diff --git a/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown index 89b584b755..a46f578feb 100644 --- a/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown +++ b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown @@ -27,16 +27,16 @@ Explanation / Result @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image ![](images/source.jpeg) --# Then if we have an image with a white background, it is good to transform it to black. This will help us to descriminate the foreground objects easier when we will apply the Distance Transform: +-# Then if we have an image with a white background, it is good to transform it to black. This will help us to discriminate the foreground objects easier when we will apply the Distance Transform: @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg ![](images/black_bg.jpeg) --# Afterwards we will sharp our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative): +-# Afterwards we will sharpen our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative): @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp ![](images/laplace.jpeg) ![](images/sharp.jpeg) --# Now we transfrom our new sharped source image to a grayscale and a binary one, respectively: +-# Now we transform our new sharpened source image to a grayscale and a binary one, respectively: @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin ![](images/bin.jpeg) diff --git a/modules/calib3d/src/calibinit.cpp b/modules/calib3d/src/calibinit.cpp index ff62abcf0d..283a9217a9 100644 --- a/modules/calib3d/src/calibinit.cpp +++ b/modules/calib3d/src/calibinit.cpp @@ -1391,7 +1391,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count, } } - // start with a corner that belongs to a quad with a signle neighbor. + // start with a corner that belongs to a quad with a single neighbor. // if we do not have such, start with a corner of a quad with two neighbors. if( !first ) first = first2; @@ -2173,7 +2173,7 @@ bool cv::findCirclesGrid2( InputArray _image, Size patternSize, boxFinder.getAsymmetricHoles(centers); break; default: - CV_Error(CV_StsBadArg, "Unkown pattern type"); + CV_Error(CV_StsBadArg, "Unknown pattern type"); } if (i != 0) diff --git a/modules/calib3d/src/circlesgrid.cpp b/modules/calib3d/src/circlesgrid.cpp index 5a787a7b05..bea180f134 100644 --- a/modules/calib3d/src/circlesgrid.cpp +++ b/modules/calib3d/src/circlesgrid.cpp @@ -619,7 +619,7 @@ bool CirclesGridFinder::findHoles() } default: - CV_Error(Error::StsBadArg, "Unkown pattern type"); + CV_Error(Error::StsBadArg, "Unknown pattern type"); } return (isDetectionCorrect()); //CV_Error( 0, "Detection is not correct" ); diff --git a/modules/calib3d/src/five-point.cpp b/modules/calib3d/src/five-point.cpp index ecc1cfcf6f..19472d49ff 100644 --- a/modules/calib3d/src/five-point.cpp +++ b/modules/calib3d/src/five-point.cpp @@ -506,7 +506,7 @@ int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2, // Do the cheirality check. // Notice here a threshold dist is used to filter // out far away points (i.e. infinite points) since - // there depth may vary between postive and negtive. + // their depth may vary between positive and negtive. std::vector allTriangulations(4); Mat Q; diff --git a/modules/calib3d/src/opencl/stereobm.cl b/modules/calib3d/src/opencl/stereobm.cl index 86e4c24d33..16386a6733 100644 --- a/modules/calib3d/src/opencl/stereobm.cl +++ b/modules/calib3d/src/opencl/stereobm.cl @@ -141,7 +141,7 @@ __kernel void stereoBM(__global const uchar * leftptr, __global const uchar * rightptr, __global uchar * dispptr, int disp_step, int disp_offset, int rows, int cols, // rows, cols of left and right images, not disp - int textureTreshold, int uniquenessRatio) + int textureThreshold, int uniquenessRatio) { int lz = get_local_id(0); int gx = get_global_id(1) * BLOCK_SIZE_X; diff --git a/modules/calib3d/src/p3p.cpp b/modules/calib3d/src/p3p.cpp index 8bf0c35945..49e679c530 100644 --- a/modules/calib3d/src/p3p.cpp +++ b/modules/calib3d/src/p3p.cpp @@ -192,7 +192,7 @@ int p3p::solve(double R[4][3][3], double t[4][3], } /// Given 3D distances between three points and cosines of 3 angles at the apex, calculates -/// the lentghs of the line segments connecting projection center (P) and the three 3D points (A, B, C). +/// the lengths of the line segments connecting projection center (P) and the three 3D points (A, B, C). /// Returned distances are for |PA|, |PB|, |PC| respectively. /// Only the solution to the main branch. /// Reference : X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" diff --git a/modules/calib3d/src/polynom_solver.cpp b/modules/calib3d/src/polynom_solver.cpp index ea43b69c7e..beb91cafc0 100644 --- a/modules/calib3d/src/polynom_solver.cpp +++ b/modules/calib3d/src/polynom_solver.cpp @@ -32,7 +32,7 @@ int solve_deg3(double a, double b, double c, double d, double & x0, double & x1, double & x2) { if (a == 0) { - // Solve second order sytem + // Solve second order system if (b == 0) { // Solve first order system if (c == 0) diff --git a/modules/calib3d/src/rho.cpp b/modules/calib3d/src/rho.cpp index 