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Fix some typos

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This commit is contained in:
Brian Wignall 2019-11-25 19:55:07 -05:00
parent ad0ab4109a
commit 9276f1910b
19 changed files with 23 additions and 23 deletions
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6
doc/tutorials/imgproc/random_generator_and_text/random_generator_and_text.markdown
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@ -210,12 +210,12 @@ Explanation
@code{.cpp}
image2 = image - Scalar::all(i)
@endcode
So, **image2** is the substraction of **image** and **Scalar::all(i)**. In fact, what happens
here is that every pixel of **image2** will be the result of substracting every pixel of
So, **image2** is the subtraction of **image** and **Scalar::all(i)**. In fact, what happens
here is that every pixel of **image2** will be the result of subtracting every pixel of
**image** minus the value of **i** (remember that for each pixel we are considering three values
such as R, G and B, so each of them will be affected)
Also remember that the substraction operation *always* performs internally a **saturate**
Also remember that the subtraction operation *always* performs internally a **saturate**
operation, which means that the result obtained will always be inside the allowed range (no
negative and between 0 and 255 for our example).

4
modules/core/include/opencv2/core/hal/msa_macros.h
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@ -502,7 +502,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
(v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__e, 31), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \
})
/* Minimum values between corresponding elements in the two vectors are written to teh returned vector. */
/* Minimum values between corresponding elements in the two vectors are written to the returned vector. */
#define msa_minq_s8(__a, __b) (__builtin_msa_min_s_b(__a, __b))
#define msa_minq_s16(__a, __b) (__builtin_msa_min_s_h(__a, __b))
#define msa_minq_s32(__a, __b) (__builtin_msa_min_s_w(__a, __b))
@ -514,7 +514,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
#define msa_minq_f32(__a, __b) (__builtin_msa_fmin_w(__a, __b))
#define msa_minq_f64(__a, __b) (__builtin_msa_fmin_d(__a, __b))
/* Maximum values between corresponding elements in the two vectors are written to teh returned vector. */
/* Maximum values between corresponding elements in the two vectors are written to the returned vector. */
#define msa_maxq_s8(__a, __b) (__builtin_msa_max_s_b(__a, __b))
#define msa_maxq_s16(__a, __b) (__builtin_msa_max_s_h(__a, __b))
#define msa_maxq_s32(__a, __b) (__builtin_msa_max_s_w(__a, __b))

4
modules/dnn/src/cuda/concat.cu
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@ -82,7 +82,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
{
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(input, N));
/* more assertions are required to fully check for vectorization possiblity; check concat() */
/* more assertions are required to fully check for vectorization possibility; check concat() */
auto kernel = raw::concat_vec<T, N>;
auto policy = make_policy(kernel, input.size() / N, 0, stream);
@ -168,7 +168,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
CV_Assert(output.rank() == input.rank());
CV_Assert(output.rank() == offsets.size());
/* squeezable axes at the begining of both tensors can be eliminated
/* squeezable axes at the beginning of both tensors can be eliminated
*
* Reasoning:
* ----------

2
modules/dnn/src/cuda/padding.cu
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@ -103,7 +103,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
CV_Assert(output.rank() == input.rank());
CV_Assert(output.rank() == ranges.size());
/* squeezable axes at the begining of both tensors can be eliminated
/* squeezable axes at the beginning of both tensors can be eliminated
*
* Reasoning:
* ----------

2
modules/dnn/src/cuda/permute.cu
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@ -83,7 +83,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
CV_Assert(input.rank() == order.size());
CV_Assert(input.size() == output.size());
/* squeezable axes at the begining of both tensors which aren't permuted can be eliminated
/* squeezable axes at the beginning of both tensors which aren't permuted can be eliminated
*
* Reasoning:
* ----------

2
modules/dnn/src/cuda/slice.cu
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@ -79,7 +79,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
CV_Assert(output.rank() == input.rank());
CV_Assert(output.rank() == offsets.size());
/* squeezable axes at the begining of both tensors can be eliminated
/* squeezable axes at the beginning of both tensors can be eliminated
*
* Reasoning:
* ----------

2
modules/dnn/src/cuda4dnn/csl/cudnn/cudnn.hpp
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@ -218,7 +218,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
*
* cuDNN frequently assumes that the first axis is the batch axis and the
* second axis is the channel axis; hence, we copy the shape of a lower rank
* tensor to the begining of `dims`
* tensor to the beginning of `dims`
*/
std::copy(start, end, std::begin(dims));

