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Merge pull request #16241 from bwignall:typo

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This commit is contained in:
Alexander Alekhin
2019-12-27 16:18:56 +00:00
parent c4c94f891e 659ffaddb4
commit 9ec3d76b21
110 changed files with 142 additions and 142 deletions
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modules/calib3d/include/opencv2/calib3d.hpp
+1 -1
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@@ -1321,7 +1321,7 @@ struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters
GridType gridType;
CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING.
CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
};
#ifndef DISABLE_OPENCV_3_COMPATIBILITY
modules/calib3d/src/calibration.cpp
+1 -1
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@@ -48,7 +48,7 @@
#include <iterator>
/*
This is stright-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
This is straight-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
that is (in a large extent) based on the paper:
Z. Zhang. "A flexible new technique for camera calibration".
IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.
modules/calib3d/src/chessboard.cpp
+2 -2
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@@ -2474,7 +2474,7 @@ int Chessboard::Board::validateCorners(const cv::Mat &data,cv::flann::Index &fla
std::vector<cv::Point2f>::const_iterator iter1 = points.begin();
for(;iter1 != points.end();++iter1)
{
// we do not have to check for NaN because of getCorners(flase)
// we do not have to check for NaN because of getCorners(false)
std::vector<cv::Point2f>::const_iterator iter2 = iter1+1;
for(;iter2 != points.end();++iter2)
if(*iter1 == *iter2)
@@ -3007,7 +3007,7 @@ Chessboard::Board Chessboard::detectImpl(const Mat& gray,std::vector<cv::Mat> &f
if(keypoints_seed.empty())
return Chessboard::Board();
// check how many points are likely a checkerbord corner
// check how many points are likely a checkerboard corner
float response = fabs(keypoints_seed.front().response*MIN_RESPONSE_RATIO);
std::vector<KeyPoint>::const_iterator seed_iter = keypoints_seed.begin();
int count = 0;
modules/calib3d/src/chessboard.hpp
+1 -1
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@@ -650,7 +650,7 @@ class Chessboard: public cv::Feature2D
bool top(bool check_empty=false); // moves one corner to the top or returns false
bool checkCorner()const; // returns true if the current corner belongs to at least one
// none empty cell
bool isNaN()const; // returns true if the currnet corner is NaN
bool isNaN()const; // returns true if the current corner is NaN
const cv::Point2f* operator*() const; // current corner coordinate
cv::Point2f* operator*(); // current corner coordinate
modules/calib3d/test/test_chesscorners_badarg.cpp
+1 -1
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@@ -94,7 +94,7 @@ void CV_ChessboardDetectorBadArgTest::run( int /*start_from */)
img = cb.clone();
initArgs();
pattern_size = Size(2,2);
errors += run_test_case( Error::StsOutOfRange, "Invlid pattern size" );
errors += run_test_case( Error::StsOutOfRange, "Invalid pattern size" );
pattern_size = cbg.cornersSize();
cb.convertTo(img, CV_32F);
modules/core/include/opencv2/core/core_c.h
+1 -1
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@@ -1309,7 +1309,7 @@ CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order,
const CvArr* delta CV_DEFAULT(NULL),
double scale CV_DEFAULT(1.) );
/** Tranposes matrix. Square matrices can be transposed in-place */
/** Transposes matrix. Square matrices can be transposed in-place */
CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst );
#define cvT cvTranspose
modules/core/include/opencv2/core/hal/intrin_avx.hpp
+1 -1
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@@ -569,7 +569,7 @@ inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b)
{ return v_int64x4(v256_blend<m>(v_uint64x4(a.val), v_uint64x4(b.val)).val); }
// shuffle
// todo: emluate 64bit
// todo: emulate 64bit
#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \
template<int m> \
inline _Tpvec v256_shuffle(const _Tpvec& a) \
modules/core/include/opencv2/core/hal/intrin_cpp.hpp
+1 -1
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@@ -73,7 +73,7 @@ implemented as a structure based on a one SIMD register.
- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char
- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short
- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int
- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsigned/signed) - int
- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
- cv::v_float32x4: four 32-bit floating point values (signed) - float
- cv::v_float64x2: two 64-bit floating point values (signed) - double
modules/core/include/opencv2/core/hal/intrin_msa.hpp
+1 -1
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@@ -1805,7 +1805,7 @@ inline v_float32x4 v_broadcast_element(const v_float32x4& a)
return v_setall_f32(v_extract_n<i>(a));
}
////// FP16 suport ///////
////// FP16 support ///////
#if CV_FP16
inline v_float32x4 v_load_expand(const float16_t* ptr)
{
modules/core/include/opencv2/core/hal/intrin_wasm.hpp
+1 -1
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@@ -94,7 +94,7 @@ struct v_uint16x8
}
ushort get0() const
{
return (ushort)wasm_i16x8_extract_lane(val, 0); // wasm_u16x8_extract_lane() unimplemeted yet
return (ushort)wasm_i16x8_extract_lane(val, 0); // wasm_u16x8_extract_lane() unimplemented yet
}
v128_t val;
modules/core/include/opencv2/core/hal/msa_macros.h
+2 -2
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@@ -50,7 +50,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
#define msa_ld1q_f32(__a) ((v4f32)__builtin_msa_ld_w(__a, 0))
#define msa_ld1q_f64(__a) ((v2f64)__builtin_msa_ld_d(__a, 0))
/* Store 64bits vector elments values to the given memory address. */
/* Store 64bits vector elements values to the given memory address. */
#define msa_st1_s8(__a, __b) (*((v8i8*)(__a)) = __b)
#define msa_st1_s16(__a, __b) (*((v4i16*)(__a)) = __b)
#define msa_st1_s32(__a, __b) (*((v2i32*)(__a)) = __b)
@@ -377,7 +377,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
})
/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector.
