mirror of
https://github.com/opencv/opencv.git
synced 2024-11-24 03:00:14 +08:00
Fix modules/ typos
Found using `codespell -q 3 -S ./3rdparty -L activ,amin,ang,atleast,childs,dof,endwhile,halfs,hist,iff,nd,od,uint`
backporting of commit: ec43292e1e
This commit is contained in:
parent
7df3141bbc
commit
fcc7d8dd4e
@ -623,7 +623,7 @@ CV_IMPL void cvProjectPoints2( const CvMat* objectPoints,
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if( (CV_MAT_TYPE(A->type) != CV_64FC1 && CV_MAT_TYPE(A->type) != CV_32FC1) ||
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A->rows != 3 || A->cols != 3 )
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CV_Error( CV_StsBadArg, "Instrinsic parameters must be 3x3 floating-point matrix" );
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CV_Error( CV_StsBadArg, "Intrinsic parameters must be 3x3 floating-point matrix" );
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cvConvert( A, &_a );
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fx = a[0]; fy = a[4];
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@ -506,7 +506,7 @@ int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2,
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// Do the cheirality check.
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// Notice here a threshold dist is used to filter
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// out far away points (i.e. infinite points) since
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// their depth may vary between positive and negtive.
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// their depth may vary between positive and negative.
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std::vector<Mat> allTriangulations(4);
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Mat Q;
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@ -650,7 +650,7 @@ void PoseSolver::makeCanonicalObjectPoints(InputArray _objectPoints, OutputArray
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if (!computeObjextSpaceR3Pts(objectPoints,R))
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{
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//we could not compute R, problably because there is a duplicate point in {objectPoints(0),objectPoints(1),objectPoints(2)}.
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//we could not compute R, probably because there is a duplicate point in {objectPoints(0),objectPoints(1),objectPoints(2)}.
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//So we compute it with the SVD (which is slower):
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computeObjextSpaceRSvD(UZero,R);
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}
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@ -191,7 +191,7 @@ void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, in
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cout << "Number of point: " << k << endl;
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cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
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cout << "Difference with noise of point: " << diff << endl;
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cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
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cout << "Maximum allowed difference: " << max_2diff << endl; cout << endl;
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}
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void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff)
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@ -204,7 +204,7 @@ void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, do
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cout << "Count of points: " << N << endl;
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cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
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cout << "Difference with noise of points: " << diff << endl;
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cout << "Maxumum allowed difference: " << max_diff << endl; cout << endl;
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cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
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}
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void CV_HomographyTest::print_information_6(int _method, int j, int N, int k, double diff, bool value)
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@ -244,7 +244,7 @@ void CV_HomographyTest::print_information_8(int _method, int j, int N, int k, in
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cout << "Number of point: " << k << " " << endl;
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cout << "Norm type using in criteria: "; if (NORM_TYPE[l] == 1) cout << "INF"; else if (NORM_TYPE[l] == 2) cout << "L1"; else cout << "L2"; cout << endl;
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cout << "Difference with noise of point: " << diff << endl;
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cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
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cout << "Maximum allowed difference: " << max_2diff << endl; cout << endl;
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}
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void CV_HomographyTest::run(int)
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@ -53,7 +53,7 @@
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which is incompatible with C
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It is OK to disable it because we only extend few plain structures with
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C++ construrtors for simpler interoperability with C++ API of the library
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C++ constructors for simpler interoperability with C++ API of the library
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*/
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# pragma warning(disable:4190)
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# elif defined __clang__ && __clang_major__ >= 3
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@ -126,7 +126,7 @@ public:
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GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator());
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GpuMat(Size size, int type, Allocator* allocator = defaultAllocator());
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//! constucts GpuMat and fills it with the specified value _s
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//! constructs GpuMat and fills it with the specified value _s
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GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator());
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GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator());
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@ -76,7 +76,7 @@ implemented as a structure based on a one SIMD register.
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- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int
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- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
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- cv::v_float32x4: four 32-bit floating point values (signed) - float
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- cv::v_float64x2: two 64-bit floating point valies (signed) - double
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- cv::v_float64x2: two 64-bit floating point values (signed) - double
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@note
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cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to
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@ -1272,7 +1272,7 @@ inline v_float32x4 v_load_expand(const float16_t* ptr)
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inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
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{
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// fixme: Is there any buitin op or intrinsic that cover "xvcvsphp"?
