Package org.opencv.core
Class Core
java.lang.Object
org.opencv.core.Core
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Nested Class Summary
Nested Classes -
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final Stringstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final Stringstatic final intstatic final intstatic final intstatic final String -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic voidCalculates the per-element absolute difference between two arrays or between an array and a scalar.static voidstatic voidCalculates the per-element sum of two arrays or an array and a scalar.static voidCalculates the per-element sum of two arrays or an array and a scalar.static voidCalculates the per-element sum of two arrays or an array and a scalar.static voidstatic voidstatic voidstatic voidOverride search data path by adding new search location Use this only to override default behavior Passed paths are used in LIFO order.static voidAppend samples search data sub directory General usage is to add OpenCV modules name (<opencv_contrib>/modules/<name>/samples/data-><name>/samples/data+modules/<name>/samples/data).static voidaddWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst) Calculates the weighted sum of two arrays.static voidaddWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst, int dtype) Calculates the weighted sum of two arrays.static voidbatchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidbatchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidbatchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidnaive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidbatchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask, int update) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidbatchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask, int update, boolean crosscheck) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: documentstatic voidbitwise_and(Mat src1, Mat src2, Mat dst) computes bitwise conjunction of the two arrays (dst = src1 & src2) Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar.static voidbitwise_and(Mat src1, Mat src2, Mat dst, Mat mask) computes bitwise conjunction of the two arrays (dst = src1 & src2) Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar.static voidbitwise_not(Mat src, Mat dst) Inverts every bit of an array.static voidbitwise_not(Mat src, Mat dst, Mat mask) Inverts every bit of an array.static voidbitwise_or(Mat src1, Mat src2, Mat dst) Calculates the per-element bit-wise disjunction of two arrays or an array and a scalar.static voidbitwise_or(Mat src1, Mat src2, Mat dst, Mat mask) Calculates the per-element bit-wise disjunction of two arrays or an array and a scalar.static voidbitwise_xor(Mat src1, Mat src2, Mat dst) Calculates the per-element bit-wise "exclusive or" operation on two arrays or an array and a scalar.static voidbitwise_xor(Mat src1, Mat src2, Mat dst, Mat mask) Calculates the per-element bit-wise "exclusive or" operation on two arrays or an array and a scalar.static intborderInterpolate(int p, int len, int borderType) Computes the source location of an extrapolated pixel.static voidBroadcast the given Mat to the given shape.static voidcalcCovarMatrix(Mat samples, Mat covar, Mat mean, int flags) Note: use #COVAR_ROWS or #COVAR_COLS flagstatic voidcalcCovarMatrix(Mat samples, Mat covar, Mat mean, int flags, int ctype) Note: use #COVAR_ROWS or #COVAR_COLS flagstatic voidcartToPolar(Mat x, Mat y, Mat magnitude, Mat angle) Calculates the magnitude and angle of 2D vectors.static voidcartToPolar(Mat x, Mat y, Mat magnitude, Mat angle, boolean angleInDegrees) Calculates the magnitude and angle of 2D vectors.static booleancheckHardwareSupport(int feature) Returns true if the specified feature is supported by the host hardware.static booleancheckRange(Mat a) Checks every element of an input array for invalid values.static booleancheckRange(Mat a, boolean quiet) Checks every element of an input array for invalid values.static booleancheckRange(Mat a, boolean quiet, double minVal) Checks every element of an input array for invalid values.static booleancheckRange(Mat a, boolean quiet, double minVal, double maxVal) Checks every element of an input array for invalid values.static voidPerforms the per-element comparison of two arrays or an array and scalar value.static voidstatic voidcompleteSymm(Mat m) Copies the lower or the upper half of a square matrix to its another half.static voidcompleteSymm(Mat m, boolean lowerToUpper) Copies the lower or the upper half of a square matrix to its another half.static voidconvertFp16(Mat src, Mat dst) Converts an array to half precision floating number.static voidconvertScaleAbs(Mat src, Mat dst) Scales, calculates absolute values, and converts the result to 8-bit.static voidconvertScaleAbs(Mat src, Mat dst, double alpha) Scales, calculates absolute values, and converts the result to 8-bit.static voidconvertScaleAbs(Mat src, Mat dst, double alpha, double beta) Scales, calculates absolute values, and converts the result to 8-bit.static voidcopyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType) Forms a border around an image.static voidcopyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType, Scalar value) Forms a border around an image.static voidThis is an overloaded member function, provided for convenience (python) Copies the matrix to another one.static intcountNonZero(Mat src) Counts non-zero array elements.static floatcubeRoot(float val) Computes the cube root of an argument.static voidPerforms a forward or inverse discrete Cosine transform of 1D or 2D array.static voidPerforms a forward or inverse discrete Cosine transform of 1D or 2D array.static doubledeterminant(Mat mtx) Returns the determinant of a square floating-point matrix.static voidPerforms a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.static voidPerforms a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.static voidPerforms a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.static voidstatic voidstatic voidPerforms per-element division of two arrays or a scalar by an array.static voidPerforms per-element division of two arrays or a scalar by an array.static voidPerforms per-element division of two arrays or a scalar by an array.static voidstatic voidstatic voidstatic booleanCalculates eigenvalues and eigenvectors of a symmetric matrix.static booleanCalculates eigenvalues and eigenvectors of a symmetric matrix.static voideigenNonSymmetric(Mat src, Mat eigenvalues, Mat eigenvectors) Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).static voidCalculates the exponent of every array element.static voidextractChannel(Mat src, Mat dst, int coi) Extracts a single channel from src (coi is 0-based index)static floatfastAtan2(float y, float x) Calculates the angle of a 2D vector in degrees.static StringTry to find requested data file Search directories: 1.static StringTry to find requested data file Search directories: 1.static StringTry to find requested data file Search directories: 1.static StringfindFileOrKeep(String relative_path) static StringfindFileOrKeep(String relative_path, boolean silentMode) static voidfindNonZero(Mat src, Mat idx) Returns the list of locations of non-zero pixels Given a binary matrix (likely returned from an operation such as threshold(), compare(), >, ==, etc, return all of the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y) For example:cv::Mat binaryImage; // input, binary image cv::Mat locations; // output, locations of non-zero pixels cv::findNonZero(binaryImage, locations); // access pixel coordinates Point pnt = locations.at<Point>(i);orcv::Mat binaryImage; // input, binary image vector<Point> locations; // output, locations of non-zero pixels cv::findNonZero(binaryImage, locations); // access pixel coordinates Point pnt = locations[i];static voidFlips a 2D array around vertical, horizontal, or both axes.static voidFlips a n-dimensional at given axisstatic voidPerforms generalized matrix multiplication.static voidPerforms generalized matrix multiplication.static StringReturns full configuration time cmake output.static StringReturns list of CPU features enabled during compilation.static longReturns the number of CPU ticks.static StringgetHardwareFeatureName(int feature) Returns feature name by ID Returns empty string if feature is not definedstatic Stringstatic intReturns the number of logical CPUs available for the process.static intReturns the number of threads used by OpenCV for parallel regions.static intgetOptimalDFTSize(int vecsize) Returns the optimal DFT size for a given vector size.static intDeprecated.Current implementation doesn't corresponding to this documentation.static longReturns the number of ticks.static doubleReturns the number of ticks per second.static intReturns major library versionstatic intReturns minor library versionstatic intReturns revision field of the library versionstatic StringReturns library version string For example "3.4.1-dev".static booleanhasNonZero(Mat src) Checks for the presence of at least one non-zero array element.static voidstd::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; cv::Mat out; cv::hconcat( matrices, out ); //out: //[1, 2, 3; // 1, 2, 3; // 1, 2, 3; // 1, 2, 3]static voidCalculates the inverse Discrete Cosine Transform of a 1D or 2D array.static voidCalculates the inverse Discrete Cosine Transform of a 1D or 2D array.static voidCalculates the inverse Discrete Fourier Transform of a 1D or 2D array.static voidCalculates the inverse Discrete Fourier Transform of a 1D or 2D array.static voidCalculates the inverse Discrete Fourier Transform of a 1D or 2D array.static voidChecks if array elements lie between the elements of two other arrays.static voidinsertChannel(Mat src, Mat dst, int coi) Inserts a single channel to dst (coi is 0-based index)static doubleFinds the inverse or pseudo-inverse of a matrix.static doubleFinds the inverse or pseudo-inverse of a matrix.static doublekmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags) Finds centers of clusters and groups input samples around the clusters.static doublekmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags, Mat centers) Finds centers of clusters and groups input samples around the clusters.static voidCalculates the natural logarithm of every array element.static voidPerforms a look-up table transform of an array.static voidCalculates the magnitude of 2D vectors.static doubleMahalanobis(Mat v1, Mat v2, Mat icovar) Calculates the Mahalanobis distance between two vectors.static voidCalculates per-element maximum of two arrays or an array and a scalar.static voidstatic ScalarCalculates an average (mean) of array elements.static ScalarCalculates an average (mean) of array elements.static voidmeanStdDev(Mat src, MatOfDouble mean, MatOfDouble stddev) Calculates a mean and standard deviation of array elements.static voidmeanStdDev(Mat src, MatOfDouble mean, MatOfDouble stddev, Mat mask) Calculates a mean and standard deviation of array elements.static voidstatic voidCalculates per-element minimum of two arrays or an array and a scalar.static voidstatic Core.MinMaxLocResultstatic Core.MinMaxLocResultstatic voidstatic voidmulSpectrums(Mat a, Mat b, Mat c, int flags) Performs the per-element multiplication of two Fourier spectrums.static voidmulSpectrums(Mat a, Mat b, Mat c, int flags, boolean conjB) Performs the per-element multiplication of two Fourier spectrums.static voidCalculates the per-element scaled product of two arrays.static voidCalculates the per-element scaled product of two arrays.static voidCalculates the per-element scaled product of two arrays.static voidstatic voidstatic voidstatic voidmulTransposed(Mat src, Mat dst, boolean aTa) Calculates the product of a matrix and its transposition.static voidmulTransposed(Mat src, Mat dst, boolean aTa, Mat delta) Calculates the product of a matrix and its transposition.static voidmulTransposed(Mat src, Mat dst, boolean aTa, Mat delta, double scale) Calculates the product of a matrix and its transposition.static voidmulTransposed(Mat src, Mat dst, boolean aTa, Mat delta, double scale, int dtype) Calculates the product of a matrix and its transposition.static doubleCalculates the absolute norm of an array.static doubleCalculates the absolute norm of an array.static doubleCalculates the absolute norm of an array.static doubleCalculates an absolute difference norm or a relative difference norm.static doubleCalculates an absolute difference norm or a relative difference norm.static doubleCalculates an absolute difference norm or a relative difference norm.static voidNormalizes the norm or value range of an array.static voidNormalizes the norm or value range of an array.static voidNormalizes the norm or value range of an array.static voidNormalizes the norm or value range of an array.static voidNormalizes the norm or value range of an array.static voidNormalizes the norm or value range of an array.static voidReplaces NaNs by given numberstatic voidReplaces NaNs by given numberstatic voidPCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat result) wrap PCA::backProjectstatic voidPCACompute(Mat data, Mat mean, Mat eigenvectors) wrap PCA::operator()static voidPCACompute(Mat data, Mat mean, Mat eigenvectors, double retainedVariance) wrap PCA::operator()static voidPCACompute(Mat data, Mat mean, Mat eigenvectors, int maxComponents) wrap PCA::operator()static voidPCACompute2(Mat data, Mat mean, Mat eigenvectors, Mat eigenvalues) wrap PCA::operator() and add eigenvalues output parameterstatic voidPCACompute2(Mat data, Mat mean, Mat eigenvectors, Mat eigenvalues, double retainedVariance) wrap PCA::operator() and add eigenvalues output parameterstatic voidPCACompute2(Mat data, Mat mean, Mat eigenvectors, Mat eigenvalues, int maxComponents) wrap PCA::operator() and add eigenvalues output parameterstatic voidPCAProject(Mat data, Mat mean, Mat eigenvectors, Mat result) wrap PCA::projectstatic voidperspectiveTransform(Mat src, Mat dst, Mat m) Performs the perspective matrix transformation of vectors.static voidCalculates the rotation angle of 2D vectors.static voidCalculates the rotation angle of 2D vectors.static voidpolarToCart(Mat magnitude, Mat angle, Mat x, Mat y) Calculates x and y coordinates of 2D vectors from their magnitude and angle.static voidpolarToCart(Mat magnitude, Mat angle, Mat x, Mat y, boolean angleInDegrees) Calculates x and y coordinates of 2D vectors from their magnitude and angle.static voidRaises every array element to a power.static doubleComputes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.static doubleComputes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.static voidFills the array with normally distributed random numbers.static voidrandShuffle(Mat dst) Shuffles the array elements randomly.static voidrandShuffle(Mat dst, double iterFactor) Shuffles the array elements randomly.static voidGenerates a single uniformly-distributed random number or an array of random numbers.static voidReduces a matrix to a vector.static voidReduces a matrix to a vector.static voidreduceArgMax(Mat src, Mat dst, int axis) Finds indices of max elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.static voidreduceArgMax(Mat src, Mat dst, int axis, boolean lastIndex) Finds indices of max elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.static voidreduceArgMin(Mat src, Mat dst, int axis) Finds indices of min elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.static voidreduceArgMin(Mat src, Mat dst, int axis, boolean lastIndex) Finds indices of min elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.static voidFills the output array with repeated copies of the input array.static voidRotates a 2D array in multiples of 90 degrees.static voidCalculates the sum of a scaled array and another array.static voidsetErrorVerbosity(boolean verbose) static voidsetIdentity(Mat mtx) Initializes a scaled identity matrix.static voidsetIdentity(Mat mtx, Scalar s) Initializes a scaled identity matrix.static voidsetNumThreads(int nthreads) OpenCV will try to set the number of threads for subsequent parallel regions.static voidsetRNGSeed(int seed) Sets state of default random number generator.static voidsetUseIPP(boolean flag) static voidsetUseIPP_NotExact(boolean flag) static voidsetUseOptimized(boolean onoff) Enables or disables the optimized code.static booleanSolves one or more linear systems or least-squares problems.static booleanSolves one or more linear systems or least-squares problems.static intsolveCubic(Mat coeffs, Mat roots) Finds the real roots of a cubic equation.static doubleFinds the real or complex roots of a polynomial equation.static doubleFinds the real or complex roots of a polynomial equation.static voidSorts each row or each column of a matrix.static voidSorts each row or each column of a matrix.static voidstatic voidCalculates a square root of array elements.static voidCalculates the per-element difference between two arrays or array and a scalar.static voidCalculates the per-element difference between two arrays or array and a scalar.static voidCalculates the per-element difference between two arrays or array and a scalar.static voidstatic voidstatic voidstatic ScalarCalculates the sum of array elements.static voidwrap SVD::backSubststatic voidwrap SVD::computestatic voidwrap SVD::computestatic ScalarReturns the trace of a matrix.static voidPerforms the matrix transformation of every array element.static voidTransposes a matrix.static voidtransposeND(Mat src, MatOfInt order, Mat dst) Transpose for n-dimensional matrices.static booleanuseIPP()proxy for hal::Choleskystatic booleanstatic booleanReturns the status of optimized code usage.static voidstd::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; cv::Mat out; cv::vconcat( matrices, out ); //out: //[1, 1, 1, 1; // 2, 2, 2, 2; // 3, 3, 3, 3]
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Field Details
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VERSION
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NATIVE_LIBRARY_NAME
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VERSION_MAJOR
public static final int VERSION_MAJOR -
VERSION_MINOR
public static final int VERSION_MINOR -
VERSION_REVISION
public static final int VERSION_REVISION -
VERSION_STATUS
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SVD_MODIFY_A
public static final int SVD_MODIFY_A- See Also:
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SVD_NO_UV
public static final int SVD_NO_UV- See Also:
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SVD_FULL_UV
public static final int SVD_FULL_UV- See Also:
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FILLED
public static final int FILLED- See Also:
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REDUCE_SUM
public static final int REDUCE_SUM- See Also:
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REDUCE_AVG
public static final int REDUCE_AVG- See Also:
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REDUCE_MAX
