@@ -136,10 +136,10 @@ Approximates an elliptic arc with a polyline.
:param center: Center of the arc.
:param axes: Half-sizes of the arc. See the :ocv:func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See the :ocv:func:`ellipse` for details.
:param axes: Half-sizes of the arc. See the :ocv:func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See the :ocv:func:`ellipse` for details.
:param startAngle: Starting angle of the elliptic arc in degrees.
:param endAngle: Ending angle of the elliptic arc in degrees.
...
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@@ -227,11 +227,11 @@ Calculates the width and height of a text string.
:param text: Input text string.
:param fontFace: Font to use. See the :ocv:func:`putText` for details.
:param fontFace: Font to use. See the :ocv:func:`putText` for details.
:param fontScale: Font scale. See the :ocv:func:`putText` for details.
:param fontScale: Font scale. See the :ocv:func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :ocv:func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :ocv:func:`putText` for details.
:param baseLine: Output parameter - y-coordinate of the baseline relative to the bottom-most text point.
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@@ -275,49 +275,49 @@ Initializes font structure (OpenCV 1.x API).
.. ocv:cfunction:: void cvInitFont( CvFont* font, int fontFace, double hscale, double vscale, double shear=0, int thickness=1, int lineType=8 )
:param font: Pointer to the font structure initialized by the function
:param font: Pointer to the font structure initialized by the function
:param fontFace: Font name identifier. Only a subset of Hershey fonts http://sources.isc.org/utils/misc/hershey-font.txt are supported now:
* **CV_FONT_HERSHEY_SIMPLEX** normal size sans-serif font
* **CV_FONT_HERSHEY_SIMPLEX** normal size sans-serif font
* **CV_FONT_HERSHEY_PLAIN** small size sans-serif font
* **CV_FONT_HERSHEY_PLAIN** small size sans-serif font
* **CV_FONT_HERSHEY_DUPLEX** normal size sans-serif font (more complex than ``CV_FONT_HERSHEY_SIMPLEX`` )
* **CV_FONT_HERSHEY_DUPLEX** normal size sans-serif font (more complex than ``CV_FONT_HERSHEY_SIMPLEX`` )
* **CV_FONT_HERSHEY_COMPLEX** normal size serif font
* **CV_FONT_HERSHEY_COMPLEX** normal size serif font
* **CV_FONT_HERSHEY_TRIPLEX** normal size serif font (more complex than ``CV_FONT_HERSHEY_COMPLEX`` )
* **CV_FONT_HERSHEY_TRIPLEX** normal size serif font (more complex than ``CV_FONT_HERSHEY_COMPLEX`` )
* **CV_FONT_HERSHEY_COMPLEX_SMALL** smaller version of ``CV_FONT_HERSHEY_COMPLEX``
* **CV_FONT_HERSHEY_COMPLEX_SMALL** smaller version of ``CV_FONT_HERSHEY_COMPLEX``
* **CV_FONT_HERSHEY_SCRIPT_SIMPLEX** hand-writing style font
* **CV_FONT_HERSHEY_SCRIPT_SIMPLEX** hand-writing style font
* **CV_FONT_HERSHEY_SCRIPT_COMPLEX** more complex variant of ``CV_FONT_HERSHEY_SCRIPT_SIMPLEX``
* **CV_FONT_HERSHEY_SCRIPT_COMPLEX** more complex variant of ``CV_FONT_HERSHEY_SCRIPT_SIMPLEX``
The parameter can be composited from one of the values above and an optional ``CV_FONT_ITALIC`` flag, which indicates italic or oblique font.
The parameter can be composited from one of the values above and an optional ``CV_FONT_ITALIC`` flag, which indicates italic or oblique font.
:param hscale: Horizontal scale. If equal to ``1.0f`` , the characters have the original width depending on the font type. If equal to ``0.5f`` , the characters are of half the original width.
:param hscale: Horizontal scale. If equal to ``1.0f`` , the characters have the original width depending on the font type. If equal to ``0.5f`` , the characters are of half the original width.