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// fixme: Is there any builtin op or intrinsic that cover "xvcvsphp"?
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#if CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM)
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vec_ushort8 vf16;
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__asm__ __volatile__ ("xvcvsphp %x0,%x1" : "=wa" (vf16) : "wf" (v.val));
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@ -151,7 +151,7 @@ number of components (vectors/matrices) of the outer vector.
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In general, type support is limited to cv::Mat types. Other types are forbidden.
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But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc.
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This data is not intented to be interpreted as an image data, or processed somehow like regular cv::Mat.
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This data is not intended to be interpreted as an image data, or processed somehow like regular cv::Mat.
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To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers.
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Custom type is wrapped as Mat-compatible `CV_8UC<N>` values (N = sizeof(T), N <= CV_CN_MAX).
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*/
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@ -2416,7 +2416,7 @@ public:
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// (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
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UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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//! constucts 2D matrix and fills it with the specified value _s.
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//! constructs 2D matrix and fills it with the specified value _s.
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UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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@ -2863,7 +2863,7 @@ public:
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`ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`.
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The methods always return a valid reference.
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If the element did not exist, it is created and initialiazed with 0.
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If the element did not exist, it is created and initialized with 0.
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*/
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//! returns reference to the specified element (1D case)
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template<typename _Tp> _Tp& ref(int i0, size_t* hashval=0);
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@ -27,7 +27,7 @@ These files can be pre-generated for target configurations of your application
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or generated by CMake on the fly (use CMAKE_BINARY_DIR for that).
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Notes:
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- H/W capability checks are still responsibility of your applcation
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- H/W capability checks are still responsibility of your application
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- runtime dispatching is not covered by this helper header
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*/
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@ -49,8 +49,8 @@ public:
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void lock(); //< acquire exclusive (writer) lock
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void unlock(); //< release exclusive (writer) lock
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void lock_shared(); //< acquire sharable (reader) lock
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void unlock_shared(); //< release sharable (reader) lock
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void lock_shared(); //< acquire shareable (reader) lock
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void unlock_shared(); //< release shareable (reader) lock
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struct Impl;
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protected:
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@ -13,7 +13,7 @@ public:
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// (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
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CV_WRAP UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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CV_WRAP UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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//! constucts 2D matrix and fills it with the specified value _s.
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//! constructs 2D matrix and fills it with the specified value _s.
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CV_WRAP UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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CV_WRAP UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
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@ -1766,7 +1766,7 @@ cvSeqInsertSlice( CvSeq* seq, int index, const CvArr* from_arr )
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CV_Error( CV_StsBadArg, "Source is not a sequence nor matrix" );
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if( !CV_IS_MAT_CONT(mat->type) || (mat->rows != 1 && mat->cols != 1) )
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CV_Error( CV_StsBadArg, "The source array must be 1d coninuous vector" );
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CV_Error( CV_StsBadArg, "The source array must be 1d continuous vector" );
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from = cvMakeSeqHeaderForArray( CV_SEQ_KIND_GENERIC, sizeof(from_header),
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CV_ELEM_SIZE(mat->type),
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@ -63,7 +63,7 @@
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#define HAL_LU_SMALL_MATRIX_THRESH 100
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#define HAL_CHOLESKY_SMALL_MATRIX_THRESH 100
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//lapack stores matrices in column-major order so transposing is neded everywhere
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//lapack stores matrices in column-major order so transposing is needed everywhere
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template <typename fptype> static inline void
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transpose_square_inplace(fptype *src, size_t src_ld, size_t m)
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{
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@ -5759,7 +5759,7 @@ public:
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static OpenCLAllocator* getOpenCLAllocator_() // call once guarantee
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{
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static OpenCLAllocator* g_allocator = new OpenCLAllocator(); // avoid destrutor call (using of this object is too wide)
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static OpenCLAllocator* g_allocator = new OpenCLAllocator(); // avoid destructor call (using of this object is too wide)
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g_isOpenCVActivated = true;
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return g_allocator;
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}
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@ -73,7 +73,7 @@ public:
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protected:
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bool test_values(const cv::Mat& src); // complex test for eigen without vectors
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bool check_full(int type); // compex test for symmetric matrix
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bool check_full(int type); // complex test for symmetric matrix
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virtual void run (int) = 0; // main testing method
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protected:
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@ -104,7 +104,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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h_t &= o_t \odot tanh(c_t), \\
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c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\
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@f}
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where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned wights.
