Fix documentation problems found by check_docs2 script
This commit is contained in:
Andrey Kamaev
parent
637397f621
commit
9498856b22
@ -38,7 +38,7 @@ doc_signatures_whitelist = [
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"CvArr", "CvFileStorage",
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# other
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"InputArray", "OutputArray",
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] + ["CvSubdiv2D", "CvQuadEdge2D", "CvSubdiv2DPoint", "cvDrawContours"]
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]
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defines = ["cvGraphEdgeIdx", "cvFree", "CV_Assert", "cvSqrt", "cvGetGraphVtx", "cvGraphVtxIdx",
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"cvCaptureFromFile", "cvCaptureFromCAM", "cvCalcBackProjectPatch", "cvCalcBackProject",
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@ -156,9 +156,10 @@ def formatSignature(s):
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argtype = re.sub(r"\s+(\*|&)$", "\\1", arg[0])
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bidx = argtype.find('[')
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if bidx < 0:
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_str += argtype + " "
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_str += argtype
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else:
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_srt += argtype[:bidx]
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_str += argtype[:bidx]
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_str += " "
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if arg[1]:
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_str += arg[1]
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else:
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@ -326,6 +327,7 @@ def process_module(module, path):
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flookup[fn[0]] = flookup_entry
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if do_python_crosscheck:
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pyclsnamespaces = ["cv." + x[3:].replace(".", "_") for x in clsnamespaces]
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for name, doc in rst.iteritems():
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decls = doc.get("decls")
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if not decls:
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@ -394,7 +396,7 @@ def process_module(module, path):
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pname = signature[1][4:signature[1].find('(')]
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cvname = "cv." + pname
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parent = None
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for cl in clsnamespaces:
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for cl in pyclsnamespaces:
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if cvname.startswith(cl + "."):
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if cl.startswith(parent or ""):
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parent = cl
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@ -685,35 +685,35 @@ findEssentialMat
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------------------
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Calculates an essential matrix from the corresponding points in two images.
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.. ocv:function:: Mat findEssentialMat( InputArray points1, InputArray points2, double focal = 1.0, Point2d pp = Point2d(0, 0), int method = FM_RANSAC, double prob = 0.999, double threshold = 1.0, OutputArray mask = noArray() )
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.. ocv:function:: Mat findEssentialMat( InputArray points1, InputArray points2, double focal=1.0, Point2d pp=Point2d(0, 0), int method=CV_RANSAC, double prob=0.999, double threshold=1.0, OutputArray mask=noArray() )
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:param points1: Array of ``N`` ``(N >= 5)`` 2D points from the first image. The point coordinates should be floating-point (single or double precision).
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:param points1: Array of ``N`` ``(N >= 5)`` 2D points from the first image. The point coordinates should be floating-point (single or double precision).
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:param points2: Array of the second image points of the same size and format as ``points1`` .
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:param focal: focal length of the camera. Note that this function assumes that ``points1`` and ``points2`` are feature points from cameras with same focal length and principle point.
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:param focal: focal length of the camera. Note that this function assumes that ``points1`` and ``points2`` are feature points from cameras with same focal length and principle point.
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:param pp: principle point of the camera.
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:param pp: principle point of the camera.
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:param method: Method for computing a fundamental matrix.
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* **CV_RANSAC** for the RANSAC algorithm.
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* **CV_LMEDS** for the LMedS algorithm.
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* **CV_RANSAC** for the RANSAC algorithm.
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* **CV_LMEDS** for the LMedS algorithm.
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:param threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
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:param prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
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:param mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
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:param mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
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This function estimates essential matrix based on an implementation of five-point algorithm [Nister03]_ [SteweniusCFS]_.
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This function estimates essential matrix based on an implementation of five-point algorithm [Nister03]_ [SteweniusCFS]_.
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The epipolar geometry is described by the following equation:
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.. math::
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[p_2; 1]^T K^T E K [p_1; 1] = 0 \\
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K =
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K =
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\begin{bmatrix}
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f & 0 & x_{pp} \\
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0 & f & y_{pp} \\
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@ -724,60 +724,60 @@ where
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:math:`E` is an essential matrix,
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:math:`p_1` and
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:math:`p_2` are corresponding points in the first and the second images, respectively.