Input is signed and outpus is unsigned. */
Input is signed and output is unsigned. */
#define msa_qrshrun_n_s16(__a, __b) \
({ \
v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__b)); \
modules/core/include/opencv2/core/opencl/opencl_info.hpp
+1 -1
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@@ -62,7 +62,7 @@ static String getDeviceTypeString(const cv::ocl::Device& device)
}
}
return "unkown";
return "unknown";
}
} // namespace
modules/core/include/opencv2/core/optim.hpp
+1 -1
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@@ -165,7 +165,7 @@ public:
/** @brief Sets the initial step that will be used in downhill simplex algorithm.
Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional
Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional
vectors that are used to determine the shape of initial simplex. Roughly said, initial point
determines the position of a simplex (it will become simplex's centroid), while step determines the
spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are
modules/core/include/opencv2/core/vsx_utils.hpp
+2 -2
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@@ -317,7 +317,7 @@ VSX_IMPL_1RG(vec_udword2, wi, vec_float4, wf, xvcvspuxds, vec_ctulo)
* Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837
*
* So we're not able to use inline asm and only use built-in functions that CLANG supports
* and use __builtin_convertvector if clang missng any of vector conversions built-in functions
* and use __builtin_convertvector if clang missing any of vector conversions built-in functions
*
* todo: clang asm template bug is fixed, need to reconsider the current workarounds.
*/
@@ -491,7 +491,7 @@ VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo)
// Only for Eigen!
/*
* changing behavior of conversion intrinsics for gcc has effect on Eigen
* so we redfine old behavior again only on gcc, clang
* so we redefine old behavior again only on gcc, clang
*/
#if !defined(__clang__) || __clang_major__ > 4
// ignoring second arg since Eigen only truncates toward zero
modules/core/src/array.cpp
+2 -2
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@@ -250,7 +250,7 @@ cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes,
for( int i = dims - 1; i >= 0; i-- )
{
if( sizes[i] < 0 )
CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
mat->dim[i].size = sizes[i];
if( step > INT_MAX )
CV_Error( CV_StsOutOfRange, "The array is too big" );
@@ -545,7 +545,7 @@ cvCreateSparseMat( int dims, const int* sizes, int type )
for( i = 0; i < dims; i++ )
{
if( sizes[i] <= 0 )
CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
}
CvSparseMat* arr = (CvSparseMat*)cvAlloc(sizeof(*arr)+MAX(0,dims-CV_MAX_DIM)*sizeof(arr->size[0]));
modules/core/src/convert_scale.simd.hpp
+2 -2
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@@ -53,7 +53,7 @@ cvtabs_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
}
}
// variant for convrsions 16f <-> ... w/o unrolling
// variant for conversions 16f <-> ... w/o unrolling
template<typename _Ts, typename _Td> inline void
cvtabs1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
Size size, float a, float b )
@@ -123,7 +123,7 @@ cvt_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
}
}
// variant for convrsions 16f <-> ... w/o unrolling
// variant for conversions 16f <-> ... w/o unrolling
template<typename _Ts, typename _Td> inline void
cvt1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
Size size, float a, float b )
modules/core/src/downhill_simplex.cpp
+1 -1
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@@ -77,7 +77,7 @@ Replaced y(1,ndim,0.0) ------> y(1,ndim+1,0.0)
***********************************************************************************************************************************
The code below was used in tesing the source code.
The code below was used in testing the source code.
Created by @SareeAlnaghy
#include <iostream>
modules/core/src/system.cpp
+3 -3
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@@ -1519,7 +1519,7 @@ public:
{
TlsAbstraction* tls = getTlsAbstraction();
if (NULL == tls)
return; // TLS signleton is not available (terminated)
return; // TLS singleton is not available (terminated)
ThreadData *pTD = tlsValue == NULL ? (ThreadData*)tls->getData() : (ThreadData*)tlsValue;
if (pTD == NULL)
return; // no OpenCV TLS data for this thread
@@ -1610,7 +1610,7 @@ public:
TlsAbstraction* tls = getTlsAbstraction();
if (NULL == tls)
return NULL; // TLS signleton is not available (terminated)
return NULL; // TLS singleton is not available (terminated)
ThreadData* threadData = (ThreadData*)tls->getData();
if(threadData && threadData->slots.size() > slotIdx)
@@ -1646,7 +1646,7 @@ public:
TlsAbstraction* tls = getTlsAbstraction();
if (NULL == tls)
return; // TLS signleton is not available (terminated)
return; // TLS singleton is not available (terminated)
ThreadData* threadData = (ThreadData*)tls->getData();
if(!threadData)
modules/dnn/include/opencv2/dnn/all_layers.hpp
+1 -1
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@@ -134,7 +134,7 @@ CV__DNN_INLINE_NS_BEGIN
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
/** @deprecated Use flag `produce_cell_output` in LayerParams.
* @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
* @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
*
* If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
modules/dnn/src/cuda/slice.cu
+1 -1
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@@ -84,7 +84,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
* Reasoning:
* ----------
* Suppose an item's indices in the output tensor is [o1, o2, ...]. The indices in the input
* tensor will be [o1 + off1, o2 + off2, ...]. The rest of the elements in the input are igored.
* tensor will be [o1 + off1, o2 + off2, ...]. The rest of the elements in the input are ignored.