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where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned weights.
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Gates are computed as follows:
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@f{eqnarray*}{
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@ -428,7 +428,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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* @param inpPin descriptor of the second layer input.
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*
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* Descriptors have the following template <DFN><layer_name>[.input_number]</DFN>:
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* - the first part of the template <DFN>layer_name</DFN> is sting name of the added layer.
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* - the first part of the template <DFN>layer_name</DFN> is string name of the added layer.
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* If this part is empty then the network input pseudo layer will be used;
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* - the second optional part of the template <DFN>input_number</DFN>
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* is either number of the layer input, either label one.
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@ -618,7 +618,7 @@ void OCL4DNNConvSpatial<Dtype>::calculateBenchmark(const UMat &bottom, UMat &ver
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// For large enough input size, we do not need to tune kernels for different
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// size. The reason is with large input size, there will be enough work items
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// to feed al the EUs.
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// FIXME for the gemm like convolution, switch back to eaxct image size.
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// FIXME for the gemm like convolution, switch back to exact image size.
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#define TUNING_SIZE(x) ((x) > 256 ? 256 : (alignSize(x, 16)))
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@ -161,7 +161,7 @@ message NodeProto {
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repeated string output = 2; // namespace Value
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// An optional identifier for this node in a graph.
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// This field MAY be absent in ths version of the IR.
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// This field MAY be absent in this version of the IR.
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optional string name = 3; // namespace Node
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// The symbolic identifier of the Operator to execute.
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@ -609,7 +609,7 @@ void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBl
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try {
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wrapper->outProms[processedOutputs].setException(std::current_exception());
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} catch(...) {
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CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
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CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
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}
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}
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}
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@ -622,7 +622,7 @@ void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBl
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try {
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wrapper->outProms[processedOutputs].setException(e);
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} catch(...) {
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CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
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CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
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}
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}
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}
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@ -40,7 +40,7 @@
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#endif
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//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
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//there is no way to suppress warnigns from IE only at this moment, so we are forced to suppress warnings globally
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//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
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#if defined(__GNUC__)
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#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
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#endif
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@ -837,7 +837,7 @@ void TFImporter::populateNet(Net dstNet)
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CV_Assert(paddings.type() == CV_32SC1);
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if (paddings.total() == 8)
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{
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// Perhabs, we have NHWC padding dimensions order.
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// Perhaps, we have NHWC padding dimensions order.
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// N H W C
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// 0 1 2 3 4 5 6 7
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std::swap(paddings.at<int32_t>(2), paddings.at<int32_t>(6));
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@ -312,7 +312,7 @@ struct TorchImporter
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fpos = THFile_position(file);
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int ktype = readInt();
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if (ktype != TYPE_STRING) //skip non-string fileds
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if (ktype != TYPE_STRING) //skip non-string fields
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{
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THFile_seek(file, fpos);
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readObject(); //key
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@ -14,7 +14,7 @@
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// Synchronize headers include statements with src/op_inf_engine.hpp
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//
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//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
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//there is no way to suppress warnigns from IE only at this moment, so we are forced to suppress warnings globally
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//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
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#if defined(__GNUC__)
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#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
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#endif
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@ -6,7 +6,7 @@
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namespace opencv_test
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{
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/* cofiguration for tests of detectors/descriptors. shared between ocl and cpu tests. */
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/* configuration for tests of detectors/descriptors. shared between ocl and cpu tests. */
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// detectors/descriptors configurations to test
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#define DETECTORS_ONLY \
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@ -578,7 +578,7 @@ public:
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private:
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/**
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* Struture representing a node in the hierarchical k-means tree.
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* Structure representing a node in the hierarchical k-means tree.
