Template "trait" class for other??sounds strange since we haven't introduced any typs before that?? OpenCV primitive data types. 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
Template "trait" class for OpenCV primitive data types. 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
:cpp:class:`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: ::
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@@ -552,7 +552,7 @@ 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.
.. cpp:function:: template<typename T, int m, int n> explicit Mat::Mat(const Matx<T, m, n>& vec, bool copyData=true)
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@@ -1477,8 +1477,7 @@ The method is used in quite a few of OpenCV functions. The point is that element
This approach, while being very simple, can boost the performance of a simple element-operation by 10-20 percents, especially if the image is rather small and the operation is quite simple.
Another OpenCV idiom in this function, a call of
:cpp:func:`Mat::create` for the destination array, already has the proper size and type.?? And while the newly allocated arrays are always continuous, you still check the destination array because
:cpp:func:`create` does not always allocate a new matrix.
:cpp:func:`Mat::create` for the destination array, that allocates the destination array unless it already has the proper size and type. And while the newly allocated arrays are always continuous, you still need to check the destination array because :cpp:func:`create` does not always allocate a new matrix.
.. list-table:: **Arithmetical Operations**??output is not in bold, why??
.. list-table:: **Arithmetical Operations**
* -
-
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@@ -1117,7 +1117,7 @@ gemm
* **GEMM_2_T** transpose ``src2``
* **GEMM_3_T** transpose ``src3``
The function performs generalized matrix multiplication similar?? to the corresponding functions ``*gemm`` in BLAS level 3. For example, ``gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`` corresponds to
The function performs generalized matrix multiplication similar to the ``gemm`` functions in BLAS level 3. For example, ``gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`` corresponds to
.. math::
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@@ -1139,10 +1139,6 @@ getConvertElem
.. cpp:function:: ConvertScaleData getConvertScaleElem(int fromType, int toType)
.. cpp:function:: typedef void (*ConvertData)(const void* from, void* to, int cn)??broken output??
.. cpp:function:: typedef void (*ConvertScaleData)(const void* from, void* to, int cn, double alpha, double beta)
Returns a conversion function for a single pixel.
:param fromType: Source pixel type.
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@@ -1161,6 +1157,12 @@ getConvertElem
The functions ``getConvertElem`` and ``getConvertScaleElem`` return pointers to the functions for converting individual pixels from one type to another. While the main function purpose is to convert single pixels (actually, for converting sparse matrices from one type to another), you can use them to convert the whole row of a dense matrix or the whole matrix at once, by setting ``cn = matrix.cols*matrix.rows*matrix.channels()`` if the matrix data is continuous.
``ConvertData`` and ``ConvertScaleData`` are defined as: ::
typedef void (*ConvertData)(const void* from, void* to, int cn)
typedef void (*ConvertScaleData)(const void* from, void* to,
The EM (Expectation Maximization) algorithm estimates the parameters of the multivariate probability density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
Consider the set of the
:math:`x_1, x_2,...,x_{N}` : N feature vectors?? from a d-dimensional Euclidean space drawn from a Gaussian mixture:
Consider the set of the N feature vectors
{ :math:`x_1, x_2,...,x_{N}` } from a d-dimensional Euclidean space drawn from a Gaussian mixture:
.. math::
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@@ -62,7 +62,7 @@ Alternatively, the algorithm may start with the M-step when the initial values f
:math:`p_{i,k}` . Often (including ML) the
:ref:`kmeans` algorithm is used for that purpose.
One of the main problems?? the EM algorithm should deal with is a large number
One of the main problems of the EM algorithm is a large number
of parameters to estimate. The majority of the parameters reside in