*
* If the size of the first axis of the input and output tensor is unity, the input and output indices
* for all the elements will be of the form be [0, o2 + off2, ...] and [0, o2, ...] respectively. Note that
modules/dnn/src/cuda4dnn/csl/cudnn/convolution.hpp
+1 -1
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@@ -227,7 +227,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
if (std::is_same<T, half>::value)
CUDA4DNN_CHECK_CUDNN(cudnnSetConvolutionMathType(descriptor, CUDNN_TENSOR_OP_MATH));
} catch (...) {
/* cudnnDestroyConvolutionDescriptor will not fail for a valid desriptor object */
/* cudnnDestroyConvolutionDescriptor will not fail for a valid descriptor object */
CUDA4DNN_CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(descriptor));
throw;
}
modules/dnn/src/cuda4dnn/csl/memory.hpp
+1 -1
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@@ -266,7 +266,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
/** page-locks \p size_in_bytes bytes of memory starting from \p ptr_
*
* Pre-conditons:
* Pre-conditions:
* - host memory should be unregistered
*/
MemoryLockGuard(void* ptr_, std::size_t size_in_bytes) {
modules/dnn/src/cuda4dnn/csl/pointer.hpp
+1 -1
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@@ -33,7 +33,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
*
* A `DevicePtr<T>` can implicitly convert to `DevicePtr<const T>`.
*
* Specalizations:
* Specializations:
* - DevicePtr<void>/DevicePtr<const void> do not support pointer arithmetic (but relational operators are provided)
* - any device pointer pointing to mutable memory is implicitly convertible to DevicePtr<void>
* - any device pointer is implicitly convertible to DevicePtr<const void>
modules/dnn/src/cuda4dnn/csl/tensor.hpp
+3 -3
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@@ -67,7 +67,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
*/
template <class T>
class Tensor {
static_assert(std::is_standard_layout<T>::value, "T must staisfy StandardLayoutType");
static_assert(std::is_standard_layout<T>::value, "T must satisfy StandardLayoutType");
public:
using value_type = typename ManagedPtr<T>::element_type;
@@ -553,7 +553,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* - [start, end) represents a forward range containing the length of the axes in order
* - the number of axis lengths must be less than or equal to the rank
* - at most one axis length is allowed for length deduction
* - the lengths provided must ensure that the total number of elements remains unchnged
* - the lengths provided must ensure that the total number of elements remains unchanged
*
* Exception Guarantee: Strong
*/
@@ -898,7 +898,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* - [start, end) represents a forward range containing length of the axes in order starting from axis zero
* - the number of axis lengths must be less than or equal to the tensor rank
* - at most one axis length is allowed for length deduction
* - the lengths provided must ensure that the total number of elements remains unchnged
* - the lengths provided must ensure that the total number of elements remains unchanged
*
* Exception Guarantee: Strong
*/
modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp
+3 -3
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@@ -35,7 +35,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* Pre-conditions:
* - \p dest and \p src must have the same shape
*
* Exception Gaurantee: Basic
* Exception Guarantee: Basic
*/
template <class T> inline
void copy(const Stream& stream, TensorSpan<T> dest, TensorView<T> src) {
@@ -50,7 +50,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* - \p A and \p B must meet the mathematical requirements for matrix multiplication
* - \p result must be large enough to hold the result
*
* Exception Gaurantee: Basic
* Exception Guarantee: Basic
*/
template <class T> inline
void gemm(const cublas::Handle& handle, T beta, TensorSpan<T> result, T alpha, bool transa, TensorView<T> A, bool transb, TensorView<T> B) {
@@ -108,7 +108,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* Pre-conditions:
* - \p A and \p result must be compatible tensors
*
* Exception Gaurantee: Basic
* Exception Guarantee: Basic
*/
template <class T> inline
void softmax(const cudnn::Handle& handle, TensorSpan<T> output, TensorView<T> input, int channel_axis, bool log) {
modules/dnn/src/cuda4dnn/primitives/pooling.hpp
+1 -1
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@@ -103,7 +103,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
CV_Assert(pooling_order == pads_end.size());
/* cuDNN rounds down by default; hence, if ceilMode is false, we do nothing
* otherwise, we add extra padding towards the end so that the convolution arithmetic yeilds
* otherwise, we add extra padding towards the end so that the convolution arithmetic yields
* the correct output size without having to deal with fancy fractional sizes
*/
auto pads_end_modified = pads_end;
modules/dnn/src/ie_ngraph.cpp
+2 -2
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@@ -622,7 +622,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
try {
wrapper->outProms[processedOutputs].setException(std::current_exception());
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
}
}
@@ -635,7 +635,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
try {
wrapper->outProms[processedOutputs].setException(e);
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
}
}
modules/dnn/src/onnx/opencv-onnx.proto
+3 -3
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@@ -116,7 +116,7 @@ message AttributeProto {
// The type field MUST be present for this version of the IR.
// For 0.0.1 versions of the IR, this field was not defined, and
// implementations needed to use has_field hueristics to determine
// implementations needed to use has_field heuristics to determine
// which value field was in use. For IR_VERSION 0.0.2 or later, this
// field MUST be set and match the f|i|s|t|... field in use. This
// change was made to accommodate proto3 implementations.
@@ -323,7 +323,7 @@ message TensorProto {
// For float and complex64 values
// Complex64 tensors are encoded as a single array of floats,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
@@ -373,7 +373,7 @@ message TensorProto {
// For double
// Complex64 tensors are encoded as a single array of doubles,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
modules/dnn/src/op_cuda.hpp
+1 -1
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@@ -350,7 +350,7 @@ namespace cv { namespace dnn {
private:
/* The same tensor memory can be reused by different layers whenever possible.
* Hence, it is possible for different backend warppers to point to the same memory.
* Hence, it is possible for different backend wrappers to point to the same memory.
* However, it may use only a part of that memory and have a different shape.
