of convex hull points is a subset of the original point set).
:param hull:Output convex hull. It is either an integer vector of indices or vector of points. In the first case the ``hull`` elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case ``hull`` elements will be the convex hull points themselves.
:param clockwise:If true, the output convex hull will be oriented clockwise. Otherwise, it will be oriented counter-clockwise. The usual screen coordinate system is assumed where the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
:param clockwise:Orientation flag. If true, the output convex hull will be oriented clockwise. Otherwise, it will be oriented counter-clockwise. The usual screen coordinate system is assumed where the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
:param returnPoints:Operation flag. In the case of matrix, when the flag is true, the function will return convex hull points, otherwise it will return indices of the convex hull points. When the output array is ``std::vector``, the flag is ignored, and the output depends on the type of the vector - ``std::vector<int>`` implies ``returnPoints=true``, ``std::vector<Point>`` implies ``returnPoints=false``.
The functions find the convex hull of a 2D point set using the Sklansky's algorithm
Sklansky82
that has
:math:`O(N logN)` or
:math:`O(N)` complexity (where
:math:`N` is the number of input points), depending on how the initial sorting is implemented (currently it is
:math:`O(N logN)` . See the OpenCV sample ``convexhull.c`` that demonstrates the usage of different function variants.
*O(N logN)* complexity in the current implementation. See the OpenCV sample ``convexhull.cpp`` that demonstrates the usage of different function variants.
:param points:Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` or ``vector<Point2f>``.
:param points:Input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function calculates the ellipse that fits (in least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed.
@ -397,13 +392,13 @@ The function calculates the ellipse that fits (in least-squares sense) a set of
:param points:Input 2D or 3D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>``, ``vector<Point2f>``, ``vector<Point3i>`` or ``vector<Point3f>``.
:param points:Input vector of 2D or 3D points, stored in ``std::vector<>`` or ``Mat``.
:param line:Output line parameters. In case of 2D fitting it should be ``Vec4f``, a vector of 4 floats ``(vx, vy, x0, y0)``, where ``(vx, vy)`` is a normalized vector collinear to the line and ``(x0, y0)`` is a point on the line. In case of 3D fitting, it should be ``Vec6f``, a vector of 6 floats ``(vx, vy, vz, x0, y0, z0)``, where ``(vx, vy, vz)`` is a normalized vector collinear to the line and ``(x0, y0, z0)`` is a point on the line.
:param line:Output line parameters. In case of 2D fitting it should be a vector of 4 elements (like ``Vec4f``) - ``(vx, vy, x0, y0)``, where ``(vx, vy)`` is a normalized vector collinear to the line and ``(x0, y0)`` is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like ``Vec6f``) - ``(vx, vy, vz, x0, y0, z0)``, where ``(vx, vy, vz)`` is a normalized vector collinear to the line and ``(x0, y0, z0)`` is a point on the line.
:param distType:Distance used by the M-estimator (see the discussion).
:param contour:Tested contour, a matrix of type ``CV_32SC2`` or ``CV_32FC2`` , or ``vector<Point>`` or ``vector<Point2f>``.
:param contour:The input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.
@ -475,11 +470,11 @@ The function tests whether the input contour is convex or not. The contour must
Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
:param points:Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` or ``vector<Point2f>``.
:param points:The input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. See the OpenCV sample ``minarea.cpp`` .
@ -487,11 +482,11 @@ The function calculates and returns the minimum-area bounding rectangle (possibl
Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
:param prevImg:The first 8-bit single-channel or 3-channel input image.
:param nextImg:The second input image of the same size and the same type as ``prevImg`` .
:param prevPts:Vector of points for which the flow needs to be found.
:param nextPts:Output vector of points containing the calculated new positions of input features in the second image.
@ -43,16 +43,16 @@ Bouguet00
calcOpticalFlowFarneback
----------------------------
..c:function:: void calcOpticalFlowFarneback( const Mat\& prevImg, const Mat\& nextImg, Mat\& flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
..cpp:function:: void calcOpticalFlowFarneback( InputArray prevImg, InputArray nextImg, InputOutputArray flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
Computes a dense optical flow using the Gunnar Farneback's algorithm.
:param prevImg:The first 8-bit single-channel input image.
:param nextImg:The second input image of the same size and the same type as ``prevImg`` .
:param flow:Computed flow image that has the same size as ``prevImg`` and type ``CV_32FC2`` .
