@ -833,7 +833,7 @@ Finds an initial camera matrix from 3D-2D point correspondences.
:param imagePoints:Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
:param npoints:The integer vector of point counters for each view.
:param pointCounts:The integer vector of point counters for each view.
:param imageSize:Image size in pixels used to initialize the principal point.
@ -919,6 +919,7 @@ Reprojects a disparity image to 3D space.
@ -673,7 +673,7 @@ Starts writing a new structure.
One and only one of the two above flags must be specified
:param CV_NODE_FLOW:the optional flag that makes sense only for YAML streams. It means that the structure is written as a flow (not as a block), which is more compact. It is recommended to use this flag for structures or arrays whose elements are all scalars.
* **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that the structure is written as a flow (not as a block), which is more compact. It is recommended to use this flag for structures or arrays whose elements are all scalars.
:param typeName:Optional parameter - the object type name. In
case of XML it is written as a ``type_id`` attribute of the
@ -792,7 +792,7 @@ Writes a file node to another file storage.
:param fs:Destination file storage
:param new_file_node:New name of the file node in the destination file storage. To keep the existing name, use :ocv:cfunc:`cvGetFileNodeName`
:param new_node_name:New name of the file node in the destination file storage. To keep the existing name, use :ocv:cfunc:`cvGetFileNodeName`
@ -20,7 +20,7 @@ Draws the found matches of keypoints from two images.
:param keypoints2:Keypoints from the second source image.
:param matches:Matches from the first image to the second one, which means that ``keypoints1[i]`` has a corresponding point in ``keypoints2[matches[i]]`` .
:param matches1to2:Matches from the first image to the second one, which means that ``keypoints1[i]`` has a corresponding point in ``keypoints2[matches[i]]`` .
:param outImg:Output image. Its content depends on the ``flags`` value defining what is drawn in the output image. See possible ``flags`` bit values below.
:param src:Source image. Supports only ``CV_8UC4`` type.
:param image:Source image. Supports only ``CV_8UC4`` type.
:param dstOrder:Integer array describing how channel values are permutated. The n-th entry of the array contains the number of the channel that is stored in the n-th channel of the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order.
@ -424,9 +426,9 @@ Applies a fixed-level threshold to each array element.
:param thresh:Threshold value.
:param maxVal:Maximum value to use with ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` threshold types.
:param maxval:Maximum value to use with ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` threshold types.
:param thresholdType:Threshold type. For details, see :ocv:func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param type:Threshold type. For details, see :ocv:func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param stream:Stream for the asynchronous version.
@ -831,11 +833,11 @@ Finds edges in an image using the [Canny86]_ algorithm.
:param edges:Output edge map. It has the same size and type as ``image`` .
:param threshold1:First threshold for the hysteresis procedure.
:param low_thresh:First threshold for the hysteresis procedure.
:param threshold2:Second threshold for the hysteresis procedure.
:param high_thresh:Second threshold for the hysteresis procedure.
:param apertureSize:Aperture size for the :ocv:func:`Sobel` operator.
:param apperture_size:Aperture size for the :ocv: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 gamma_correction:Flag to specify whether the gamma correction preprocessing is required or not.
@ -275,7 +275,7 @@ Detects objects of different sizes in the input image.
:param image:Matrix of type ``CV_8U`` containing an image where objects should be detected.
:param objects:Buffer to store detected objects (rectangles). If it is empty, it is allocated with the default size. If not empty, the function searches not more than N objects, where ``N = sizeof(objectsBufer's data)/sizeof(cv::Rect)``.
:param objectsBuf:Buffer to store detected objects (rectangles). If it is empty, it is allocated with the default size. If not empty, the function searches not more than N objects, where ``N = sizeof(objectsBufer's data)/sizeof(cv::Rect)``.
:param scaleFactor:Value to specify how much the image size is reduced at each image scale.
:param imageSize:Camera view image size in pixels.
:param imgsize: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.
@ -143,17 +143,18 @@ The function supports multi-channel images. Each channel is processed independen
phaseCorrelate
-------------------------------
--------------
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion esitimation. For more information please see http://en.wikipedia.org/wiki/Phase\_correlation .
Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with ``getOptimalDFTSize``.
Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with :ocv:func:`getOptimalDFTSize`.
..ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
..ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
..ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, int mode, int method, Point offset=Point())
..ocv:cfunction:: int cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** firstContour, int headerSize=sizeof(CvContour), int mode=CV_RETR_LIST, int method=CV_CHAIN_APPROX_SIMPLE, CvPoint offset=cvPoint(0, 0) )
: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 :ocv:func:`compare` , :ocv:func:`inRange` , :ocv:func:`threshold` , :ocv:func:`adaptiveThreshold` , :ocv: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.
:param hiararchy:Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each contour ``contours[i]`` , the elements ``hierarchy[i][0]`` , ``hiearchy[i][1]`` , ``hiearchy[i][2]`` , and ``hiearchy[i][3]`` are set to 0-based indices in ``contours`` of the next and previous contours at the same hierarchical level: the first child contour and the parent contour, respectively. If for a contour ``i`` there are no next, previous, parent, or nested contours, the corresponding elements of ``hierarchy[i]`` will be negative.
:param hierarchy:Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each contour ``contours[i]`` , the elements ``hierarchy[i][0]`` , ``hiearchy[i][1]`` , ``hiearchy[i][2]`` , and ``hiearchy[i][3]`` are set to 0-based indices in ``contours`` of the next and previous contours at the same hierarchical level: the first child contour and the parent contour, respectively. If for a contour ``i`` there are no next, previous, parent, or nested contours, the corresponding elements of ``hierarchy[i]`` will be negative.
:param contour:Input vector of 2D points (contour vertices), stored in ``std::vector`` or ``Mat``.
:param orientation:Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is ``false``, which means that the absolute value is returned.
:param oriented:Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is ``false``, which means that the absolute value is returned.
The function computes a contour area. Similarly to
:ocv: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
@ -202,7 +202,9 @@ Constructs MLP with the specified topology.
:param activateFunc:Parameter specifying the activation function for each neuron: one of ``CvANN_MLP::IDENTITY``, ``CvANN_MLP::SIGMOID_SYM``, and ``CvANN_MLP::GAUSSIAN``.
:param fparam1/fparam2:Free parameters of the activation function, :math:`\alpha` and :math:`\beta`, respectively. See the formulas in the introduction section.
:param fparam1:Free parameter of the activation function, :math:`\alpha`. See the formulas in the introduction section.
:param fparam2:Free parameter of the activation function, :math:`\beta`. See the formulas in the introduction section.
The method creates an MLP network with the specified topology and assigns the same activation function to all the neurons.
:param returnDFVal:Specifies a type of the return value. If ``true`` and the problem is 2-class classification then the method returns the decision function value that is signed distance to the margin, else the function returns a class label (classification) or estimated function value (regression).
Andrey Kamaev
13 years ago