*
* We store the common information such as device tensor and its corresponding host memory in
modules/dnn/src/vkcom/src/context.cpp
+1 -1
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@@ -243,7 +243,7 @@ Context::Context()
queueCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
queueCreateInfo.queueFamilyIndex = kQueueFamilyIndex;
queueCreateInfo.queueCount = 1; // create one queue in this family. We don't need more.
float queuePriorities = 1.0; // we only have one queue, so this is not that imporant.
float queuePriorities = 1.0; // we only have one queue, so this is not that important.
queueCreateInfo.pQueuePriorities = &queuePriorities;
VkDeviceCreateInfo deviceCreateInfo = {};
modules/features2d/include/opencv2/features2d.hpp
+1 -1
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@@ -398,7 +398,7 @@ code which is distributed under GPL.
class CV_EXPORTS_W MSER : public Feature2D
{
public:
/** @brief Full consturctor for %MSER detector
/** @brief Full constructor for %MSER detector
@param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
@param _min_area prune the area which smaller than minArea
modules/features2d/src/kaze/nldiffusion_functions.h
+1 -1
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@@ -36,7 +36,7 @@ void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int
// Nonlinear diffusion filtering scalar step
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
// For non-maxima suppresion
// For non-maxima suppression
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
// Image downsampling
modules/features2d/src/mser.cpp
+1 -1
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@@ -983,7 +983,7 @@ extractMSER_8uC3( const Mat& src,
double s = (double)(lr->size-lr->sizei)/(lr->dt-lr->di);
if ( s < lr->s )
{
// skip the first one and check stablity
// skip the first one and check stability
if ( i > lr->reinit+1 && MSCRStableCheck( lr, params ) )
{
if ( lr->tmsr == NULL )
modules/flann/include/opencv2/flann/simplex_downhill.h
+1 -1
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@@ -131,7 +131,7 @@ float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
}
if (val_r<vals[0]) {
// value is smaller than smalest in simplex
// value is smaller than smallest in simplex
// expand some more to see if it drops further
for (int i=0; i<n; ++i) {
modules/gapi/doc/00-root.markdown
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@@ -95,7 +95,7 @@ Internally, cv::GComputation::apply() compiles the captured graph for
the given input parameters and executes the compiled graph on data
immediately.
There is a number important concepts can be outlines with this examle:
There is a number important concepts can be outlines with this example:
* Graph declaration and graph execution are distinct steps;
* Graph is built implicitly from a sequence of G-API expressions;
* G-API supports function-like calls -- e.g. cv::gapi::resize(), and
modules/gapi/doc/01-background.markdown
+1 -1
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@@ -36,7 +36,7 @@ software optimization due to diffent costs of memory access on modern
computer architectures -- the more data is reused in the first level
cache, the more efficient pipeline is.
Definitely the aforementioned techinques can be applied manually --
Definitely the aforementioned techniques can be applied manually --
but it requires extra skills and knowledge of the target platform and
the algorithm implementation changes irrevocably -- becoming more
specific, less flexible, and harder to extend and maintain.
modules/gapi/doc/slides/gapi_overview.org
+1 -1
View File
@@ -242,7 +242,7 @@ Graph *protocol* defines what arguments a computation was defined on
- A type name (every operation is a C++ type);
- Operation signature (similar to ~std::function<>~);
- Operation identifier (a string);
- Metadata callback -- desribe what is the output value format(s),
- Metadata callback -- describe what is the output value format(s),
given the input and arguments.
- Use ~OpType::on(...)~ to use a new kernel ~OpType~ to construct graphs.
#+LaTeX: {\footnotesize
modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp
+1 -1
View File
@@ -124,7 +124,7 @@ protected:
F m_f;
};
// FIXME: This is an ugly ad-hoc imlpementation. TODO: refactor
// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
namespace detail
{
modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp
+1 -1
View File
@@ -39,7 +39,7 @@ namespace fluid
*/
GAPI_EXPORTS cv::gapi::GBackend backend();
/** @} */
} // namespace flud
} // namespace fluid
} // namespace gapi
modules/gapi/include/opencv2/gapi/gcompiled.hpp
+1 -1
View File
@@ -148,7 +148,7 @@ public:
* @param outs vector of output cv::Mat objects to produce by the
* computation.
*
* Numbers of elements in ins/outs vectos must match numbers of
* Numbers of elements in ins/outs vectors must match numbers of
* inputs/outputs which were used to define the source GComputation.
*/
void operator() (const std::vector<cv::Mat> &ins, // Compatibility overload
modules/gapi/include/opencv2/gapi/gcomputation.hpp
+2 -2
View File
@@ -314,7 +314,7 @@ public:
* @param args compilation arguments for underlying compilation
* process.
*
* Numbers of elements in ins/outs vectos must match numbers of
* Numbers of elements in ins/outs vectors must match numbers of
* inputs/outputs which were used to define this GComputation.
*/
void apply(const std::vector<cv::Mat>& ins, // Compatibility overload
@@ -373,7 +373,7 @@ public:
// template<typename... Ts>
// GCompiled compile(const Ts&... metas, GCompileArgs &&args)
//
// But not all compilers can hande this (and seems they shouldn't be able to).
// But not all compilers can handle this (and seems they shouldn't be able to).
// FIXME: SFINAE looks ugly in the generated documentation
/**
* @overload
modules/gapi/include/opencv2/gapi/gtype_traits.hpp
+1 -1
View File
@@ -101,7 +101,7 @@ namespace detail
template<> struct GTypeOf<cv::gapi::own::Scalar> { using type = cv::GScalar; };
template<typename U> struct GTypeOf<std::vector<U> > { using type = cv::GArray<U>; };
// FIXME: This is not quite correct since IStreamSource may produce not only Mat but also Scalar
// and vector data. TODO: Extend the type dispatchig on these types too.
