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Merge pull request #14724 from asashour:typos
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94ec0e9b74
@ -72,7 +72,7 @@ public:
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// are geometrically consistent. We check if every 3 correspondences sets
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// fulfills the constraint.
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//
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// The usefullness of this constraint is explained in the paper:
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// The usefulness of this constraint is explained in the paper:
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//
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// "Speeding-up homography estimation in mobile devices"
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// Journal of Real-Time Image Processing. 2013. DOI: 10.1007/s11554-012-0314-1
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@ -122,10 +122,10 @@ private:
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* For highest accuracy the Jacobian should be computed at the centroid of the point correspondences (see the IPPE paper for the explanation of this).
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* For a point (ux,uy) on the object plane, suppose the homography H maps (ux,uy) to a point (p,q) in the image (in normalized pixel coordinates).
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* The Jacobian matrix [J00, J01; J10,J11] is the Jacobian of the mapping evaluated at (ux,uy).
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* @param j00 Homography jacobian coefficent at (ux,uy)
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* @param j01 Homography jacobian coefficent at (ux,uy)
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* @param j10 Homography jacobian coefficent at (ux,uy)
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* @param j11 Homography jacobian coefficent at (ux,uy)
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* @param j00 Homography jacobian coefficient at (ux,uy)
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* @param j01 Homography jacobian coefficient at (ux,uy)
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* @param j10 Homography jacobian coefficient at (ux,uy)
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* @param j11 Homography jacobian coefficient at (ux,uy)
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* @param p The x coordinate of point (ux,uy) mapped into the image (undistorted and normalized position)
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* @param q The y coordinate of point (ux,uy) mapped into the image (undistorted and normalized position)
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*/
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@ -145,7 +145,7 @@ protected:
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if (fail)
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{
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// commented according to vp123's recomendation. TODO - improve accuaracy
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// commented according to vp123's recommendation. TODO - improve accuracy
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//ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss
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}
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ts->printf( cvtest::TS::LOG, "%d) DistCoeff exp=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1, k2, p1, p2, k3);
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@ -579,7 +579,7 @@ CvNArrayIterator;
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#define CV_NO_CN_CHECK 2
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#define CV_NO_SIZE_CHECK 4
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/** initializes iterator that traverses through several arrays simulteneously
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/** initializes iterator that traverses through several arrays simultaneously
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(the function together with cvNextArraySlice is used for
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N-ari element-wise operations) */
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CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs,
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@ -392,7 +392,7 @@ inline unsigned RNG::next()
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return (unsigned)state;
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}
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//! returns the next unifomly-distributed random number of the specified type
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//! returns the next uniformly-distributed random number of the specified type
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template<typename _Tp> static inline _Tp randu()
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{
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return (_Tp)theRNG();
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@ -219,10 +219,10 @@ converge to it. Another obvious restriction is that it should be possible to com
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a function at any point, thus it is preferable to have analytic expression for gradient and
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computational burden should be born by the user.
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The latter responsibility is accompilished via the getGradient method of a
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The latter responsibility is accomplished via the getGradient method of a
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MinProblemSolver::Function interface (which represents function being optimized). This method takes
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point a point in *n*-dimensional space (first argument represents the array of coordinates of that
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point) and comput its gradient (it should be stored in the second argument as an array).
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point) and compute its gradient (it should be stored in the second argument as an array).
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@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
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@ -3368,7 +3368,7 @@ cvTreeToNodeSeq( const void* first, int header_size, CvMemStorage* storage )
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typedef struct CvTreeNode
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{
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int flags; /* micsellaneous flags */
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int flags; /* miscellaneous flags */
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int header_size; /* size of sequence header */
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struct CvTreeNode* h_prev; /* previous sequence */
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struct CvTreeNode* h_next; /* next sequence */
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@ -422,7 +422,7 @@ void log64f(const double *src, double *dst, int n)
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#define EXPPOLY_32F_A0 .9670371139572337719125840413672004409288e-2
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// the code below uses _mm_cast* intrinsics, which are not avialable on VS2005
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// the code below uses _mm_cast* intrinsics, which are not available on VS2005
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#if (defined _MSC_VER && _MSC_VER < 1500) || \
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(!defined __APPLE__ && defined __GNUC__ && __GNUC__*100 + __GNUC_MINOR__ < 402)
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#undef CV_SSE2
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@ -215,7 +215,7 @@ size_t ExifReader::getFieldSize ()
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* @brief Filling m_exif member with exif directory elements
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* This is internal function and is not exposed to client
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*
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* @return The function doesn't return any value. In case of unsiccessful parsing
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* @return The function doesn't return any value. In case of unsuccessful parsing
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* the m_exif member is not filled up
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*/
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void ExifReader::parseExif()
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@ -51,7 +51,7 @@
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// developed by Greg Ward. It handles the conversions between rgbe and
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// pixels consisting of floats. The data is assumed to be an array of floats.
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// By default there are three floats per pixel in the order red, green, blue.
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// (RGBE_DATA_??? values control this.) Only the mimimal header reading and
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// (RGBE_DATA_??? values control this.) Only the minimal header reading and
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// writing is implemented. Each routine does error checking and will return
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// a status value as defined below. This code is intended as a skeleton so
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// feel free to modify it to suit your needs.
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@ -8284,8 +8284,8 @@ DeathTest::TestRole WindowsDeathTest::AssumeRole() {
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GTEST_DEATH_TEST_CHECK_(::CreateProcessA(
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executable_path,
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const_cast<char*>(command_line.c_str()),
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NULL, // Retuned process handle is not inheritable.
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NULL, // Retuned thread handle is not inheritable.
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NULL, // Returned process handle is not inheritable.
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NULL, // Returned thread handle is not inheritable.
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TRUE, // Child inherits all inheritable handles (for write_handle_).
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0x0, // Default creation flags.
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NULL, // Inherit the parent's environment.
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@ -550,7 +550,7 @@ double cv::findTransformECC(InputArray templateImage,
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const double correlation = templateZM.dot(imageWarped);
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// calculate enhanced correlation coefficiont (ECC)->rho
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// calculate enhanced correlation coefficient (ECC)->rho
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last_rho = rho;
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rho = correlation/(imgNorm*tmpNorm);
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if (cvIsNaN(rho)) {
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@ -1283,7 +1283,7 @@ void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
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levels1 /= 2;
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}
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// ensure that pyramid has reqired padding
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// ensure that pyramid has required padding
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if(levels1 > 0)
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{
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Size fullSize;
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@ -1311,7 +1311,7 @@ void SparsePyrLKOpticalFlowImpl::calc( InputArray _prevImg, InputArray _nextImg,
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levels2 /= 2;
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}
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// ensure that pyramid has reqired padding
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// ensure that pyramid has required padding
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if(levels2 > 0)
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{
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Size fullSize;
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@ -177,7 +177,7 @@ int main( int argc, char* argv[] )
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else
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capture.set(CAP_OPENNI_IR_GENERATOR_PRESENT, false);
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// Print some avalible device settings.
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// Print some available device settings.
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if (capture.get(CAP_OPENNI_DEPTH_GENERATOR_PRESENT))
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{
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cout << "\nDepth generator output mode:" << endl <<
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