:param vscale: Vertical scale. If equal to ``1.0f`` , the characters have the original height depending on the font type. If equal to ``0.5f`` , the characters are of half the original height.
:param vscale: Vertical scale. If equal to ``1.0f`` , the characters have the original height depending on the font type. If equal to ``0.5f`` , the characters are of half the original height.
:param shear: Approximate tangent of the character slope relative to the vertical line. A zero value means a non-italic font, ``1.0f`` means about a 45 degree slope, etc.
:param shear: Approximate tangent of the character slope relative to the vertical line. A zero value means a non-italic font, ``1.0f`` means about a 45 degree slope, etc.
:param thickness: Thickness of the text strokes
:param thickness: Thickness of the text strokes
:param lineType: Type of the strokes, see :ocv:func:`line` description
:param lineType: Type of the strokes, see :ocv:func:`line` description
The function initializes the font structure that can be passed to text rendering functions.
.. seealso:: :ocv:cfunc:`PutText`
.. _Line:
.. _Line:
line
--------
...
...
@@ -416,7 +416,7 @@ Draws a simple, thick, or filled up-right rectangle.
:param pt1: Vertex of the rectangle.
:param pt2: Vertex of the rectangle opposite to ``pt1`` .
:param r: Alternative specification of the drawn rectangle.
:param color: Rectangle color or brightness (grayscale image).
...
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@@ -441,7 +441,7 @@ Draws several polygonal curves.
.. ocv:cfunction:: void cvPolyLine( CvArr* img, CvPoint** pts, int* npts, int contours, int isClosed, CvScalar color, int thickness=1, int lineType=8, int shift=0 )
@@ -95,7 +95,7 @@ Computes the cube root of an argument.
.. ocv:cfunction:: float cvCbrt(float val)
.. ocv:pyoldfunction:: cv.Cbrt(val)-> float
.. ocv:pyoldfunction:: cv.Cbrt(value)-> float
:param val: A function argument.
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@@ -151,7 +151,7 @@ Determines if the argument is Infinity.
.. ocv:cfunction:: int cvIsInf(double value)
.. ocv:pyoldfunction:: cv.IsInf(value)-> int
:param value: The input floating-point value
:param value: The input floating-point value
The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) and 0 otherwise.
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@@ -162,7 +162,7 @@ Determines if the argument is Not A Number.
.. ocv:cfunction:: int cvIsNaN(double value)
.. ocv:pyoldfunction:: cv.IsNaN(value)-> int
:param value: The input floating-point value
:param value: The input floating-point value
The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 otherwise.
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@@ -186,8 +186,8 @@ Signals an error and raises an exception.
:param exc: Exception to throw.
:param status: Error code. Normally, it is a negative value. The list of pre-defined error codes can be found in ``cxerror.h`` .
:param status: Error code. Normally, it is a negative value. The list of pre-defined error codes can be found in ``cxerror.h`` .
:param err_msg: Text of the error message.
:param args: ``printf`` -like formatted error message in parentheses.
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@@ -209,7 +209,7 @@ The macro ``CV_Error_`` can be used to construct an error message on-fly to incl
Exception
---------
.. ocv:class:: Exception
.. ocv:class:: Exception : public std::exception
Exception class passed to an error. ::
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@@ -261,7 +261,7 @@ Deallocates a memory buffer.
.. ocv:cfunction:: void cvFree( void** pptr )
:param ptr: Pointer to the allocated buffer.
:param pptr: Double pointer to the allocated buffer
The function deallocates the buffer allocated with :ocv:func:`fastMalloc` . If NULL pointer is passed, the function does nothing. C version of the function clears the pointer ``*pptr`` to avoid problems with double memory deallocation.
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@@ -286,10 +286,10 @@ Returns true if the specified feature is supported by the host hardware.
@@ -167,7 +167,7 @@ Finds the best match for each descriptor from a query set.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image ``trainDescCollection[i]``.
In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by ``DescriptorMatcher::add`` is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by ``DescriptorMatcher::add`` is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
...
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@@ -193,7 +193,7 @@ Finds the k best matches for each descriptor from a query set.
:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
These extended variants of :ocv:func:`DescriptorMatcher::match` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See :ocv:func:`DescriptorMatcher::match` for the details about query and train descriptors.
These extended variants of :ocv:func:`DescriptorMatcher::match` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See :ocv:func:`DescriptorMatcher::match` for the details about query and train descriptors.
...
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@@ -218,7 +218,7 @@ For each query descriptor, finds the training descriptors not farther than the s
:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
:param maxDistance: Threshold for the distance between matched descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than ``maxDistance``. Found matches are returned in the distance increasing order.
:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters but with empty train data.
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@@ -241,15 +241,15 @@ Creates a descriptor matcher of a given type with the default parameters (using
:param descriptorMatcherType: Descriptor matcher type. Now the following matcher types are supported:
*
*
``BruteForce`` (it uses ``L2`` )
*
*
``BruteForce-L1``
*
*
``BruteForce-Hamming``
*
*
``BruteForce-Hamming(2)``
*
*
``FlannBased``
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@@ -258,7 +258,7 @@ Creates a descriptor matcher of a given type with the default parameters (using
BFMatcher
-----------------
.. ocv:class::BFMatcher
.. ocv:class::BFMatcher : public DescriptorMatcher
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets. ::
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@@ -267,16 +267,16 @@ BFMatcher::BFMatcher
--------------------
Brute-force matcher constructor.
.. ocv:function:: BFMatcher::BFMatcher( int distanceType, bool crossCheck=false )
.. ocv:function:: BFMatcher::BFMatcher( int normType, bool crossCheck=false )
:param distanceType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
:param crossCheck: If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMathcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
FlannBasedMatcher
-----------------
.. ocv:class:: FlannBasedMatcher
.. ocv:class:: FlannBasedMatcher : public DescriptorMatcher
Flann-based descriptor matcher. This matcher trains :ocv:class:`flann::Index_` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. ``FlannBasedMatcher`` does not support masking permissible matches of descriptor sets because ``flann::Index`` does not support this. ::
Abstract base class for 2D image feature detectors. ::
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@@ -156,7 +156,7 @@ for example: ``"GridFAST"``, ``"PyramidSTAR"`` .
FastFeatureDetector
-------------------
.. ocv:class:: FastFeatureDetector
.. ocv:class:: FastFeatureDetector : public FeatureDetector
Wrapping class for feature detection using the
:ocv:func:`FAST` method. ::
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@@ -252,7 +252,7 @@ Wrapping class for feature detection using the
DenseFeatureDetector
--------------------
.. ocv:class:: DenseFeatureDetector
.. ocv:class:: DenseFeatureDetector : public FeatureDetector
Class for generation of image features which are distributed densely and regularly over the image. ::
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@@ -279,7 +279,7 @@ The detector generates several levels (in the amount of ``featureScaleLevels``)
SimpleBlobDetector
-------------------
.. ocv:class:: SimpleBlobDetector
.. ocv:class:: SimpleBlobDetector : public FeatureDetector
Class for extracting blobs from an image. ::
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@@ -344,7 +344,7 @@ Default values of parameters are tuned to extract dark circular blobs.
GridAdaptedFeatureDetector
--------------------------
.. ocv:class:: GridAdaptedFeatureDetector
.. ocv:class:: GridAdaptedFeatureDetector : public FeatureDetector
Class adapting a detector to partition the source image into a grid and detect points in each cell. ::
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@@ -369,7 +369,7 @@ Class adapting a detector to partition the source image into a grid and detect p
PyramidAdaptedFeatureDetector
-----------------------------
.. ocv:class:: PyramidAdaptedFeatureDetector
.. ocv:class:: PyramidAdaptedFeatureDetector : public FeatureDetector
Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class for detectors that are not inherently scaled. ::
...
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@@ -387,7 +387,7 @@ Class adapting a detector to detect points over multiple levels of a Gaussian py
DynamicAdaptedFeatureDetector
-----------------------------
.. ocv:class:: DynamicAdaptedFeatureDetector
.. ocv:class:: DynamicAdaptedFeatureDetector : public FeatureDetector
Adaptively adjusting detector that iteratively detects features until the desired number is found. ::
.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features, int max_features, int max_iters )
.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 )
:param adjuster: :ocv:class:`AdjusterAdapter` that detects features and adjusts parameters.