// and vector data. TODO: Extend the type dispatching on these types too.
template<> struct GTypeOf<cv::gapi::wip::IStreamSource::Ptr> { using type = cv::GMat;};
template<class T> using g_type_of_t = typename GTypeOf<T>::type;
modules/gapi/include/opencv2/gapi/ocl/goclkernel.hpp
+1 -1
View File
@@ -94,7 +94,7 @@ protected:
F m_f;
};
// FIXME: This is an ugly ad-hoc imlpementation. TODO: refactor
// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
namespace detail
{
modules/gapi/include/opencv2/gapi/streaming/cap.hpp
+1 -1
View File
@@ -35,7 +35,7 @@ namespace wip {
* This class implements IStreamSource interface.
* Its constructor takes the same parameters as cv::VideoCapture does.
*
* Please make sure that videoio OpenCV module is avaiable before using
* Please make sure that videoio OpenCV module is available before using
* this in your application (G-API doesn't depend on it directly).
*
* @note stream sources are passed to G-API via shared pointers, so
modules/gapi/samples/draw_example.cpp
+1 -1
View File
@@ -7,7 +7,7 @@
int main(int argc, char *argv[])
{
if (argc < 2) {
std::cerr << "Filename requried" << std::endl;
std::cerr << "Filename required" << std::endl;
return 1;
}
modules/gapi/src/api/ft_render.cpp
+3 -3
View File
@@ -61,13 +61,13 @@ cv::Size cv::gapi::wip::draw::FTTextRender::Priv::getTextSize(const std::wstring
// or decrement (for right-to-left writing) the pen position after a
// glyph has been rendered when processing text
//
// widht (bitmap->width) - The width of glyph
// width (bitmap->width) - The width of glyph
//
//
// Algorihm to compute size of the text bounding box:
// Algorithm to compute size of the text bounding box:
//
// 1) Go through all symbols and shift pen position and save glyph parameters (left, advance, width)
// If left + pen postion < 0 set left to 0. For example it's maybe happened
// If left + pen position < 0 set left to 0. For example it's maybe happened
// if we print first letter 'J' or any other letter with negative 'left'
// We want to render glyph in pen position + left, so we must't allow it to be negative
//
modules/gapi/src/api/render_ocv.cpp
+2 -2
View File
@@ -184,7 +184,7 @@ void drawPrimitivesOCV(cv::Mat& in,
cv::Point org(0, mask.rows - baseline);
cv::putText(mask, tp.text, org, tp.ff, tp.fs, 255, tp.thick);
// Org is bottom left point, trasform it to top left point for blendImage
// Org is bottom left point, transform it to top left point for blendImage
cv::Point tl(tp.org.x, tp.org.y - mask.size().height + baseline);
blendTextMask(in, mask, tl, tp.color);
@@ -208,7 +208,7 @@ void drawPrimitivesOCV(cv::Mat& in,
cv::Point org(0, mask.rows - baseline);
ftpr->putText(mask, ftp.text, org, ftp.fh);
// Org is bottom left point, trasform it to top left point for blendImage
// Org is bottom left point, transform it to top left point for blendImage
cv::Point tl(ftp.org.x, ftp.org.y - mask.size().height + baseline);
blendTextMask(in, mask, tl, color);
modules/gapi/src/backends/fluid/gfluidimgproc.cpp
+1 -1
View File
@@ -1823,7 +1823,7 @@ GAPI_FLUID_KERNEL(GFluidBayerGR2RGB, cv::gapi::imgproc::GBayerGR2RGB, false)
}
};
} // namespace fliud
} // namespace fluid
} // namespace gapi
} // namespace cv
modules/gapi/src/backends/fluid/gfluidimgproc_func.dispatch.cpp
+1 -1
View File
@@ -209,7 +209,7 @@ RUN_MEDBLUR3X3_IMPL( float)
#undef RUN_MEDBLUR3X3_IMPL
} // namespace fliud
} // namespace fluid
} // namespace gapi
} // namespace cv
modules/gapi/src/backends/fluid/gfluidutils.hpp
+1 -1
View File
@@ -25,7 +25,7 @@ using cv::gapi::own::rintd;
//--------------------------------
//
// Macros for mappig of data types
// Macros for mapping of data types
//
//--------------------------------
modules/gapi/src/backends/ie/giebackend.cpp
+1 -1
View File
@@ -185,7 +185,7 @@ struct IEUnit {
// The practice shows that not all inputs and not all outputs
// are mandatory to specify in IE model.
// So what we're concerned here about is:
// if opeation's (not topology's) input/output number is
// if operation's (not topology's) input/output number is
// greater than 1, then we do care about input/output layer
// names. Otherwise, names are picked up automatically.
// TODO: Probably this check could be done at the API entry point? (gnet)
modules/gapi/src/backends/ocl/goclimgproc.hpp
+1 -1
View File
@@ -15,7 +15,7 @@
namespace cv { namespace gimpl {
// NB: This is what a "Kernel Package" from the origianl Wiki doc should be.
// NB: This is what a "Kernel Package" from the original Wiki doc should be.
void loadOCLImgProc(std::map<std::string, cv::GOCLKernel> &kmap);
}}
modules/gapi/src/compiler/passes/islands.cpp
+2 -2
View File
@@ -32,7 +32,7 @@ namespace
//
// In this case, Data object is part of Island A if and only if:
// - Data object's producer is part of Island A,
// - AND any of Data obejct's consumers is part of Island A.
// - AND any of Data object's consumers is part of Island A.
//
// Op["island0"] --> Data[ ? ] --> Op["island0"]
// :
@@ -147,7 +147,7 @@ void cv::gimpl::passes::checkIslands(ade::passes::PassContext &ctx)
// Run the recursive traversal process as described in 5/a-d.
// This process is like a flood-fill traversal for island.