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*/
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struct Node
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{
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@ -547,7 +547,7 @@ public:
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private:
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/**
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* Struture representing a node in the hierarchical k-means tree.
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* Structure representing a node in the hierarchical k-means tree.
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*/
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struct KMeansNode
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{
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@ -301,7 +301,7 @@ public:
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unsigned int index_;
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};
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/** Default cosntructor */
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/** Default constructor */
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UniqueResultSet() :
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is_full_(false), worst_distance_(std::numeric_limits<DistanceType>::max())
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{
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@ -1806,7 +1806,7 @@ static gboolean icvOnMouse( GtkWidget *widget, GdkEvent *event, gpointer user_da
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else if( event->type == GDK_SCROLL )
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{
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#if defined(GTK_VERSION3_4)
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// NOTE: in current implementation doesn't possible to put into callback function delta_x and delta_y separetely
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// NOTE: in current implementation doesn't possible to put into callback function delta_x and delta_y separately
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double delta = (event->scroll.delta_x + event->scroll.delta_y);
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cv_event = (event->scroll.delta_y!=0) ? CV_EVENT_MOUSEHWHEEL : CV_EVENT_MOUSEWHEEL;
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#else
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@ -1219,7 +1219,7 @@ cvShowImage( const char* name, const CvArr* arr )
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dst_ptr, (size.cx * channels + 3) & -4 );
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cvConvertImage( image, &dst, origin == 0 ? CV_CVTIMG_FLIP : 0 );
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// ony resize window if needed
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// only resize window if needed
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if (changed_size)
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icvUpdateWindowPos(window);
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InvalidateRect(window->hwnd, 0, 0);
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@ -52,7 +52,7 @@
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#include <stdio.h>
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#include <setjmp.h>
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// the following defines are a hack to avoid multiple problems with frame ponter handling and setjmp
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// the following defines are a hack to avoid multiple problems with frame pointer handling and setjmp
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// see http://gcc.gnu.org/ml/gcc/2011-10/msg00324.html for some details
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#define mingw_getsp(...) 0
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#define __builtin_frame_address(...) 0
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@ -62,7 +62,7 @@ namespace cv {
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#define MAX_PAM_HEADER_IDENITFIER_LENGTH 8
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#define MAX_PAM_HEADER_VALUE_LENGTH 255
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/* PAM header fileds */
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/* PAM header fields */
