@@ -20,9 +20,9 @@ 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: The first threshold for the hysteresis procedure.
:param threshold1: First threshold for the hysteresis procedure.
:param threshold2: The second threshold for the hysteresis procedure.
:param threshold2: Second threshold for the hysteresis procedure.
:param apertureSize: Aperture size for the :ocv:func:`Sobel` operator.
...
...
@@ -65,7 +65,7 @@ For every pixel
where the derivatives are computed using the
:ocv:func:`Sobel` operator.
After that it finds eigenvectors and eigenvalues of
After that, it finds eigenvectors and eigenvalues of
:math:`M` and stores them in the destination image as
:math:`(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)` where
...
...
@@ -112,7 +112,7 @@ The function runs the Harris edge detector on the image. Similarly to
:ocv:func:`cornerMinEigenVal` and
:ocv:func:`cornerEigenValsAndVecs` , for each pixel
:math:`(x, y)` it calculates a
:math:`2\times2` gradient covariation matrix
:math:`2\times2` gradient covariance matrix
:math:`M^{(x,y)}` over a
:math:`\texttt{blockSize} \times \texttt{blockSize}` neighborhood. Then, it computes the following characteristic:
...
...
@@ -146,7 +146,7 @@ Calculates the minimal eigenvalue of gradient matrices for corner detection.
:param boderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
The function is similar to
:ocv:func:`cornerEigenValsAndVecs` but it calculates and stores only the minimal eigenvalue of the covariation matrix of derivatives, that is,
:ocv:func:`cornerEigenValsAndVecs` but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is,
:math:`\min(\lambda_1, \lambda_2)` in terms of the formulae in the
:ocv:func:`cornerEigenValsAndVecs` description.
...
...
@@ -174,7 +174,7 @@ Refines the corner locations.
:param criteria: Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after ``criteria.maxCount`` iterations or when the corner position moves by less than ``criteria.epsilon`` on some iteration.
The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as shown on the picture below.
The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as shown on the figure below.
.. image:: pics/cornersubpix.png
...
...
@@ -262,7 +262,7 @@ The function finds the most prominent corners in the image or in the specified i
The remaining corners are sorted by the quality measure in the descending order.
#.
Then the function throws away each corner for which there is a stronger corner at a distance less than ``maxDistance``.
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.
...
...
@@ -291,21 +291,23 @@ Finds circles in a grayscale image using the Hough transform.
:param circles: Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
:param method: The detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
: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.
: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 :ocv:func:`Canny` edge detector (the lower one is twice smaller).
:param param1: First method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the higher threshold of the two passed to the :ocv: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
:param param2: 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.
:param minRadius: Minimum circle radius.
:param maxRadius: Maximum circle radius.
The function finds circles in a grayscale image using a modification of the Hough transform. Here is a short usage example: ::
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: ::
#include <cv.h>
#include <highgui.h>
...
...
@@ -372,31 +374,31 @@ Finds lines in a binary image using the standard Hough transform.
:param stn: For the multi-scale Hough transform, it is a divisor for the distance resolution ``theta``.
:param method: The Hough transform variant, one of the following:
: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 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 ``CV_32SC4`` 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``
* **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: The first method-dependent parameter:
:param param1: First method-dependent parameter:
* For the classical Hough transform it is not used (0).
* For the classical Hough transform, it is not used (0).
* For the probabilistic Hough transform it is the minimum line length.
* For the probabilistic Hough transform, it is the minimum line length.
* For the multi-scale Hough transform it is ``srn``.
* For the multi-scale Hough transform, it is ``srn``.
:param param2: The second method-dependent parameter:
:param param2: Second method-dependent parameter:
* For the classical Hough transform it is not used (0).
* 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 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``.
* 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.
See also the example in :ocv:func:`HoughLinesP` description.
...
...
@@ -426,7 +428,7 @@ Finds line segments in a binary image using the probabilistic Hough transform.
The function implements the probabilistic Hough transform algorithm for line detection, described in
[Matas00]_. See the line detection example below: ::
/* This is a standalone program. Pass an image name as a first parameter
/* This is a standalone program. Pass an image name as the first parameter
of the program. Switch between standard and probabilistic Hough transform
by changing "#if 1" to "#if 0" and back */
#include <cv.h>
...
...
@@ -527,10 +529,10 @@ The corners can be found as local maximums of the functions, as shown below: ::
dilate(corners, dilated_corners, Mat(), 1);
Mat corner_mask = corners == dilated_corners;
.. [Canny86] J. Canny. A Computational Approach to Edge Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6), pp. 679-698 (1986).
