@@ -16,7 +16,7 @@ Today it is common to have a digital video recording system at your disposal. Th
The source code
===============
As a test case where to show off these using OpenCV I've created a small program that reads in two video files and performs a similarity check between them. This is something you could use to check just how well a new video compressing algorithms works. Let there be a reference (original) video like :download:`this small Megamind clip <../../../../samples/cpp/tutorial_code/highgui/video-input-psnr-ssim/video/Megamind.avi>` and :download:`a compressed version of it <../../../../samples/cpp/tutorial_code/highgui/video-input-psnr-ssim/video/Megamind_bugy.avi>`. You may also find the source code and these video file in the :file:`samples/cpp/tutorial_code/highgui/video-input-psnr-ssim/` folder of the OpenCV source library.
As a test case where to show off these using OpenCV I've created a small program that reads in two video files and performs a similarity check between them. This is something you could use to check just how well a new video compressing algorithms works. Let there be a reference (original) video like :download:`this small Megamind clip <../../../../samples/cpp/tutorial_code/HighGUI/video-input-psnr-ssim/video/Megamind.avi>` and :download:`a compressed version of it <../../../../samples/cpp/tutorial_code/HighGUI/video-input-psnr-ssim/video/Megamind_bugy.avi>`. You may also find the source code and these video file in the :file:`samples/cpp/tutorial_code/HighGUI/video-input-psnr-ssim/` folder of the OpenCV source library.
Retrieve the current parameters values in a *Retina::RetinaParameters* structure
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@@ -325,7 +325,7 @@ Retina::RetinaParameters
.. ocv:struct:: Retina::RetinaParameters
This structure merges all the parameters that can be adjusted threw the **Retina::setup()**, **Retina::setupOPLandIPLParvoChannel** and **Retina::setupIPLMagnoChannel** setup methods
Parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel. ::
Parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel. ::
@@ -166,6 +166,12 @@ field of the set is the total number of nodes both occupied and free. When an oc
``CvSet`` is used to represent graphs (:ocv:struct:`CvGraph`), sparse multi-dimensional arrays (:ocv:struct:`CvSparseMat`), and planar subdivisions (:ocv:struct:`CvSubdiv2D`).
CvSetElem
---------
.. ocv:struct:: CvSetElem
The structure is represent single element of :ocv:struct:`CvSet`. It consists of two fields: element data pointer and flags.
CvGraph
-------
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@@ -174,6 +180,24 @@ CvGraph
The structure ``CvGraph`` is a base for graphs used in OpenCV 1.x. It inherits from
:ocv:struct:`CvSet`, that is, it is considered as a set of vertices. Besides, it contains another set as a member, a set of graph edges. Graphs in OpenCV are represented using adjacency lists format.
CvGraphVtx
----------
.. ocv:struct:: CvGraphVtx
The structure represents single vertex in :ocv:struct:`CvGraph`. It consists of two filds: pointer to first edge and flags.
CvGraphEdge
-----------
.. ocv:struct:: CvGraphEdge
The structure represents edge in :ocv:struct:`CvGraph`. Each edge consists of:
- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively);
- Two pointers to next edges for the starting and ending vertices, where
next[0] points to the next edge in the vtx[0] adjacency list and
next[1] points to the next edge in the vtx[1] adjacency list;
* **SVD::NO_UV** indicates that only a vector of singular values ``w`` is to be processed, while ``u`` and ``vt`` will be set to empty matrices.
* **SVD::FULL_UV** when the matrix is not square, by default the algorithm produces ``u`` and ``vt`` matrices of sufficiently large size for the further ``A`` reconstruction; if, however, ``FULL_UV`` flag is specified, ``u`` and ``vt``will be full-size square orthogonal matrices.
* **SVD::FULL_UV** when the matrix is not square, by default the algorithm produces ``u`` and ``vt`` matrices of sufficiently large size for the further ``A`` reconstruction; if, however, ``FULL_UV`` flag is specified, ``u`` and ``vt`` will be full-size square orthogonal matrices.
The first constructor initializes an empty ``SVD`` structure. The second constructor initializes an empty ``SVD`` structure and then calls
.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 )
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@@ -484,7 +483,7 @@ Example: ::
AdjusterAdapter::good
-------------------------
---------------------
Returns false if the detector parameters cannot be adjusted any more.
@@ -6,6 +6,7 @@ Fast Approximate Nearest Neighbor Search
This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. More information about FLANN can be found in [Muja2009]_ .
.. [Muja2009] Marius Muja, David G. Lowe. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration, 2009
:param fps: Framerate of the created video stream.
:param params: Encoder parameters. See :ocv:class:`gpu::VideoWriter_GPU::EncoderParams` .
:param params: Encoder parameters. See :ocv:struct:`gpu::VideoWriter_GPU::EncoderParams` .
:param format: Surface format of input frames ( ``SF_UYVY`` , ``SF_YUY2`` , ``SF_YV12`` , ``SF_NV12`` , ``SF_IYUV`` , ``SF_BGR`` or ``SF_GRAY``). BGR or gray frames will be converted to YV12 format before encoding, frames with other formats will be used as is.
The weak tree classifier, a component of the boosted tree classifier :ocv:class:`CvBoost`, is a derivative of :ocv:class:`CvDTree`. Normally, there is no need to use the weak classifiers directly. However, they can be accessed as elements of the sequence :ocv:member:`CvBoost::weak`, retrieved by :ocv:func:`CvBoost::get_weak_predictors`.
The weak tree classifier, a component of the boosted tree classifier :ocv:class:`CvBoost`, is a derivative of :ocv:class:`CvDTree`. Normally, there is no need to use the weak classifiers directly. However, they can be accessed as elements of the sequence ``CvBoost::weak``, retrieved by :ocv:func:`CvBoost::get_weak_predictors`.
.. note:: In case of LogitBoost and Gentle AdaBoost, each weak predictor is a regression tree, rather than a classification tree. Even in case of Discrete AdaBoost and Real AdaBoost, the ``CvBoostTree::predict`` return value (:ocv:member:`CvDTreeNode::value`) is not an output class label. A negative value "votes" for class #0, a positive value - for class #1. The votes are weighted. The weight of each individual tree may be increased or decreased using the method ``CvBoostTree::scale``.
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@@ -233,4 +233,3 @@ CvBoost::get_data
Returns used train data of the boosted tree classifier.
@@ -148,7 +148,7 @@ Brute-force descriptor matcher. For each descriptor in the first set, this match
The class ``BruteForceMatcher_OCL_base`` has an interface similar to the class :ocv:class:`DescriptorMatcher`. It has two groups of ``match`` methods: for matching descriptors of one image with another image or with an image set. Also, all functions have an alternative to save results either to the GPU memory or to the CPU memory. ``BruteForceMatcher_OCL_base`` supports only the ``L1<float>``, ``L2<float>``, and ``Hamming`` distance types.
@@ -56,7 +56,7 @@ Class providing memory buffers for :ocv:func:`ocl::matchTemplate` function, plus
You can use field `user_block_size` to set specific block size for :ocv:func:`ocl::matchTemplate` function. If you leave its default value `Size(0,0)` then automatic estimation of block size will be used (which is optimized for speed). By varying `user_block_size` you can reduce memory requirements at the cost of speed.
ocl::matchTemplate
----------------------
------------------
Computes a proximity map for a raster template and an image where the template is searched for.
