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@ -88,7 +88,7 @@ struct CvVectors
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#if 0
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/* A structure, representing the lattice range of statmodel parameters.
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It is used for optimizing statmodel parameters by cross-validation method.
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The lattice is logarithmic, so <step> must be greater then 1. */
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The lattice is logarithmic, so <step> must be greater than 1. */
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typedef struct CvParamLattice
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{
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double min_val;
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@ -158,7 +158,7 @@ protected:
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/* The structure, representing the grid range of statmodel parameters.
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It is used for optimizing statmodel accuracy by varying model parameters,
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the accuracy estimate being computed by cross-validation.
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The grid is logarithmic, so <step> must be greater then 1. */
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The grid is logarithmic, so <step> must be greater than 1. */
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class CvMLData;
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@ -12,7 +12,7 @@ python gen_pattern.py -o out.svg -r 11 -c 8 -T circles -s 20.0 -R 5.0 -u mm -w 2
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-u, --units - mm, inches, px, m (default mm)
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-w, --page_width - page width in units (default 216)
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-h, --page_height - page height in units (default 279)
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-a, --page_size - page size (default A4), supercedes -h -w arguments
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-a, --page_size - page size (default A4), supersedes -h -w arguments
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-H, --help - show help
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"""
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@ -12,7 +12,7 @@ Theory
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We know SIFT uses 128-dim vector for descriptors. Since it is using floating point numbers, it takes
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basically 512 bytes. Similarly SURF also takes minimum of 256 bytes (for 64-dim). Creating such a
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vector for thousands of features takes a lot of memory which are not feasible for resouce-constraint
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vector for thousands of features takes a lot of memory which are not feasible for resource-constraint
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applications especially for embedded systems. Larger the memory, longer the time it takes for
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matching.
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@ -2164,7 +2164,7 @@ inline void RHO_HEST_REFC::refine(void){
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* order to compute a candidate homography (newH).
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*
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* The system above is solved by Cholesky decomposition of a
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* sufficently-damped JtJ into a lower-triangular matrix (and its
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* sufficiently-damped JtJ into a lower-triangular matrix (and its
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* transpose), whose inverse is then computed. This inverse (and its
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* transpose) then multiply Jte in order to find dH.
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*/
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@ -103,7 +103,7 @@ double memory deallocation.
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CV_EXPORTS void fastFree(void* ptr);
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/*!
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The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree()
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The STL-compliant memory Allocator based on cv::fastMalloc() and cv::fastFree()
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*/
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template<typename _Tp> class Allocator
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{
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@ -2266,7 +2266,7 @@ inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
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v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]);
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}
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////// FP16 suport ///////
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////// FP16 support ///////
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inline v_reg<float, V_TypeTraits<float>::nlanes128>
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v_load_expand(const float16_t* ptr)
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@ -1635,7 +1635,7 @@ inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_flo
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}
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#endif
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////// FP16 suport ///////
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////// FP16 support ///////
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#if CV_FP16
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inline v_float32x4 v_load_expand(const float16_t* ptr)
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{
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@ -33,7 +33,7 @@ String dumpInputArray(InputArray argument)
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}
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catch (...)
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{
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ss << " ERROR: exception occured, dump is non-complete"; // need to properly support different kinds
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ss << " ERROR: exception occurred, dump is non-complete"; // need to properly support different kinds
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}
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return ss.str();
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}
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@ -70,7 +70,7 @@ CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument)
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}
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catch (...)
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{
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ss << " ERROR: exception occured, dump is non-complete"; // need to properly support different kinds
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ss << " ERROR: exception occurred, dump is non-complete"; // need to properly support different kinds
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}
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return ss.str();
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}
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@ -100,7 +100,7 @@ CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument)
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}
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catch (...)
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{
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ss << " ERROR: exception occured, dump is non-complete"; // need to properly support different kinds
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ss << " ERROR: exception occurred, dump is non-complete"; // need to properly support different kinds
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}
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return ss.str();
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}
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@ -137,7 +137,7 @@ CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argume
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}
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catch (...)
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{
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ss << " ERROR: exception occured, dump is non-complete"; // need to properly support different kinds
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ss << " ERROR: exception occurred, dump is non-complete"; // need to properly support different kinds
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}
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return ss.str();
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}
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@ -250,7 +250,7 @@ BinaryFunc getCopyMaskFunc(size_t esz);
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// There is some mess in code with vectors representation.
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// Both vector-column / vector-rows are used with dims=2 (as Mat2D always).
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// Reshape matrices if neccessary (in case of vectors) and returns size with scaled width.
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// Reshape matrices if necessary (in case of vectors) and returns size with scaled width.
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Size getContinuousSize2D(Mat& m1, int widthScale=1);
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Size getContinuousSize2D(Mat& m1, Mat& m2, int widthScale=1);
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Size getContinuousSize2D(Mat& m1, Mat& m2, Mat& m3, int widthScale=1);
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@ -344,7 +344,7 @@ cv::String findDataFile(const cv::String& relative_path,
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#if defined OPENCV_INSTALL_PREFIX && defined OPENCV_DATA_INSTALL_PATH
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cv::String install_dir(OPENCV_INSTALL_PREFIX);
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// use core/world module path and verify that library is running from installation directory
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// It is neccessary to avoid touching of unrelated common /usr/local path
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// It is necessary to avoid touching of unrelated common /usr/local path
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if (module_path.empty()) // can't determine
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module_path = install_dir;
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if (isSubDirectory(install_dir, module_path) || isSubDirectory(utils::fs::canonical(install_dir), utils::fs::canonical(module_path)))
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@ -119,7 +119,7 @@ message AttributeProto {
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// implementations needed to use has_field hueristics to determine
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// which value field was in use. For IR_VERSION 0.0.2 or later, this
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// field MUST be set and match the f|i|s|t|... field in use. This
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// change was made to accomodate proto3 implementations.
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// change was made to accommodate proto3 implementations.
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optional AttributeType type = 20; // discriminator that indicates which field below is in use
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// Exactly ONE of the following fields must be present for this version of the IR
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@ -120,7 +120,7 @@ public:
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\f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f]
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where \f$n\f$ is the maximal index satisfying
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\f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f]
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The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
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The grid is logarithmic, so logStep must always be greater than 1. Default value is 1.
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*/
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CV_PROP_RW double logStep;
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@ -99,7 +99,7 @@ static void checkParamGrid(const ParamGrid& pg)
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if( pg.minVal < DBL_EPSILON )
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CV_Error( CV_StsBadArg, "Lower bound of the grid must be positive" );
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if( pg.logStep < 1. + FLT_EPSILON )
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CV_Error( CV_StsBadArg, "Grid step must greater then 1" );
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CV_Error( CV_StsBadArg, "Grid step must greater than 1" );
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}
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// SVM training parameters
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@ -2171,7 +2171,7 @@ void videoInput::setPhyCon(int id, int conn){
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// ----------------------------------------------------------------------
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// Check that we are not trying to setup a non-existant device
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// Check that we are not trying to setup a non-existent device
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// Then start the graph building!
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// ----------------------------------------------------------------------
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