public static final int REDUCE_MAX- See Also:
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REDUCE_MIN
public static final int REDUCE_MIN- See Also:
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RNG_UNIFORM
public static final int RNG_UNIFORM- See Also:
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RNG_NORMAL
public static final int RNG_NORMAL- See Also:
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BORDER_CONSTANT
public static final int BORDER_CONSTANT- See Also:
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BORDER_REPLICATE
public static final int BORDER_REPLICATE- See Also:
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BORDER_REFLECT
public static final int BORDER_REFLECT- See Also:
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BORDER_WRAP
public static final int BORDER_WRAP- See Also:
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BORDER_REFLECT_101
public static final int BORDER_REFLECT_101- See Also:
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BORDER_TRANSPARENT
public static final int BORDER_TRANSPARENT- See Also:
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BORDER_REFLECT101
public static final int BORDER_REFLECT101- See Also:
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BORDER_DEFAULT
public static final int BORDER_DEFAULT- See Also:
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BORDER_ISOLATED
public static final int BORDER_ISOLATED- See Also:
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CMP_EQ
public static final int CMP_EQ- See Also:
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CMP_GT
public static final int CMP_GT- See Also:
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CMP_GE
public static final int CMP_GE- See Also:
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CMP_LT
public static final int CMP_LT- See Also:
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CMP_LE
public static final int CMP_LE- See Also:
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CMP_NE
public static final int CMP_NE- See Also:
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COVAR_SCRAMBLED
public static final int COVAR_SCRAMBLED- See Also:
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COVAR_NORMAL
public static final int COVAR_NORMAL- See Also:
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COVAR_USE_AVG
public static final int COVAR_USE_AVG- See Also:
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COVAR_SCALE
public static final int COVAR_SCALE- See Also:
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COVAR_ROWS
public static final int COVAR_ROWS- See Also:
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COVAR_COLS
public static final int COVAR_COLS- See Also:
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DECOMP_LU
public static final int DECOMP_LU- See Also:
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DECOMP_SVD
public static final int DECOMP_SVD- See Also:
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DECOMP_EIG
public static final int DECOMP_EIG- See Also:
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DECOMP_CHOLESKY
public static final int DECOMP_CHOLESKY- See Also:
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DECOMP_QR
public static final int DECOMP_QR- See Also:
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DECOMP_NORMAL
public static final int DECOMP_NORMAL- See Also:
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StsOk
public static final int StsOk- See Also:
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StsBackTrace
public static final int StsBackTrace- See Also:
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StsError
public static final int StsError- See Also:
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StsInternal
public static final int StsInternal- See Also:
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StsNoMem
public static final int StsNoMem- See Also:
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StsBadArg
public static final int StsBadArg- See Also:
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StsBadFunc
public static final int StsBadFunc- See Also:
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StsNoConv
public static final int StsNoConv- See Also:
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StsAutoTrace
public static final int StsAutoTrace- See Also:
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HeaderIsNull
public static final int HeaderIsNull- See Also:
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BadImageSize
public static final int BadImageSize- See Also:
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BadOffset
public static final int BadOffset- See Also:
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BadDataPtr
public static final int BadDataPtr- See Also:
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BadStep
public static final int BadStep- See Also:
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BadModelOrChSeq
public static final int BadModelOrChSeq- See Also:
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BadNumChannels
public static final int BadNumChannels- See Also:
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BadNumChannel1U
public static final int BadNumChannel1U- See Also:
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BadDepth
public static final int BadDepth- See Also:
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BadAlphaChannel
public static final int BadAlphaChannel- See Also:
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BadOrder
public static final int BadOrder- See Also:
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BadOrigin
public static final int BadOrigin- See Also:
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BadAlign
public static final int BadAlign- See Also:
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BadCallBack
public static final int BadCallBack- See Also:
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BadTileSize
public static final int BadTileSize- See Also:
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BadCOI
public static final int BadCOI- See Also:
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BadROISize
public static final int BadROISize- See Also:
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MaskIsTiled
public static final int MaskIsTiled- See Also:
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StsNullPtr
public static final int StsNullPtr- See Also:
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StsVecLengthErr
public static final int StsVecLengthErr- See Also:
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StsFilterStructContentErr
public static final int StsFilterStructContentErr- See Also:
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StsKernelStructContentErr
public static final int StsKernelStructContentErr- See Also:
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StsFilterOffsetErr
public static final int StsFilterOffsetErr- See Also:
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StsBadSize
public static final int StsBadSize- See Also:
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StsDivByZero
public static final int StsDivByZero- See Also:
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StsInplaceNotSupported
public static final int StsInplaceNotSupported- See Also:
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StsObjectNotFound
public static final int StsObjectNotFound- See Also:
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StsUnmatchedFormats
public static final int StsUnmatchedFormats- See Also:
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StsBadFlag
public static final int StsBadFlag- See Also:
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StsBadPoint
public static final int StsBadPoint- See Also:
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StsBadMask
public static final int StsBadMask- See Also:
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StsUnmatchedSizes
public static final int StsUnmatchedSizes- See Also:
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StsUnsupportedFormat
public static final int StsUnsupportedFormat- See Also:
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StsOutOfRange
public static final int StsOutOfRange- See Also:
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StsParseError
public static final int StsParseError- See Also:
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StsNotImplemented
public static final int StsNotImplemented- See Also:
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StsBadMemBlock
public static final int StsBadMemBlock- See Also:
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StsAssert
public static final int StsAssert- See Also:
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GpuNotSupported
public static final int GpuNotSupported- See Also:
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GpuApiCallError
public static final int GpuApiCallError- See Also:
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OpenGlNotSupported
public static final int OpenGlNotSupported- See Also:
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OpenGlApiCallError
public static final int OpenGlApiCallError- See Also:
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OpenCLApiCallError
public static final int OpenCLApiCallError- See Also:
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OpenCLDoubleNotSupported
public static final int OpenCLDoubleNotSupported- See Also:
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OpenCLInitError
public static final int OpenCLInitError- See Also:
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OpenCLNoAMDBlasFft
public static final int OpenCLNoAMDBlasFft- See Also:
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Formatter_FMT_DEFAULT
public static final int Formatter_FMT_DEFAULT- See Also:
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Formatter_FMT_MATLAB
public static final int Formatter_FMT_MATLAB- See Also:
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Formatter_FMT_CSV
public static final int Formatter_FMT_CSV- See Also:
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Formatter_FMT_PYTHON
public static final int Formatter_FMT_PYTHON- See Also:
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Formatter_FMT_NUMPY
public static final int Formatter_FMT_NUMPY- See Also:
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Formatter_FMT_C
public static final int Formatter_FMT_C- See Also:
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GEMM_1_T
public static final int GEMM_1_T- See Also:
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GEMM_2_T
public static final int GEMM_2_T- See Also:
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GEMM_3_T
public static final int GEMM_3_T- See Also:
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KMEANS_RANDOM_CENTERS
public static final int KMEANS_RANDOM_CENTERS- See Also:
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KMEANS_PP_CENTERS
public static final int KMEANS_PP_CENTERS- See Also:
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KMEANS_USE_INITIAL_LABELS
public static final int KMEANS_USE_INITIAL_LABELS- See Also:
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NORM_INF
public static final int NORM_INF- See Also:
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NORM_L1
public static final int NORM_L1- See Also:
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NORM_L2
public static final int NORM_L2- See Also:
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NORM_L2SQR
public static final int NORM_L2SQR- See Also:
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NORM_HAMMING
public static final int NORM_HAMMING- See Also:
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NORM_HAMMING2
public static final int NORM_HAMMING2- See Also:
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NORM_TYPE_MASK
public static final int NORM_TYPE_MASK- See Also:
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NORM_RELATIVE
public static final int NORM_RELATIVE- See Also:
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NORM_MINMAX
public static final int NORM_MINMAX- See Also:
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PCA_DATA_AS_ROW
public static final int PCA_DATA_AS_ROW- See Also:
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PCA_DATA_AS_COL
public static final int PCA_DATA_AS_COL- See Also:
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PCA_USE_AVG
public static final int PCA_USE_AVG- See Also:
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Param_INT
public static final int Param_INT- See Also:
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Param_BOOLEAN
public static final int Param_BOOLEAN- See Also:
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Param_REAL
public static final int Param_REAL- See Also:
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Param_STRING
public static final int Param_STRING- See Also:
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Param_MAT
public static final int Param_MAT- See Also:
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Param_MAT_VECTOR
public static final int Param_MAT_VECTOR- See Also:
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Param_ALGORITHM
public static final int Param_ALGORITHM- See Also:
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Param_FLOAT
public static final int Param_FLOAT- See Also:
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Param_UNSIGNED_INT
public static final int Param_UNSIGNED_INT- See Also:
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Param_UINT64
public static final int Param_UINT64- See Also:
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Param_UCHAR
public static final int Param_UCHAR- See Also:
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Param_SCALAR
public static final int Param_SCALAR- See Also:
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REDUCE_SUM2
public static final int REDUCE_SUM2- See Also:
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DFT_INVERSE
public static final int DFT_INVERSE- See Also:
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DFT_SCALE
public static final int DFT_SCALE- See Also:
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DFT_ROWS
public static final int DFT_ROWS- See Also:
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DFT_COMPLEX_OUTPUT
public static final int DFT_COMPLEX_OUTPUT- See Also:
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DFT_REAL_OUTPUT
public static final int DFT_REAL_OUTPUT- See Also:
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DFT_COMPLEX_INPUT
public static final int DFT_COMPLEX_INPUT- See Also:
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DCT_INVERSE
public static final int DCT_INVERSE- See Also:
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DCT_ROWS
public static final int DCT_ROWS- See Also:
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ROTATE_90_CLOCKWISE
public static final int ROTATE_90_CLOCKWISE- See Also:
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ROTATE_180
public static final int ROTATE_180- See Also:
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ROTATE_90_COUNTERCLOCKWISE
public static final int ROTATE_90_COUNTERCLOCKWISE- See Also:
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SORT_EVERY_ROW
public static final int SORT_EVERY_ROW- See Also:
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SORT_EVERY_COLUMN
public static final int SORT_EVERY_COLUMN- See Also:
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SORT_ASCENDING
public static final int SORT_ASCENDING- See Also:
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SORT_DESCENDING
public static final int SORT_DESCENDING- See Also:
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Constructor Details
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Core
public Core()
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Method Details
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cubeRoot
public static float cubeRoot(float val) Computes the cube root of an argument. The function cubeRoot computes \(\sqrt[3]{\texttt{val}}\). Negative arguments are handled correctly. NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for single-precision data.- Parameters:
val- A function argument.- Returns:
- automatically generated
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fastAtan2
public static float fastAtan2(float y, float x) Calculates the angle of a 2D vector in degrees. The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.- Parameters:
y- y-coordinate of the vector.x- x-coordinate of the vector.- Returns:
- automatically generated
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useIPP
public static boolean useIPP()proxy for hal::Cholesky- Returns:
- automatically generated
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setUseIPP
public static void setUseIPP(boolean flag) -
getIppVersion
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useIPP_NotExact
public static boolean useIPP_NotExact() -
setUseIPP_NotExact
public static void setUseIPP_NotExact(boolean flag) -
borderInterpolate
public static int borderInterpolate(int p, int len, int borderType) Computes the source location of an extrapolated pixel. The function computes and returns the coordinate of a donor pixel corresponding to the specified extrapolated pixel when using the specified extrapolation border mode. For example, if you use cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it looks like:float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), borderInterpolate(-5, img.cols, cv::BORDER_WRAP));Normally, the function is not called directly. It is used inside filtering functions and also in copyMakeBorder.- Parameters:
p- 0-based coordinate of the extrapolated pixel along one of the axes, likely <0 or >= lenlen- Length of the array along the corresponding axis.borderType- Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and #BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless of p and len. SEE: copyMakeBorder- Returns:
- automatically generated
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copyMakeBorder
public static void copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType, Scalar value) Forms a border around an image. The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when src is already in the middle of dst . In this case, the function does not copy src itself but simply constructs the border, for example:// let border be the same in all directions int border=2; // constructs a larger image to fit both the image and the border Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); // select the middle part of it w/o copying data Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); // convert image from RGB to grayscale cvtColor(rgb, gray, COLOR_RGB2GRAY); // form a border in-place copyMakeBorder(gray, gray_buf, border, border, border, border, BORDER_REPLICATE); // now do some custom filtering ... ...Note: When the source image is a part (ROI) of a bigger image, the function will try to use the pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as if src was not a ROI, use borderType | #BORDER_ISOLATED.- Parameters:
src- Source image.dst- Destination image of the same type as src and the size Size(src.cols+left+right, src.rows+top+bottom) .top- the top pixelsbottom- the bottom pixelsleft- the left pixelsright- Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs to be built.borderType- Border type. See borderInterpolate for details.value- Border value if borderType==BORDER_CONSTANT . SEE: borderInterpolate
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copyMakeBorder
public static void copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType) Forms a border around an image. The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when src is already in the middle of dst . In this case, the function does not copy src itself but simply constructs the border, for example:// let border be the same in all directions int border=2; // constructs a larger image to fit both the image and the border Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); // select the middle part of it w/o copying data Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); // convert image from RGB to grayscale cvtColor(rgb, gray, COLOR_RGB2GRAY); // form a border in-place copyMakeBorder(gray, gray_buf, border, border, border, border, BORDER_REPLICATE); // now do some custom filtering ... ...Note: When the source image is a part (ROI) of a bigger image, the function will try to use the pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as if src was not a ROI, use borderType | #BORDER_ISOLATED.- Parameters:
src- Source image.dst- Destination image of the same type as src and the size Size(src.cols+left+right, src.rows+top+bottom) .top- the top pixelsbottom- the bottom pixelsleft- the left pixelsright- Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs to be built.borderType- Border type. See borderInterpolate for details. SEE: borderInterpolate
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add
Calculates the per-element sum of two arrays or an array and a scalar. The function add calculates:- Sum of two arrays when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) whereIis a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 + src2; dst += src1; // equivalent to add(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit floating-point array. Depth of the output array is determined by the dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case, the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.add(src,X)meansadd(src,(X,X,X,X)).add(src,(X,))meansadd(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and number of channels as the input array(s); the depth is defined by dtype or src1/src2.mask- optional operation mask - 8-bit single channel array, that specifies elements of the output array to be changed.dtype- optional depth of the output array (see the discussion below). SEE: subtract, addWeighted, scaleAdd, Mat::convertTo
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add
Calculates the per-element sum of two arrays or an array and a scalar. The function add calculates:- Sum of two arrays when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) whereIis a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 + src2; dst += src1; // equivalent to add(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit floating-point array. Depth of the output array is determined by the dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case, the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.add(src,X)meansadd(src,(X,X,X,X)).add(src,(X,))meansadd(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and number of channels as the input array(s); the depth is defined by dtype or src1/src2.mask- optional operation mask - 8-bit single channel array, that specifies elements of the output array to be changed. SEE: subtract, addWeighted, scaleAdd, Mat::convertTo
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add
Calculates the per-element sum of two arrays or an array and a scalar. The function add calculates:- Sum of two arrays when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) whereIis a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 + src2; dst += src1; // equivalent to add(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit floating-point array. Depth of the output array is determined by the dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case, the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.add(src,X)meansadd(src,(X,X,X,X)).add(src,(X,))meansadd(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and number of channels as the input array(s); the depth is defined by dtype or src1/src2. output array to be changed. SEE: subtract, addWeighted, scaleAdd, Mat::convertTo
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subtract
Calculates the per-element difference between two arrays or array and a scalar. The function subtract calculates:- Difference between two arrays, when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same number of
elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same number of
elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) - The reverse difference between a scalar and an array in the case of
SubRS: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 - src2; dst -= src1; // equivalent to subtract(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of the output array is determined by dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.subtract(src,X)meanssubtract(src,(X,X,X,X)).subtract(src,(X,))meanssubtract(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array of the same size and the same number of channels as the input array.mask- optional operation mask; this is an 8-bit single channel array that specifies elements of the output array to be changed.dtype- optional depth of the output array SEE: add, addWeighted, scaleAdd, Mat::convertTo
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subtract
Calculates the per-element difference between two arrays or array and a scalar. The function subtract calculates:- Difference between two arrays, when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same number of
elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same number of
elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) - The reverse difference between a scalar and an array in the case of
SubRS: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 - src2; dst -= src1; // equivalent to subtract(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of the output array is determined by dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.subtract(src,X)meanssubtract(src,(X,X,X,X)).subtract(src,(X,))meanssubtract(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array of the same size and the same number of channels as the input array.mask- optional operation mask; this is an 8-bit single channel array that specifies elements of the output array to be changed. SEE: add, addWeighted, scaleAdd, Mat::convertTo
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subtract
Calculates the per-element difference between two arrays or array and a scalar. The function subtract calculates:- Difference between two arrays, when both input arrays have the same size and the same number of channels: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\)
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same number of
elements as
src1.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\) - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same number of
elements as
src2.channels(): \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\) - The reverse difference between a scalar and an array in the case of
SubRS: \(\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
dst = src1 - src2; dst -= src1; // equivalent to subtract(dst, src1, dst);The input arrays and the output array can all have the same or different depths. For example, you can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of the output array is determined by dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case the output array will have the same depth as the input array, be it src1, src2 or both. Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.subtract(src,X)meanssubtract(src,(X,X,X,X)).subtract(src,(X,))meanssubtract(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array of the same size and the same number of channels as the input array. of the output array to be changed. SEE: add, addWeighted, scaleAdd, Mat::convertTo
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multiply
Calculates the per-element scaled product of two arrays. The function multiply calculates the per-element product of two arrays: \(\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\) There is also a REF: MatrixExpressions -friendly variant of the first function. See Mat::mul . For a not-per-element matrix product, see gemm . Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.multiply(src,X)meansmultiply(src,(X,X,X,X)).multiply(src,(X,))meansmultiply(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.dst- output array of the same size and type as src1.scale- optional scale factor.dtype- optional depth of the output array SEE: add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, Mat::convertTo
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multiply
Calculates the per-element scaled product of two arrays. The function multiply calculates the per-element product of two arrays: \(\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\) There is also a REF: MatrixExpressions -friendly variant of the first function. See Mat::mul . For a not-per-element matrix product, see gemm . Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.multiply(src,X)meansmultiply(src,(X,X,X,X)).multiply(src,(X,))meansmultiply(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.dst- output array of the same size and type as src1.scale- optional scale factor. SEE: add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, Mat::convertTo
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multiply
Calculates the per-element scaled product of two arrays. The function multiply calculates the per-element product of two arrays: \(\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\) There is also a REF: MatrixExpressions -friendly variant of the first function. See Mat::mul . For a not-per-element matrix product, see gemm . Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.multiply(src,X)meansmultiply(src,(X,X,X,X)).multiply(src,(X,))meansmultiply(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.dst- output array of the same size and type as src1. SEE: add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, Mat::convertTo
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divide
Performs per-element division of two arrays or a scalar by an array. The function cv::divide divides one array by another: \(\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\) or a scalar by an array when there is no src1 : \(\texttt{dst(I) = saturate(scale/src2(I))}\) Different channels of multi-channel arrays are processed independently. For integer types when src2(I) is zero, dst(I) will also be zero. Note: In case of floating point data there is no special defined behavior for zero src2(I) values. Regular floating-point division is used. Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.divide(src,X)meansdivide(src,(X,X,X,X)).divide(src,(X,))meansdivide(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and type as src1.dst- output array of the same size and type as src2.scale- scalar factor.dtype- optional depth of the output array; if -1, dst will have depth src2.depth(), but in case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). SEE: multiply, add, subtract
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divide
Performs per-element division of two arrays or a scalar by an array. The function cv::divide divides one array by another: \(\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\) or a scalar by an array when there is no src1 : \(\texttt{dst(I) = saturate(scale/src2(I))}\) Different channels of multi-channel arrays are processed independently. For integer types when src2(I) is zero, dst(I) will also be zero. Note: In case of floating point data there is no special defined behavior for zero src2(I) values. Regular floating-point division is used. Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.divide(src,X)meansdivide(src,(X,X,X,X)).divide(src,(X,))meansdivide(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and type as src1.dst- output array of the same size and type as src2. case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). SEE: multiply, add, subtractscale- scalar factor.
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divide
Performs per-element division of two arrays or a scalar by an array. The function cv::divide divides one array by another: \(\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\) or a scalar by an array when there is no src1 : \(\texttt{dst(I) = saturate(scale/src2(I))}\) Different channels of multi-channel arrays are processed independently. For integer types when src2(I) is zero, dst(I) will also be zero. Note: In case of floating point data there is no special defined behavior for zero src2(I) values. Regular floating-point division is used. Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.divide(src,X)meansdivide(src,(X,X,X,X)).divide(src,(X,))meansdivide(src,(X,0,0,0)).- Parameters:
src1- first input array.src2- second input array of the same size and type as src1.dst- output array of the same size and type as src2. case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). SEE: multiply, add, subtract
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divide
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divide
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scaleAdd
Calculates the sum of a scaled array and another array. The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates the sum of a scaled array and another array: \(\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\) The function can also be emulated with a matrix expression, for example:Mat A(3, 3, CV_64F); ... A.row(0) = A.row(1)*2 + A.row(2);- Parameters:
src1- first input array.alpha- scale factor for the first array.src2- second input array of the same size and type as src1.dst- output array of the same size and type as src1. SEE: add, addWeighted, subtract, Mat::dot, Mat::convertTo
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addWeighted
public static void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst, int dtype) Calculates the weighted sum of two arrays. The function addWeighted calculates the weighted sum of two arrays as follows: \(\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. The function can be replaced with a matrix expression:dst = src1*alpha + src2*beta + gamma;Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow.- Parameters:
src1- first input array.alpha- weight of the first array elements.src2- second input array of the same size and channel number as src1.beta- weight of the second array elements.gamma- scalar added to each sum.dst- output array that has the same size and number of channels as the input arrays.dtype- optional depth of the output array; when both input arrays have the same depth, dtype can be set to -1, which will be equivalent to src1.depth(). SEE: add, subtract, scaleAdd, Mat::convertTo
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addWeighted
public static void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst) Calculates the weighted sum of two arrays. The function addWeighted calculates the weighted sum of two arrays as follows: \(\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. The function can be replaced with a matrix expression:dst = src1*alpha + src2*beta + gamma;Note: Saturation is not applied when the output array has the depth CV_32S. You may even get result of an incorrect sign in the case of overflow.- Parameters:
src1- first input array.alpha- weight of the first array elements.src2- second input array of the same size and channel number as src1.beta- weight of the second array elements.gamma- scalar added to each sum.dst- output array that has the same size and number of channels as the input arrays. can be set to -1, which will be equivalent to src1.depth(). SEE: add, subtract, scaleAdd, Mat::convertTo
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convertScaleAbs
Scales, calculates absolute values, and converts the result to 8-bit. On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type: \(\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\) In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example:Mat_<float> A(30,30); randu(A, Scalar(-100), Scalar(100)); Mat_<float> B = A*5 + 3; B = abs(B); // Mat_<float> B = abs(A*5+3) will also do the job, // but it will allocate a temporary matrix- Parameters:
src- input array.dst- output array.alpha- optional scale factor.beta- optional delta added to the scaled values. SEE: Mat::convertTo, cv::abs(const Mat&)
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convertScaleAbs
Scales, calculates absolute values, and converts the result to 8-bit. On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type: \(\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\) In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example:Mat_<float> A(30,30); randu(A, Scalar(-100), Scalar(100)); Mat_<float> B = A*5 + 3; B = abs(B); // Mat_<float> B = abs(A*5+3) will also do the job, // but it will allocate a temporary matrix- Parameters:
src- input array.dst- output array.alpha- optional scale factor. SEE: Mat::convertTo, cv::abs(const Mat&)
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convertScaleAbs
Scales, calculates absolute values, and converts the result to 8-bit. On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type: \(\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\) In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example:Mat_<float> A(30,30); randu(A, Scalar(-100), Scalar(100)); Mat_<float> B = A*5 + 3; B = abs(B); // Mat_<float> B = abs(A*5+3) will also do the job, // but it will allocate a temporary matrix- Parameters:
src- input array.dst- output array. SEE: Mat::convertTo, cv::abs(const Mat&)
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convertFp16
Converts an array to half precision floating number. This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. The format of half precision floating point is defined in IEEE 754-2008.- Parameters:
src- input array.dst- output array.
