Merge remote-tracking branch 'upstream/3.4' into merge-3.4
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@@ -291,9 +291,9 @@ if src was not a ROI, use borderType | #BORDER_ISOLATED.
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@param src Source image.
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@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
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src.rows+top+bottom) .
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@param top
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@param bottom
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@param left
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@param top the top pixels
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@param bottom the bottom pixels
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@param left the left pixels
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@param right Parameter specifying how many pixels in each direction from the source image rectangle
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to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
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to be built.
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@@ -579,7 +579,7 @@ CvNArrayIterator;
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#define CV_NO_CN_CHECK 2
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#define CV_NO_SIZE_CHECK 4
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/** initializes iterator that traverses through several arrays simulteneously
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/** initializes iterator that traverses through several arrays simultaneously
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(the function together with cvNextArraySlice is used for
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N-ari element-wise operations) */
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CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs,
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@@ -357,10 +357,10 @@ Cv64suf;
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# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
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#endif
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#ifdef CVAPI_EXPORTS
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# if (defined _WIN32 || defined WINCE || defined __CYGWIN__)
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#ifndef CV_EXPORTS
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# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined(CVAPI_EXPORTS)
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# define CV_EXPORTS __declspec(dllexport)
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# elif defined __GNUC__ && __GNUC__ >= 4
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# elif defined __GNUC__ && __GNUC__ >= 4 && (defined(CVAPI_EXPORTS) || defined(__APPLE__))
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# define CV_EXPORTS __attribute__ ((visibility ("default")))
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# endif
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#endif
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@@ -398,7 +398,7 @@ inline unsigned RNG::next()
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return (unsigned)state;
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}
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//! returns the next unifomly-distributed random number of the specified type
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//! returns the next uniformly-distributed random number of the specified type
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template<typename _Tp> static inline _Tp randu()
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{
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return (_Tp)theRNG();
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@@ -219,10 +219,10 @@ converge to it. Another obvious restriction is that it should be possible to com
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a function at any point, thus it is preferable to have analytic expression for gradient and
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computational burden should be born by the user.
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The latter responsibility is accompilished via the getGradient method of a
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The latter responsibility is accomplished via the getGradient method of a
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MinProblemSolver::Function interface (which represents function being optimized). This method takes
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point a point in *n*-dimensional space (first argument represents the array of coordinates of that
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point) and comput its gradient (it should be stored in the second argument as an array).
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point) and compute its gradient (it should be stored in the second argument as an array).
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@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
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