// If there's to distint successful flood-fills happened for the same island
// If there's to distinct successful flood-fills happened for the same island
// name, there are two islands with this name.
std::stack<ade::NodeHandle> stack;
stack.push(tagged_nh);
modules/gapi/src/executor/gstreamingexecutor.cpp
+1 -1
View File
@@ -198,7 +198,7 @@ void sync_data(cv::GRunArgs &results, cv::GRunArgsP &outputs)
// "Stop" is received.
//
// Queue reader is the class which encapsulates all this logic and
// provies threads with a managed storage and an easy API to obtain
// provides threads with a managed storage and an easy API to obtain
// data.
class QueueReader
{
modules/gapi/test/common/gapi_core_tests.hpp
+1 -1
View File
@@ -67,7 +67,7 @@ inline std::ostream& operator<<(std::ostream& os, bitwiseOp op)
// initMatsRandU - function that is used to initialize input/output data
// FIXTURE_API(mathOp,bool,double,bool) - test-specific parameters (types)
// 4 - number of test-specific parameters
// opType, testWithScalar, scale, doReverseOp - test-spcific parameters (names)
// opType, testWithScalar, scale, doReverseOp - test-specific parameters (names)
//
// We get:
// 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function
modules/gapi/test/common/gapi_core_tests_inl.hpp
+2 -2
View File
@@ -294,7 +294,7 @@ TEST_P(Polar2CartTest, AccuracyTest)
// expect of single-precision elementary functions implementation.
//
// However, good idea is making such threshold configurable: parameter
// of this test - which a specific test istantiation could setup.
// of this test - which a specific test instantiation could setup.
//
// Note that test instantiation for the OpenCV back-end could even let
// the threshold equal to zero, as CV back-end calls the same kernel.
@@ -340,7 +340,7 @@ TEST_P(Cart2PolarTest, AccuracyTest)
// expect of single-precision elementary functions implementation.
//
// However, good idea is making such threshold configurable: parameter
// of this test - which a specific test istantiation could setup.
// of this test - which a specific test instantiation could setup.
//
// Note that test instantiation for the OpenCV back-end could even let
// the threshold equal to zero, as CV back-end calls the same kernel.
modules/gapi/test/common/gapi_imgproc_tests.hpp
+1 -1
View File
@@ -19,7 +19,7 @@ namespace opencv_test
// initMatrixRandN - function that is used to initialize input/output data
// FIXTURE_API(CompareMats,int,int) - test-specific parameters (types)
// 3 - number of test-specific parameters
// cmpF, kernSize, borderType - test-spcific parameters (names)
// cmpF, kernSize, borderType - test-specific parameters (names)
//
// We get:
// 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function
modules/gapi/test/gapi_async_test.cpp
+1 -1
View File
@@ -426,7 +426,7 @@ struct output_args_lifetime : ::testing::Test{
static constexpr const int num_of_requests = 20;
};
TYPED_TEST_CASE_P(output_args_lifetime);
//There are intentionaly no actual checks (asserts and verify) in output_args_lifetime tests.
//There are intentionally no actual checks (asserts and verify) in output_args_lifetime tests.
//They are more of example use-cases than real tests. (ASAN/valgrind can still catch issues here)
TYPED_TEST_P(output_args_lifetime, callback){
modules/gapi/test/gapi_smoke_test.cpp
+1 -1
View File
@@ -64,7 +64,7 @@ TEST(GAPI, Mat_Recreate)
EXPECT_EQ(m3.at<uchar>(0, 0), m4.at<uchar>(0, 0));
// cv::Mat::create must be NOOP if we don't change the meta,
// even if the origianl mat is created from handle.
// even if the original mat is created from handle.
m4.create(3, 3, CV_8U);
EXPECT_EQ(m3.rows, m4.rows);
EXPECT_EQ(m3.cols, m4.cols);
modules/imgproc/include/opencv2/imgproc/imgproc_c.h
+1 -1
View File
@@ -1151,7 +1151,7 @@ CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype );
/** @brief Returns the polygon points which make up the given ellipse.
The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than
sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than
0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
number of points stored into 'pts' is returned by this function.
@see cv::ellipse2Poly
modules/imgproc/src/approx.cpp
+1 -1
View File
@@ -630,7 +630,7 @@ approxPolyDP_( const Point_<T>* src_contour, int count0, Point_<T>* dst_contour,
WRITE_PT( src_contour[count-1] );
// last stage: do final clean-up of the approximated contour -
// remove extra points on the [almost] stright lines.
// remove extra points on the [almost] straight lines.