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LUT
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: \(\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\) where \(d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\)- Parameters:
src- input array of 8-bit elements.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.dst- output array of the same size and number of channels as src, and the same depth as lut. SEE: convertScaleAbs, Mat::convertTo
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sumElems
Calculates the sum of array elements. The function cv::sum calculates and returns the sum of array elements, independently for each channel.- Parameters:
src- input array that must have from 1 to 4 channels. SEE: countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce- Returns:
- automatically generated
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hasNonZero
Checks for the presence of at least one non-zero array element. The function returns whether there are non-zero elements in src- Parameters:
src- single-channel array. SEE: mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix- Returns:
- automatically generated
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countNonZero
Counts non-zero array elements. The function returns the number of non-zero elements in src : \(\sum _{I: \; \texttt{src} (I) \ne0 } 1\)- Parameters:
src- single-channel array. SEE: mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix- Returns:
- automatically generated
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findNonZero
Returns the list of locations of non-zero pixels Given a binary matrix (likely returned from an operation such as threshold(), compare(), >, ==, etc, return all of the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y) For example:cv::Mat binaryImage; // input, binary image cv::Mat locations; // output, locations of non-zero pixels cv::findNonZero(binaryImage, locations); // access pixel coordinates Point pnt = locations.at<Point>(i);orcv::Mat binaryImage; // input, binary image vector<Point> locations; // output, locations of non-zero pixels cv::findNonZero(binaryImage, locations); // access pixel coordinates Point pnt = locations[i];- Parameters:
src- single-channel arrayidx- the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
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mean
Calculates an average (mean) of array elements. The function cv::mean calculates the mean value M of array elements, independently for each channel, and return it: \(\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\) When all the mask elements are 0's, the function returns Scalar::all(0)- Parameters:
src- input array that should have from 1 to 4 channels so that the result can be stored in Scalar_ .mask- optional operation mask. SEE: countNonZero, meanStdDev, norm, minMaxLoc- Returns:
- automatically generated
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mean
Calculates an average (mean) of array elements. The function cv::mean calculates the mean value M of array elements, independently for each channel, and return it: \(\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\) When all the mask elements are 0's, the function returns Scalar::all(0)- Parameters:
src- input array that should have from 1 to 4 channels so that the result can be stored in Scalar_ . SEE: countNonZero, meanStdDev, norm, minMaxLoc- Returns:
- automatically generated
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meanStdDev
Calculates a mean and standard deviation of array elements. The function cv::meanStdDev calculates the mean and the standard deviation M of array elements independently for each channel and returns it via the output parameters: \(\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\) When all the mask elements are 0's, the function returns mean=stddev=Scalar::all(0). Note: The calculated standard deviation is only the diagonal of the complete normalized covariance matrix. If the full matrix is needed, you can reshape the multi-channel array M x N to the single-channel array M\*N x mtx.channels() (only possible when the matrix is continuous) and then pass the matrix to calcCovarMatrix .- Parameters:
src- input array that should have from 1 to 4 channels so that the results can be stored in Scalar_ 's.mean- output parameter: calculated mean value.stddev- output parameter: calculated standard deviation.mask- optional operation mask. SEE: countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
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meanStdDev
Calculates a mean and standard deviation of array elements. The function cv::meanStdDev calculates the mean and the standard deviation M of array elements independently for each channel and returns it via the output parameters: \(\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\) When all the mask elements are 0's, the function returns mean=stddev=Scalar::all(0). Note: The calculated standard deviation is only the diagonal of the complete normalized covariance matrix. If the full matrix is needed, you can reshape the multi-channel array M x N to the single-channel array M\*N x mtx.channels() (only possible when the matrix is continuous) and then pass the matrix to calcCovarMatrix .- Parameters:
src- input array that should have from 1 to 4 channels so that the results can be stored in Scalar_ 's.mean- output parameter: calculated mean value.stddev- output parameter: calculated standard deviation. SEE: countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
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norm
Calculates the absolute norm of an array. This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{1}, L_{2} \) and \( L_{\infty} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows \(align*} \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 \) and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is \(align*} \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. \) The following graphic shows all values for the three norm functions \(\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\) and \(\| r(x) \|_{L_\infty}\). It is notable that the \( L_{1} \) norm forms the upper and the \( L_{\infty} \) norm forms the lower border for the example function \( r(x) \).  When the mask parameter is specified and it is not empty, the norm is If normType is not specified, #NORM_L2 is used. calculated only over the region specified by the mask. Multi-channel input arrays are treated as single-channel arrays, that is, the results for all channels are combined. Hamming norms can only be calculated with CV_8U depth arrays.- Parameters:
src1- first input array.normType- type of the norm (see #NormTypes).mask- optional operation mask; it must have the same size as src1 and CV_8UC1 type.- Returns:
- automatically generated
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norm
Calculates the absolute norm of an array. This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{1}, L_{2} \) and \( L_{\infty} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows \(align*} \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 \) and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is \(align*} \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. \) The following graphic shows all values for the three norm functions \(\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\) and \(\| r(x) \|_{L_\infty}\). It is notable that the \( L_{1} \) norm forms the upper and the \( L_{\infty} \) norm forms the lower border for the example function \( r(x) \).  When the mask parameter is specified and it is not empty, the norm is If normType is not specified, #NORM_L2 is used. calculated only over the region specified by the mask. Multi-channel input arrays are treated as single-channel arrays, that is, the results for all channels are combined. Hamming norms can only be calculated with CV_8U depth arrays.- Parameters:
src1- first input array.normType- type of the norm (see #NormTypes).- Returns:
- automatically generated
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norm
Calculates the absolute norm of an array. This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{1}, L_{2} \) and \( L_{\infty} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows \(align*} \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 \) and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is \(align*} \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. \) The following graphic shows all values for the three norm functions \(\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\) and \(\| r(x) \|_{L_\infty}\). It is notable that the \( L_{1} \) norm forms the upper and the \( L_{\infty} \) norm forms the lower border for the example function \( r(x) \).  When the mask parameter is specified and it is not empty, the norm is If normType is not specified, #NORM_L2 is used. calculated only over the region specified by the mask. Multi-channel input arrays are treated as single-channel arrays, that is, the results for all channels are combined. Hamming norms can only be calculated with CV_8U depth arrays.- Parameters:
src1- first input array.- Returns:
- automatically generated
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norm
Calculates an absolute difference norm or a relative difference norm. This version of cv::norm calculates the absolute difference norm or the relative difference norm of arrays src1 and src2. The type of norm to calculate is specified using #NormTypes.- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.normType- type of the norm (see #NormTypes).mask- optional operation mask; it must have the same size as src1 and CV_8UC1 type.- Returns:
- automatically generated
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norm
Calculates an absolute difference norm or a relative difference norm. This version of cv::norm calculates the absolute difference norm or the relative difference norm of arrays src1 and src2. The type of norm to calculate is specified using #NormTypes.- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.normType- type of the norm (see #NormTypes).- Returns:
- automatically generated
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norm
Calculates an absolute difference norm or a relative difference norm. This version of cv::norm calculates the absolute difference norm or the relative difference norm of arrays src1 and src2. The type of norm to calculate is specified using #NormTypes.- Parameters:
src1- first input array.src2- second input array of the same size and the same type as src1.- Returns:
- automatically generated
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PSNR
Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. The arrays must have the same type. The PSNR is calculated as follows: \( \texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } \) where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data) and MSE is the mean squared error between the two arrays.- Parameters:
src1- first input array.src2- second input array of the same size as src1.R- the maximum pixel value (255 by default)- Returns:
- automatically generated
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PSNR
Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. The arrays must have the same type. The PSNR is calculated as follows: \( \texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } \) where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data) and MSE is the mean squared error between the two arrays.- Parameters:
src1- first input array.src2- second input array of the same size as src1.- Returns:
- automatically generated
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batchDistance
public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask, int update, boolean crosscheck) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generatednormType- automatically generatedK- automatically generatedmask- automatically generatedupdate- automatically generatedcrosscheck- automatically generated
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batchDistance
public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask, int update) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generatednormType- automatically generatedK- automatically generatedmask- automatically generatedupdate- automatically generated
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batchDistance
public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generatednormType- automatically generatedK- automatically generatedmask- automatically generated
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batchDistance
public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K) naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generatednormType- automatically generatedK- automatically generated
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batchDistance
naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generatednormType- automatically generated
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batchDistance
naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document- Parameters:
src1- automatically generatedsrc2- automatically generateddist- automatically generateddtype- automatically generatednidx- automatically generated
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normalize
public static void normalize(Mat src, Mat dst, double alpha, double beta, int norm_type, int dtype, Mat mask) Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src .alpha- norm value to normalize to or the lower range boundary in case of the range normalization.beta- upper range boundary in case of the range normalization; it is not used for the norm normalization.norm_type- normalization type (see cv::NormTypes).dtype- when negative, the output array has the same type as src; otherwise, it has the same number of channels as src and the depth =CV_MAT_DEPTH(dtype).mask- optional operation mask. SEE: norm, Mat::convertTo, SparseMat::convertTo
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normalize
Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src .alpha- norm value to normalize to or the lower range boundary in case of the range normalization.beta- upper range boundary in case of the range normalization; it is not used for the norm normalization.norm_type- normalization type (see cv::NormTypes).dtype- when negative, the output array has the same type as src; otherwise, it has the same number of channels as src and the depth =CV_MAT_DEPTH(dtype). SEE: norm, Mat::convertTo, SparseMat::convertTo
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normalize
Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src .alpha- norm value to normalize to or the lower range boundary in case of the range normalization.beta- upper range boundary in case of the range normalization; it is not used for the norm normalization.norm_type- normalization type (see cv::NormTypes). number of channels as src and the depth =CV_MAT_DEPTH(dtype). SEE: norm, Mat::convertTo, SparseMat::convertTo
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normalize
Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src .alpha- norm value to normalize to or the lower range boundary in case of the range normalization.beta- upper range boundary in case of the range normalization; it is not used for the norm normalization. number of channels as src and the depth =CV_MAT_DEPTH(dtype). SEE: norm, Mat::convertTo, SparseMat::convertTo
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normalize
Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src .alpha- norm value to normalize to or the lower range boundary in case of the range normalization. normalization. number of channels as src and the depth =CV_MAT_DEPTH(dtype). SEE: norm, Mat::convertTo, SparseMat::convertTo
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normalize
Normalizes the norm or value range of an array. The function cv::normalize normalizes scale and shift the input array elements so that \(\| \texttt{dst} \| _{L_p}= \texttt{alpha}\) (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that \(\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\) when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo. In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level. Possible usage with some positive example data:vector<double> positiveData = { 2.0, 8.0, 10.0 }; vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; // Norm to probability (total count) // sum(numbers) = 20.0 // 2.0 0.1 (2.0/20.0) // 8.0 0.4 (8.0/20.0) // 10.0 0.5 (10.0/20.0) normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); // Norm to unit vector: ||positiveData|| = 1.0 // 2.0 0.15 // 8.0 0.62 // 10.0 0.77 normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); // Norm to max element // 2.0 0.2 (2.0/10.0) // 8.0 0.8 (8.0/10.0) // 10.0 1.0 (10.0/10.0) normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); // Norm to range [0.0;1.0] // 2.0 0.0 (shift to left border) // 8.0 0.75 (6.0/8.0) // 10.0 1.0 (shift to right border) normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);- Parameters:
src- input array.dst- output array of the same size as src . normalization. normalization. number of channels as src and the depth =CV_MAT_DEPTH(dtype). SEE: norm, Mat::convertTo, SparseMat::convertTo
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reduceArgMin
Finds indices of min elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.- Parameters:
src- input single-channel array.dst- output array of type CV_32SC1 with the same dimensionality as src, except for axis being reduced - it should be set to 1.axis- axis to reduce along. SEE: reduceArgMax, minMaxLoc, min, max, compare, reducelastIndex- whether to get the index of first or last occurrence of min.
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reduceArgMin
Finds indices of min elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.- Parameters:
src- input single-channel array.dst- output array of type CV_32SC1 with the same dimensionality as src, except for axis being reduced - it should be set to 1.axis- axis to reduce along. SEE: reduceArgMax, minMaxLoc, min, max, compare, reduce
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reduceArgMax
Finds indices of max elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.- Parameters:
src- input single-channel array.dst- output array of type CV_32SC1 with the same dimensionality as src, except for axis being reduced - it should be set to 1.axis- axis to reduce along. SEE: reduceArgMin, minMaxLoc, min, max, compare, reducelastIndex- whether to get the index of first or last occurrence of max.
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reduceArgMax
Finds indices of max elements along provided axis Note: - If input or output array is not continuous, this function will create an internal copy. - NaN handling is left unspecified, see patchNaNs(). - The returned index is always in bounds of input matrix.- Parameters:
src- input single-channel array.dst- output array of type CV_32SC1 with the same dimensionality as src, except for axis being reduced - it should be set to 1.axis- axis to reduce along. SEE: reduceArgMin, minMaxLoc, min, max, compare, reduce
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reduce
Reduces a matrix to a vector. The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. In case of #REDUCE_SUM, #REDUCE_SUM2 and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes. The following code demonstrates its usage for a single channel matrix. SNIPPET: snippets/core_reduce.cpp example And the following code demonstrates its usage for a two-channel matrix. SNIPPET: snippets/core_reduce.cpp example2- Parameters:
src- input 2D matrix.dst- output vector. Its size and type is defined by dim and dtype parameters.dim- dimension index along which the matrix is reduced. 0 means that the matrix is reduced to a single row. 1 means that the matrix is reduced to a single column.rtype- reduction operation that could be one of #ReduceTypesdtype- when negative, the output vector will have the same type as the input matrix, otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). SEE: repeat, reduceArgMin, reduceArgMax
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reduce
Reduces a matrix to a vector. The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. In case of #REDUCE_SUM, #REDUCE_SUM2 and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes. The following code demonstrates its usage for a single channel matrix. SNIPPET: snippets/core_reduce.cpp example And the following code demonstrates its usage for a two-channel matrix. SNIPPET: snippets/core_reduce.cpp example2- Parameters:
src- input 2D matrix.dst- output vector. Its size and type is defined by dim and dtype parameters.dim- dimension index along which the matrix is reduced. 0 means that the matrix is reduced to a single row. 1 means that the matrix is reduced to a single column.rtype- reduction operation that could be one of #ReduceTypes otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). SEE: repeat, reduceArgMin, reduceArgMax
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merge
- Parameters:
mv- input vector of matrices to be merged; all the matrices in mv must have the same size and the same depth.dst- output array of the same size and the same depth as mv[0]; The number of channels will be the total number of channels in the matrix array.
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split
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mixChannels
- Parameters:
src- input array or vector of matrices; all of the matrices must have the same size and the same depth.dst- output array or vector of matrices; all the matrices must be allocated; their size and depth must be the same as in src[0].fromTo- array of index pairs specifying which channels are copied and where; fromTo[k\*2] is a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to src[0].channels()-1, the second input image channels are indexed from src[0].channels() to src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is filled with zero .
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extractChannel
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insertChannel
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flip
Flips a 2D array around vertical, horizontal, or both axes. The function cv::flip flips the array in one of three different ways (row and column indices are 0-based): \(\texttt{dst} _{ij} = \left\{ \begin{array}{l l} \texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ \texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ \end{array} \right.\) The example scenarios of using the function are the following: Vertical flipping of the image (flipCode == 0) to switch between top-left and bottom-left image origin. This is a typical operation in video processing on Microsoft Windows\* OS. Horizontal flipping of the image with the subsequent horizontal shift and absolute difference calculation to check for a vertical-axis symmetry (flipCode > 0). Simultaneous horizontal and vertical flipping of the image with the subsequent shift and absolute difference calculation to check for a central symmetry (flipCode < 0). Reversing the order of point arrays (flipCode > 0 or flipCode == 0).- Parameters:
src- input array.dst- output array of the same size and type as src.flipCode- a flag to specify how to flip the array; 0 means flipping around the x-axis and positive value (for example, 1) means flipping around y-axis. Negative value (for example, -1) means flipping around both axes. SEE: transpose , repeat , completeSymm
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flipND
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broadcast
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rotate
Rotates a 2D array in multiples of 90 degrees. The function cv::rotate rotates the array in one of three different ways: Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE). Rotate by 180 degrees clockwise (rotateCode = ROTATE_180). Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).- Parameters:
src- input array.dst- output array of the same type as src. The size is the same with ROTATE_180, and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.rotateCode- an enum to specify how to rotate the array; see the enum #RotateFlags SEE: transpose , repeat , completeSymm, flip, RotateFlags
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repeat
Fills the output array with repeated copies of the input array. The function cv::repeat duplicates the input array one or more times along each of the two axes: \(\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\) The second variant of the function is more convenient to use with REF: MatrixExpressions.- Parameters:
src- input array to replicate.ny- Flag to specify how many times thesrcis repeated along the vertical axis.nx- Flag to specify how many times thesrcis repeated along the horizontal axis.dst- output array of the same type assrc. SEE: cv::reduce
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hconcat
std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; cv::Mat out; cv::hconcat( matrices, out ); //out: //[1, 2, 3; // 1, 2, 3; // 1, 2, 3; // 1, 2, 3]- Parameters:
src- input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.dst- output array. It has the same number of rows and depth as the src, and the sum of cols of the src. same depth.
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vconcat
std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; cv::Mat out; cv::vconcat( matrices, out ); //out: //[1, 1, 1, 1; // 2, 2, 2, 2; // 3, 3, 3, 3]- Parameters:
src- input array or vector of matrices. all of the matrices must have the same number of cols and the same depthdst- output array. It has the same number of cols and depth as the src, and the sum of rows of the src. same depth.