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The result of this function may be passed further to ``decomposeEssentialMat()`` or ``recoverPose()`` to recover the relative pose between cameras.
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The result of this function may be passed further to ``decomposeEssentialMat()`` or ``recoverPose()`` to recover the relative pose between cameras.
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decomposeEssentialMat
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-------------------------
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Decompose an essential matrix to possible rotations and translation.
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Decompose an essential matrix to possible rotations and translation.
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.. ocv:function:: void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t )
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:param E: The input essential matrix.
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:param E: The input essential matrix.
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:param R1: One possible rotation matrix.
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:param R1: One possible rotation matrix.
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:param R2: Another possible rotation matrix.
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:param R2: Another possible rotation matrix.
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:param t: One possible translation.
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:param t: One possible translation.
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This function decompose an essential matrix ``E`` using svd decomposition [HartleyZ00]_. Generally 4 possible poses exists for a given ``E``.
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They are
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:math:`[R_1, t]`,
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:math:`[R_1, -t]`,
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:math:`[R_2, t]`,
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:math:`[R_2, -t]`.
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This function decompose an essential matrix ``E`` using svd decomposition [HartleyZ00]_. Generally 4 possible poses exists for a given ``E``.
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They are
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:math:`[R_1, t]`,
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:math:`[R_1, -t]`,
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:math:`[R_2, t]`,
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:math:`[R_2, -t]`.
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recoverPose
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---------------
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Recover relative camera rotation and translation from an estimated essential matrix and the corresponding points in two images, using cheirality check.
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Returns the number of inliers which pass the check.
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Recover relative camera rotation and translation from an estimated essential matrix and the corresponding points in two images, using cheirality check.
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Returns the number of inliers which pass the check.
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.. ocv:function:: int recoverPose( InputArray E, InputArray points1, InputArray points2, OutputArray R, OutputArray t, double focal = 1.0, Point2d pp = Point2d(0, 0), InputOutputArray mask = noArray())
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:param E: The input essential matrix.
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:param E: The input essential matrix.
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:param points1: Array of ``N`` 2D points from the first image. The point coordinates should be floating-point (single or double precision).
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:param points2: Array of the second image points of the same size and format as ``points1`` .
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:param R: Recovered relative rotation.
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:param R: Recovered relative rotation.
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:param t: Recoverd relative translation.
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:param t: Recoverd relative translation.
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:param focal: Focal length of the camera. Note that this function assumes that ``points1`` and ``points2`` are feature points from cameras with same focal length and principle point.
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:param focal: Focal length of the camera. Note that this function assumes that ``points1`` and ``points2`` are feature points from cameras with same focal length and principle point.
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:param pp: Principle point of the camera.
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:param pp: Principle point of the camera.
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:param mask: Input/output mask for inliers in ``points1`` and ``points2``.
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If it is not empty, then it marks inliers in ``points1`` and ``points2`` for then given essential matrix ``E``.
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Only these inliers will be used to recover pose.
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In the output mask only inliers which pass the cheirality check.
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This function decomposes an essential matrix using ``decomposeEssentialMat()`` and then verifies possible pose hypotheses by doing cheirality check.
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The cheirality check basically means that the triangulated 3D points should have positive depth. Some details can be found from [Nister03]_.
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:param mask: Input/output mask for inliers in ``points1`` and ``points2``.
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If it is not empty, then it marks inliers in ``points1`` and ``points2`` for then given essential matrix ``E``.
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Only these inliers will be used to recover pose.
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In the output mask only inliers which pass the cheirality check.
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This function can be used to process output ``E`` and ``mask`` from ``findEssentialMat()``.
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This function decomposes an essential matrix using ``decomposeEssentialMat()`` and then verifies possible pose hypotheses by doing cheirality check.
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The cheirality check basically means that the triangulated 3D points should have positive depth. Some details can be found from [Nister03]_.
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This function can be used to process output ``E`` and ``mask`` from ``findEssentialMat()``.