:param pyrScale:Parameter specifying the image scale (<1) to build pyramids for each image. ``pyrScale=0.5`` means a classical pyramid, where each next layer is twice smaller than the previous one.
:param levels:Number of pyramid layers including the initial image. ``levels=1`` means that no extra layers are created and only the original images are used.
@ -81,7 +81,7 @@ The function finds an optical flow for each ``prevImg`` pixel using the alorithm
Updates the motion history image by a moving silhouette.
@ -102,8 +102,8 @@ The function updates the motion history image as follows:
That is, MHI pixels where the motion occurs are set to the current ``timestamp`` , while the pixels where the motion happened last time a long time ago are cleared.
The function, together with
:func:`calcMotionGradient` and
:func:`calcGlobalOrientation` , implements a motion templates technique described in
:cpp:func:`calcMotionGradient` and
:cpp:func:`calcGlobalOrientation` , implements a motion templates technique described in
Davis97
and
Bradski00
@ -114,7 +114,7 @@ See also the OpenCV sample ``motempl.c`` that demonstrates the use of all the mo
:func:`phase` are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the ``mask`` is filled to indicate pixels where the computed angle is valid.
:cpp:func:`fastArctan` and
:cpp:func:`phase` are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the ``mask`` is filled to indicate pixels where the computed angle is valid.
Calculates a global motion orientation in a selected region.
:param orientation:Motion gradient orientation image calculated by the function :func:`calcMotionGradient` .
:param orientation:Motion gradient orientation image calculated by the function :cpp:func:`calcMotionGradient` .
:param mask:Mask image. It may be a conjunction of a valid gradient mask, also calculated by :func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
:param mask:Mask image. It may be a conjunction of a valid gradient mask, also calculated by :cpp:func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
:param mhi:Motion history image calculated by :func:`updateMotionHistory` .
:param mhi:Motion history image calculated by :cpp:func:`updateMotionHistory` .
:param timestamp:Timestamp passed to :func:`updateMotionHistory` .
:param timestamp:Timestamp passed to :cpp:func:`updateMotionHistory` .
:param duration:Maximum duration of a motion track in milliseconds, passed to :func:`updateMotionHistory` .
:param duration:Maximum duration of a motion track in milliseconds, passed to :cpp:func:`updateMotionHistory` .
The function calculates an average
motion direction in the selected region and returns the angle between
@ -171,21 +171,21 @@ weight and the motion occurred in the past has a smaller weight, as recorded in
:param probImage:Back projection of the object histogram. See :func:`calcBackProject` .
:param probImage:Back projection of the object histogram. See :cpp:func:`calcBackProject` .
:param window:Initial search window.
:param criteria:Stop criteria for the underlying :func:`meanShift` .
:param criteria:Stop criteria for the underlying :cpp:func:`meanShift` .
The function implements the CAMSHIFT object tracking algrorithm
Bradski98
.
First, it finds an object center using
:func:`meanShift` and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with ``RotatedRect::boundingRect()`` .
:cpp:func:`meanShift` and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with ``RotatedRect::boundingRect()`` .
See the OpenCV sample ``camshiftdemo.c`` that tracks colored objects.
@ -193,23 +193,23 @@ See the OpenCV sample ``camshiftdemo.c`` that tracks colored objects.
..cpp:function:: int meanShift( InputArray probImage, Rect& window, TermCriteria criteria )
Finds an object on a back projection image.
:param probImage:Back projection of the object histogram. See :func:`calcBackProject` for details.
:param probImage:Back projection of the object histogram. See :cpp:func:`calcBackProject` for details.
:param window:Initial search window.
:param criteria:Stop criteria for the iterative search algorithm.
The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in ``window`` of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations ``criteria.maxCount`` is done or until the window center shifts by less than ``criteria.epsilon`` . The algorithm is used inside
:func:`CamShift` and, unlike
:func:`CamShift` , the search window size or orientation do not change during the search. You can simply pass the output of
:func:`calcBackProject` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise (for example, by retrieving connected components with
:func:`findContours` , throwing away contours with small area (
:func:`contourArea` ), and rendering the remaining contours with
:func:`drawContours` ).
:cpp:func:`CamShift` and, unlike
:cpp:func:`CamShift` , the search window size or orientation do not change during the search. You can simply pass the output of
:cpp:func:`calcBackProject` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise (for example, by retrieving connected components with
:cpp:func:`findContours` , throwing away contours with small area (
:cpp:func:`contourArea` ), and rendering the remaining contours with
Vadim Pisarevsky
14 years ago