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typedef enum {
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PAM_HEADER_NONE,
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PAM_HEADER_COMMENT,
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@ -72,7 +72,7 @@
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#pragma warning( disable: 4611 )
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#endif
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// the following defines are a hack to avoid multiple problems with frame ponter handling and setjmp
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// the following defines are a hack to avoid multiple problems with frame pointer handling and setjmp
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// see http://gcc.gnu.org/ml/gcc/2011-10/msg00324.html for some details
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#define mingw_getsp(...) 0
|
||||
#define __builtin_frame_address(...) 0
|
||||
|
@ -28,7 +28,7 @@ PERF_TEST_P(Image_RhoStep_ThetaStep_Threshold, HoughLines,
|
||||
|
||||
Canny(image, image, 32, 128);
|
||||
|
||||
// add some syntetic lines:
|
||||
// add some synthetic lines:
|
||||
line(image, Point(0, 0), Point(image.cols, image.rows), Scalar::all(255), 3);
|
||||
line(image, Point(image.cols, 0), Point(image.cols/2, image.rows), Scalar::all(255), 3);
|
||||
|
||||
@ -89,7 +89,7 @@ PERF_TEST_P(Image_RhoStep_ThetaStep_Threshold, HoughLines3f,
|
||||
|
||||
Canny(image, image, 32, 128);
|
||||
|
||||
// add some syntetic lines:
|
||||
// add some synthetic lines:
|
||||
line(image, Point(0, 0), Point(image.cols, image.rows), Scalar::all(255), 3);
|
||||
line(image, Point(image.cols, 0), Point(image.cols/2, image.rows), Scalar::all(255), 3);
|
||||
|
||||
|
@ -603,7 +603,7 @@ HoughLinesProbabilistic( Mat& image,
|
||||
if( k > 0 )
|
||||
dx = -dx, dy = -dy;
|
||||
|
||||
// walk along the line using fixed-point arithmetics,
|
||||
// walk along the line using fixed-point arithmetic,
|
||||
// stop at the image border or in case of too big gap
|
||||
for( ;; x += dx, y += dy )
|
||||
{
|
||||
@ -651,7 +651,7 @@ HoughLinesProbabilistic( Mat& image,
|
||||
if( k > 0 )
|
||||
dx = -dx, dy = -dy;
|
||||
|
||||
// walk along the line using fixed-point arithmetics,
|
||||
// walk along the line using fixed-point arithmetic,
|
||||
// stop at the image border or in case of too big gap
|
||||
for( ;; x += dx, y += dy )
|
||||
{
|
||||
@ -968,7 +968,7 @@ void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, i
|
||||
createTrigTable( numangle, min_theta, theta_step,
|
||||
irho, tabSin, tabCos );
|
||||
|
||||
// stage 1. fill accumlator
|
||||
// stage 1. fill accumulator
|
||||
for( i = 0; i < (int)point.size(); i++ )
|
||||
for(int n = 0; n < numangle; n++ )
|
||||
{
|
||||
|
@ -269,7 +269,7 @@ static bool ippMorph(int op, int src_type, int dst_type,
|
||||
return false;
|
||||
|
||||
// Multiple iterations on small mask is not effective in current integration
|
||||
// Implace imitation for 3x3 kernel is not efficient
|
||||
// Inplace imitation for 3x3 kernel is not efficient
|
||||
// Advanced morphology for small mask introduces degradations
|
||||
if((iterations > 1 || src_data == dst_data || (op != MORPH_ERODE && op != MORPH_DILATE)) && kernel_width*kernel_height < 25)
|
||||
return false;
|
||||
|
@ -104,7 +104,7 @@ static void rotatingCalipers( const Point2f* points, int n, int mode, float* out
|
||||
/* rotating calipers sides will always have coordinates
|
||||
(a,b) (-b,a) (-a,-b) (b, -a)
|
||||
*/
|
||||
/* this is a first base bector (a,b) initialized by (1,0) */
|
||||
/* this is a first base vector (a,b) initialized by (1,0) */
|
||||
float orientation = 0;
|
||||
float base_a;
|
||||
float base_b = 0;
|
||||
|
@ -77,7 +77,7 @@ PARAM_TEST_CASE(Canny, Channels, ApertureSize, L2gradient, UseRoi)
|
||||
void generateTestData()
|
||||
{
|
||||
Mat img = readImageType("shared/fruits.png", CV_8UC(cn));
|
||||
ASSERT_FALSE(img.empty()) << "cann't load shared/fruits.png";
|
||||
ASSERT_FALSE(img.empty()) << "can't load shared/fruits.png";
|
||||
|
||||
Size roiSize = img.size();
|
||||
int type = img.type();
|
||||
|
@ -357,7 +357,7 @@ PARAM_TEST_CASE(CannyVX, ImagePath, ApertureSize, L2gradient)
|
||||
void loadImage()
|
||||
{
|
||||
src = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(src.empty()) << "cann't load image: " << imgPath;
|
||||