.. [Canny86] J. Canny. *A Computational Approach to Edge Detection*, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6), pp. 679-698 (1986).
.. [Matas00] Matas, J. and Galambos, C. and Kittler, J.V., “Robust Detection of Lines Using the Progressive Probabilistic Hough Transform”. CVIU 78 1, pp 119-137 (2000)
.. [Matas00] Matas, J. and Galambos, C. and Kittler, J.V., *Robust Detection of Lines Using the Progressive Probabilistic Hough Transform*. CVIU 78 1, pp 119-137 (2000)
.. [Shi94] J. Shi and C. Tomasi. Good features to track. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, June 1994.
.. [Shi94] J. Shi and C. Tomasi. *Good Features to Track*. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, June 1994.
.. [Yuen90] Yuen, H. K. and Princen, J. and Illingworth, J. and Kittler, J., “Comparative study of Hough transform methods for circle finding”. Image Vision Comput. 8 1, pp 71–77 (1990)
\ No newline at end of file
.. [Yuen90] Yuen, H. K. and Princen, J. and Illingworth, J. and Kittler, J., *Comparative study of Hough transform methods for circle finding*. Image Vision Comput. 8 1, pp 71–77 (1990)
@@ -22,13 +22,13 @@ Calculates a histogram of a set of arrays.
:param narrays: Number of source arrays.
:param channels: List of the ``dims`` channels used to compute the histogram. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1`` etc.
:param channels: List of the ``dims`` channels used to compute the histogram. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1``, and so on.
:param mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size as ``arrays[i]`` . The non-zero mask elements mark the array elements counted in the histogram.
:param hist: Output histogram, which is a dense or sparse ``dims`` -dimensional array.
:param dims: Histogram dimensionality that must be positive and not greater than ``CV_MAX_DIMS`` (=32 in the current OpenCV version).
:param dims: Histogram dimensionality that must be positive and not greater than ``CV_MAX_DIMS`` (equal to 32 in the current OpenCV version).
:param histSize: Array of histogram sizes in each dimension.
...
...
@@ -119,7 +119,7 @@ Calculates the back projection of a histogram.
:param narrays: Number of source arrays.
:param channels: The list of channels that are used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1`` and so on.
:param channels: The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1``, and so on.
:param hist: Input histogram that can be dense or sparse.
:param signature1: The first signature, a :math:`\texttt{size1}\times \texttt{dims}+1` floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used.
:param signature1: First signature, a :math:`\texttt{size1}\times \texttt{dims}+1` floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used.
:param signature2: The second signature of the same format as ``signature1`` , though the number of rows may be different. The total weights may be different, in this case an extra "dummy" point is added to either ``signature1`` or ``signature2`` .
:param signature2: Second signature of the same format as ``signature1`` , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either ``signature1`` or ``signature2`` .
:param distType: Used metric. ``CV_DIST_L1, CV_DIST_L2`` , and ``CV_DIST_C`` stand for one of the standard metrics; ``CV_DIST_USER`` means that a pre-calculated cost matrix ``cost`` is used.
:param distType: Used metric. ``CV_DIST_L1, CV_DIST_L2`` , and ``CV_DIST_C`` stand for one of the standard metrics. ``CV_DIST_USER`` means that a pre-calculated cost matrix ``cost`` is used.
:param distFunc: custom distance function, supported by the old interface. ``CvDistanceFunction`` is defined as: ::
:param distFunc: Custom distance function supported by the old interface. ``CvDistanceFunction`` is defined as: ::
typedef float (CV_CDECL * CvDistanceFunction)( const float* a,
const float* b, void* userdata );
where ``a`` and ``b`` are point coordinates and ``userdata`` is the same as the last parameter.
:param cost: The user-defined :math:`\texttt{size1}\times \texttt{size2}` cost matrix. Also, if a cost matrix is used, lower boundary ``lowerBound`` can not be calculated, because it needs a metric function.
:param cost: User-defined :math:`\texttt{size1}\times \texttt{size2}` cost matrix. Also, if a cost matrix is used, lower boundary ``lowerBound`` cannot be calculated because it needs a metric function.
:param lowerBound: Optional input/output parameter: lower boundary of distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (i.e. the signature matrices have a single column). The user **must** initialize ``*lowerBound`` . If the calculated distance between mass centers is greater or equal to ``*lowerBound`` (it means that the signatures are far enough) the function does not calculate EMD. In any case ``*lowerBound`` is set to the calculated distance between mass centers on return. Thus, if user wants to calculate both distance between mass centers and EMD, ``*lowerBound`` should be set to 0.
