From ff7b604a250ae46ec413c86f3ede7b1314894f02 Mon Sep 17 00:00:00 2001 From: Andrey Kamaev Date: Tue, 19 Feb 2013 12:01:27 +0400 Subject: [PATCH] Fix several inconsistent declarations identified by check_doc2 script --- modules/core/doc/basic_structures.rst | 17 +++++++++-------- .../doc/utility_and_system_functions_and_macros.rst | 4 ++-- .../doc/common_interfaces_of_feature_detectors.rst | 2 +- modules/gpu/doc/data_structures.rst | 6 +++--- modules/ml/doc/boosting.rst | 8 ++++---- 5 files changed, 19 insertions(+), 18 deletions(-) diff --git a/modules/core/doc/basic_structures.rst b/modules/core/doc/basic_structures.rst index 2e68022..054aa2a 100644 --- a/modules/core/doc/basic_structures.rst +++ b/modules/core/doc/basic_structures.rst @@ -2064,10 +2064,11 @@ SparseMat::SparseMat Various SparseMat constructors. .. ocv:function:: SparseMat::SparseMat() -.. ocv:function:: SparseMat::SparseMat(int dims, const int* _sizes, int _type) -.. ocv:function:: SparseMat::SparseMat(const SparseMat& m) -.. ocv:function:: SparseMat::SparseMat(const Mat& m, bool try1d=false) -.. ocv:function:: SparseMat::SparseMat(const CvSparseMat* m) +.. ocv:function:: SparseMat::SparseMat( int dims, const int* _sizes, int _type ) +.. ocv:function:: SparseMat::SparseMat( const SparseMat& m ) +.. ocv:function:: SparseMat::SparseMat( const Mat& m ) +.. ocv:function:: SparseMat::SparseMat( const CvSparseMat* m ) + :param m: Source matrix for copy constructor. If m is dense matrix (ocv:class:`Mat`) then it will be converted to sparse representation. :param dims: Array dimensionality. @@ -2081,12 +2082,12 @@ SparseMat object destructor. .. ocv:function:: SparseMat::~SparseMat() -SparseMat::operator = ---------------------- +SparseMat::operator= +-------------------- Provides sparse matrix assignment operators. -.. ocv:function:: SparseMat& SparseMat::operator=(const SparseMat& m) -.. ocv:function:: SparseMat& SparseMat::operator=(const Mat& m) +.. ocv:function:: SparseMat& SparseMat::operator = (const SparseMat& m) +.. ocv:function:: SparseMat& SparseMat::operator = (const Mat& m) The last variant is equivalent to the corresponding constructor with try1d=false. diff --git a/modules/core/doc/utility_and_system_functions_and_macros.rst b/modules/core/doc/utility_and_system_functions_and_macros.rst index 9055415..54198b0 100644 --- a/modules/core/doc/utility_and_system_functions_and_macros.rst +++ b/modules/core/doc/utility_and_system_functions_and_macros.rst @@ -444,9 +444,9 @@ If ``threads == 0``, OpenCV will disable threading optimizations and run all it' functions sequentially. Passing ``threads < 0`` will reset threads number to system default. This function must be called outside of parallel region. -.. ocv:function:: void setNumThreads(int threads) +.. ocv:function:: void setNumThreads(int nthreads) - :param threads: Number of threads used by OpenCV. + :param nthreads: Number of threads used by OpenCV. OpenCV will try to run it's functions with specified threads number, but some behaviour differs from framework: diff --git a/modules/features2d/doc/common_interfaces_of_feature_detectors.rst b/modules/features2d/doc/common_interfaces_of_feature_detectors.rst index 8804bde..81c72d3 100644 --- a/modules/features2d/doc/common_interfaces_of_feature_detectors.rst +++ b/modules/features2d/doc/common_interfaces_of_feature_detectors.rst @@ -531,7 +531,7 @@ StarAdjuster SurfAdjuster ------------ -.. ocv:class:: SurfAdjuster: public AdjusterAdapter +.. ocv:class:: SurfAdjuster : public AdjusterAdapter :ocv:class:`AdjusterAdapter` for ``SurfFeatureDetector``. :: diff --git a/modules/gpu/doc/data_structures.rst b/modules/gpu/doc/data_structures.rst index 1291cf9..5699723 100644 --- a/modules/gpu/doc/data_structures.rst +++ b/modules/gpu/doc/data_structures.rst @@ -352,9 +352,9 @@ gpu::Stream::enqueueMemSet -------------------------- Initializes or sets device memory to a value. -.. ocv:function:: void gpu::Stream::enqueueMemSet(const GpuMat& src, Scalar val) +.. ocv:function:: void gpu::Stream::enqueueMemSet( GpuMat& src, Scalar val ) -.. ocv:function:: void gpu::Stream::enqueueMemSet(const GpuMat& src, Scalar val, const GpuMat& mask) +.. ocv:function:: void gpu::Stream::enqueueMemSet( GpuMat& src, Scalar val, const GpuMat& mask ) @@ -362,7 +362,7 @@ gpu::Stream::enqueueConvert --------------------------- Converts matrix type, ex from float to uchar depending on type. -.. ocv:function:: void gpu::Stream::enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0) +.. ocv:function:: void gpu::Stream::enqueueConvert( const GpuMat& src, GpuMat& dst, int dtype, double a=1, double b=0 ) diff --git a/modules/ml/doc/boosting.rst b/modules/ml/doc/boosting.rst index 6dd40f7..7c5bc83 100644 --- a/modules/ml/doc/boosting.rst +++ b/modules/ml/doc/boosting.rst @@ -169,11 +169,11 @@ CvBoost::predict ---------------- Predicts a response for an input sample. -.. ocv:function:: float CvBoost::predict( const Mat& sample, const Mat& missing=Mat(), const Range& slice=Range::all(), bool raw_mode=false, bool return_sum=false ) const +.. ocv:function:: float CvBoost::predict( const cv::Mat& sample, const cv::Mat& missing=Mat(), const cv::Range& slice=Range::all(), bool rawMode=false, bool returnSum=false ) const .. ocv:function:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false, bool return_sum=false ) const -.. ocv:pyfunction:: cv2.Boost.predict(sample[, missing[, slice[, raw_mode[, return_sum]]]]) -> retval +.. ocv:pyfunction:: cv2.Boost.predict(sample[, missing[, slice[, rawMode[, returnSum]]]]) -> retval :param sample: Input sample. @@ -183,9 +183,9 @@ Predicts a response for an input sample. :param slice: Continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. - :param raw_mode: Normally, it should be set to ``false``. + :param rawMode: Normally, it should be set to ``false``. - :param return_sum: If ``true`` then return sum of votes instead of the class label. + :param returnSum: If ``true`` then return sum of votes instead of the class label. The method runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting. -- 2.7.4