ASSERT_FALSE(src.empty()) << "can't load image: " << imgPath;
|
||||
}
|
||||
};
|
||||
|
||||
|
@ -268,7 +268,7 @@ class AsyncServiceHelper
|
||||
}
|
||||
else
|
||||
{
|
||||
Log.d(TAG, "Wating for package installation");
|
||||
Log.d(TAG, "Waiting for package installation");
|
||||
}
|
||||
|
||||
Log.d(TAG, "Unbind from service");
|
||||
|
@ -501,10 +501,10 @@ public class OpenCVTestCase extends TestCase {
|
||||
double maxDiff = Core.norm(diff, Core.NORM_INF);
|
||||
|
||||
if (isEqualityMeasured)
|
||||
assertTrue("Max difference between expected and actiual Mats is "+ maxDiff + ", that bigger than " + eps,
|
||||
assertTrue("Max difference between expected and actual Mats is "+ maxDiff + ", that bigger than " + eps,
|
||||
maxDiff <= eps);
|
||||
else
|
||||
assertFalse("Max difference between expected and actiual Mats is "+ maxDiff + ", that less than " + eps,
|
||||
assertFalse("Max difference between expected and actual Mats is "+ maxDiff + ", that less than " + eps,
|
||||
maxDiff <= eps);
|
||||
}
|
||||
|
||||
|
@ -527,10 +527,10 @@ public class OpenCVTestCase extends TestCase {
|
||||
double maxDiff = Core.norm(diff, Core.NORM_INF);
|
||||
|
||||
if (isEqualityMeasured)
|
||||
assertTrue("Max difference between expected and actiual Mats is "+ maxDiff + ", that bigger than " + eps,
|
||||
assertTrue("Max difference between expected and actual Mats is "+ maxDiff + ", that bigger than " + eps,
|
||||
maxDiff <= eps);
|
||||
else
|
||||
assertFalse("Max difference between expected and actiual Mats is "+ maxDiff + ", that less than " + eps,
|
||||
assertFalse("Max difference between expected and actual Mats is "+ maxDiff + ", that less than " + eps,
|
||||
maxDiff <= eps);
|
||||
}
|
||||
|
||||
|
@ -3,7 +3,7 @@
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
|
||||
|
@ -3,7 +3,7 @@
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
|
||||
|
@ -69,7 +69,7 @@
|
||||
//
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
|
||||
@ -92,7 +92,7 @@ QUnit.test('test_imgProc', function(assert) {
|
||||
binView[0] = 10;
|
||||
cv.calcHist(source, channels, mask, hist, histSize, ranges, false);
|
||||
|
||||
// hist should contains a N X 1 arrary.
|
||||
// hist should contains a N X 1 array.
|
||||
let size = hist.size();
|
||||
assert.equal(size.height, 256);
|
||||
assert.equal(size.width, 1);
|
||||
|
@ -69,7 +69,7 @@
|
||||
//
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
|
||||
|
@ -69,7 +69,7 @@
|
||||
//
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
cv.FS_createLazyFile('/', 'haarcascade_frontalface_default.xml', // eslint-disable-line new-cap
|
||||
'haarcascade_frontalface_default.xml', true, false);
|
||||
|
@ -68,7 +68,7 @@
|
||||
//
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
QUnit.module('Utils', {});
|
||||
|
@ -68,7 +68,7 @@
|
||||
//
|
||||
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
// The envrionment is Node.js
|
||||
// The environment is Node.js
|
||||
var cv = require('./opencv.js'); // eslint-disable-line no-var
|
||||
}
|
||||
|
||||
|
@ -433,7 +433,7 @@ Logistic Regression {#ml_intro_lr}
|
||||
ML implements logistic regression, which is a probabilistic classification technique. Logistic
|
||||
Regression is a binary classification algorithm which is closely related to Support Vector Machines
|
||||
(SVM). Like SVM, Logistic Regression can be extended to work on multi-class classification problems
|
||||
like digit recognition (i.e. recognizing digitis like 0,1 2, 3,... from the given images). This
|
||||
like digit recognition (i.e. recognizing digits like 0,1 2, 3,... from the given images). This
|
||||
version of Logistic Regression supports both binary and multi-class classifications (for multi-class
|
||||
it creates a multiple 2-class classifiers). In order to train the logistic regression classifier,
|
||||
Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see
|
||||
|
@ -1760,7 +1760,7 @@ Note that the parameters margin regularization, initial step size, and step decr
|
||||
|
||||
To use SVMSGD algorithm do as follows:
|
||||
|
||||
- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
|
||||
- first, create the SVMSGD object. The algorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
|
||||
setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
|
||||
|
||||
- then the SVM model can be trained using the train features and the correspondent labels by the method train().