:param lowerBound: Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You **must** initialize ``*lowerBound`` . If the calculated distance between mass centers is greater or equal to ``*lowerBound`` (it means that the signatures are far enough), the function does not calculate EMD. In any case ``*lowerBound`` is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, ``*lowerBound`` should be set to 0.
:param flow: The resultant :math:`\texttt{size1} \times \texttt{size2}` flow matrix: :math:`\texttt{flow}_{i,j}` is a flow from :math:`i` th point of ``signature1`` to :math:`j` th point of ``signature2`` .
:param flow: Resultant :math:`\texttt{size1} \times \texttt{size2}` flow matrix: :math:`\texttt{flow}_{i,j}` is a flow from :math:`i` -th point of ``signature1`` to :math:`j` -th point of ``signature2`` .
:param userdata: Optional pointer directly passed to the custom distance function.
...
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@@ -292,7 +293,7 @@ The algorithm normalizes the brightness and increases the contrast of the image.
Extra Histogram Functions (C API)
---------------------------------
In the rest of the section additional C functions operating on ``CvHistogram`` are described.
The rest of the section describes additional C functions operating on ``CvHistogram``.
CalcBackProjectPatch
--------------------
...
...
@@ -302,19 +303,19 @@ Locates a template within an image by using a histogram comparison.
:param images: Source images (though, you may pass CvMat** as well)
:param images: Source images (though, you may pass CvMat** as well).
:param dst: Destination image
:param dst: Destination image.
:param patch_size: Size of the patch slid though the source image
:param patch_size: Size of the patch slid though the source image.
:param hist: Histogram
:param hist: Histogram.
:param method: Comparison method, passed to :ocv:cfunc:`CompareHist` (see description of that function)
:param method: Comparison method passed to :ocv:cfunc:`CompareHist` (see the function description).
:param factor: Normalization factor for histograms, will affect the normalization scale of the destination image, pass 1 if unsure
:param factor: Normalization factor for histograms that affects the normalization scale of the destination image. Pass 1 if not sure.
The function calculates the back projection by comparing histograms of the source image patches with the given histogram. The function is similar to :ocv:func:`MatchTemplate`, but instead of comparing raster patch with all its possible positions within the search window, the function ``CalcBackProjectPatch`` compares histograms. Below is the diagram of the algorithm: ::
The function calculates the back projection by comparing histograms of the source image patches with the given histogram. The function is similar to :ocv:func:`MatchTemplate`, but instead of comparing the raster patch with all its possible positions within the search window, the function ``CalcBackProjectPatch`` compares histograms. See the algorithm diagram below: ::
.. image:: pics/backprojectpatch.png
...
...
@@ -327,15 +328,15 @@ Divides one histogram by another.
The function makes a copy of the histogram. If the second histogram pointer ``*dst`` is NULL, a new histogram of the same size as ``src`` is created. Otherwise, both histograms must have equal types and sizes. Then the function copies the source histogram's bin values to the destination histogram and sets the same bin value ranges as in ``src``.
The function makes a copy of the histogram. If the second histogram pointer ``*dst`` is NULL, a new histogram of the same size as ``src`` is created. Otherwise, both histograms must have equal types and sizes. Then the function copies the bin values of the source histogram to the destination histogram and sets the same bin value ranges as in ``src``.
:param sizes: Array of the histogram dimension sizes
:param sizes: Array of the histogram dimension sizes.
:param type: Histogram representation format: ``CV_HIST_ARRAY`` means that the histogram data is represented as a multi-dimensional dense array CvMatND; ``CV_HIST_SPARSE`` means that histogram data is represented as a multi-dimensional sparse array CvSparseMat
:param type: Histogram representation format. ``CV_HIST_ARRAY`` means that the histogram data is represented as a multi-dimensional dense array CvMatND. ``CV_HIST_SPARSE`` means that histogram data is represented as a multi-dimensional sparse array ``CvSparseMat``.
:param ranges: Array of ranges for the histogram bins. Its meaning depends on the ``uniform`` parameter value. The ranges are used for when the histogram is calculated or backprojected to determine which histogram bin corresponds to which value/tuple of values from the input image(s)
:param ranges: Array of ranges for the histogram bins. Its meaning depends on the ``uniform`` parameter value. The ranges are used when the histogram is calculated or backprojected to determine which histogram bin corresponds to which value/tuple of values from the input image(s).
