org.opencv.core

## Class Core

• public class Core
extends Object
• ### Constructor Detail

• #### Core

public Core()
• ### Method Detail

• #### mean

public static Scalar mean(Mat src,
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_ .
Returns:
automatically generated
• #### mean

public static Scalar mean(Mat src)
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
• #### sumElems

public static Scalar sumElems(Mat src)
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
• #### trace

public static Scalar trace(Mat mtx)
Returns the trace of a matrix. The function cv::trace returns the sum of the diagonal elements of the matrix mtx . $$\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)$$
Parameters:
mtx - input matrix.
Returns:
automatically generated
• #### getBuildInformation

public static String 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
• #### getHardwareFeatureName

public static String getHardwareFeatureName(int feature)
Returns feature name by ID Returns empty string if feature is not defined
Parameters:
feature - automatically generated
Returns:
automatically generated
• #### getVersionString

public static String getVersionString()
Returns library version string For example "3.4.1-dev". SEE: getMajorVersion, getMinorVersion, getRevisionVersion
Returns:
automatically generated
• #### getIppVersion

public static String getIppVersion()
• #### findFile

public static String findFile(String relative_path,
boolean required,
boolean silentMode)
Try to find requested data file Search directories: 1. Directories passed via addSamplesDataSearchPath() 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 &lt;source&gt;/{,data,samples/data} directories if build directory is detected or the current directory is in source tree. 6. Scan &lt;install&gt;/share/OpenCV directory if install directory is detected. SEE: cv::utils::findDataFile
Parameters:
relative_path - Relative path to data file
required - Specify "file not found" handling. If true, function prints information message and raises cv::Exception. If false, function returns empty result
silentMode - Disables messages
Returns:
Returns path (absolute or relative to the current directory) or empty string if file is not found
• #### findFile

public static String findFile(String relative_path,
boolean required)
Try to find requested data file Search directories: 1. Directories passed via addSamplesDataSearchPath() 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 &lt;source&gt;/{,data,samples/data} directories if build directory is detected or the current directory is in source tree. 6. Scan &lt;install&gt;/share/OpenCV directory if install directory is detected. SEE: cv::utils::findDataFile
Parameters:
relative_path - Relative path to data file
required - 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
• #### findFile

public static String findFile(String relative_path)
Try to find requested data file Search directories: 1. Directories passed via addSamplesDataSearchPath() 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 &lt;source&gt;/{,data,samples/data} directories if build directory is detected or the current directory is in source tree. 6. Scan &lt;install&gt;/share/OpenCV directory 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

public static String findFileOrKeep(String relative_path,
boolean silentMode)
• #### findFileOrKeep

public static String findFileOrKeep(String relative_path)
• #### checkRange

public static boolean checkRange(Mat a,
boolean quiet,
double minVal,
double maxVal)
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
• #### checkRange

public static boolean checkRange(Mat a,
boolean quiet,
double minVal)
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
• #### checkRange

public static boolean checkRange(Mat a,
boolean quiet)
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
• #### checkRange

public static boolean checkRange(Mat a)
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
• #### eigen

public static boolean eigen(Mat src,
Mat eigenvalues,
Mat eigenvectors)
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
• #### eigen

public static boolean eigen(Mat src,
Mat eigenvalues)
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
• #### solve

public static boolean solve(Mat src1,
Mat src2,
Mat dst,
int flags)
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
• #### solve

public static boolean solve(Mat src1,
Mat src2,
Mat dst)
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
• #### useIPP

public static boolean useIPP()
proxy for hal::Cholesky
Returns:
automatically generated
• #### useIPP_NE

public static boolean useIPP_NE()
• #### useIPP_NotExact

public static boolean useIPP_NotExact()
• #### Mahalanobis

public static double Mahalanobis(Mat v1,
Mat v2,
Mat icovar)
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
• #### PSNR

public static double PSNR(Mat src1,
Mat src2)
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. Arrays must have depth CV_8U. 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 CV_8U (255) 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
• #### determinant

public static double determinant(Mat mtx)
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
• #### 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
• #### invert

public static double invert(Mat src,
Mat dst,
int flags)
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
• #### invert

public static double invert(Mat src,
Mat dst)
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
• #### 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::KmeansFlags
centers - 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.
• #### 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.
• #### norm

public static double norm(Mat src1,
Mat src2,
int normType,
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
• #### norm

public static double norm(Mat src1,
Mat src2,
int normType)
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
• #### norm

public static double norm(Mat src1,
Mat src2)
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
• #### norm

public static double norm(Mat src1,
int normType,
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 $$r(x)$$. ![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) 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
• #### norm

public static double norm(Mat src1,
int normType)
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 $$r(x)$$. ![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) 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
• #### norm

public static double norm(Mat src1)
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 $$r(x)$$. ![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) 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
• #### solvePoly

public static double solvePoly(Mat coeffs,
Mat roots,
int maxIters)
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
• #### solvePoly

public static double solvePoly(Mat coeffs,
Mat roots)
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
• #### 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
• #### 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:
x - x-coordinate of the vector.
y - y-coordinate of the vector.
Returns:
automatically generated
• #### 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 >= len
len - 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
• #### countNonZero

public static int countNonZero(Mat src)
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

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
• #### getNumberOfCPUs

public static int getNumberOfCPUs()
Returns the number of logical CPUs available for the process.
Returns:
automatically generated
• #### 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

@Deprecated
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.
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
• #### getVersionMajor

public static int getVersionMajor()
Returns major library version
Returns:
automatically generated
• #### getVersionMinor

public static int getVersionMinor()
Returns minor library version
Returns:
automatically generated
• #### getVersionRevision

public static int getVersionRevision()
Returns revision field of the library version
Returns:
automatically generated
• #### solveCubic

public static int solveCubic(Mat coeffs,
Mat roots)
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: $$x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0$$
The roots are stored in the roots array.
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.
• #### 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
• #### 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
• #### LUT

public static void LUT(Mat src,
Mat lut,
Mat dst)
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
• #### PCABackProject

public static void PCABackProject(Mat data,
Mat mean,
Mat eigenvectors,
Mat result)
wrap PCA::backProject
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
result - automatically generated
• #### 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 generated
mean - automatically generated
eigenvectors - automatically generated
eigenvalues - automatically generated
retainedVariance - automatically generated
• #### 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 generated
mean - automatically generated
eigenvectors - automatically generated
eigenvalues - automatically generated
maxComponents - automatically generated
• #### PCACompute2

public static void PCACompute2(Mat data,
Mat mean,
Mat eigenvectors,
Mat eigenvalues)
wrap PCA::operator() and add eigenvalues output parameter
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
eigenvalues - automatically generated
• #### PCACompute

public static void PCACompute(Mat data,
Mat mean,
Mat eigenvectors,
double retainedVariance)
wrap PCA::operator()
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
retainedVariance - automatically generated
• #### PCACompute

public static void PCACompute(Mat data,
Mat mean,
Mat eigenvectors,
int maxComponents)
wrap PCA::operator()
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
maxComponents - automatically generated
• #### PCACompute

public static void PCACompute(Mat data,
Mat mean,
Mat eigenvectors)
wrap PCA::operator()
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
• #### PCAProject

public static void PCAProject(Mat data,
Mat mean,
Mat eigenvectors,
Mat result)
wrap PCA::project
Parameters:
data - automatically generated
mean - automatically generated
eigenvectors - automatically generated
result - automatically generated
• #### SVBackSubst

public static void SVBackSubst(Mat w,
Mat u,
Mat vt,
Mat rhs,
Mat dst)
wrap SVD::backSubst
Parameters:
w - automatically generated
u - automatically generated
vt - automatically generated
rhs - automatically generated
dst - automatically generated
• #### SVDecomp

public static void SVDecomp(Mat src,
Mat w,
Mat u,
Mat vt,
int flags)
wrap SVD::compute
Parameters:
src - automatically generated
w - automatically generated
u - automatically generated
vt - automatically generated
flags - automatically generated
• #### SVDecomp

public static void SVDecomp(Mat src,
Mat w,
Mat u,
Mat vt)
wrap SVD::compute
Parameters:
src - automatically generated
w - automatically generated
u - automatically generated
vt - automatically generated
• #### absdiff

public static void absdiff(Mat src1,
Mat src2,
Mat dst)
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 in src1: $$\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)$$ 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 in src2: $$\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)$$ 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.
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&)
• #### absdiff

public static void absdiff(Mat src1,
Scalar src2,
Mat dst)

Mat src2,
Mat dst,
int dtype)
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(): $$\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0$$ where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
The first function in the list above can be replaced with matrix expressions: 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.
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

Mat src2,
Mat dst,
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(): $$\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0$$ where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
The first function in the list above can be replaced with matrix expressions: 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.
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

Mat src2,
Mat dst)
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(): $$\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0$$ where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.
The first function in the list above can be replaced with matrix expressions: 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.
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

Scalar src2,
Mat dst,
int dtype)

Scalar src2,
Mat dst,

Scalar src2,
Mat dst)

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

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
• #### batchDistance

public static void batchDistance(Mat src1,
Mat src2,
Mat dist,
int dtype,
Mat nidx,
int normType,
int K,
int update,
boolean crosscheck)
naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document
Parameters:
src1 - automatically generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
normType - automatically generated
K - automatically generated
update - automatically generated
crosscheck - automatically generated
• #### batchDistance

public static void batchDistance(Mat src1,
Mat src2,
Mat dist,
int dtype,
Mat nidx,
int normType,
int K,
int update)
naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document
Parameters:
src1 - automatically generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
normType - automatically generated
K - automatically generated
update - automatically generated
• #### 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 generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
normType - automatically generated
K - automatically generated
• #### 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 generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
normType - automatically generated
K - automatically generated
• #### batchDistance

public static void batchDistance(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: document
Parameters:
src1 - automatically generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
normType - automatically generated
• #### batchDistance

public static void batchDistance(Mat src1,
Mat src2,
Mat dist,
int dtype,
Mat nidx)
naive nearest neighbor finder see http://en.wikipedia.org/wiki/Nearest_neighbor_search TODO: document
Parameters:
src1 - automatically generated
src2 - automatically generated
dist - automatically generated
dtype - automatically generated
nidx - automatically generated
• #### bitwise_and

public static void bitwise_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. 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 as src1.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 as src2.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.
• #### bitwise_and

public static void bitwise_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. 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 as src1.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 as src2.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.
• #### bitwise_not

public static void bitwise_not(Mat src,
Mat dst,
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.
• #### bitwise_not

public static void bitwise_not(Mat src,
Mat dst)
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.
• #### bitwise_or

public static void bitwise_or(Mat src1,
Mat src2,
Mat dst,
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 as src1.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 as src2.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.
• #### bitwise_or

public static void bitwise_or(Mat src1,
Mat src2,
Mat dst)
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 as src1.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 as src2.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.
• #### bitwise_xor

public static void bitwise_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. 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 as src1.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 as src2.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.
• #### bitwise_xor

public static void bitwise_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. 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 as src1.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 as src2.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.
• #### calcCovarMatrix

public static void calcCovarMatrix(Mat samples,
Mat covar,
Mat mean,
int flags,
int ctype)
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
ctype - type of the matrixl; it equals 'CV_64F' by default.
• #### calcCovarMatrix

public static void calcCovarMatrix(Mat samples,
Mat covar,
Mat mean,
int flags)
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
• #### cartToPolar

public static void cartToPolar(Mat x,
Mat y,
Mat magnitude,
Mat angle,
boolean angleInDegrees)
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
• #### cartToPolar

public static void cartToPolar(Mat x,
Mat y,
Mat magnitude,
Mat angle)
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
• #### compare

public static void compare(Mat src1,
Mat src2,
Mat dst,
int cmpop)
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
• #### compare

public static void compare(Mat src1,
Scalar src2,
Mat dst,
int cmpop)
• #### completeSymm

public static void completeSymm(Mat m,
boolean lowerToUpper)
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
• #### completeSymm

public static void completeSymm(Mat m)
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
• #### convertFp16

public static void convertFp16(Mat src,
Mat dst)
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.
• #### convertScaleAbs

public static void convertScaleAbs(Mat src,
Mat dst,
double alpha,
double beta)
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&)
• #### convertScaleAbs

public static void convertScaleAbs(Mat src,
Mat dst,
double alpha)
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&)
• #### convertScaleAbs

public static void convertScaleAbs(Mat src,
Mat dst)
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&)
• #### 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 pixels
bottom - the bottom pixels
left - the left pixels
right - 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
• #### 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 pixels
bottom - the bottom pixels
left - the left pixels
right - 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
• #### dct

public static void dct(Mat src,
Mat dst,
int flags)
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: $$X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}$$
The function chooses the mode of operation by looking at the flags and size of the input array:
• 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.
Note: Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you can pad the array when necessary. Also, the function performance depends very much, and not monotonically, on the array size (see getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT of a vector of size N/2 . Thus, the optimal DCT size N1 >= N can be calculated as: 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
• #### dct

public static void dct(Mat src,
Mat dst)
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: $$X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}$$
The function chooses the mode of operation by looking at the flags and size of the input array:
• 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.
Note: Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you can pad the array when necessary. Also, the function performance depends very much, and not monotonically, on the array size (see getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT of a vector of size N/2 . Thus, the optimal DCT size N1 >= N can be calculated as: 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
• #### dft

public static void dft(Mat src,
Mat dst,
int flags,
int nonzeroRows)
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: $$\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}$$
In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here is how 2D *CCS* spectrum looks: $$\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}$$ In case of 1D transform of a real vector, the output looks like the first row of the matrix above. So, the function chooses an operation mode depending on the flags and size of the input 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.
If #DFT_SCALE is set, the scaling is done after the transformation. Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize method. The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: 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.
All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by using them, you can get the performance even better than with the above theoretically optimal implementation. Though, those two functions actually calculate cross-correlation, not convolution, so you need to "flip" the second convolution operand B vertically and horizontally using flip . Note:
• 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 - 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
• #### dft

public static void dft(Mat src,
Mat dst,
int flags)
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: $$\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}$$
In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here is how 2D *CCS* spectrum looks: $$\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}$$ In case of 1D transform of a real vector, the output looks like the first row of the matrix above. So, the function chooses an operation mode depending on the flags and size of the input 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.
If #DFT_SCALE is set, the scaling is done after the transformation. Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize method. The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: 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.
All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by using them, you can get the performance even better than with the above theoretically optimal implementation. Though, those two functions actually calculate cross-correlation, not convolution, so you need to "flip" the second convolution operand B vertically and horizontally using flip . Note:
• 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
• #### dft

public static void dft(Mat src,
Mat dst)
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: $$\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}$$
In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here is how 2D *CCS* spectrum looks: $$\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}$$ In case of 1D transform of a real vector, the output looks like the first row of the matrix above. So, the function chooses an operation mode depending on the flags and size of the input 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.
If #DFT_SCALE is set, the scaling is done after the transformation. Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize method. The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: 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.
All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by using them, you can get the performance even better than with the above theoretically optimal implementation. Though, those two functions actually calculate cross-correlation, not convolution, so you need to "flip" the second convolution operand B vertically and horizontally using flip . Note:
• 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
• #### divide

public static void divide(Mat src1,
Mat src2,
Mat dst,
double scale,
int dtype)
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 : $$\texttt{dst(I) = saturate(scale/src2(I))}$$ When src2(I) is zero, dst(I) will also be zero. Different channels of multi-channel arrays are processed independently. 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.
src2 - second input array of the same size and type as src1.
scale - scalar factor.
dst - output array of the same size and type as src2.
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
• #### divide

public static void divide(Mat src1,
Mat src2,
Mat dst,
double scale)
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 : $$\texttt{dst(I) = saturate(scale/src2(I))}$$ When src2(I) is zero, dst(I) will also be zero. Different channels of multi-channel arrays are processed independently. 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.
src2 - second input array of the same size and type as src1.
scale - scalar factor.
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
• #### divide

public static void divide(Mat src1,
Mat src2,
Mat dst)
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 : $$\texttt{dst(I) = saturate(scale/src2(I))}$$ When src2(I) is zero, dst(I) will also be zero. Different channels of multi-channel arrays are processed independently. 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.
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
• #### divide

public static void divide(Mat src1,
Scalar src2,
Mat dst,
double scale,
int dtype)
• #### divide

public static void divide(Mat src1,
Scalar src2,
Mat dst,
double scale)
• #### divide

public static void divide(Mat src1,
Scalar src2,
Mat dst)
• #### divide

public static void divide(double scale,
Mat src2,
Mat dst,
int dtype)
• #### divide

public static void divide(double scale,
Mat src2,
Mat dst)
• #### eigenNonSymmetric

public static void eigenNonSymmetric(Mat src,
Mat eigenvalues,
Mat eigenvectors)
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
• #### exp

public static void exp(Mat src,
Mat dst)
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
• #### extractChannel

public static void extractChannel(Mat src,
Mat dst,
int coi)
Extracts a single channel from src (coi is 0-based index)
Parameters:
src - input array
dst - output array
coi - index of channel to extract SEE: mixChannels, split
• #### findNonZero

public static void findNonZero(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); or cv::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 array (type CV_8UC1)
idx - the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
• #### flip

public static void flip(Mat src,
Mat dst,
int flipCode)
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
• #### 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
• #### gemm

public static void gemm(Mat src1,
Mat src2,
double alpha,
Mat src3,
double beta,
Mat dst)
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
• #### hconcat

public static void hconcat(List<Mat> src,
Mat dst)
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.
• #### idct

public static void idct(Mat src,
Mat dst,
int flags)
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
• #### idct

public static void idct(Mat src,
Mat dst)
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
• #### idft

public static void idft(Mat src,
Mat dst,
int flags,
int nonzeroRows)
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.
• #### idft

public static void idft(Mat src,
Mat dst,
int flags)
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.
• #### idft

public static void idft(Mat src,
Mat dst)
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.
• #### inRange

public static void inRange(Mat src,
Scalar lowerb,
Scalar upperb,
Mat dst)
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.
That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the specified 1D, 2D, 3D, ... box and 0 otherwise. When the lower and/or upper boundary parameters are scalars, the indexes (I) at lowerb and upperb in the above formulas should be omitted.
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.
• #### insertChannel

public static void insertChannel(Mat src,
Mat dst,
int coi)
Inserts a single channel to dst (coi is 0-based index)
Parameters:
src - input array
dst - output array
coi - index of channel for insertion SEE: mixChannels, merge
• #### log

public static void log(Mat src,
Mat dst)
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
• #### magnitude

public static void magnitude(Mat x,
Mat y,
Mat 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
• #### max

public static void max(Mat src1,
Mat src2,
Mat dst)
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
• #### max

public static void max(Mat src1,
Scalar src2,
Mat dst)
• #### meanStdDev

public static void meanStdDev(Mat src,
MatOfDouble mean,
MatOfDouble stddev,
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.
• #### meanStdDev

public static void meanStdDev(Mat src,
MatOfDouble mean,
MatOfDouble stddev)
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
• #### merge

public static void merge(List<Mat> mv,
Mat dst)
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.
• #### min

public static void min(Mat src1,
Mat src2,
Mat dst)
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
• #### min

public static void min(Mat src1,
Scalar src2,
Mat dst)
• #### mixChannels

public static void mixChannels(List<Mat> src,
List<Mat> dst,
MatOfInt fromTo)
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 .
• #### mulSpectrums

public static void mulSpectrums(Mat a,
Mat b,
Mat c,
int flags,
boolean conjB)
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 a 0 as value.
conjB - optional flag that conjugates the second input array before the multiplication (true) or not (false).
• #### mulSpectrums

public static void mulSpectrums(Mat a,
Mat b,
Mat c,
int flags)
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 a 0 as value. or not (false).
• #### mulTransposed

public static void mulTransposed(Mat src,
Mat dst,
boolean aTa,
Mat delta,
double scale,
int dtype)
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
• #### mulTransposed

public static void mulTransposed(Mat src,
Mat dst,
boolean aTa,
Mat delta,
double scale)
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
• #### mulTransposed

public static void mulTransposed(Mat src,
Mat dst,
boolean aTa,
Mat delta)
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
• #### mulTransposed

public static void mulTransposed(Mat src,
Mat dst,
boolean aTa)
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
• #### multiply

public static void multiply(Mat src1,
Mat src2,
Mat dst,
double scale,
int dtype)
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.
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
• #### multiply

public static void multiply(Mat src1,
Mat src2,
Mat dst,
double scale)
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.
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.
• #### multiply

public static void multiply(Mat src1,
Mat src2,
Mat dst)
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.
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
• #### multiply

public static void multiply(Mat src1,
Scalar src2,
Mat dst,
double scale,
int dtype)
• #### multiply

public static void multiply(Mat src1,
Scalar src2,
Mat dst,
double scale)
• #### multiply

public static void multiply(Mat src1,
Scalar src2,
Mat dst)
• #### normalize

public static void normalize(Mat src,
Mat dst,
double alpha,
double beta,
int norm_type,
int dtype,
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).
• #### normalize

public static void normalize(Mat src,
Mat dst,
double alpha,
double beta,
int norm_type,
int dtype)
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
• #### normalize

public static void normalize(Mat src,
Mat dst,
double alpha,
double beta,
int norm_type)
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
• #### normalize

public static void normalize(Mat src,
Mat dst,
double alpha,
double beta)
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
• #### normalize

public static void normalize(Mat src,
Mat dst,
double alpha)
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
• #### normalize

public static void normalize(Mat src,
Mat dst)
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
• #### patchNaNs

public static void patchNaNs(Mat a,
double val)
converts NaN's to the given number
Parameters:
a - automatically generated
val - automatically generated
• #### patchNaNs

public static void patchNaNs(Mat a)
converts NaN's to the given number
Parameters:
a - automatically generated
• #### perspectiveTransform

public static void perspectiveTransform(Mat src,
Mat dst,
Mat m)
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
• #### phase

public static void phase(Mat x,
Mat y,
Mat angle,
boolean angleInDegrees)
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.
• #### phase

public static void phase(Mat x,
Mat y,
Mat angle)
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.
• #### polarToCart

public static void polarToCart(Mat magnitude,
Mat angle,
Mat x,
Mat y,
boolean angleInDegrees)
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
• #### polarToCart

public static void polarToCart(Mat magnitude,
Mat angle,
Mat x,
Mat y)
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
• #### pow

public static void pow(Mat src,
double power,
Mat dst)
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
• #### randShuffle

public static void randShuffle(Mat dst,
double iterFactor)
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
• #### randShuffle

public static void randShuffle(Mat dst)
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
• #### randn

public static void randn(Mat dst,
double mean,
double stddev)
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
• #### randu

public static void randu(Mat dst,
double low,
double high)
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
• #### reduce

public static void reduce(Mat src,
Mat dst,
int dim,
int rtype,
int dtype)
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 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
dtype - 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
• #### reduce

public static void reduce(Mat src,
Mat dst,
int dim,
int rtype)
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 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
• #### repeat

public static void repeat(Mat src,
int ny,
int nx,
Mat dst)
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 the src is repeated along the vertical axis.
nx - Flag to specify how many times the src is repeated along the horizontal axis.
dst - output array of the same type as src. SEE: cv::reduce
• #### rotate

public static void rotate(Mat src,
Mat dst,
int rotateCode)
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

double alpha,
Mat src2,
Mat dst)
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
• #### setErrorVerbosity

public static void setErrorVerbosity(boolean verbose)
• #### setIdentity

public static void setIdentity(Mat mtx,
Scalar s)
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=
• #### setIdentity

public static void setIdentity(Mat mtx)
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=

OpenCV will try to set the number of threads for the next parallel region. If threads == 0, OpenCV will disable threading optimizations and run all it's functions sequentially. Passing threads < 0 will reset threads number to system default. This function must be called outside of parallel region. 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:
• #### 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
• #### sort

public static void sort(Mat src,
Mat dst,
int flags)
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
• #### sortIdx

public static void sortIdx(Mat src,
Mat dst,
int flags)
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
• #### split

public static void split(Mat m,
List<Mat> mv)
Parameters:
m - input multi-channel array.
mv - output vector of arrays; the arrays themselves are reallocated, if needed.
• #### sqrt

public static void sqrt(Mat src,
Mat dst)
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.
• #### subtract

public static void subtract(Mat src1,
Mat src2,
Mat dst,
int dtype)
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.
The first function in the list above can be replaced with matrix expressions: 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.
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.
• #### subtract

public static void subtract(Mat src1,
Mat src2,
Mat dst,
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.
The first function in the list above can be replaced with matrix expressions: 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.
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.
• #### subtract

public static void subtract(Mat src1,
Mat src2,
Mat dst)
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.
The first function in the list above can be replaced with matrix expressions: 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.
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
• #### subtract

public static void subtract(Mat src1,
Scalar src2,
Mat dst,
int dtype)
• #### subtract

public static void subtract(Mat src1,
Scalar src2,
Mat dst,
• #### subtract

public static void subtract(Mat src1,
Scalar src2,
Mat dst)
• #### transform

public static void transform(Mat src,
Mat dst,
Mat m)
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
• #### transpose

public static void transpose(Mat src,
Mat dst)
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.
• #### vconcat

public static void vconcat(List<Mat> src,
Mat dst)
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 depth
dst - output array. It has the same number of cols and depth as the src, and the sum of rows of the src. same depth.
• #### setUseIPP

public static void setUseIPP(boolean flag)
• #### setUseIPP_NE

public static void setUseIPP_NE(boolean flag)
• #### setUseIPP_NotExact

public static void setUseIPP_NotExact(boolean flag)