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fix:make zerocolor edge

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lennlouisgeek
2026-02-04 03:18:20 +08:00
parent ca3545b8b0
commit 001685b633
157 changed files with 31832 additions and 32681 deletions
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110
3rdpart/OpenCV/include/opencv2/core.hpp
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@@ -60,16 +60,11 @@
/**
@defgroup core Core functionality
The Core module is the backbone of OpenCV, offering fundamental data structures, matrix operations,
and utility functions that other modules depend on. It’s essential for handling image data,
performing mathematical computations, and managing memory efficiently within the OpenCV ecosystem.
@{
@defgroup core_basic Basic structures
@defgroup core_array Operations on arrays
@defgroup core_async Asynchronous API
@defgroup core_xml XML/YAML/JSON Persistence
@defgroup core_xml XML/YAML Persistence
@defgroup core_cluster Clustering
@defgroup core_utils Utility and system functions and macros
@{
@@ -81,6 +76,7 @@ performing mathematical computations, and managing memory efficiently within the
@defgroup core_utils_samples Utility functions for OpenCV samples
@}
@defgroup core_opengl OpenGL interoperability
@defgroup core_ipp Intel IPP Asynchronous C/C++ Converters
@defgroup core_optim Optimization Algorithms
@defgroup core_directx DirectX interoperability
@defgroup core_eigen Eigen support
@@ -100,7 +96,6 @@ performing mathematical computations, and managing memory efficiently within the
@{
@defgroup core_parallel_backend Parallel backends API
@}
@defgroup core_quaternion Quaternion
@}
*/
@@ -128,12 +123,12 @@ public:
Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used.
*/
Exception(int _code, const String& _err, const String& _func, const String& _file, int _line);
virtual ~Exception() CV_NOEXCEPT;
virtual ~Exception() throw();
/*!
\return the error description and the context as a text string.
*/
virtual const char *what() const CV_NOEXCEPT CV_OVERRIDE;
virtual const char *what() const throw() CV_OVERRIDE;
void formatMessage();
String msg; ///< the formatted error message
@@ -168,7 +163,7 @@ enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independe
//! @} core_utils
//! @addtogroup core_array
//! @addtogroup core
//! @{
//! Covariation flags
@@ -207,6 +202,27 @@ enum CovarFlags {
COVAR_COLS = 16
};
//! @addtogroup core_cluster
//! @{
//! k-Means flags
enum KmeansFlags {
/** Select random initial centers in each attempt.*/
KMEANS_RANDOM_CENTERS = 0,
/** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/
KMEANS_PP_CENTERS = 2,
/** During the first (and possibly the only) attempt, use the
user-supplied labels instead of computing them from the initial centers. For the second and
further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
to specify the exact method.*/
KMEANS_USE_INITIAL_LABELS = 1
};
//! @} core_cluster
//! @addtogroup core_array
//! @{
enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix.
REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix.
REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix.
@@ -214,12 +230,19 @@ enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/column
REDUCE_SUM2 = 4 //!< the output is the sum of all squared rows/columns of the matrix.
};
//! @} core_array
/** @brief Swaps two matrices
*/
CV_EXPORTS void swap(Mat& a, Mat& b);
/** @overload */
CV_EXPORTS void swap( UMat& a, UMat& b );
//! @} core
//! @addtogroup core_array
//! @{
/** @brief Computes the source location of an extrapolated pixel.
The function computes and returns the coordinate of a donor pixel corresponding to the specified
@@ -463,6 +486,10 @@ The function can also be emulated with a matrix expression, for example:
*/
CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
Check @ref tutorial_trackbar "the corresponding tutorial" for more details
*/
/** @brief Calculates the weighted sum of two arrays.
The function addWeighted calculates the weighted sum of two arrays as follows:
@@ -530,19 +557,15 @@ The format of half precision floating point is defined in IEEE 754-2008.
*/
CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst);
/** @example samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
Check @ref tutorial_how_to_scan_images "the corresponding tutorial" for more details
*/
/** @brief Performs a look-up table transform of an array.