|
||||
|
@ -614,7 +614,7 @@ protected:
|
||||
|
||||
if( data.empty() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "File with spambase dataset cann't be read.\n");
|
||||
ts->printf(cvtest::TS::LOG, "File with spambase dataset can't be read.\n");
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
|
@ -721,7 +721,7 @@ class FuncInfo(object):
|
||||
aname, argno = v.py_outlist[0]
|
||||
code_ret = "return pyopencv_from(%s)" % (aname,)
|
||||
else:
|
||||
# ther is more than 1 return parameter; form the tuple out of them
|
||||
# there is more than 1 return parameter; form the tuple out of them
|
||||
fmtspec = "N"*len(v.py_outlist)
|
||||
backcvt_arg_list = []
|
||||
for aname, argno in v.py_outlist:
|
||||
|
@ -585,7 +585,7 @@ $(function(){
|
||||
$(tbl_row).remove()
|
||||
})
|
||||
if($("tbody tr", tbl).length == 0) {
|
||||
$("<tr><td colspan='"+$("thead tr:first th", tbl).length+"'>No results mathing your search criteria</td></tr>")
|
||||
$("<tr><td colspan='"+$("thead tr:first th", tbl).length+"'>No results matching your search criteria</td></tr>")
|
||||
.appendTo($("tbody", tbl))
|
||||
}
|
||||
}
|
||||
|
@ -90,7 +90,7 @@ static void image_jacobian_homo_ECC(const Mat& src1, const Mat& src2,
|
||||
|
||||
|
||||
//instead of dividing each block with den,
|
||||
//just pre-devide the block of gradients (it's more efficient)
|
||||
//just pre-divide the block of gradients (it's more efficient)
|
||||
|
||||
Mat src1Divided_;
|
||||
Mat src2Divided_;
|
||||
|
@ -48,7 +48,7 @@
|
||||
#define GRIDSIZE 3
|
||||
#define LSx 8
|
||||
#define LSy 8
|
||||
// defeine local memory sizes
|
||||
// define local memory sizes
|
||||
#define LM_W (LSx*GRIDSIZE+2)
|
||||
#define LM_H (LSy*GRIDSIZE+2)
|
||||
#define BUFFER (LSx*LSy)
|
||||
|
@ -58,7 +58,7 @@ RIFF ('AVI '
|
||||
{xxdb|xxdc|xxpc|xxwb}
|
||||
xx - stream number: 00, 01, 02, ...
|
||||
db - uncompressed video frame
|
||||
dc - commpressed video frame
|
||||
dc - compressed video frame
|
||||
pc - palette change
|
||||
wb - audio frame
|
||||
|
||||
@ -139,7 +139,7 @@ class BitStream;
|
||||
// {xxdb|xxdc|xxpc|xxwb}
|
||||
// xx - stream number: 00, 01, 02, ...
|
||||
// db - uncompressed video frame
|
||||
// dc - commpressed video frame
|
||||
// dc - compressed video frame
|
||||
// pc - palette change
|
||||
// wb - audio frame
|
||||
|
||||
|
@ -299,10 +299,10 @@ bool CvCaptureCAM_Aravis::grabFrame()
|
||||
size_t buffer_size;
|
||||
framebuffer = (void*)arv_buffer_get_data (arv_buffer, &buffer_size);
|
||||
|
||||
// retieve image size properites
|
||||
// retrieve image size properites
|
||||
arv_buffer_get_image_region (arv_buffer, &xoffset, &yoffset, &width, &height);
|
||||
|
||||
// retieve image ID set by camera
|
||||
// retrieve image ID set by camera
|
||||
frameID = arv_buffer_get_frame_id(arv_buffer);
|
||||
|
||||
arv_stream_push_buffer(stream, arv_buffer);
|
||||
|
@ -1191,7 +1191,7 @@ CvVideoWriter_AVFoundation::CvVideoWriter_AVFoundation(const char* filename, int
|
||||
NSError *error = nil;
|
||||
|
||||
|
||||
// Make sure the file does not already exist. Necessary to overwirte??
|
||||
// Make sure the file does not already exist. Necessary to overwrite??
|
||||
/*
|
||||
NSFileManager *fileManager = [NSFileManager defaultManager];
|
||||
if ([fileManager fileExistsAtPath:path]){
|
||||
@ -1233,7 +1233,7 @@ CvVideoWriter_AVFoundation::CvVideoWriter_AVFoundation(const char* filename, int
|
||||
|
||||
if(mMovieWriter.status == AVAssetWriterStatusFailed){
|
||||
NSLog(@"%@", [mMovieWriter.error localizedDescription]);
|
||||
// TODO: error handling, cleanup. Throw execption?