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In this scenario, ``points1`` and ``points2`` are the same input for ``findEssentialMat()``. ::
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// Example. Estimation of fundamental matrix using the RANSAC algorithm
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@ -792,14 +792,14 @@ In this scenario, ``points1`` and ``points2`` are the same input for ``findEssen
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points2[i] = ...;
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}
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double focal = 1.0;
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cv::Point2d pp(0.0, 0.0);
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Mat E, R, t, mask;
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double focal = 1.0;
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cv::Point2d pp(0.0, 0.0);
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Mat E, R, t, mask;
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E = findEssentialMat(points1, points2, focal, pp, CV_RANSAC, 0.999, 1.0, mask);
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recoverPose(E, points1, points2, R, t, focal, pp, mask);
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recoverPose(E, points1, points2, R, t, focal, pp, mask);
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findHomography
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------------------
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@ -1593,11 +1593,11 @@ The function reconstructs 3-dimensional points (in homogeneous coordinates) by u
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.. [Hartley99] Hartley, R.I., Theory and Practice of Projective Rectification. IJCV 35 2, pp 115-127 (1999)
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.. [HartleyZ00] Hartley, R. and Zisserman, A. Multiple View Geomtry in Computer Vision, Cambridge University Press, 2000.
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.. [HartleyZ00] Hartley, R. and Zisserman, A. Multiple View Geomtry in Computer Vision, Cambridge University Press, 2000.
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.. [HH08] Hirschmuller, H. Stereo Processing by Semiglobal Matching and Mutual Information, PAMI(30), No. 2, February 2008, pp. 328-341.
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.. [Nister03] Nistér, D. An efficient solution to the five-point relative pose problem, CVPR 2003.
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.. [Nister03] Nistér, D. An efficient solution to the five-point relative pose problem, CVPR 2003.
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.. [SteweniusCFS] Stewénius, H., Calibrated Fivepoint solver. http://www.vis.uky.edu/~stewe/FIVEPOINT/
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@ -692,7 +692,7 @@ cvarrToMat
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----------
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Converts ``CvMat``, ``IplImage`` , or ``CvMatND`` to ``Mat``.
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.. ocv:function:: Mat cvarrToMat( const CvArr* arr, bool copyData=false, bool allowND=true, int coiMode=0 )
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.. ocv:function:: Mat cvarrToMat( const CvArr* arr, bool copyData=false, bool allowND=true, int coiMode=0, AutoBuffer<double>* buf=0 )
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:param arr: input ``CvMat``, ``IplImage`` , or ``CvMatND``.
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@ -7,7 +7,8 @@ FAST
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----
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Detects corners using the FAST algorithm
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true, type=FastFeatureDetector::TYPE_9_16 )
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression, int type )
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:param image: grayscale image where keypoints (corners) are detected.
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@ -703,14 +703,10 @@ Calculates histogram for one channel 8-bit image.
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.. ocv:function:: void gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null())
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.. ocv:function:: void gpu::calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null())
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:param src: Source image.
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:param hist: Destination histogram with one row, 256 columns, and the ``CV_32SC1`` type.
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:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
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:param stream: Stream for the asynchronous version.
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@ -721,8 +717,6 @@ Equalizes the histogram of a grayscale image.
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.. ocv:function:: void gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
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.. ocv:function:: void gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null())
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.. ocv:function:: void gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null())
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:param src: Source image.
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@ -881,7 +881,7 @@ CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circ
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//! finds arbitrary template in the grayscale image using Generalized Hough Transform
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//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
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//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
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class CV_EXPORTS GeneralizedHough_GPU : public Algorithm
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class CV_EXPORTS GeneralizedHough_GPU : public cv::Algorithm
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{
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public:
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static Ptr<GeneralizedHough_GPU> create(int method);
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@ -1554,7 +1554,7 @@ protected:
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};
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// Implementation of soft (stage-less) cascaded detector.