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@@ -443,7 +443,7 @@ The constructor
AdjusterAdapter
---------------
.. ocv:class:: AdjusterAdapter
.. ocv:class:: AdjusterAdapter : public FeatureDetector
Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ocv:class:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ocv:class:`FeatureDetector` that enables adjusting parameters after feature detection. ::
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@@ -522,7 +522,7 @@ Creates an adjuster adapter by name
FastAdjuster
------------
.. ocv:class:: FastAdjuster
.. ocv:class:: FastAdjuster : public AdjusterAdapter
:ocv:class:`AdjusterAdapter` for :ocv:class:`FastFeatureDetector`. This class decreases or increases the threshold value by 1. ::
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@@ -535,7 +535,7 @@ FastAdjuster
StarAdjuster
------------
.. ocv:class:: StarAdjuster
.. ocv:class:: StarAdjuster : public AdjusterAdapter
:ocv:class:`AdjusterAdapter` for :ocv:class:`StarFeatureDetector`. This class adjusts the ``responseThreshhold`` of ``StarFeatureDetector``. ::
@@ -3,7 +3,7 @@ Common Interfaces of Generic Descriptor Matchers
.. highlight:: cpp
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to matching descriptors
that cannot be represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. It does not make any assumptions about descriptor representation.
@@ -151,12 +151,12 @@ Classifies keypoints from a query set.
:param trainKeypoints: Keypoints from a train image.
The method classifies each keypoint from a query set. The first variant of the method takes a train image and its keypoints as an input argument. The second variant uses the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method.
The methods do the following:
#.
Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set.
#.
Set the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set.
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@@ -195,7 +195,7 @@ Finds the ``k`` best matches for each query keypoint.
The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
@@ -31,7 +31,7 @@ Draws the found matches of keypoints from two images.
:param matchesMask: Mask determining which matches are drawn. If the mask is empty, all matches are drawn.
:param flags: Flags setting drawing features. Possible ``flags`` bit values are defined by ``DrawMatchesFlags``.
This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). The structure ``DrawMatchesFlags`` is defined as follows:
@@ -24,7 +24,7 @@ Detects corners using the FAST algorithm by [Rosten06]_.
MSER
----
.. ocv:class:: MSER
.. ocv:class:: MSER : public FeatureDetector
Maximally stable extremal region extractor. ::
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@@ -50,7 +50,7 @@ http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http:/
ORB
---
.. ocv:class:: ORB
.. ocv:class:: ORB : public Feature2D
Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
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@@ -60,39 +60,37 @@ ORB::ORB
--------
The ORB constructor
.. ocv:function:: ORB::ORB()
.. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31)
:param nfeatures: The maximum number of features to retain.
:param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
:param nlevels: The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
:param edgeThreshold: This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
:param firstLevel: It should be 0 in the current implementation.
:param WTA_K: The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
:param scoreType: The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
:param patchSize: size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
ORB::operator()
---------------
Finds keypoints in an image and computes their descriptors
@@ -338,7 +338,7 @@ Blocks the current CPU thread until all operations in the stream are complete.
gpu::StreamAccessor
-------------------
.. ocv:class:: gpu::StreamAccessor
.. ocv:struct:: gpu::StreamAccessor
Class that enables getting ``cudaStream_t`` from :ocv:class:`gpu::Stream` and is declared in ``stream_accessor.hpp`` because it is the only public header that depends on the CUDA Runtime API. Including it brings a dependency to your code. ::
@@ -129,11 +129,11 @@ The function ``imwrite`` saves the image to the specified file. The image format
:ocv:func:`cvtColor` to convert it before saving. Or, use the universal XML I/O functions to save the image to XML or YAML format.