:param uniform: Uniformity flag; if not 0, the histogram has evenly
:param uniform: Uniformity flag. If not zero, the histogram has evenly
spaced bins and for every :math:`0<=i<cDims` ``ranges[i]``
is an array of two numbers: lower and upper boundaries for the i-th
histogram dimension.
The whole range [lower,upper] is then split
into ``dims[i]`` equal parts to determine the ``i-th`` input
into ``dims[i]`` equal parts to determine the ``i``-th input
tuple value ranges for every histogram bin. And if ``uniform=0`` ,
then ``i-th`` element of ``ranges`` array contains ``dims[i]+1`` elements: :math:`\texttt{lower}_0, \texttt{upper}_0,
then the ``i``-th element of the ``ranges`` array contains ``dims[i]+1`` elements: :math:`\texttt{lower}_0, \texttt{upper}_0,
where :math:`\texttt{lower}_j` and :math:`\texttt{upper}_j`
are lower and upper
boundaries of ``i-th`` input tuple value for ``j-th``
boundaries of the ``i``-th input tuple value for the ``j``-th
bin, respectively. In either case, the input values that are beyond
the specified range for a histogram bin are not counted by :ocv:cfunc:`CalcHist` and filled with 0 by :ocv:cfunc:`CalcBackProject`
the specified range for a histogram bin are not counted by :ocv:cfunc:`CalcHist` and filled with 0 by :ocv:cfunc:`CalcBackProject`.
The function creates a histogram of the specified size and returns a pointer to the created histogram. If the array ``ranges`` is 0, the histogram bin ranges must be specified later via the function :ocv:cfunc:`SetHistBinRanges`. Though :ocv:cfunc:`CalcHist` and :ocv:cfunc:`CalcBackProject` may process 8-bit images without setting bin ranges, they assume thy are equally spaced in 0 to 255 bins.
The function creates a histogram of the specified size and returns a pointer to the created histogram. If the array ``ranges`` is 0, the histogram bin ranges must be specified later via the function :ocv:cfunc:`SetHistBinRanges`. Though :ocv:cfunc:`CalcHist` and :ocv:cfunc:`CalcBackProject` may process 8-bit images without setting bin ranges, they assume they are equally spaced in 0 to 255 bins.
GetHistValue*D
...
...
@@ -415,15 +416,15 @@ Returns a pointer to the histogram bin.
@@ -438,7 +439,7 @@ Returns a pointer to the histogram bin.
..
The macros ``GetHistValue`` return a pointer to the specified bin of the 1D, 2D, 3D or N-D histogram. In the case of a sparse histogram the function creates a new bin and sets it to 0, unless it exists already.
The macros ``GetHistValue`` return a pointer to the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function creates a new bin and sets it to 0, unless it exists already.
GetMinMaxHistValue
...
...
@@ -449,17 +450,17 @@ Finds the minimum and maximum histogram bins.
:param min_value: Pointer to the minimum value of the histogram
:param min_value: Pointer to the minimum value of the histogram.
:param max_value: Pointer to the maximum value of the histogram
:param max_value: Pointer to the maximum value of the histogram.
:param min_idx: Pointer to the array of coordinates for the minimum
:param min_idx: Pointer to the array of coordinates for the minimum.
:param max_idx: Pointer to the array of coordinates for the maximum
:param max_idx: Pointer to the array of coordinates for the maximum.
The function finds the minimum and maximum histogram bins and their positions. All of output arguments are optional. Among several extremas with the same value the ones with the minimum index (in lexicographical order) are returned. In the case of several maximums or minimums, the earliest in lexicographical order (extrema locations) is returned.
The function finds the minimum and maximum histogram bins and their positions. All of output arguments are optional. Among several extremas with the same value the ones with the minimum index (in the lexicographical order) are returned. In case of several maximums or minimums, the earliest in the lexicographical order (extrema locations) is returned.
MakeHistHeaderForArray
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...
@@ -468,17 +469,17 @@ Makes a histogram out of an array.
.. ocv:cfunction:: CvHistogram* cvMakeHistHeaderForArray( int dims, int* sizes, CvHistogram* hist, float* data, float** ranges=NULL, int uniform=1 )
:param dims: Number of histogram dimensions
:param dims: Number of the histogram dimensions.
:param sizes: Array of the histogram dimension sizes
:param sizes: Array of the histogram dimension sizes.
:param hist: The histogram header initialized by the function
:param hist: Histogram header initialized by the function.
:param data: Array that will be used to store histogram bins
:param data: Array used to store histogram bins.