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class CV_EXPORTS SCascade : public Algorithm
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class CV_EXPORTS SCascade : public cv::Algorithm
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{
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public:
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@ -1,7 +1,7 @@
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#/usr/bin/env python
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import os, sys, re, string, fnmatch
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allmodules = ["core", "flann", "imgproc", "ml", "highgui", "video", "features2d", "calib3d", "objdetect", "legacy", "contrib", "gpu", "androidcamera", "java", "python", "stitching", "ts", "photo", "nonfree", "videostab", "ocl"]
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allmodules = ["core", "flann", "imgproc", "ml", "highgui", "video", "features2d", "calib3d", "objdetect", "legacy", "contrib", "gpu", "androidcamera", "java", "python", "stitching", "ts", "photo", "nonfree", "videostab", "ocl", "softcascade"]
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verbose = False
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show_warnings = True
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show_errors = True
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@ -25,37 +25,37 @@ The sample has been rejected if it fall rejection threshold. So stageless cascad
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.. [BMTG12] Rodrigo Benenson, Markus Mathias, Radu Timofte and Luc Van Gool. Pedestrian detection at 100 frames per second. IEEE CVPR, 2012.
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Detector
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softcascade::Detector
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-------------------
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.. ocv:class:: Detector
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.. ocv:class:: Detector : public Algorithm
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Implementation of soft (stageless) cascaded detector. ::
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class CV_EXPORTS_W Detector : public Algorithm
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class Detector : public Algorithm
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{
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public:
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enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT};
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CV_WRAP Detector(double minScale = 0.4, double maxScale = 5., int scales = 55, int rejCriteria = 1);
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CV_WRAP virtual ~Detector();
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Detector(double minScale = 0.4, double maxScale = 5., int scales = 55, int rejCriteria = 1);
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virtual ~Detector();
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cv::AlgorithmInfo* info() const;
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CV_WRAP virtual bool load(const FileNode& fileNode);
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CV_WRAP virtual void read(const FileNode& fileNode);
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virtual bool load(const FileNode& fileNode);
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virtual void read(const FileNode& fileNode);
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virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
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CV_WRAP virtual void detect(InputArray image, InputArray rois, CV_OUT OutputArray rects, CV_OUT OutputArray confs) const;
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virtual void detect(InputArray image, InputArray rois, CV_OUT OutputArray rects, CV_OUT OutputArray confs) const;
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}
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Detector::Detector
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softcascade::Detector::Detector
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----------------------------------------
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An empty cascade will be created.
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.. ocv:function:: Detector::Detector(float minScale = 0.4f, float maxScale = 5.f, int scales = 55, int rejCriteria = 1)
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.. ocv:function:: softcascade::Detector::Detector( double minScale=0.4, double maxScale=5., int scales=55, int rejCriteria=1 )
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.. ocv:pyfunction:: cv2.Detector.Detector(minScale[, maxScale[, scales[, rejCriteria]]]) -> cascade
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.. ocv:pyfunction:: cv2.softcascade_Detector([minScale[, maxScale[, scales[, rejCriteria]]]]) -> <softcascade_Detector object>
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:param minScale: a minimum scale relative to the original size of the image on which cascade will be applied.
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@ -67,35 +67,35 @@ An empty cascade will be created.
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Detector::~Detector
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softcascade::Detector::~Detector
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-----------------------------------------
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Destructor for Detector.
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.. ocv:function:: Detector::~Detector()
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.. ocv:function:: softcascade::Detector::~Detector()
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Detector::load
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--------------------------
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softcascade::Detector::load
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---------------------------
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Load cascade from FileNode.
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.. ocv:function:: bool Detector::load(const FileNode& fileNode)
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.. ocv:function:: bool softcascade::Detector::load(const FileNode& fileNode)
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.. ocv:pyfunction:: cv2.Detector.load(fileNode)
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.. ocv:pyfunction:: cv2.softcascade_Detector.load(fileNode) -> retval
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:param fileNode: File node from which the soft cascade are read.
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Detector::detect
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---------------------------
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softcascade::Detector::detect
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-----------------------------
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Apply cascade to an input frame and return the vector of Detection objects.
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.. ocv:function:: void Detector::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
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.. ocv:function:: void softcascade::Detector::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
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.. ocv:function:: void Detector::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
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.. ocv:function:: void softcascade::Detector::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
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.. ocv:pyfunction:: cv2.Detector.detect(image, rois) -> (rects, confs)
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.. ocv:pyfunction:: cv2.softcascade_Detector.detect(image, rois[, rects[, confs]]) -> rects, confs
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:param image: a frame on which detector will be applied.