The function LUT fills the output array with values from the look-up table. Indices of the entries
are taken from the input array. That is, the function processes each element of src as follows:
\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f]
where
\f[d = \forkthree{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\) or \(\texttt{CV_16U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}{32768}{if \(\texttt{src}\) has depth \(\texttt{CV_16S}\)}\f]
@param src input array of 8-bit or 16-bit integer elements.
@param lut look-up table of 256 elements (if src has depth CV_8U or CV_8S) or 65536 elements(if src has depth CV_16U or CV_16S); in case of multi-channel input array, the table should
\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f]
@param src input array of 8-bit elements.
@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
either have a single channel (in this case the same table is used for all channels) or the same
number of channels as in the input array.
@param dst output array of the same size and number of channels as src, and the same depth as lut.
@@ -805,11 +828,6 @@ Possible usage with some positive example data:
normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
@endcode
@note Due to rounding issues, min-max normalization can result in values outside provided boundaries.
If exact range conformity is needed, following workarounds can be used:
- use double floating point precision (dtype = CV_64F)
- manually clip values (`cv::max(res, left_bound, res)`, `cv::min(res, right_bound, res)` or `np.clip`)
@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
@@ -837,21 +855,13 @@ CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, i
/** @brief Finds the global minimum and maximum in an array.
The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
extrema are searched across the whole array or, if mask is not an empty array, in the specified
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region.
In C++, if the input is multi-channel, you should omit the minLoc, maxLoc, and mask arguments
(i.e. leave them as NULL, NULL, and noArray() respectively). These arguments are not
supported for multi-channel input arrays. If working with multi-channel input and you
need the minLoc, maxLoc, or mask arguments, then use Mat::reshape first to reinterpret
the array as single-channel. Alternatively, you can extract the particular channel using either
extractImageCOI, mixChannels, or split.
In Python, multi-channel input is not supported at all due to a limitation in the
binding generation process (there is no way to set minLoc and maxLoc to NULL). A
workaround is to operate on each channel individually or to use NumPy to achieve the same
functionality.
The function do not work with multi-channel arrays. If you need to find minimum or maximum
elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI, or
mixChannels, or split.
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@@ -1711,13 +1721,10 @@ elements.
*/
CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0,
double minVal = -DBL_MAX, double maxVal = DBL_MAX);
/** @brief Replaces NaNs (Not-a-Number values) in a matrix with the specified value.
This function modifies the input matrix in-place.
The input matrix must be of type `CV_32F` or `CV_64F`; other types are not supported.
@param a Input/output matrix (CV_32F or CV_64F type).
@param val Value used to replace NaNs (defaults to 0).
/** @brief Replaces NaNs by given number
@param a input/output matrix (CV_32F type).
@param val value to convert the NaNs
*/
CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0);
@@ -2012,8 +2019,8 @@ The function solveCubic finds the real roots of a cubic equation:
The roots are stored in the roots array.
@param coeffs equation coefficients, an array of 3 or 4 elements.
@param roots output array of real roots that has 0, 1, 2 or 3 elements.
@return number of real roots. It can be -1 (all real numbers), 0, 1, 2 or 3.
@param roots output array of real roots that has 1 or 3 elements.
@return number of real roots. It can be 0, 1 or 2.
*/
CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots);
@@ -3070,21 +3077,8 @@ private:
//! @addtogroup core_cluster
//! @{
//! k-means flags
enum KmeansFlags {
/** Select random initial centers in each attempt.*/
KMEANS_RANDOM_CENTERS = 0,
/** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/
KMEANS_PP_CENTERS = 2,
/** During the first (and possibly the only) attempt, use the
user-supplied labels instead of computing them from the initial centers. For the second and
further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
to specify the exact method.*/
KMEANS_USE_INITIAL_LABELS = 1
};
/** @example samples/cpp/kmeans.cpp
An example on k-means clustering
An example on K-means clustering
*/
/** @brief Finds centers of clusters and groups input samples around the clusters.
@@ -3094,7 +3088,7 @@ and groups the input samples around the clusters. As an output, \f$\texttt{bestL
0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix.
@note
- (Python) An example on k-means clustering can be found at
- (Python) An example on K-means clustering can be found at
opencv_source_code/samples/python/kmeans.py
@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
Examples of this array can be: