Utility and System Functions and Macros

alignPtr

Aligns a pointer to the specified number of bytes.

C++: template<typename _Tp> _Tp* alignPtr(_Tp* ptr, int n=sizeof(_Tp))

Parameters:

• ptr – Aligned pointer.
• n – Alignment size that must be a power of two.

The function returns the aligned pointer of the same type as the input pointer:

alignSize

Aligns a buffer size to the specified number of bytes.

C++: size_t alignSize(size_t sz, int n)

Parameters:

• sz – Buffer size to align.
• n – Alignment size that must be a power of two.

The function returns the minimum number that is greater or equal to sz and is divisble by n :

allocate

Allocates an array of elements.

C++: template<typename _Tp> _Tp* allocate(size_t n)

Parameters:

• n – Number of elements to allocate.

The generic function allocate allocates a buffer for the specified number of elements. For each element, the default constructor is called.

deallocate

Deallocates an array of elements.

C++: template<typename _Tp> void deallocate(_Tp* ptr, size_t n)

Parameters:

• ptr – Pointer to the deallocated buffer.
• n – Number of elements in the buffer.

The generic function deallocate deallocates the buffer allocated with allocate() . The number of elements must match the number passed to allocate() .

fastAtan2

Calculates the angle of a 2D vector in degrees.

C++: float fastAtan2(float y, float x)

Python: cv2.fastAtan2(y, x) → retval

C: float cvFastArctan(float y, float x)

Python: cv.FastArctan(y, x) → float

Parameters:

• x – x-coordinate of the vector.
• y – y-coordinate of the vector.

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.

cubeRoot

Computes the cube root of an argument.

C++: float cubeRoot(float val)

Python: cv2.cubeRoot(val) → retval

C: float cvCbrt(float val)

Python: cv.Cbrt(val) → float

Parameters:val – A function argument.

The function cubeRoot computes . Negative arguments are handled correctly. NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for single-precision data.

Ceil

Rounds floating-point number to the nearest integer not smaller than the original.

C: int cvCeil(double value)

Python: cv.Ceil(value) → int

Parameters:value – floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the result is not defined.

The function computes an integer i such that:

Floor

Rounds floating-point number to the nearest integer not larger than the original.

C: int cvFloor(double value)

Python: cv.Floor(value) → int

Parameters:value – floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the result is not defined.

The function computes an integer i such that:

Round

Rounds floating-point number to the nearest integer

C: int cvRound(double value)

Python: cv.Round(value) → int

Parameters:value – floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the result is not defined.

IsInf

Determines if the argument is Infinity.

C: int cvIsInf(double value)

Python: cv.IsInf(value) → int

Parameters:value – The input floating-point value

The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) and 0 otherwise.

IsNaN

Determines if the argument is Not A Number.

C: int cvIsNaN(double value)

Python: cv.IsNaN(value) → int

Parameters:value – The input floating-point value

The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 otherwise.

CV_Assert

Checks a condition at runtime and throws exception if it fails

C++: CV_Assert(expr None)

The macros CV_Assert (and CV_DbgAssert) evaluate the specified expression. If it is 0, the macros raise an error (see error() ). The macro CV_Assert checks the condition in both Debug and Release configurations while CV_DbgAssert is only retained in the Debug configuration.

error

Signals an error and raises an exception.

C++: void error(const Exception& exc)

C: int cvError(int status, const char* funcName, const char* errMsg, const char* filename, int line)

Parameters:

• exc – Exception to throw.
• code – Error code. Normally, it is a negative value. The list of pre-defined error codes can be found in cxerror.h .
• msg – Text of the error message.
• args – printf -like formatted error message in parentheses.

The function and the helper macros CV_Error and CV_Error_:

#define CV_Error( code, msg ) error(...)
#define CV_Error_( code, args ) error(...)

call the error handler. Currently, the error handler prints the error code ( exc.code ), the context ( exc.file,``exc.line`` ), and the error message exc.err to the standard error stream stderr . In the Debug configuration, it then provokes memory access violation, so that the execution stack and all the parameters can be analyzed by the debugger. In the Release configuration, the exception exc is thrown.

The macro CV_Error_ can be used to construct an error message on-fly to include some dynamic information, for example:

// note the extra parentheses around the formatted text message
CV_Error_(CV_StsOutOfRange,
    ("the matrix element (
    i, j, mtx.at<float>(i,j)))

Exception

class Exception

Exception class passed to an error.

class  Exception
{
public:
    // various constructors and the copy operation
    Exception() { code = 0; line = 0; }
    Exception(int _code, const string& _err,
              const string& _func, const string& _file, int _line);
    Exception(const Exception& exc);
    Exception& operator = (const Exception& exc);

    // the error code
    int code;
    // the error text message
    string err;
    // function name where the error happened
    string func;
    // the source file name where the error happened
    string file;
    // the source file line where the error happened
    int line;
};

The class Exception encapsulates all or almost all necessary information about the error happened in the program. The exception is usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. See error() .

fastMalloc

Allocates an aligned memory buffer.

C++: void* fastMalloc(size_t size)

C: void* cvAlloc(size_t size)

Parameters:

• size – Allocated buffer size.

The function allocates the buffer of the specified size and returns it. When the buffer size is 16 bytes or more, the returned buffer is aligned to 16 bytes.

fastFree

Deallocates a memory buffer.

C++: void fastFree(void* ptr)

C: void cvFree(void** pptr)

Parameters:

• ptr – Pointer to the allocated buffer.
• pptr – Double pointer to the allocated buffer

The function deallocates the buffer allocated with fastMalloc() . If NULL pointer is passed, the function does nothing. C version of the function clears the pointer *pptr to avoid problems with double memory deallocation.

format

Returns a text string formatted using the printf-like expression.

string format( const char* fmt, ... )

Parameters:

• fmt – printf -compatible formatting specifiers.

The function acts like sprintf but forms and returns an STL string. It can be used to form an error message in the Exception() constructor.

checkHardwareSupport

Returns true if the specified feature is supported by the host hardware.

C++: bool checkHardwareSupport(int feature)

C: int cvCheckHardwareSupport(int feature)

Python: checkHardwareSupport(feature) → Bool

Parameters:feature –

The feature of interest, one of:

• CV_CPU_MMX - MMX
• CV_CPU_SSE - SSE
• CV_CPU_SSE2 - SSE 2
• CV_CPU_SSE3 - SSE 3
• CV_CPU_SSSE3 - SSSE 3
• CV_CPU_SSE4_1 - SSE 4.1
• CV_CPU_SSE4_2 - SSE 4.2
• CV_CPU_POPCNT - POPCOUNT
• CV_CPU_AVX - AVX

The function returns true if the host hardware supports the specified feature. When user calls setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code in OpenCV.

getNumThreads

Returns the number of threads used by OpenCV.

C++: int getNumThreads()

The function returns the number of threads that is used by OpenCV.

See also

setNumThreads(), getThreadNum()

getThreadNum

Returns the index of the currently executed thread.

C++: int getThreadNum()

The function returns a 0-based index of the currently executed thread. The function is only valid inside a parallel OpenMP region. When OpenCV is built without OpenMP support, the function always returns 0.

See also

setNumThreads(), getNumThreads() .

getTickCount

Returns the number of ticks.

C++: int64 getTickCount()

Python: cv2.getTickCount() → retval

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 also the tick frequency.

getTickFrequency

Returns the number of ticks per second.

C++: double getTickFrequency()

Python: cv2.getTickFrequency() → retval

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();

getCPUTickCount

Returns the number of CPU ticks.

C++: int64 getCPUTickCount()

Python: cv2.getCPUTickCount() → retval

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.

saturate_cast

Template function for accurate conversion from one primitive type to another.

C++: template<...> _Tp saturate_cast(_Tp2 v)

Parameters:

• v – Function parameter.

The functions saturate_cast resemble the standard C++ cast operations, such as static_cast<T>() and others. They perform an efficient and accurate conversion from one primitive type to another (see the introduction chapter). saturate in the name means that when the input value v is out of the range of the target type, the result is not formed just by taking low bits of the input, but instead the value is clipped. For example:

uchar a = saturate_cast<uchar>(-100); // a = 0 (UCHAR_MIN)
short b = saturate_cast<short>(33333.33333); // b = 32767 (SHRT_MAX)

Such clipping is done when the target type is unsigned char , signed char , unsigned short or signed short . For 32-bit integers, no clipping is done.

When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), the floating-point value is first rounded to the nearest integer and then clipped if needed (when the target type is 8- or 16-bit).

This operation is used in the simplest or most complex image processing functions in OpenCV.

See also

add(), subtract(), multiply(), divide(), Mat::convertTo()

setNumThreads

Sets the number of threads used by OpenCV.

C++: void setNumThreads(int nthreads)

Parameters:

• nthreads – Number of threads used by OpenCV.

The function sets the number of threads used by OpenCV in parallel OpenMP regions. If nthreads=0 , the function uses the default number of threads that is usually equal to the number of the processing cores.

See also

getNumThreads(), getThreadNum()

setUseOptimized

Enables or disables the optimized code.

C++: void setUseOptimized(bool onoff)

Python: cv2.setUseOptimized(onoff) → None

C: int cvUseOptimized(int onoff)

Parameters:

• onoff – The boolean flag specifying whether the optimized code should be used (onoff=true) or not (onoff=false).

The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX, and other instructions on the platforms that support it). It sets a global flag that is further checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only safe to call the function on the very top level in your application where you can be sure that no other OpenCV function is currently executed.

By default, the optimized code is enabled unless you disable it in CMake. The current status can be retrieved using useOptimized.

useOptimized

Returns the status of optimized code usage.

C++: bool useOptimized()

Python: cv2.useOptimized() → retval

The function returns true if the optimized code is enabled. Otherwise, it returns false.