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bitwise_and
computes bitwise conjunction of the two arrays (dst = src1 & src2) Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar. The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays.mask- optional operation mask, 8-bit single channel array, that specifies elements of the output array to be changed.
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bitwise_and
computes bitwise conjunction of the two arrays (dst = src1 & src2) Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar. The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays. specifies elements of the output array to be changed.
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bitwise_or
Calculates the per-element bit-wise disjunction of two arrays or an array and a scalar. The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays.mask- optional operation mask, 8-bit single channel array, that specifies elements of the output array to be changed.
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bitwise_or
Calculates the per-element bit-wise disjunction of two arrays or an array and a scalar. The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays. specifies elements of the output array to be changed.
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bitwise_xor
Calculates the per-element bit-wise "exclusive or" operation on two arrays or an array and a scalar. The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" operation for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the 2nd and 3rd cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays.mask- optional operation mask, 8-bit single channel array, that specifies elements of the output array to be changed.
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bitwise_xor
Calculates the per-element bit-wise "exclusive or" operation on two arrays or an array and a scalar. The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" operation for: Two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) An array and a scalar when src2 is constructed from Scalar or has the same number of elements assrc1.channels(): \(\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\) A scalar and an array when src1 is constructed from Scalar or has the same number of elements assrc2.channels(): \(\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\) In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the 2nd and 3rd cases above, the scalar is first converted to the array type.- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as the input arrays. specifies elements of the output array to be changed.
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bitwise_not
Inverts every bit of an array. The function cv::bitwise_not calculates per-element bit-wise inversion of the input array: \(\texttt{dst} (I) = \neg \texttt{src} (I)\) In case of a floating-point input array, its machine-specific bit representation (usually IEEE754-compliant) is used for the operation. In case of multi-channel arrays, each channel is processed independently.- Parameters:
src- input array.dst- output array that has the same size and type as the input array.mask- optional operation mask, 8-bit single channel array, that specifies elements of the output array to be changed.
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bitwise_not
Inverts every bit of an array. The function cv::bitwise_not calculates per-element bit-wise inversion of the input array: \(\texttt{dst} (I) = \neg \texttt{src} (I)\) In case of a floating-point input array, its machine-specific bit representation (usually IEEE754-compliant) is used for the operation. In case of multi-channel arrays, each channel is processed independently.- Parameters:
src- input array.dst- output array that has the same size and type as the input array. specifies elements of the output array to be changed.
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absdiff
Calculates the per-element absolute difference between two arrays or between an array and a scalar. The function cv::absdiff calculates: Absolute difference between two arrays when they have the same size and type: \(\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\) Absolute difference between an array and a scalar when the second array is constructed from Scalar or has as many elements as the number of channels insrc1: \(\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\) Absolute difference between a scalar and an array when the first array is constructed from Scalar or has as many elements as the number of channels insrc2: \(\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\) where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. Note: Saturation is not applied when the arrays have the depth CV_32S. You may even get a negative value in the case of overflow. Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.absdiff(src,X)meansabsdiff(src,(X,X,X,X)).absdiff(src,(X,))meansabsdiff(src,(X,0,0,0)).- Parameters:
src1- first input array or a scalar.src2- second input array or a scalar.dst- output array that has the same size and type as input arrays. SEE: cv::abs(const Mat&)
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copyTo
This is an overloaded member function, provided for convenience (python) Copies the matrix to another one. When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.- Parameters:
src- source matrix.dst- Destination matrix. If it does not have a proper size or type before the operation, it is reallocated.mask- Operation mask of the same size as \*this. Its non-zero elements indicate which matrix elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.
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inRange
Checks if array elements lie between the elements of two other arrays. The function checks the range as follows:- For every element of a single-channel input array: \(\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\)
- For two-channel arrays: \(\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\)
- and so forth.
- Parameters:
src- first input array.lowerb- inclusive lower boundary array or a scalar.upperb- inclusive upper boundary array or a scalar.dst- output array of the same size as src and CV_8U type.
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compare
Performs the per-element comparison of two arrays or an array and scalar value. The function compares: Elements of two arrays when src1 and src2 have the same size: \(\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\) Elements of src1 with a scalar src2 when src2 is constructed from Scalar or has a single element: \(\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\) src1 with elements of src2 when src1 is constructed from Scalar or has a single element: \(\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\) When the comparison result is true, the corresponding element of output array is set to 255. The comparison operations can be replaced with the equivalent matrix expressions:Mat dst1 = src1 >= src2; Mat dst2 = src1 < 8; ...- Parameters:
src1- first input array or a scalar; when it is an array, it must have a single channel.src2- second input array or a scalar; when it is an array, it must have a single channel.dst- output array of type ref CV_8U that has the same size and the same number of channels as the input arrays.cmpop- a flag, that specifies correspondence between the arrays (cv::CmpTypes) SEE: checkRange, min, max, threshold
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min
Calculates per-element minimum of two arrays or an array and a scalar. The function cv::min calculates the per-element minimum of two arrays: \(\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\) or array and a scalar: \(\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\)- Parameters:
src1- first input array.src2- second input array of the same size and type as src1.dst- output array of the same size and type as src1. SEE: max, compare, inRange, minMaxLoc
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max
Calculates per-element maximum of two arrays or an array and a scalar. The function cv::max calculates the per-element maximum of two arrays: \(\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\) or array and a scalar: \(\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\)- Parameters:
src1- first input array.src2- second input array of the same size and type as src1 .dst- output array of the same size and type as src1. SEE: min, compare, inRange, minMaxLoc, REF: MatrixExpressions
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sqrt
Calculates a square root of array elements. The function cv::sqrt calculates a square root of each input array element. In case of multi-channel arrays, each channel is processed independently. The accuracy is approximately the same as of the built-in std::sqrt .- Parameters:
src- input floating-point array.dst- output array of the same size and type as src.
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pow
Raises every array element to a power. The function cv::pow raises every element of the input array to power : \(\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\) So, for a non-integer power exponent, the absolute values of input array elements are used. However, it is possible to get true values for negative values using some extra operations. In the example below, computing the 5th root of array src shows:Mat mask = src < 0; pow(src, 1./5, dst); subtract(Scalar::all(0), dst, dst, mask);For some values of power, such as integer values, 0.5 and -0.5, specialized faster algorithms are used. Special values (NaN, Inf) are not handled.- Parameters:
src- input array.power- exponent of power.dst- output array of the same size and type as src. SEE: sqrt, exp, log, cartToPolar, polarToCart
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exp
Calculates the exponent of every array element. The function cv::exp calculates the exponent of every element of the input array: \(\texttt{dst} [I] = e^{ src(I) }\) The maximum relative error is about 7e-6 for single-precision input and less than 1e-10 for double-precision input. Currently, the function converts denormalized values to zeros on output. Special values (NaN, Inf) are not handled.- Parameters:
src- input array.dst- output array of the same size and type as src. SEE: log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
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log
Calculates the natural logarithm of every array element. The function cv::log calculates the natural logarithm of every element of the input array: \(\texttt{dst} (I) = \log (\texttt{src}(I)) \) Output on zero, negative and special (NaN, Inf) values is undefined.- Parameters:
src- input array.dst- output array of the same size and type as src . SEE: exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
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polarToCart
Calculates x and y coordinates of 2D vectors from their magnitude and angle. The function cv::polarToCart calculates the Cartesian coordinates of each 2D vector represented by the corresponding elements of magnitude and angle: \(\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\) The relative accuracy of the estimated coordinates is about 1e-6.- Parameters:
magnitude- input floating-point array of magnitudes of 2D vectors; it can be an empty matrix (=Mat()), in this case, the function assumes that all the magnitudes are =1; if it is not empty, it must have the same size and type as angle.angle- input floating-point array of angles of 2D vectors.x- output array of x-coordinates of 2D vectors; it has the same size and type as angle.y- output array of y-coordinates of 2D vectors; it has the same size and type as angle.angleInDegrees- when true, the input angles are measured in degrees, otherwise, they are measured in radians. SEE: cartToPolar, magnitude, phase, exp, log, pow, sqrt
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polarToCart
Calculates x and y coordinates of 2D vectors from their magnitude and angle. The function cv::polarToCart calculates the Cartesian coordinates of each 2D vector represented by the corresponding elements of magnitude and angle: \(\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\) The relative accuracy of the estimated coordinates is about 1e-6.- Parameters:
magnitude- input floating-point array of magnitudes of 2D vectors; it can be an empty matrix (=Mat()), in this case, the function assumes that all the magnitudes are =1; if it is not empty, it must have the same size and type as angle.angle- input floating-point array of angles of 2D vectors.x- output array of x-coordinates of 2D vectors; it has the same size and type as angle.y- output array of y-coordinates of 2D vectors; it has the same size and type as angle. degrees, otherwise, they are measured in radians. SEE: cartToPolar, magnitude, phase, exp, log, pow, sqrt
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cartToPolar
Calculates the magnitude and angle of 2D vectors. The function cv::cartToPolar calculates either the magnitude, angle, or both for every 2D vector (x(I),y(I)): \(\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\) The angles are calculated with accuracy about 0.3 degrees. For the point (0,0), the angle is set to 0.- Parameters:
x- array of x-coordinates; this must be a single-precision or double-precision floating-point array.y- array of y-coordinates, that must have the same size and same type as x.magnitude- output array of magnitudes of the same size and type as x.angle- output array of angles that has the same size and type as x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).angleInDegrees- a flag, indicating whether the angles are measured in radians (which is by default), or in degrees. SEE: Sobel, Scharr
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cartToPolar
Calculates the magnitude and angle of 2D vectors. The function cv::cartToPolar calculates either the magnitude, angle, or both for every 2D vector (x(I),y(I)): \(\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\) The angles are calculated with accuracy about 0.3 degrees. For the point (0,0), the angle is set to 0.- Parameters:
x- array of x-coordinates; this must be a single-precision or double-precision floating-point array.y- array of y-coordinates, that must have the same size and same type as x.magnitude- output array of magnitudes of the same size and type as x.angle- output array of angles that has the same size and type as x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). in radians (which is by default), or in degrees. SEE: Sobel, Scharr
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phase
Calculates the rotation angle of 2D vectors. The function cv::phase calculates the rotation angle of each 2D vector that is formed from the corresponding elements of x and y : \(\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\) The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , the corresponding angle(I) is set to 0.- Parameters:
x- input floating-point array of x-coordinates of 2D vectors.y- input array of y-coordinates of 2D vectors; it must have the same size and the same type as x.angle- output array of vector angles; it has the same size and same type as x .angleInDegrees- when true, the function calculates the angle in degrees, otherwise, they are measured in radians.
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phase
Calculates the rotation angle of 2D vectors. The function cv::phase calculates the rotation angle of each 2D vector that is formed from the corresponding elements of x and y : \(\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\) The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , the corresponding angle(I) is set to 0.- Parameters:
x- input floating-point array of x-coordinates of 2D vectors.y- input array of y-coordinates of 2D vectors; it must have the same size and the same type as x.angle- output array of vector angles; it has the same size and same type as x . degrees, otherwise, they are measured in radians.
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magnitude
Calculates the magnitude of 2D vectors. The function cv::magnitude calculates the magnitude of 2D vectors formed from the corresponding elements of x and y arrays: \(\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\)- Parameters:
x- floating-point array of x-coordinates of the vectors.y- floating-point array of y-coordinates of the vectors; it must have the same size as x.magnitude- output array of the same size and type as x. SEE: cartToPolar, polarToCart, phase, sqrt
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checkRange
Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal >- DBL_MAX and maxVal < DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.
- Parameters:
a- input array.quiet- a flag, indicating whether the functions quietly return false when the array elements are out of range or they throw an exception. elements.minVal- inclusive lower boundary of valid values range.maxVal- exclusive upper boundary of valid values range.- Returns:
- automatically generated
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checkRange
Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal >- DBL_MAX and maxVal < DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.
- Parameters:
a- input array.quiet- a flag, indicating whether the functions quietly return false when the array elements are out of range or they throw an exception. elements.minVal- inclusive lower boundary of valid values range.- Returns:
- automatically generated
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checkRange
Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal >- DBL_MAX and maxVal < DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.
- Parameters:
a- input array.quiet- a flag, indicating whether the functions quietly return false when the array elements are out of range or they throw an exception. elements.- Returns:
- automatically generated
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checkRange
Checks every element of an input array for invalid values. The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal >- DBL_MAX and maxVal < DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.
- Parameters:
a- input array. are out of range or they throw an exception. elements.- Returns:
- automatically generated
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patchNaNs
Replaces NaNs by given number- Parameters:
a- input/output matrix (CV_32F type).val- value to convert the NaNs
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patchNaNs
Replaces NaNs by given number- Parameters:
a- input/output matrix (CV_32F type).