It is possible to store PNG images with an alpha channel using this function. To do this, create 8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535. The sample below shows how to create such a BGRA image and store to PNG file. It also demonstrates how to set custom compression parameters ::
The methods/functions decode and return the just grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more frames in video file), the methods return false and the functions return NULL pointer.
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@@ -322,11 +322,11 @@ Grabs, decodes and returns the next video frame.
The methods/functions combine :ocv:func:`VideoCapture::grab` and :ocv:func:`VideoCapture::retrieve` in one call. This is the most convenient method for reading video files or capturing data from decode and return the just grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more frames in video file), the methods return false and the functions return NULL pointer.
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@@ -335,15 +335,15 @@ The methods/functions combine :ocv:func:`VideoCapture::grab` and :ocv:func:`Vide
:param fourcc: 4-character code of codec used to compress the frames. For example, ``CV_FOURCC('P','I','M,'1')`` is a MPEG-1 codec, ``CV_FOURCC('M','J','P','G')`` is a motion-jpeg codec etc.
:param fps: Framerate of the created video stream.
:param fps: Framerate of the created video stream.
:param frameSize: Size of the video frames.
:param frameSize: Size of the video frames.
:param isColor: If it is not zero, the encoder will expect and encode color frames, otherwise it will work with grayscale frames (the flag is currently supported on Windows only).
:param isColor: If it is not zero, the encoder will expect and encode color frames, otherwise it will work with grayscale frames (the flag is currently supported on Windows only).
The constructors/functions initialize video writers. On Linux FFMPEG is used to write videos; on Windows FFMPEG or VFW is used; on MacOSX QTKit is used.
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@@ -526,9 +526,9 @@ Writes the next video frame
.. ocv:cfunction:: int cvWriteFrame( CvVideoWriter* writer, const IplImage* image )
:param trackbarname: Name of the created trackbar.
...
...
@@ -27,7 +27,7 @@ Creates a trackbar and attaches it to the specified window.
The function ``createTrackbar`` creates a trackbar (a slider or range control) with the specified name and range, assigns a variable ``value`` to be a position synchronized with the trackbar and specifies the callback function ``onChange`` to be called on the trackbar position change. The created trackbar is displayed in the specified window ``winname``.
.. note::
**[Qt Backend Only]** ``winname`` can be empty (or NULL) if the trackbar should be attached to the control panel.
Clicking the label of each trackbar enables editing the trackbar values manually.
:param onMouse: Mouse callback. See OpenCV samples, such as http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param param: The optional parameter passed to the callback.
@@ -233,5 +233,5 @@ The function ``waitKey`` waits for a key event infinitely (when
This function is the only method in HighGUI that can fetch and handle events, so it needs to be called periodically for normal event processing unless HighGUI is used within an environment that takes care of event processing.
.. note::
The function only works if there is at least one HighGUI window created and the window is active. If there are several HighGUI windows, any of them can be active.
:param image: Input 8-bit or floating-point 32-bit, single-channel image.
:param eigImage: The parameter is ignored.
:param tempImage: The parameter is ignored.
:param corners: Output vector of detected corners.
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@@ -244,9 +247,9 @@ Determines strong corners on an image.
: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 :ocv:func:`cornerEigenValsAndVecs` .
:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :ocv:func:`cornerHarris`) or :ocv:func:`cornerMinEigenVal`.
:param k: Free parameter of the Harris detector.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]_:
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@@ -255,7 +258,7 @@ 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
:ocv:func:`cornerMinEigenVal` or
:ocv:func:`cornerHarris` .
#.
Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
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@@ -268,16 +271,16 @@ The function finds the most prominent corners in the image or in the specified i
#.
Function throws away each corner for which there is a stronger corner at a distance less than ``maxDistance``.
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`` .
.. seealso::
:ocv:func:`cornerMinEigenVal`,
:ocv:func:`cornerHarris`,
:ocv:func:`calcOpticalFlowPyrLK`,
:ocv:func:`cornerMinEigenVal`,
:ocv:func:`cornerHarris`,
:ocv:func:`calcOpticalFlowPyrLK`,
:ocv:func:`estimateRigidTransform`,
...