is_closed = is_closed0;
count = new_count;
pos = is_closed ? count - 1 : 0;
modules/imgproc/src/shapedescr.cpp
+2 -2
View File
@@ -776,7 +776,7 @@ cv::RotatedRect cv::fitEllipseDirect( InputArray _points )
namespace cv
{
// Calculates bounding rectagnle of a point set or retrieves already calculated
// Calculates bounding rectangle of a point set or retrieves already calculated
static Rect pointSetBoundingRect( const Mat& points )
{
int npoints = points.checkVector(2);
@@ -1392,7 +1392,7 @@ cvFitEllipse2( const CvArr* array )
return cvBox2D(cv::fitEllipse(points));
}
/* Calculates bounding rectagnle of a point set or retrieves already calculated */
/* Calculates bounding rectangle of a point set or retrieves already calculated */
CV_IMPL CvRect
cvBoundingRect( CvArr* array, int update )
{
modules/imgproc/test/test_approxpoly.cpp
+1 -1
View File
@@ -325,7 +325,7 @@ void CV_ApproxPolyTest::run( int /*start_from*/ )
if( DstSeq == NULL )
{
ts->printf( cvtest::TS::LOG,
"cvApproxPoly returned NULL for contour #%d, espilon = %g\n", i, Eps );
"cvApproxPoly returned NULL for contour #%d, epsilon = %g\n", i, Eps );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
goto _exit_;
} // if( DstSeq == NULL )
modules/imgproc/test/test_intersection.cpp
+1 -1
View File
@@ -60,7 +60,7 @@ namespace opencv_test { namespace {
// 6 - partial intersection, rectangle on top of different size
// 7 - full intersection, rectangle fully enclosed in the other
// 8 - partial intersection, rectangle corner just touching. point contact
// 9 - partial intersetion. rectangle side by side, line contact
// 9 - partial intersection. rectangle side by side, line contact
static void compare(const std::vector<Point2f>& test, const std::vector<Point2f>& target)
{
modules/java/android_sdk/CMakeLists.txt
+2 -2
View File
@@ -44,7 +44,7 @@ foreach(file ${seed_project_files_rel})
endforeach()
list(APPEND depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Andoid SDK) source files" ${depends})
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Android SDK) source files" ${depends})
file(REMOVE "${OPENCV_DEPHELPER}/${the_module}_android_source_copy") # force rebuild after CMake run
set(depends ${the_module}_android_source_copy "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")
@@ -134,7 +134,7 @@ foreach(file ${__files_rel})
endforeach()
list(APPEND depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Andoid SDK) source files" ${depends})
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Android SDK) source files" ${depends})
file(REMOVE "${OPENCV_DEPHELPER}/${the_module}_android_source_copy") # force rebuild after CMake run
set(depends ${the_module}_android_source_copy "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")
modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java
+1 -1
View File
@@ -248,7 +248,7 @@ public abstract class CameraBridgeViewBase extends SurfaceView implements Surfac
/**
* This method is provided for clients, so they can disable camera connection and stop
* the delivery of frames even though the surface view itself is not destroyed and still stays on the scren
* the delivery of frames even though the surface view itself is not destroyed and still stays on the screen
*/
public void disableView() {
synchronized(mSyncObject) {
modules/js/perf/README.md
+1 -1
View File
@@ -32,4 +32,4 @@ To run performance tests, please launch a local web server in <build_dir>/bin fo
Navigate the web browser to the kernel page you want to test, like http://localhost:8080/perf/imgproc/cvtcolor.html.
You can input the paramater, and then click the `Run` button to run the specific case, or it will run all the cases.
You can input the parameter, and then click the `Run` button to run the specific case, or it will run all the cases.
modules/ml/include/opencv2/ml.hpp
+1 -1
View File
@@ -1683,7 +1683,7 @@ public:
/** @brief This function returns the trained parameters arranged across rows.
For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of
For a two class classification problem, it returns a row matrix. It returns learnt parameters of
the Logistic Regression as a matrix of type CV_32F.
*/
CV_WRAP virtual Mat get_learnt_thetas() const = 0;
modules/python/test/test_algorithm_rw.py
+1 -1
View File
@@ -1,5 +1,5 @@
#!/usr/bin/env python
"""Algorithm serializaion test."""
"""Algorithm serialization test."""
import tempfile
import os
import cv2 as cv
modules/python/test/test_cuda.py
+1 -1
View File
@@ -181,7 +181,7 @@ class cuda_test(NewOpenCVTests):
self.assertTrue('GpuMat' in str(type(gpu_mat)), msg=type(gpu_mat))
#TODO: print(cv.utils.dumpInputArray(gpu_mat)) # - no support for GpuMat
# not checking output, therefore sepearate tests for different signatures is unecessary
# not checking output, therefore sepearate tests for different signatures is unnecessary
ret, _gpu_mat2 = reader.nextFrame(gpu_mat)
#TODO: self.assertTrue(gpu_mat == gpu_mat2)
self.assertTrue(ret)
modules/python/test/test_persistence.py
+1 -1
View File
@@ -1,5 +1,5 @@
#!/usr/bin/env python
""""Core serializaion tests."""
""""Core serialization tests."""
import tempfile
import os
import cv2 as cv
modules/stitching/include/opencv2/stitching/detail/matchers.hpp
+2 -2
View File
@@ -215,14 +215,14 @@ finds two best matches for each feature and leaves the best one only if the
ratio between descriptor distances is greater than the threshold match_conf.
Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
transformation (affine trasformation estimate will be placed in matches_info).
transformation (affine transformation estimate will be placed in matches_info).
@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
*/
class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
{
public:
/** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
/** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
between images
@param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
modules/ts/include/opencv2/ts/ts_gtest.h
+1 -1
View File
@@ -11367,7 +11367,7 @@ void UniversalTersePrint(const T& value, ::std::ostream* os) {
// NUL-terminated string.
template <typename T>
void UniversalPrint(const T& value, ::std::ostream* os) {
// A workarond for the bug in VC++ 7.1 that prevents us from instantiating
// A workaround for the bug in VC++ 7.1 that prevents us from instantiating
// UniversalPrinter with T directly.
typedef T T1;
UniversalPrinter<T1>::Print(value, os);
modules/ts/misc/run_android.py
+2 -2
View File
@@ -94,11 +94,11 @@ class Aapt(Tool):
# get test instrumentation info
instrumentation_tag = [t for t in tags if t.startswith("instrumentation ")]
if not instrumentation_tag:
raise Err("Can not find instrumentation detials in: %s", exe)
raise Err("Can not find instrumentation details in: %s", exe)
res.pkg_runner = re.search(r"^[ ]+A: android:name\(0x[0-9a-f]{8}\)=\"(?P<runner>.*?)\" \(Raw: \"(?P=runner)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("runner")
res.pkg_target = re.search(r"^[ ]+A: android:targetPackage\(0x[0-9a-f]{8}\)=\"(?P<pkg>.*?)\" \(Raw: \"(?P=pkg)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("pkg")
if not res.pkg_name or not res.pkg_runner or not res.pkg_target:
raise Err("Can not find instrumentation detials in: %s", exe)
raise Err("Can not find instrumentation details in: %s", exe)
return res
modules/ts/src/ts.cpp
+1 -1
View File
@@ -452,7 +452,7 @@ int BadArgTest::run_test_case( int expected_code, const string& _descr )
{
thrown = true;
if (e.code != expected_code &&
e.code != cv::Error::StsError && e.code != cv::Error::StsAssert // Exact error codes support will be dropped. Checks should provide proper text messages intead.
e.code != cv::Error::StsError && e.code != cv::Error::StsAssert // Exact error codes support will be dropped. Checks should provide proper text messages instead.