|
||||
// TODO: error handling, cleanup. Throw exception?
|
||||
// return;
|
||||
}
|
||||
|
||||
|
@ -1184,7 +1184,7 @@ CvVideoWriter_AVFoundation::CvVideoWriter_AVFoundation(const std::string &filena
|
||||
NSError *error = nil;
|
||||
|
||||
|
||||
// Make sure the file does not already exist. Necessary to overwirte??
|
||||
// Make sure the file does not already exist. Necessary to overwrite??
|
||||
/*
|
||||
NSFileManager *fileManager = [NSFileManager defaultManager];
|
||||
if ([fileManager fileExistsAtPath:path]){
|
||||
|
@ -480,7 +480,7 @@ class videoInput{
|
||||
bool setupDeviceFourcc(int deviceID, int w, int h,int fourcc);
|
||||
|
||||
//These two are only for capture cards
|
||||
//USB and Firewire cameras souldn't specify connection
|
||||
//USB and Firewire cameras shouldn't specify connection
|
||||
bool setupDevice(int deviceID, int connection);
|
||||
bool setupDevice(int deviceID, int w, int h, int connection);
|
||||
|
||||
|
@ -1193,7 +1193,7 @@ bool CvCapture_MSMF::grabFrame()
|
||||
{
|
||||
if (streamIndex != dwStreamIndex)
|
||||
{
|
||||
CV_LOG_DEBUG(NULL, "videoio(MSMF): Wrong stream readed. Abort capturing");
|
||||
CV_LOG_DEBUG(NULL, "videoio(MSMF): Wrong stream read. Abort capturing");
|
||||
close();
|
||||
}
|
||||
else if (flags & MF_SOURCE_READERF_ERROR)
|
||||
|
@ -668,7 +668,7 @@ void BitStream::writeBlock()
|
||||
}
|
||||
|
||||
size_t BitStream::getPos() const {
|
||||
return safe_int_cast<size_t>(m_current - m_start, "Failed to determine AVI bufer position: value is out of range") + m_pos;
|
||||
return safe_int_cast<size_t>(m_current - m_start, "Failed to determine AVI buffer position: value is out of range") + m_pos;
|
||||
}
|
||||
|
||||
void BitStream::putByte(int val)
|
||||
|
@ -49,8 +49,8 @@ private:
|
||||
unsigned char __k;
|
||||
};
|
||||
|
||||
static_assert(sizeof(Guid) == sizeof(::_GUID), "Incorect size for Guid");
|
||||
static_assert(sizeof(__rcGUID_t) == sizeof(::_GUID), "Incorect size for __rcGUID_t");
|
||||
static_assert(sizeof(Guid) == sizeof(::_GUID), "Incorrect size for Guid");
|
||||
static_assert(sizeof(__rcGUID_t) == sizeof(::_GUID), "Incorrect size for __rcGUID_t");
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
inline Guid::Guid() : __a(0), __b(0), __c(0), __d(0), __e(0), __f(0), __g(0), __h(0), __i(0), __j(0), __k(0)
|
||||
|
@ -1,7 +1,7 @@
|
||||
package org.opencv.engine;
|
||||
|
||||
/**
|
||||
* Class provides Java interface to OpenCV Engine Service. Is synchronious with native OpenCVEngine class.
|
||||
* Class provides Java interface to OpenCV Engine Service. Is synchronous with native OpenCVEngine class.
|
||||
*/
|
||||
interface OpenCVEngineInterface
|
||||
{
|
||||
|
@ -31,7 +31,7 @@ from __future__ import print_function
|
||||
import glob, re, os, os.path, shutil, string, sys, argparse, traceback, multiprocessing
|
||||
from subprocess import check_call, check_output, CalledProcessError
|
||||
|
||||
IPHONEOS_DEPLOYMENT_TARGET='8.0' # default, can be changed via command line options or environemnt variable
|
||||
IPHONEOS_DEPLOYMENT_TARGET='8.0' # default, can be changed via command line options or environment variable
|
||||
|
||||
def execute(cmd, cwd = None):
|
||||
print("Executing: %s in %s" % (cmd, cwd), file=sys.stderr)
|
||||
|
Loading…
Reference in New Issue
Block a user