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gemm
public static void gemm(Mat src1, Mat src2, double alpha, Mat src3, double beta, Mat dst, int flags) Performs generalized matrix multiplication. The function cv::gemm performs generalized matrix multiplication similar to the gemm functions in BLAS level 3. For example,gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)corresponds to \(\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\) In case of complex (two-channel) data, performed a complex matrix multiplication. The function can be replaced with a matrix expression. For example, the above call can be replaced with:dst = alpha*src1.t()*src2 + beta*src3.t();- Parameters:
src1- first multiplied input matrix that could be real(CV_32FC1, CV_64FC1) or complex(CV_32FC2, CV_64FC2).src2- second multiplied input matrix of the same type as src1.alpha- weight of the matrix product.src3- third optional delta matrix added to the matrix product; it should have the same type as src1 and src2.beta- weight of src3.dst- output matrix; it has the proper size and the same type as input matrices.flags- operation flags (cv::GemmFlags) SEE: mulTransposed , transform
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gemm
Performs generalized matrix multiplication. The function cv::gemm performs generalized matrix multiplication similar to the gemm functions in BLAS level 3. For example,gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)corresponds to \(\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\) In case of complex (two-channel) data, performed a complex matrix multiplication. The function can be replaced with a matrix expression. For example, the above call can be replaced with:dst = alpha*src1.t()*src2 + beta*src3.t();- Parameters:
src1- first multiplied input matrix that could be real(CV_32FC1, CV_64FC1) or complex(CV_32FC2, CV_64FC2).src2- second multiplied input matrix of the same type as src1.alpha- weight of the matrix product.src3- third optional delta matrix added to the matrix product; it should have the same type as src1 and src2.beta- weight of src3.dst- output matrix; it has the proper size and the same type as input matrices. SEE: mulTransposed , transform
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mulTransposed
Calculates the product of a matrix and its transposition. The function cv::mulTransposed calculates the product of src and its transposition: \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\) if aTa=true , and \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\) otherwise. The function is used to calculate the covariance matrix. With zero delta, it can be used as a faster substitute for general matrix product A\*B when B=A'- Parameters:
src- input single-channel matrix. Note that unlike gemm, the function can multiply not only floating-point matrices.dst- output square matrix.aTa- Flag specifying the multiplication ordering. See the description below.delta- Optional delta matrix subtracted from src before the multiplication. When the matrix is empty ( delta=noArray() ), it is assumed to be zero, that is, nothing is subtracted. If it has the same size as src , it is simply subtracted. Otherwise, it is "repeated" (see repeat ) to cover the full src and then subtracted. Type of the delta matrix, when it is not empty, must be the same as the type of created output matrix. See the dtype parameter description below.scale- Optional scale factor for the matrix product.dtype- Optional type of the output matrix. When it is negative, the output matrix will have the same type as src . Otherwise, it will be type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . SEE: calcCovarMatrix, gemm, repeat, reduce
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mulTransposed
Calculates the product of a matrix and its transposition. The function cv::mulTransposed calculates the product of src and its transposition: \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\) if aTa=true , and \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\) otherwise. The function is used to calculate the covariance matrix. With zero delta, it can be used as a faster substitute for general matrix product A\*B when B=A'- Parameters:
src- input single-channel matrix. Note that unlike gemm, the function can multiply not only floating-point matrices.dst- output square matrix.aTa- Flag specifying the multiplication ordering. See the description below.delta- Optional delta matrix subtracted from src before the multiplication. When the matrix is empty ( delta=noArray() ), it is assumed to be zero, that is, nothing is subtracted. If it has the same size as src , it is simply subtracted. Otherwise, it is "repeated" (see repeat ) to cover the full src and then subtracted. Type of the delta matrix, when it is not empty, must be the same as the type of created output matrix. See the dtype parameter description below.scale- Optional scale factor for the matrix product. the output matrix will have the same type as src . Otherwise, it will be type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . SEE: calcCovarMatrix, gemm, repeat, reduce
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mulTransposed
Calculates the product of a matrix and its transposition. The function cv::mulTransposed calculates the product of src and its transposition: \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\) if aTa=true , and \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\) otherwise. The function is used to calculate the covariance matrix. With zero delta, it can be used as a faster substitute for general matrix product A\*B when B=A'- Parameters:
src- input single-channel matrix. Note that unlike gemm, the function can multiply not only floating-point matrices.dst- output square matrix.aTa- Flag specifying the multiplication ordering. See the description below.delta- Optional delta matrix subtracted from src before the multiplication. When the matrix is empty ( delta=noArray() ), it is assumed to be zero, that is, nothing is subtracted. If it has the same size as src , it is simply subtracted. Otherwise, it is "repeated" (see repeat ) to cover the full src and then subtracted. Type of the delta matrix, when it is not empty, must be the same as the type of created output matrix. See the dtype parameter description below. the output matrix will have the same type as src . Otherwise, it will be type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . SEE: calcCovarMatrix, gemm, repeat, reduce
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mulTransposed
Calculates the product of a matrix and its transposition. The function cv::mulTransposed calculates the product of src and its transposition: \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\) if aTa=true , and \(\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\) otherwise. The function is used to calculate the covariance matrix. With zero delta, it can be used as a faster substitute for general matrix product A\*B when B=A'- Parameters:
src- input single-channel matrix. Note that unlike gemm, the function can multiply not only floating-point matrices.dst- output square matrix.aTa- Flag specifying the multiplication ordering. See the description below. multiplication. When the matrix is empty ( delta=noArray() ), it is assumed to be zero, that is, nothing is subtracted. If it has the same size as src , it is simply subtracted. Otherwise, it is "repeated" (see repeat ) to cover the full src and then subtracted. Type of the delta matrix, when it is not empty, must be the same as the type of created output matrix. See the dtype parameter description below. the output matrix will have the same type as src . Otherwise, it will be type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . SEE: calcCovarMatrix, gemm, repeat, reduce
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transpose
Transposes a matrix. The function cv::transpose transposes the matrix src : \(\texttt{dst} (i,j) = \texttt{src} (j,i)\) Note: No complex conjugation is done in case of a complex matrix. It should be done separately if needed.- Parameters:
src- input array.dst- output array of the same type as src.
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transposeND
Transpose for n-dimensional matrices. Note: Input should be continuous single-channel matrix.- Parameters:
src- input array.order- a permutation of [0,1,..,N-1] where N is the number of axes of src. The i’th axis of dst will correspond to the axis numbered order[i] of the input.dst- output array of the same type as src.
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transform
Performs the matrix transformation of every array element. The function cv::transform performs the matrix transformation of every element of the array src and stores the results in dst : \(\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\) (when m.cols=src.channels() ), or \(\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\) (when m.cols=src.channels()+1 ) Every element of the N -channel array src is interpreted as N -element vector that is transformed using the M x N or M x (N+1) matrix m to M-element vector - the corresponding element of the output array dst . The function may be used for geometrical transformation of N -dimensional points, arbitrary linear color space transformation (such as various kinds of RGB to YUV transforms), shuffling the image channels, and so forth.- Parameters:
src- input array that must have as many channels (1 to 4) as m.cols or m.cols-1.dst- output array of the same size and depth as src; it has as many channels as m.rows.m- transformation 2x2 or 2x3 floating-point matrix. SEE: perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective
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perspectiveTransform
Performs the perspective matrix transformation of vectors. The function cv::perspectiveTransform transforms every element of src by treating it as a 2D or 3D vector, in the following way: \((x, y, z) \rightarrow (x'/w, y'/w, z'/w)\) where \((x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\) and \(w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\) Here a 3D vector transformation is shown. In case of a 2D vector transformation, the z component is omitted. Note: The function transforms a sparse set of 2D or 3D vectors. If you want to transform an image using perspective transformation, use warpPerspective . If you have an inverse problem, that is, you want to compute the most probable perspective transformation out of several pairs of corresponding points, you can use getPerspectiveTransform or findHomography .- Parameters:
src- input two-channel or three-channel floating-point array; each element is a 2D/3D vector to be transformed.dst- output array of the same size and type as src.m- 3x3 or 4x4 floating-point transformation matrix. SEE: transform, warpPerspective, getPerspectiveTransform, findHomography
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completeSymm
Copies the lower or the upper half of a square matrix to its another half. The function cv::completeSymm copies the lower or the upper half of a square matrix to its another half. The matrix diagonal remains unchanged:- \(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i > j\) if lowerToUpper=false
- \(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i < j\) if lowerToUpper=true
- Parameters:
m- input-output floating-point square matrix.lowerToUpper- operation flag; if true, the lower half is copied to the upper half. Otherwise, the upper half is copied to the lower half. SEE: flip, transpose
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completeSymm
Copies the lower or the upper half of a square matrix to its another half. The function cv::completeSymm copies the lower or the upper half of a square matrix to its another half. The matrix diagonal remains unchanged:- \(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i > j\) if lowerToUpper=false
- \(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i < j\) if lowerToUpper=true
- Parameters:
m- input-output floating-point square matrix. the upper half. Otherwise, the upper half is copied to the lower half. SEE: flip, transpose
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setIdentity
Initializes a scaled identity matrix. The function cv::setIdentity initializes a scaled identity matrix: \(\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\) The function can also be emulated using the matrix initializers and the matrix expressions:Mat A = Mat::eye(4, 3, CV_32F)*5; // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]- Parameters:
mtx- matrix to initialize (not necessarily square).s- value to assign to diagonal elements. SEE: Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
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setIdentity
Initializes a scaled identity matrix. The function cv::setIdentity initializes a scaled identity matrix: \(\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\) The function can also be emulated using the matrix initializers and the matrix expressions:Mat A = Mat::eye(4, 3, CV_32F)*5; // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]- Parameters:
mtx- matrix to initialize (not necessarily square). SEE: Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
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determinant
Returns the determinant of a square floating-point matrix. The function cv::determinant calculates and returns the determinant of the specified matrix. For small matrices ( mtx.cols=mtx.rows<=3 ), the direct method is used. For larger matrices, the function uses LU factorization with partial pivoting. For symmetric positively-determined matrices, it is also possible to use eigen decomposition to calculate the determinant.- Parameters:
mtx- input matrix that must have CV_32FC1 or CV_64FC1 type and square size. SEE: trace, invert, solve, eigen, REF: MatrixExpressions- Returns:
- automatically generated
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trace
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invert
Finds the inverse or pseudo-inverse of a matrix. The function cv::invert inverts the matrix src and stores the result in dst . When the matrix src is singular or non-square, the function calculates the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is minimal, where I is an identity matrix. In case of the #DECOMP_LU method, the function returns non-zero value if the inverse has been successfully calculated and 0 if src is singular. In case of the #DECOMP_SVD method, the function returns the inverse condition number of src (the ratio of the smallest singular value to the largest singular value) and 0 if src is singular. The SVD method calculates a pseudo-inverse matrix if src is singular. Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with non-singular square matrices that should also be symmetrical and positively defined. In this case, the function stores the inverted matrix in dst and returns non-zero. Otherwise, it returns 0.- Parameters:
src- input floating-point M x N matrix.dst- output matrix of N x M size and the same type as src.flags- inversion method (cv::DecompTypes) SEE: solve, SVD- Returns:
- automatically generated
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invert
Finds the inverse or pseudo-inverse of a matrix. The function cv::invert inverts the matrix src and stores the result in dst . When the matrix src is singular or non-square, the function calculates the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is minimal, where I is an identity matrix. In case of the #DECOMP_LU method, the function returns non-zero value if the inverse has been successfully calculated and 0 if src is singular. In case of the #DECOMP_SVD method, the function returns the inverse condition number of src (the ratio of the smallest singular value to the largest singular value) and 0 if src is singular. The SVD method calculates a pseudo-inverse matrix if src is singular. Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with non-singular square matrices that should also be symmetrical and positively defined. In this case, the function stores the inverted matrix in dst and returns non-zero. Otherwise, it returns 0.- Parameters:
src- input floating-point M x N matrix.dst- output matrix of N x M size and the same type as src. SEE: solve, SVD- Returns:
- automatically generated
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solve
Solves one or more linear systems or least-squares problems. The function cv::solve solves a linear system or least-squares problem (the latter is possible with SVD or QR methods, or by specifying the flag #DECOMP_NORMAL ): \(\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\) If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 if src1 (or \(\texttt{src1}^T\texttt{src1}\) ) is non-singular. Otherwise, it returns 0. In the latter case, dst is not valid. Other methods find a pseudo-solution in case of a singular left-hand side part. Note: If you want to find a unity-norm solution of an under-defined singular system \(\texttt{src1}\cdot\texttt{dst}=0\) , the function solve will not do the work. Use SVD::solveZ instead.- Parameters:
src1- input matrix on the left-hand side of the system.src2- input matrix on the right-hand side of the system.dst- output solution.flags- solution (matrix inversion) method (#DecompTypes) SEE: invert, SVD, eigen- Returns:
- automatically generated
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solve
Solves one or more linear systems or least-squares problems. The function cv::solve solves a linear system or least-squares problem (the latter is possible with SVD or QR methods, or by specifying the flag #DECOMP_NORMAL ): \(\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\) If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 if src1 (or \(\texttt{src1}^T\texttt{src1}\) ) is non-singular. Otherwise, it returns 0. In the latter case, dst is not valid. Other methods find a pseudo-solution in case of a singular left-hand side part. Note: If you want to find a unity-norm solution of an under-defined singular system \(\texttt{src1}\cdot\texttt{dst}=0\) , the function solve will not do the work. Use SVD::solveZ instead.- Parameters:
src1- input matrix on the left-hand side of the system.src2- input matrix on the right-hand side of the system.dst- output solution. SEE: invert, SVD, eigen- Returns:
- automatically generated
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sort
Sorts each row or each column of a matrix. The function cv::sort sorts each matrix row or each matrix column in ascending or descending order. So you should pass two operation flags to get desired behaviour. If you want to sort matrix rows or columns lexicographically, you can use STL std::sort generic function with the proper comparison predicate.- Parameters:
src- input single-channel array.dst- output array of the same size and type as src.flags- operation flags, a combination of #SortFlags SEE: sortIdx, randShuffle
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sortIdx
Sorts each row or each column of a matrix. The function cv::sortIdx sorts each matrix row or each matrix column in the ascending or descending order. So you should pass two operation flags to get desired behaviour. Instead of reordering the elements themselves, it stores the indices of sorted elements in the output array. For example:Mat A = Mat::eye(3,3,CV_32F), B; sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); // B will probably contain // (because of equal elements in A some permutations are possible): // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]- Parameters:
src- input single-channel array.dst- output integer array of the same size as src.flags- operation flags that could be a combination of cv::SortFlags SEE: sort, randShuffle
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solveCubic
Finds the real roots of a cubic equation. The function solveCubic finds the real roots of a cubic equation:- if coeffs is a 4-element vector: \(\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\)
- if coeffs is a 3-element vector: \(x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\)
- Parameters:
coeffs- equation coefficients, an array of 3 or 4 elements.roots- output array of real roots that has 1 or 3 elements.- Returns:
- number of real roots. It can be 0, 1 or 2.
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solvePoly
Finds the real or complex roots of a polynomial equation. The function cv::solvePoly finds real and complex roots of a polynomial equation: \(\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\)- Parameters:
coeffs- array of polynomial coefficients.roots- output (complex) array of roots.maxIters- maximum number of iterations the algorithm does.- Returns:
- automatically generated
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solvePoly
Finds the real or complex roots of a polynomial equation. The function cv::solvePoly finds real and complex roots of a polynomial equation: \(\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\)- Parameters:
coeffs- array of polynomial coefficients.roots- output (complex) array of roots.- Returns:
- automatically generated
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eigen
Calculates eigenvalues and eigenvectors of a symmetric matrix. The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric matrix src:src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()Note: Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.- Parameters:
src- input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical (src ^T^ == src).eigenvalues- output vector of eigenvalues of the same type as src; the eigenvalues are stored in the descending order.eigenvectors- output matrix of eigenvectors; it has the same size and type as src; the eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. SEE: eigenNonSymmetric, completeSymm , PCA- Returns:
- automatically generated
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eigen
Calculates eigenvalues and eigenvectors of a symmetric matrix. The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric matrix src:src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()Note: Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.- Parameters:
src- input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical (src ^T^ == src).eigenvalues- output vector of eigenvalues of the same type as src; the eigenvalues are stored in the descending order. eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. SEE: eigenNonSymmetric, completeSymm , PCA- Returns:
- automatically generated
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eigenNonSymmetric
Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). Note: Assumes real eigenvalues. The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()- Parameters:
src- input matrix (CV_32FC1 or CV_64FC1 type).eigenvalues- output vector of eigenvalues (type is the same type as src).eigenvectors- output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. SEE: eigen
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calcCovarMatrix
Note: use #COVAR_ROWS or #COVAR_COLS flag- Parameters:
samples- samples stored as rows/columns of a single matrix.covar- output covariance matrix of the type ctype and square size.mean- input or output (depending on the flags) array as the average value of the input vectors.flags- operation flags as a combination of #CovarFlagsctype- type of the matrixl; it equals 'CV_64F' by default.