)
{
ts->printf(TS::LOG, "%s (test case #%d): the error code %d is different from the expected %d\n",
modules/video/src/bgfg_KNN.cpp
+2 -2
View File
@@ -110,7 +110,7 @@ public:
//set parameters
// N - the number of samples stored in memory per model
nN = defaultNsamples;
//kNN - k nearest neighbour - number on NN for detcting background - default K=[0.1*nN]
//kNN - k nearest neighbour - number on NN for detecting background - default K=[0.1*nN]
nkNN=MAX(1,cvRound(0.1*nN*3+0.40));
//Tb - Threshold Tb*kernelwidth
@@ -292,7 +292,7 @@ protected:
//less important parameters - things you might change but be careful
////////////////////////
int nN;//totlal number of samples
int nkNN;//number on NN for detcting background - default K=[0.1*nN]
int nkNN;//number on NN for detecting background - default K=[0.1*nN]
//shadow detection parameters
bool bShadowDetection;//default 1 - do shadow detection
modules/video/src/bgfg_gaussmix2.cpp
+1 -1
View File
@@ -181,7 +181,7 @@ public:
//! computes a background image which are the mean of all background gaussians
virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
//! re-initiaization method
//! re-initialization method
void initialize(Size _frameSize, int _frameType)
{
frameSize = _frameSize;
modules/videoio/include/opencv2/videoio/legacy/constants_c.h
+2 -2
View File
@@ -225,8 +225,8 @@ enum
CV_CAP_PROP_XI_COOLING = 466, // Start camera cooling.
CV_CAP_PROP_XI_TARGET_TEMP = 467, // Set sensor target temperature for cooling.
CV_CAP_PROP_XI_CHIP_TEMP = 468, // Camera sensor temperature
CV_CAP_PROP_XI_HOUS_TEMP = 469, // Camera housing tepmerature
CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, // Camera housing back side tepmerature
CV_CAP_PROP_XI_HOUS_TEMP = 469, // Camera housing temperature
CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, // Camera housing back side temperature
CV_CAP_PROP_XI_SENSOR_BOARD_TEMP = 596, // Camera sensor board temperature
CV_CAP_PROP_XI_CMS = 470, // Mode of color management system.
CV_CAP_PROP_XI_APPLY_CMS = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
modules/videoio/src/cap_aravis.cpp
+1 -1
View File
@@ -300,7 +300,7 @@ bool CvCaptureCAM_Aravis::grabFrame()
size_t buffer_size;
framebuffer = (void*)arv_buffer_get_data (arv_buffer, &buffer_size);
// retrieve image size properites
// retrieve image size properties
arv_buffer_get_image_region (arv_buffer, &xoffset, &yoffset, &width, &height);
// retrieve image ID set by camera
modules/videoio/src/cap_avfoundation.mm
+1 -1
View File
@@ -1298,7 +1298,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
provider, NULL, false, kCGRenderingIntentDefault);
//CGImage -> CVPixelBufferRef coversion
//CGImage -> CVPixelBufferRef conversion
CVPixelBufferRef pixelBuffer = NULL;
CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
int status = CVPixelBufferCreateWithBytes(NULL,
modules/videoio/src/cap_avfoundation_mac.mm
+2 -2
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@@ -814,7 +814,7 @@ bool CvCaptureFile::setupReadingAt(CMTime position) {
if (mMode == CV_CAP_MODE_BGR || mMode == CV_CAP_MODE_RGB) {
// For CV_CAP_MODE_BGR, read frames as BGRA (AV Foundation's YUV->RGB conversion is slightly faster than OpenCV's CV_YUV2BGR_YV12)
// kCVPixelFormatType_32ABGR is reportedly faster on OS X, but OpenCV doesn't have a CV_ABGR2BGR conversion.
// kCVPixelFormatType_24RGB is significanly slower than kCVPixelFormatType_32BGRA.
// kCVPixelFormatType_24RGB is significantly slower than kCVPixelFormatType_32BGRA.
pixelFormat = kCVPixelFormatType_32BGRA;
mFormat = CV_8UC3;
} else if (mMode == CV_CAP_MODE_GRAY) {
@@ -1332,7 +1332,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
provider, NULL, false, kCGRenderingIntentDefault);
//CGImage -> CVPixelBufferRef coversion
//CGImage -> CVPixelBufferRef conversion
CVPixelBufferRef pixelBuffer = NULL;
CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
int status = CVPixelBufferCreateWithBytes(NULL,
modules/videoio/src/cap_gstreamer.cpp
+1 -1
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@@ -953,7 +953,7 @@ bool GStreamerCapture::open(const String &filename_)
* \return property value
*
* There are two ways the properties can be retrieved. For seek-based properties we can query the pipeline.
* For frame-based properties, we use the caps of the lasst receivef sample. This means that some properties
* For frame-based properties, we use the caps of the last receivef sample. This means that some properties
* are not available until a first frame was received
*/
double GStreamerCapture::getProperty(int propId) const