2
modules/dnn/src/cuda4dnn/csl/tensor.hpp
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@ -53,7 +53,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
* "TensorType" is used when only meta-information such as the size or shape is required, i.e. the data won't be touched
*/
/** if the \p axis is a negative index, the equivalent postive index is returned; otherwise, returns \p axis */
/** if the \p axis is a negative index, the equivalent positive index is returned; otherwise, returns \p axis */
CUDA4DNN_HOST_DEVICE constexpr std::size_t clamp_axis(int axis, std::size_t rank) {
return axis < 0 ? axis + rank : axis;
}

2
modules/dnn/src/cuda4dnn/primitives/normalize_bbox.hpp
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@ -41,7 +41,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
/* 1 for L1 norm, 2 for L2 norm */
std::size_t norm;
/* epsilon to use to avoid divison by zero */
/* epsilon to use to avoid division by zero */
T eps;
};

2
modules/dnn/src/cuda4dnn/primitives/pooling.hpp
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@ -168,7 +168,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
* copying the input to a bigger tensor and padding the ends manually
*
* But we first try to avoid the transformation using cuDNN's flexibility. cuDNN can accept a smaller or
* a bigger output shape. This effectively allows us to have arbitary padding at the right.
* a bigger output shape. This effectively allows us to have arbitrary padding at the right.
*/
if (std::any_of(std::begin(padding_left), std::end(padding_left), is_not_zero))
{

2
modules/dnn/src/op_cuda.hpp
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@ -65,7 +65,7 @@ namespace cv { namespace dnn {
* \param[out] destTensor destination tensor
* \param stream CUDA stream to use for the memory transfer
*
* The memory copy starts from begining \p srcMat. The number of elements copied is
* The memory copy starts from beginning \p srcMat. The number of elements copied is
* equal to the number of elements in \p destTensor.
*
* Pre-conditions:

2
modules/gapi/test/common/gapi_core_tests.hpp
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@ -73,7 +73,7 @@ inline std::ostream& operator<<(std::ostream& os, bitwiseOp op)
// 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function
// - available in test body
// 2. Input/output matrices will be initialized by initMatsRandU (in this fixture)
// 3. Specific parameters: opType, testWithScalar, scale, doReverseOp of correponding types
// 3. Specific parameters: opType, testWithScalar, scale, doReverseOp of corresponding types
// - created (and initialized) automatically
// - available in test body
// Note: all parameter _values_ (e.g. type CV_8UC3) are set via INSTANTIATE_TEST_CASE_P macro

2
modules/gapi/test/common/gapi_imgproc_tests.hpp
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@ -25,7 +25,7 @@ namespace opencv_test
// 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function
// - available in test body
// 2. Input/output matrices will be initialized by initMatrixRandN (in this fixture)
// 3. Specific parameters: cmpF, kernSize, borderType of correponding types
// 3. Specific parameters: cmpF, kernSize, borderType of corresponding types
// - created (and initialized) automatically
// - available in test body
// Note: all parameter _values_ (e.g. type CV_8UC3) are set via INSTANTIATE_TEST_CASE_P macro

2
modules/gapi/test/common/gapi_operators_tests.hpp
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@ -195,7 +195,7 @@ g_api_ocv_pair_mat_mat opXor = {std::string{"operator^"},
// 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function
// - available in test body
// 2. Input/output matrices will be initialized by initMatsRandU (in this fixture)
// 3. Specific parameters: cmpF, op of correponding types
// 3. Specific parameters: cmpF, op of corresponding types
// - created (and initialized) automatically
// - available in test body
// Note: all parameter _values_ (e.g. type CV_8UC3) are set via INSTANTIATE_TEST_CASE_P macro

2
modules/gapi/test/streaming/gapi_streaming_tests.cpp
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@ -540,7 +540,7 @@ TEST(GAPI_Streaming_Types, XChangeVector)
auto fluid_kernels = cv::gapi::core::fluid::kernels();
fluid_kernels.include<TypesTest::FluidAddV>();
// Here OCV takes precedense over Fluid, whith SubC & SumV remaining
// Here OCV takes precedense over Fluid, with SubC & SumV remaining
// in Fluid.
auto kernels = cv::gapi::combine(fluid_kernels, ocv_kernels);

2
modules/imgproc/test/test_watershed.cpp
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@ -83,7 +83,7 @@ void CV_WatershedTest::run( int /* start_from */)
Point* p = (Point*)cvGetSeqElem(cnts, 0);
//expected image was added with 1 in order to save to png
//so now we substract 1 to get real color
//so now we subtract 1 to get real color
if(!exp.empty())
colors.push_back(exp.ptr(p->y)[p->x] - 1);
}