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@ -108,37 +108,39 @@ Apply cascade to an input frame and return the vector of Detection objects.
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:param confs: an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th confidence.
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|
||||
|
||||
ChannelFeatureBuilder
|
||||
---------------------
|
||||
.. ocv:class:: ChannelFeatureBuilder
|
||||
softcascade::ChannelFeatureBuilder
|
||||
----------------------------------
|
||||
.. ocv:class:: softcascade::ChannelFeatureBuilder : public Algorithm
|
||||
|
||||
Public interface for of soft (stageless) cascaded detector. ::
|
||||
|
||||
class CV_EXPORTS_W ChannelFeatureBuilder : public Algorithm
|
||||
class ChannelFeatureBuilder : public Algorithm
|
||||
{
|
||||
public:
|
||||
virtual ~ChannelFeatureBuilder();
|
||||
|
||||
CV_WRAP_AS(compute) virtual void operator()(InputArray src, CV_OUT OutputArray channels) const = 0;
|
||||
virtual void operator()(InputArray src, CV_OUT OutputArray channels) const = 0;
|
||||
|
||||
CV_WRAP static cv::Ptr<ChannelFeatureBuilder> create();
|
||||
static cv::Ptr<ChannelFeatureBuilder> create();
|
||||
};
|
||||
|
||||
|
||||
ChannelFeatureBuilder:~ChannelFeatureBuilder
|
||||
--------------------------------------------
|
||||
softcascade::ChannelFeatureBuilder:~ChannelFeatureBuilder
|
||||
---------------------------------------------------------
|
||||
Destructor for ChannelFeatureBuilder.
|
||||
|
||||
.. ocv:function:: ChannelFeatureBuilder::~ChannelFeatureBuilder()
|
||||
.. ocv:function:: softcascade::ChannelFeatureBuilder::~ChannelFeatureBuilder()
|
||||
|
||||
.. ocv:pyfunction:: cv2.softcascade_ChannelFeatureBuilder_create() -> retval
|
||||
|
||||
|
||||
ChannelFeatureBuilder::operator()
|
||||
---------------------------------
|
||||
softcascade::ChannelFeatureBuilder::operator()
|
||||
----------------------------------------------
|
||||
Create channel feature integrals for input image.
|
||||
|
||||
.. ocv:function:: void ChannelFeatureBuilder::operator()(InputArray src, OutputArray channels) const
|
||||
.. ocv:function:: void softcascade::ChannelFeatureBuilder::operator()(InputArray src, OutputArray channels) const
|
||||
|
||||
.. ocv:pyfunction:: cv2.ChannelFeatureBuilder.compute(src, channels) -> None
|
||||
.. ocv:pyfunction:: cv2.softcascade_ChannelFeatureBuilder.compute(src, channels) -> None
|
||||
|
||||
:param src source frame
|
||||
|
||||
|
||||
@ -7,13 +7,13 @@ Soft Cascade Detector Training
|
||||
--------------------------------------------
|
||||
|
||||
|
||||
Octave
|
||||
-----------------
|
||||
.. ocv:class:: Octave
|
||||
softcascade::Octave
|
||||
-------------------
|
||||
.. ocv:class:: softcascade::Octave : public Algorithm
|
||||
|
||||
Public interface for soft cascade training algorithm. ::
|
||||
|
||||
class CV_EXPORTS Octave : public Algorithm
|
||||
class Octave : public Algorithm
|
||||
{
|
||||
public:
|
||||
|
||||
@ -37,17 +37,17 @@ Public interface for soft cascade training algorithm. ::
|
||||
|
||||
|
||||
|
||||
Octave::~Octave
|
||||
softcascade::Octave::~Octave
|
||||
---------------------------------------
|
||||
Destructor for Octave.
|
||||
|
||||
.. ocv:function:: Octave::~Octave()
|
||||
.. ocv:function:: softcascade::Octave::~Octave()
|
||||
|
||||
|
||||
Octave::train
|
||||
------------------------
|
||||
softcascade::Octave::train
|
||||
--------------------------
|
||||
|
||||
.. ocv:function:: bool Octave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
|
||||
.. ocv:function:: bool softcascade::Octave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
|
||||
|
||||
:param dataset an object that allows communicate for training set.