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calcCovarMatrix
Note: use #COVAR_ROWS or #COVAR_COLS flag- Parameters:
samples- samples stored as rows/columns of a single matrix.covar- output covariance matrix of the type ctype and square size.mean- input or output (depending on the flags) array as the average value of the input vectors.flags- operation flags as a combination of #CovarFlags
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PCACompute
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PCACompute
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PCACompute2
public static void PCACompute2(Mat data, Mat mean, Mat eigenvectors, Mat eigenvalues, int maxComponents) wrap PCA::operator() and add eigenvalues output parameter- Parameters:
data- automatically generatedmean- automatically generatedeigenvectors- automatically generatedeigenvalues- automatically generatedmaxComponents- automatically generated
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PCACompute2
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PCACompute
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PCACompute2
public static void PCACompute2(Mat data, Mat mean, Mat eigenvectors, Mat eigenvalues, double retainedVariance) wrap PCA::operator() and add eigenvalues output parameter- Parameters:
data- automatically generatedmean- automatically generatedeigenvectors- automatically generatedeigenvalues- automatically generatedretainedVariance- automatically generated
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PCAProject
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PCABackProject
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SVDecomp
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SVDecomp
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SVBackSubst
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Mahalanobis
Calculates the Mahalanobis distance between two vectors. The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: \(d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\) The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using the invert function (preferably using the #DECOMP_SVD method, as the most accurate).- Parameters:
v1- first 1D input vector.v2- second 1D input vector.icovar- inverse covariance matrix.- Returns:
- automatically generated
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dft
Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. The function cv::dft performs one of the following:- Forward the Fourier transform of a 1D vector of N elements: \(Y = F^{(N)} \cdot X,\) where \(F^{(N)}_{jk}=\exp(-2\pi i j k/N)\) and \(i=\sqrt{-1}\)
- Inverse the Fourier transform of a 1D vector of N elements: \(\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\) where \(F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\)
- Forward the 2D Fourier transform of a M x N matrix: \(Y = F^{(M)} \cdot X \cdot F^{(N)}\)
- Inverse the 2D Fourier transform of a M x N matrix: \(\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\)
- If #DFT_ROWS is set or the input array has a single row or single column, the function performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. Otherwise, it performs a 2D transform.
- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or 2D transform:
- When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as input.
- When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as input. In case of 2D transform, it uses the packed format as shown above. In case of a single 1D transform, it looks like the first row of the matrix above. In case of multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix looks like the first row of the matrix above.
- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the output is a complex array of the same size as input. The function performs a forward or inverse 1D or 2D transform of the whole input array or each row of the input array independently, depending on the flags DFT_INVERSE and DFT_ROWS.
- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT is set, the output is a real array of the same size as input. The function performs a 1D or 2D inverse transformation of the whole input array or each individual row, depending on the flags #DFT_INVERSE and #DFT_ROWS.
void convolveDFT(InputArray A, InputArray B, OutputArray C) { // reallocate the output array if needed C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); Size dftSize; // calculate the size of DFT transform dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); // allocate temporary buffers and initialize them with 0's Mat tempA(dftSize, A.type(), Scalar::all(0)); Mat tempB(dftSize, B.type(), Scalar::all(0)); // copy A and B to the top-left corners of tempA and tempB, respectively Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); A.copyTo(roiA); Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); B.copyTo(roiB); // now transform the padded A & B in-place; // use "nonzeroRows" hint for faster processing dft(tempA, tempA, 0, A.rows); dft(tempB, tempB, 0, B.rows); // multiply the spectrums; // the function handles packed spectrum representations well mulSpectrums(tempA, tempB, tempA); // transform the product back from the frequency domain. // Even though all the result rows will be non-zero, // you need only the first C.rows of them, and thus you // pass nonzeroRows == C.rows dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); // now copy the result back to C. tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); // all the temporary buffers will be deallocated automatically }To optimize this sample, consider the following approaches:- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) rightmost columns of the matrices.
- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. To do this, you need to split the output array C into multiple tiles. For each tile, estimate which parts of A and B are required to calculate convolution in this tile. If the tiles in C are too small, the speed will decrease a lot because of repeated work. In the ultimate case, when each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and there is also a slowdown because of bad cache locality. So, there is an optimal tile size somewhere in the middle.
- If different tiles in C can be calculated in parallel and, thus, the convolution is done by parts, the loop can be threaded.
- An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp
- (Python) An example using the dft functionality to perform Wiener deconvolution can be found at opencv_source/samples/python/deconvolution.py
- (Python) An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python/dft.py
- Parameters:
src- input array that could be real or complex.dst- output array whose size and type depends on the flags .flags- transformation flags, representing a combination of the #DftFlagsnonzeroRows- when the parameter is not zero, the function assumes that only the first nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the rows more efficiently and save some time; this technique is very useful for calculating array cross-correlation or convolution using DFT. SEE: dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , magnitude , phase
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dft
Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. The function cv::dft performs one of the following:- Forward the Fourier transform of a 1D vector of N elements: \(Y = F^{(N)} \cdot X,\) where \(F^{(N)}_{jk}=\exp(-2\pi i j k/N)\) and \(i=\sqrt{-1}\)
- Inverse the Fourier transform of a 1D vector of N elements: \(\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\) where \(F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\)
- Forward the 2D Fourier transform of a M x N matrix: \(Y = F^{(M)} \cdot X \cdot F^{(N)}\)
- Inverse the 2D Fourier transform of a M x N matrix: \(\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\)
- If #DFT_ROWS is set or the input array has a single row or single column, the function performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. Otherwise, it performs a 2D transform.
- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or 2D transform:
- When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as input.
- When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as input. In case of 2D transform, it uses the packed format as shown above. In case of a single 1D transform, it looks like the first row of the matrix above. In case of multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix looks like the first row of the matrix above.
- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the output is a complex array of the same size as input. The function performs a forward or inverse 1D or 2D transform of the whole input array or each row of the input array independently, depending on the flags DFT_INVERSE and DFT_ROWS.
- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT is set, the output is a real array of the same size as input. The function performs a 1D or 2D inverse transformation of the whole input array or each individual row, depending on the flags #DFT_INVERSE and #DFT_ROWS.
void convolveDFT(InputArray A, InputArray B, OutputArray C) { // reallocate the output array if needed C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); Size dftSize; // calculate the size of DFT transform dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); // allocate temporary buffers and initialize them with 0's Mat tempA(dftSize, A.type(), Scalar::all(0)); Mat tempB(dftSize, B.type(), Scalar::all(0)); // copy A and B to the top-left corners of tempA and tempB, respectively Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); A.copyTo(roiA); Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); B.copyTo(roiB); // now transform the padded A & B in-place; // use "nonzeroRows" hint for faster processing dft(tempA, tempA, 0, A.rows); dft(tempB, tempB, 0, B.rows); // multiply the spectrums; // the function handles packed spectrum representations well mulSpectrums(tempA, tempB, tempA); // transform the product back from the frequency domain. // Even though all the result rows will be non-zero, // you need only the first C.rows of them, and thus you // pass nonzeroRows == C.rows dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); // now copy the result back to C. tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); // all the temporary buffers will be deallocated automatically }To optimize this sample, consider the following approaches:- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) rightmost columns of the matrices.
- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. To do this, you need to split the output array C into multiple tiles. For each tile, estimate which parts of A and B are required to calculate convolution in this tile. If the tiles in C are too small, the speed will decrease a lot because of repeated work. In the ultimate case, when each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and there is also a slowdown because of bad cache locality. So, there is an optimal tile size somewhere in the middle.
- If different tiles in C can be calculated in parallel and, thus, the convolution is done by parts, the loop can be threaded.
- An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp
- (Python) An example using the dft functionality to perform Wiener deconvolution can be found at opencv_source/samples/python/deconvolution.py
- (Python) An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python/dft.py
- Parameters:
src- input array that could be real or complex.dst- output array whose size and type depends on the flags .flags- transformation flags, representing a combination of the #DftFlags nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the rows more efficiently and save some time; this technique is very useful for calculating array cross-correlation or convolution using DFT. SEE: dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , magnitude , phase
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dft
Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. The function cv::dft performs one of the following:- Forward the Fourier transform of a 1D vector of N elements: \(Y = F^{(N)} \cdot X,\) where \(F^{(N)}_{jk}=\exp(-2\pi i j k/N)\) and \(i=\sqrt{-1}\)
- Inverse the Fourier transform of a 1D vector of N elements: \(\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\) where \(F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\)
- Forward the 2D Fourier transform of a M x N matrix: \(Y = F^{(M)} \cdot X \cdot F^{(N)}\)
- Inverse the 2D Fourier transform of a M x N matrix: \(\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\)
- If #DFT_ROWS is set or the input array has a single row or single column, the function performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. Otherwise, it performs a 2D transform.
- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or 2D transform:
- When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as input.
- When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as input. In case of 2D transform, it uses the packed format as shown above. In case of a single 1D transform, it looks like the first row of the matrix above. In case of multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix looks like the first row of the matrix above.
- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the output is a complex array of the same size as input. The function performs a forward or inverse 1D or 2D transform of the whole input array or each row of the input array independently, depending on the flags DFT_INVERSE and DFT_ROWS.
- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT is set, the output is a real array of the same size as input. The function performs a 1D or 2D inverse transformation of the whole input array or each individual row, depending on the flags #DFT_INVERSE and #DFT_ROWS.
void convolveDFT(InputArray A, InputArray B, OutputArray C) { // reallocate the output array if needed C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); Size dftSize; // calculate the size of DFT transform dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); // allocate temporary buffers and initialize them with 0's Mat tempA(dftSize, A.type(), Scalar::all(0)); Mat tempB(dftSize, B.type(), Scalar::all(0)); // copy A and B to the top-left corners of tempA and tempB, respectively Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); A.copyTo(roiA); Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); B.copyTo(roiB); // now transform the padded A & B in-place; // use "nonzeroRows" hint for faster processing dft(tempA, tempA, 0, A.rows); dft(tempB, tempB, 0, B.rows); // multiply the spectrums; // the function handles packed spectrum representations well mulSpectrums(tempA, tempB, tempA); // transform the product back from the frequency domain. // Even though all the result rows will be non-zero, // you need only the first C.rows of them, and thus you // pass nonzeroRows == C.rows dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); // now copy the result back to C. tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); // all the temporary buffers will be deallocated automatically }To optimize this sample, consider the following approaches:- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) rightmost columns of the matrices.
- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. To do this, you need to split the output array C into multiple tiles. For each tile, estimate which parts of A and B are required to calculate convolution in this tile. If the tiles in C are too small, the speed will decrease a lot because of repeated work. In the ultimate case, when each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and there is also a slowdown because of bad cache locality. So, there is an optimal tile size somewhere in the middle.
- If different tiles in C can be calculated in parallel and, thus, the convolution is done by parts, the loop can be threaded.
- An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp
- (Python) An example using the dft functionality to perform Wiener deconvolution can be found at opencv_source/samples/python/deconvolution.py
- (Python) An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python/dft.py
- Parameters:
src- input array that could be real or complex.dst- output array whose size and type depends on the flags . nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the rows more efficiently and save some time; this technique is very useful for calculating array cross-correlation or convolution using DFT. SEE: dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , magnitude , phase
-
idft
Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of dft or idft explicitly to make these transforms mutually inverse. SEE: dft, dct, idct, mulSpectrums, getOptimalDFTSize- Parameters:
src- input floating-point real or complex array.dst- output array whose size and type depend on the flags.flags- operation flags (see dft and #DftFlags).nonzeroRows- number of dst rows to process; the rest of the rows have undefined content (see the convolution sample in dft description.
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idft
Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of dft or idft explicitly to make these transforms mutually inverse. SEE: dft, dct, idct, mulSpectrums, getOptimalDFTSize- Parameters:
src- input floating-point real or complex array.dst- output array whose size and type depend on the flags.flags- operation flags (see dft and #DftFlags). the convolution sample in dft description.
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idft
Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of dft or idft explicitly to make these transforms mutually inverse. SEE: dft, dct, idct, mulSpectrums, getOptimalDFTSize- Parameters:
src- input floating-point real or complex array.dst- output array whose size and type depend on the flags. the convolution sample in dft description.
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dct
Performs a forward or inverse discrete Cosine transform of 1D or 2D array. The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D floating-point array:- Forward Cosine transform of a 1D vector of N elements: \(Y = C^{(N)} \cdot X\) where \(C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\) and \(\alpha_0=1\), \(\alpha_j=2\) for *j > 0*.
- Inverse Cosine transform of a 1D vector of N elements: \(X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\) (since \(C^{(N)}\) is an orthogonal matrix, \(C^{(N)} \cdot \left(C^{(N)}\right)^T = I\) )
- Forward 2D Cosine transform of M x N matrix: \(Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\)
- Inverse 2D Cosine transform of M x N matrix: \(X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\)
- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it is an inverse 1D or 2D transform.
- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
- If the array is a single column or a single row, the function performs a 1D transform.
- If none of the above is true, the function performs a 2D transform.
size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } N1 = getOptimalDCTSize(N);- Parameters:
src- input floating-point array.dst- output array of the same size and type as src .flags- transformation flags as a combination of cv::DftFlags (DCT_*) SEE: dft , getOptimalDFTSize , idct
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dct
Performs a forward or inverse discrete Cosine transform of 1D or 2D array. The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D floating-point array:- Forward Cosine transform of a 1D vector of N elements: \(Y = C^{(N)} \cdot X\) where \(C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\) and \(\alpha_0=1\), \(\alpha_j=2\) for *j > 0*.
- Inverse Cosine transform of a 1D vector of N elements: \(X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\) (since \(C^{(N)}\) is an orthogonal matrix, \(C^{(N)} \cdot \left(C^{(N)}\right)^T = I\) )
- Forward 2D Cosine transform of M x N matrix: \(Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\)
- Inverse 2D Cosine transform of M x N matrix: \(X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\)
- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it is an inverse 1D or 2D transform.
- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
- If the array is a single column or a single row, the function performs a 1D transform.