:param ranges: Histogram bin ranges, see :ocv:cfunc:`CreateHist`
:param ranges: Histogram bin ranges. See :ocv:cfunc:`CreateHist` for details.
:param uniform: Uniformity flag, see :ocv:cfunc:`CreateHist`
:param uniform: Uniformity flag. See :ocv:cfunc:`CreateHist` for details.
The function initializes the histogram, whose header and bins are allocated by the user. :ocv:cfunc:`ReleaseHist` does not need to be called afterwards. Only dense histograms can be initialized this way. The function returns ``hist``.
The macros return the value of the specified bin of the 1D, 2D, 3D or N-D histogram. In the case of a sparse histogram the function returns 0, if the bin is not present in the histogram no new bin is created.
The macros return the value of the specified bin of the 1D, 2D, 3D, or N-D histogram. In case of a sparse histogram, the function returns 0. If the bin is not present in the histogram, no new bin is created.
:param hist: Double pointer to the released histogram
:param hist: Double pointer to the released histogram.
The function releases the histogram (header and the data). The pointer to the histogram is cleared by the function. If ``*hist`` pointer is already ``NULL``, the function does nothing.
...
...
@@ -539,13 +540,13 @@ Sets the bounds of the histogram bins.
:param ranges: Array of bin ranges arrays, see :ocv:cfunc:`CreateHist`
:param ranges: Array of bin ranges arrays. See :ocv:cfunc:`CreateHist` for details.
:param uniform: Uniformity flag, see :ocv:cfunc:`CreateHist`
:param uniform: Uniformity flag. See :ocv:cfunc:`CreateHist` for details.
The function is a stand-alone function for setting bin ranges in the histogram. For a more detailed description of the parameters ``ranges`` and ``uniform`` see the :ocv:cfunc:`CalcHist` function, that can initialize the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or the backproject of the histogram is calculated.
This is a standalone function for setting bin ranges in the histogram. For a more detailed description of the parameters ``ranges`` and ``uniform``, see the :ocv:cfunc:`CalcHist` function that can initialize the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or the backproject of the histogram is calculated.
:param contour: Input contour. Currently, only integer point coordinates are allowed
:param contour: Input contour. Currently, only integer point coordinates are allowed.
:param hist: Calculated histogram; must be two-dimensional
:param hist: Calculated histogram. It must be two-dimensional.
The function calculates a 2D pair-wise geometrical histogram (PGH), described in [Iivarinen97]_ for the contour. The algorithm considers every pair of contour
edges. The angle between the edges and the minimum/maximum distances
are determined for every pair. To do this each of the edges in turn
are determined for every pair. To do this, each of the edges in turn
is taken as the base, while the function loops through all the other
edges. When the base edge and any other edge are considered, the minimum
and maximum distances from the points on the non-base edge and line of
...
...
@@ -584,6 +585,6 @@ of the histogram in which all the bins that correspond to the distance
between the calculated minimum and maximum distances are incremented
(that is, the histogram is transposed relatively to the definition in the original paper). The histogram can be used for contour matching.
.. [RubnerSept98] Y. Rubner. C. Tomasi, L.J. Guibas. The Earth Mover’s Distance as a Metric for Image Retrieval. Technical Report STAN-CS-TN-98-86, Department of Computer Science, Stanford University, September 1998.
.. [RubnerSept98] Y. Rubner. C. Tomasi, L.J. Guibas. *The Earth Mover’s Distance as a Metric for Image Retrieval*. Technical Report STAN-CS-TN-98-86, Department of Computer Science, Stanford University, September 1998.
.. [Iivarinen97] Jukka Iivarinen, Markus Peura, Jaakko Srel, and Ari Visa. Comparison of Combined Shape Descriptors for Irregular Objects, 8th British Machine Vision Conference, BMVC'97. http://www.cis.hut.fi/research/IA/paper/publications/bmvc97/bmvc97.html
\ No newline at end of file
.. [Iivarinen97] Jukka Iivarinen, Markus Peura, Jaakko Srel, and Ari Visa. *Comparison of Combined Shape Descriptors for Irregular Objects*, 8th British Machine Vision Conference, BMVC'97. http://www.cis.hut.fi/research/IA/paper/publications/bmvc97/bmvc97.html
:param array: A raster image (single-channel, 8-bit or floating-point 2D array) or an array ( :math:`1 \times N` or :math:`N \times 1` ) of 2D points (``Point`` or ``Point2f`` ).
:param array: Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( :math:`1 \times N` or :math:`N \times 1` ) of 2D points (``Point`` or ``Point2f`` ).
:param binaryImage: If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.