|
||||
|
||||
@ -59,19 +59,19 @@ Octave::train
|
||||
|
||||
|
||||
|
||||
Octave::setRejectThresholds
|
||||
--------------------------------------
|
||||
softcascade::Octave::setRejectThresholds
|
||||
----------------------------------------
|
||||
|
||||
.. ocv:function:: void Octave::setRejectThresholds(OutputArray thresholds)
|
||||
.. ocv:function:: void softcascade::Octave::setRejectThresholds(OutputArray thresholds)
|
||||
|
||||
:param thresholds an output array of resulted rejection vector. Have same size as number of trained stages.
|
||||
|
||||
|
||||
Octave::write
|
||||
------------------------
|
||||
softcascade::Octave::write
|
||||
--------------------------
|
||||
|
||||
.. ocv:function:: void Octave::train(cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const
|
||||
.. ocv:function:: void Octave::train( CvFileStorage* fs, string name) const
|
||||
.. ocv:function:: void softcascade::Octave::train(cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const
|
||||
.. ocv:function:: void softcascade::Octave::train( CvFileStorage* fs, string name) const
|
||||
|
||||
:param fs an output file storage to store trained detector.
|
||||
|
||||
@ -82,13 +82,13 @@ Octave::write
|
||||
:param name a name of root node for trained detector.
|
||||
|
||||
|
||||
FeaturePool
|
||||
-----------
|
||||
.. ocv:class:: FeaturePool
|
||||
softcascade::FeaturePool
|
||||
------------------------
|
||||
.. ocv:class:: softcascade::FeaturePool
|
||||
|
||||
Public interface for feature pool. This is a hight level abstraction for training random feature pool. ::
|
||||
|
||||
class CV_EXPORTS FeaturePool
|
||||
class FeaturePool
|
||||
{
|
||||
public:
|
||||
|
||||
@ -99,42 +99,42 @@ Public interface for feature pool. This is a hight level abstraction for trainin
|
||||
|
||||
};
|
||||
|
||||
FeaturePool::size
|
||||
-----------------
|
||||
softcascade::FeaturePool::size
|
||||
------------------------------
|
||||
|
||||
Returns size of feature pool.
|
||||
|
||||
.. ocv:function:: int FeaturePool::size() const
|
||||
.. ocv:function:: int softcascade::FeaturePool::size() const
|
||||
|
||||
|
||||
|
||||
FeaturePool::~FeaturePool
|
||||
-------------------------
|
||||
softcascade::FeaturePool::~FeaturePool
|
||||
--------------------------------------
|
||||
|
||||
FeaturePool destructor.
|
||||
|
||||
.. ocv:function:: int FeaturePool::~FeaturePool()
|
||||
.. ocv:function:: softcascade::FeaturePool::~FeaturePool()
|
||||
|
||||
|
||||
|
||||
FeaturePool::write
|
||||
------------------
|
||||
softcascade::FeaturePool::write
|
||||
-------------------------------
|
||||
|
||||
Write specified feature from feature pool to file storage.
|
||||
|
||||
.. ocv:function:: void FeaturePool::write( cv::FileStorage& fs, int index) const
|
||||
.. ocv:function:: void softcascade::FeaturePool::write( cv::FileStorage& fs, int index) const
|
||||
|
||||
:param fs an output file storage to store feature.
|
||||
|
||||
:param index an index of feature that should be stored.
|
||||
|
||||
|
||||
FeaturePool::apply
|
||||
------------------
|
||||
softcascade::FeaturePool::apply
|
||||
-------------------------------
|
||||
|
||||
Compute feature on integral channel image.
|
||||
|
||||
.. ocv:function:: float FeaturePool::apply(int fi, int si, const Mat& channels) const
|
||||
.. ocv:function:: float softcascade::FeaturePool::apply(int fi, int si, const Mat& channels) const
|
||||
|
||||
:param fi an index of feature that should be computed.
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user