- If none of the above is true, the function performs a 2D transform.
size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } N1 = getOptimalDCTSize(N);- Parameters:
src- input floating-point array.dst- output array of the same size and type as src . SEE: dft , getOptimalDFTSize , idct
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idct
Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).- Parameters:
src- input floating-point single-channel array.dst- output array of the same size and type as src.flags- operation flags. SEE: dct, dft, idft, getOptimalDFTSize
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idct
Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).- Parameters:
src- input floating-point single-channel array.dst- output array of the same size and type as src. SEE: dct, dft, idft, getOptimalDFTSize
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mulSpectrums
Performs the per-element multiplication of two Fourier spectrums. The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex matrices that are results of a real or complex Fourier transform. The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are simply multiplied (per element) with an optional conjugation of the second-array elements. When the arrays are real, they are assumed to be CCS-packed (see dft for details).- Parameters:
a- first input array.b- second input array of the same size and type as src1 .c- output array of the same size and type as src1 .flags- operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a0as value.conjB- optional flag that conjugates the second input array before the multiplication (true) or not (false).
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mulSpectrums
Performs the per-element multiplication of two Fourier spectrums. The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex matrices that are results of a real or complex Fourier transform. The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are simply multiplied (per element) with an optional conjugation of the second-array elements. When the arrays are real, they are assumed to be CCS-packed (see dft for details).- Parameters:
a- first input array.b- second input array of the same size and type as src1 .c- output array of the same size and type as src1 .flags- operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a0as value. or not (false).
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getOptimalDFTSize
public static int getOptimalDFTSize(int vecsize) Returns the optimal DFT size for a given vector size. DFT performance is not a monotonic function of a vector size. Therefore, when you calculate convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to pad the input data with zeros to get a bit larger array that can be transformed much faster than the original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) are also processed quite efficiently. The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize so that the DFT of a vector of size N can be processed efficiently. In the current implementation N = 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. The function returns a negative number if vecsize is too large (very close to INT_MAX ). While the function cannot be used directly to estimate the optimal vector size for DCT transform (since the current DCT implementation supports only even-size vectors), it can be easily processed as getOptimalDFTSize((vecsize+1)/2)\*2.- Parameters:
vecsize- vector size. SEE: dft , dct , idft , idct , mulSpectrums- Returns:
- automatically generated
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setRNGSeed
public static void setRNGSeed(int seed) Sets state of default random number generator. The function cv::setRNGSeed sets state of default random number generator to custom value.- Parameters:
seed- new state for default random number generator SEE: RNG, randu, randn
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randu
Generates a single uniformly-distributed random number or an array of random numbers. Non-template variant of the function fills the matrix dst with uniformly-distributed random numbers from the specified range: \(\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\)- Parameters:
dst- output array of random numbers; the array must be pre-allocated.low- inclusive lower boundary of the generated random numbers.high- exclusive upper boundary of the generated random numbers. SEE: RNG, randn, theRNG
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randn
Fills the array with normally distributed random numbers. The function cv::randn fills the matrix dst with normally distributed random numbers with the specified mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the value range of the output array data type.- Parameters:
dst- output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.mean- mean value (expectation) of the generated random numbers.stddev- standard deviation of the generated random numbers; it can be either a vector (in which case a diagonal standard deviation matrix is assumed) or a square matrix. SEE: RNG, randu
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randShuffle
Shuffles the array elements randomly. The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .- Parameters:
dst- input/output numerical 1D array.iterFactor- scale factor that determines the number of random swap operations (see the details below). instead. SEE: RNG, sort
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randShuffle
Shuffles the array elements randomly. The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .- Parameters:
dst- input/output numerical 1D array. below). instead. SEE: RNG, sort
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kmeans
public static double kmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags, Mat centers) Finds centers of clusters and groups input samples around the clusters. The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \(\texttt{bestLabels}_i\) contains a 0-based cluster index for the sample stored in the \(i^{th}\) row of the samples matrix. Note:- (Python) An example on K-means clustering can be found at opencv_source_code/samples/python/kmeans.py
- Parameters:
data- Data for clustering. An array of N-Dimensional points with float coordinates is needed. Examples of this array can be:- Mat points(count, 2, CV_32F);
- Mat points(count, 1, CV_32FC2);
- Mat points(1, count, CV_32FC2);
- std::vector<cv::Point2f> points(sampleCount);
K- Number of clusters to split the set by.bestLabels- Input/output integer array that stores the cluster indices for every sample.criteria- The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.attempts- Flag to specify the number of times the algorithm is executed using different initial labellings. The algorithm returns the labels that yield the best compactness (see the last function parameter).flags- Flag that can take values of cv::KmeansFlagscenters- Output matrix of the cluster centers, one row per each cluster center.- Returns:
- The function returns the compactness measure that is computed as \(\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\) after every attempt. The best (minimum) value is chosen and the corresponding labels and the compactness value are returned by the function. Basically, you can use only the core of the function, set the number of attempts to 1, initialize labels each time using a custom algorithm, pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.
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kmeans
public static double kmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags) Finds centers of clusters and groups input samples around the clusters. The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \(\texttt{bestLabels}_i\) contains a 0-based cluster index for the sample stored in the \(i^{th}\) row of the samples matrix. Note:- (Python) An example on K-means clustering can be found at opencv_source_code/samples/python/kmeans.py
- Parameters:
data- Data for clustering. An array of N-Dimensional points with float coordinates is needed. Examples of this array can be:- Mat points(count, 2, CV_32F);
- Mat points(count, 1, CV_32FC2);
- Mat points(1, count, CV_32FC2);
- std::vector<cv::Point2f> points(sampleCount);
K- Number of clusters to split the set by.bestLabels- Input/output integer array that stores the cluster indices for every sample.criteria- The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.attempts- Flag to specify the number of times the algorithm is executed using different initial labellings. The algorithm returns the labels that yield the best compactness (see the last function parameter).flags- Flag that can take values of cv::KmeansFlags- Returns:
- The function returns the compactness measure that is computed as \(\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\) after every attempt. The best (minimum) value is chosen and the corresponding labels and the compactness value are returned by the function. Basically, you can use only the core of the function, set the number of attempts to 1, initialize labels each time using a custom algorithm, pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.
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getNumThreads
public static int getNumThreads()Returns the number of threads used by OpenCV for parallel regions. Always returns 1 if OpenCV is built without threading support. The exact meaning of return value depends on the threading framework used by OpenCV library:TBB- The number of threads, that OpenCV will try to use for parallel regions. If there is any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns default number of threads used by TBB library.OpenMP- An upper bound on the number of threads that could be used to form a new team.Concurrency- The number of threads, that OpenCV will try to use for parallel regions.GCD- Unsupported; returns the GCD thread pool limit (512) for compatibility.C=- The number of threads, that OpenCV will try to use for parallel regions, if before called setNumThreads with threads > 0, otherwise returns the number of logical CPUs, available for the process. SEE: setNumThreads, getThreadNum
- Returns:
- automatically generated
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setNumThreads
public static void setNumThreads(int nthreads) OpenCV will try to set the number of threads for subsequent parallel regions. If threads == 1, OpenCV will disable threading optimizations and run all it's functions sequentially. Passing threads < 0 will reset threads number to system default. The function is not thread-safe. It must not be called in parallel region or concurrent threads. OpenCV will try to run its functions with specified threads number, but some behaviour differs from framework:TBB- User-defined parallel constructions will run with the same threads number, if another is not specified. If later on user creates his own scheduler, OpenCV will use it.OpenMP- No special defined behaviour.Concurrency- If threads == 1, OpenCV will disable threading optimizations and run its functions sequentially.GCD- Supports only values <= 0.C=- No special defined behaviour.
- Parameters:
nthreads- Number of threads used by OpenCV. SEE: getNumThreads, getThreadNum
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getThreadNum
Deprecated.Current implementation doesn't corresponding to this documentation. The exact meaning of the return value depends on the threading framework used by OpenCV library:TBB- Unsupported with current 4.1 TBB release. Maybe will be supported in future.OpenMP- The thread number, within the current team, of the calling thread.Concurrency- An ID for the virtual processor that the current context is executing on (0 for master thread and unique number for others, but not necessary 1,2,3,...).GCD- System calling thread's ID. Never returns 0 inside parallel region.C=- The index of the current parallel task. SEE: setNumThreads, getNumThreads
Returns the index of the currently executed thread within the current parallel region. Always returns 0 if called outside of parallel region.- Returns:
- automatically generated
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getBuildInformation
Returns full configuration time cmake output. Returned value is raw cmake output including version control system revision, compiler version, compiler flags, enabled modules and third party libraries, etc. Output format depends on target architecture.- Returns:
- automatically generated
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getVersionString
Returns library version string For example "3.4.1-dev". SEE: getMajorVersion, getMinorVersion, getRevisionVersion- Returns:
- automatically generated
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getVersionMajor
public static int getVersionMajor()Returns major library version- Returns:
- automatically generated
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getVersionMinor
public static int getVersionMinor()Returns minor library version- Returns:
- automatically generated
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getVersionRevision
public static int getVersionRevision()Returns revision field of the library version- Returns:
- automatically generated
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getTickCount
public static long getTickCount()Returns the number of ticks. The function returns the number of ticks after the certain event (for example, when the machine was turned on). It can be used to initialize RNG or to measure a function execution time by reading the tick count before and after the function call. SEE: getTickFrequency, TickMeter- Returns:
- automatically generated
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getTickFrequency
public static double getTickFrequency()Returns the number of ticks per second. The function returns the number of ticks per second. That is, the following code computes the execution time in seconds:double t = (double)getTickCount(); // do something ... t = ((double)getTickCount() - t)/getTickFrequency();SEE: getTickCount, TickMeter- Returns:
- automatically generated
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getCPUTickCount
public static long getCPUTickCount()Returns the number of CPU ticks. The function returns the current number of CPU ticks on some architectures (such as x86, x64, PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU with its own counter. So, theoretically (and practically) the subsequent calls to the function do not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU frequency depending on the load, the number of CPU clocks spent in some code cannot be directly converted to time units. Therefore, getTickCount is generally a preferable solution for measuring execution time.- Returns:
- automatically generated
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checkHardwareSupport
public static boolean checkHardwareSupport(int feature) Returns true if the specified feature is supported by the host hardware. The function returns true if the host hardware supports the specified feature. When user calls setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code in OpenCV.- Parameters:
feature- The feature of interest, one of cv::CpuFeatures- Returns:
- automatically generated
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getHardwareFeatureName
Returns feature name by ID Returns empty string if feature is not defined- Parameters:
feature- automatically generated- Returns:
- automatically generated
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getCPUFeaturesLine
Returns list of CPU features enabled during compilation. Returned value is a string containing space separated list of CPU features with following markers:- no markers - baseline features
- prefix
*- features enabled in dispatcher - suffix
?- features enabled but not available in HW
SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?- Returns:
- automatically generated
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getNumberOfCPUs
public static int getNumberOfCPUs()Returns the number of logical CPUs available for the process.- Returns:
- automatically generated
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setUseOptimized
public static void setUseOptimized(boolean onoff) Enables or disables the optimized code. The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, and other instructions on the platforms that support it). It sets a global flag that is further checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only safe to call the function on the very top level in your application where you can be sure that no other OpenCV function is currently executed. By default, the optimized code is enabled unless you disable it in CMake. The current status can be retrieved using useOptimized.- Parameters:
onoff- The boolean flag specifying whether the optimized code should be used (onoff=true) or not (onoff=false).
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useOptimized
public static boolean useOptimized()Returns the status of optimized code usage. The function returns true if the optimized code is enabled. Otherwise, it returns false.- Returns:
- automatically generated
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findFile
Try to find requested data file Search directories: 1. Directories passed viaaddSamplesDataSearchPath()2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable 3. OPENCV_SAMPLES_DATA_PATH environment variable If parameter value is not empty and nothing is found then stop searching. 4. Detects build/install path based on: a. current working directory (CWD) b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) 5. Scan<source>/{,data,samples/data}directories if build directory is detected or the current directory is in source tree. 6. Scan<install>/share/OpenCVdirectory if install directory is detected. SEE: cv::utils::findDataFile- Parameters:
relative_path- Relative path to data filerequired- Specify "file not found" handling. If true, function prints information message and raises cv::Exception. If false, function returns empty resultsilentMode- Disables messages- Returns:
- Returns path (absolute or relative to the current directory) or empty string if file is not found
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findFile
Try to find requested data file Search directories: 1. Directories passed viaaddSamplesDataSearchPath()2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable 3. OPENCV_SAMPLES_DATA_PATH environment variable If parameter value is not empty and nothing is found then stop searching. 4. Detects build/install path based on: a. current working directory (CWD) b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) 5. Scan<source>/{,data,samples/data}directories if build directory is detected or the current directory is in source tree. 6. Scan<install>/share/OpenCVdirectory if install directory is detected. SEE: cv::utils::findDataFile- Parameters:
relative_path- Relative path to data filerequired- Specify "file not found" handling. If true, function prints information message and raises cv::Exception. If false, function returns empty result- Returns:
- Returns path (absolute or relative to the current directory) or empty string if file is not found
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findFile
Try to find requested data file Search directories: 1. Directories passed viaaddSamplesDataSearchPath()2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable 3. OPENCV_SAMPLES_DATA_PATH environment variable If parameter value is not empty and nothing is found then stop searching. 4. Detects build/install path based on: a. current working directory (CWD) b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) 5. Scan<source>/{,data,samples/data}directories if build directory is detected or the current directory is in source tree. 6. Scan<install>/share/OpenCVdirectory if install directory is detected. SEE: cv::utils::findDataFile- Parameters:
relative_path- Relative path to data file If true, function prints information message and raises cv::Exception. If false, function returns empty result- Returns:
- Returns path (absolute or relative to the current directory) or empty string if file is not found
-
findFileOrKeep
-
findFileOrKeep
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addSamplesDataSearchPath
Override search data path by adding new search location Use this only to override default behavior Passed paths are used in LIFO order.- Parameters:
path- Path to used samples data
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addSamplesDataSearchSubDirectory
Append samples search data sub directory General usage is to add OpenCV modules name (<opencv_contrib>/modules/<name>/samples/data-><name>/samples/data+modules/<name>/samples/data). Passed subdirectories are used in LIFO order.- Parameters:
subdir- samples data sub directory
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setErrorVerbosity
public static void setErrorVerbosity(boolean verbose) -
add
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add
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add
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subtract
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subtract
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subtract
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multiply
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multiply
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multiply
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divide
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divide
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divide
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absdiff
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compare
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min
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max
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minMaxLoc
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minMaxLoc
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