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@@ -287,16 +290,16 @@ Finds circles in a grayscale image using the Hough transform.
.. ocv: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 )
.. ocv:cfunction:: CvSeq* cvHoughCircles( CvArr* image, CvMemStorage* circleStorage, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
.. ocv:cfunction:: CvSeq* cvHoughCircles( CvArr* image, void* circle_storage, int method, double dp, double min_dist, double param1=100, double param2=100, int min_radius=0, int max_radius=0 )
:param circles: Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
:param circleStorage: In C function this is a memory storage that will contain the output sequence of found circles.
:param method: Detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
:param dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if ``dp=1`` , the accumulator has the same resolution as the input image. If ``dp=2`` , the accumulator has half as big width and height.
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@@ -311,7 +314,7 @@ Finds circles in a grayscale image using the Hough transform.
:param maxRadius: Maximum circle radius.
The function finds circles in a grayscale image using a modification of the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: ::
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@@ -362,7 +365,7 @@ Finds lines in a binary image using the standard Hough transform.
@@ -379,31 +382,31 @@ Finds lines in a binary image using the standard Hough transform.
:param srn: For the multi-scale Hough transform, it is a divisor for the distance resolution ``rho`` . The coarse accumulator distance resolution is ``rho`` and the accurate accumulator resolution is ``rho/srn`` . If both ``srn=0`` and ``stn=0`` , the classical Hough transform is used. Otherwise, both these parameters should be positive.
:param stn: For the multi-scale Hough transform, it is a divisor for the distance resolution ``theta``.
:param method: One of the following Hough transform variants:
* **CV_HOUGH_STANDARD** classical or standard Hough transform. Every line is represented by two floating-point numbers :math:`(\rho, \theta)` , where :math:`\rho` is a distance between (0,0) point and the line, and :math:`\theta` is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of ``CV_32FC2`` type
* **CV_HOUGH_PROBABILISTIC** probabilistic Hough transform (more efficient in case if the picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of the ``CV_32SC4`` type.
:param method: One of the following Hough transform variants:
* **CV_HOUGH_STANDARD** classical or standard Hough transform. Every line is represented by two floating-point numbers :math:`(\rho, \theta)` , where :math:`\rho` is a distance between (0,0) point and the line, and :math:`\theta` is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of ``CV_32FC2`` type
* **CV_HOUGH_PROBABILISTIC** probabilistic Hough transform (more efficient in case if the picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of the ``CV_32SC4`` type.
* **CV_HOUGH_MULTI_SCALE** multi-scale variant of the classical Hough transform. The lines are encoded the same way as ``CV_HOUGH_STANDARD``.
:param param1: First method-dependent parameter:
* For the classical Hough transform, it is not used (0).
* For the probabilistic Hough transform, it is the minimum line length.
* For the multi-scale Hough transform, it is ``srn``.
:param param2: Second method-dependent parameter:
* For the multi-scale Hough transform, it is ``srn``.
:param param2: Second method-dependent parameter:
* For the classical Hough transform, it is not used (0).
* For the probabilistic Hough transform, it is the maximum gap between line segments lying on the same line to treat them as a single line segment (that is, to join them).
* For the multi-scale Hough transform, it is ``stn``.
The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.
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@@ -501,21 +504,22 @@ preCornerDetect
---------------
Calculates a feature map for corner detection.
.. ocv:function:: void preCornerDetect( InputArray src, OutputArray dst, int apertureSize, int borderType=BORDER_DEFAULT )
.. ocv:function:: void preCornerDetect( InputArray src, OutputArray dst, int ksize, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.matchTemplate(image, templ, method[, result]) -> result
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@@ -19,7 +19,7 @@ Compares a template against overlapped image regions.
:param templ: Searched template. It must be not greater than the source image and have the same data type.
:param result: Map of comparison results. It must be single-channel 32-bit floating-point. If ``image`` is :math:`W \times H` and ``templ`` is :math:`w \times h` , then ``result`` is :math:`(W-w+1) \times (H-h+1)` .
:param method: Parameter specifying the comparison method (see below).
The function slides through ``image`` , compares the