5d42868043..7126e2f196 100644 --- a/modules/calib3d/src/rho.cpp +++ b/modules/calib3d/src/rho.cpp @@ -443,7 +443,7 @@ static inline void sacSub8x1 (float* Hout, /** * External access to context constructor. * - * @return A pointer to the context if successful; NULL if an error occured. + * @return A pointer to the context if successful; NULL if an error occurred. */ Ptr rhoInit(void){ @@ -1205,7 +1205,7 @@ inline void RHO_HEST_REFC::PROSACGoToNextPhase(void){ /** * Get a sample according to PROSAC rules. Namely: - * - If we're past the phase end interation, select randomly 4 out of the first + * - If we're past the phase end interaction, select randomly 4 out of the first * phNum matches. * - Otherwise, select match phNum-1 and select randomly the 3 others out of * the first phNum-1 matches. @@ -1742,7 +1742,7 @@ inline void RHO_HEST_REFC::updateBounds(void){ } /** - * Ouput the best model so far to the output argument. + * Output the best model so far to the output argument. * * Reads (direct): arg.finalH, best.H, arg.inl, best.inl, arg.N * Reads (callees): arg.finalH, arg.inl, arg.N @@ -1762,7 +1762,7 @@ inline void RHO_HEST_REFC::outputModel(void){ } /** - * Ouput a zeroed H to the output argument. + * Output a zeroed H to the output argument. * * Reads (direct): arg.finalH, arg.inl, arg.N * Reads (callees): None. diff --git a/modules/calib3d/test/test_cameracalibration_tilt.cpp b/modules/calib3d/test/test_cameracalibration_tilt.cpp index 30570eb119..5ac5dd985d 100644 --- a/modules/calib3d/test/test_cameracalibration_tilt.cpp +++ b/modules/calib3d/test/test_cameracalibration_tilt.cpp @@ -80,7 +80,7 @@ protected: static const double m_pointTargetDist; static const int m_pointTargetNum; - /** image distance coresponding to working distance */ + /** image distance corresponding to working distance */ double m_imageDistance; /** image tilt angle corresponding to the tilt of the object plane */ double m_imageTiltDegree; @@ -202,7 +202,7 @@ void cameraCalibrationTiltTest::SetUp() double nearFarFactorImage[2] = { aperture/(aperture - circleConfusion), aperture/(aperture + circleConfusion)}; - // on the object side - points that determin the field of + // on the object side - points that determine the field of // view std::vector fovBottomTop(6); std::vector::iterator itFov = fovBottomTop.begin(); @@ -552,7 +552,7 @@ void showVec(const std::string& name, const INPUT& in, const cv::Mat& est) For given camera matrix and distortion coefficients - project point target in different positions onto the sensor - add pixel noise -- estimate camera modell with noisy measurements +- estimate camera model with noisy measurements - compare result with initial model parameter Parameter are differently affected by the noise @@ -623,7 +623,7 @@ TEST_F(cameraCalibrationTiltTest, calibrateCamera) cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50000, 1e-14); - // modell coice + // model choice int flag = cv::CALIB_FIX_ASPECT_RATIO | // cv::CALIB_RATIONAL_MODEL | diff --git a/modules/calib3d/test/test_posit.cpp b/modules/calib3d/test/test_posit.cpp index a2f22f1ce3..5fef2ef904 100644 --- a/modules/calib3d/test/test_posit.cpp +++ b/modules/calib3d/test/test_posit.cpp @@ -94,7 +94,7 @@ void CV_POSITTest::run( int start_from ) const float flFocalLength = 760.f; const float flEpsilon = 0.5f; - /* Initilization */ + /* Initialization */ criteria.type = CV_TERMCRIT_EPS|CV_TERMCRIT_ITER; criteria.epsilon = flEpsilon; criteria.max_iter = 10000; diff --git a/modules/calib3d/test/test_stereomatching.cpp b/modules/calib3d/test/test_stereomatching.cpp index 75444e3c25..601e0cb93b 100644 --- a/modules/calib3d/test/test_stereomatching.cpp +++ b/modules/calib3d/test/test_stereomatching.cpp @@ -41,7 +41,7 @@ /* This is a regression test for stereo matching algorithms. This test gets some quality metrics - discribed in "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms". + described in "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms". Daniel Scharstein, Richard Szeliski */ diff --git a/modules/core/include/opencv2/core/cuda.hpp b/modules/core/include/opencv2/core/cuda.hpp index 42a3b2710c..e6cb79edaf 100644 --- a/modules/core/include/opencv2/core/cuda.hpp +++ b/modules/core/include/opencv2/core/cuda.hpp @@ -382,7 +382,7 @@ the DefaultAllocator since the stack for pool1 is full. @endcode If a third stream is declared in the above example, allocating with #getBuffer -within that stream will also be carried out by the DefaultAllocator becuase we've run out of +within that stream will also be carried out by the DefaultAllocator because we've run out of stacks. @code diff --git a/modules/core/include/opencv2/core/cuda/block.hpp b/modules/core/include/opencv2/core/cuda/block.hpp index 330cf1d7bb..c277f0ea9c 100644 --- a/modules/core/include/opencv2/core/cuda/block.hpp +++ b/modules/core/include/opencv2/core/cuda/block.hpp @@ -106,7 +106,7 @@ namespace cv { namespace cuda { namespace device } template - static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op) + static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op) { int STRIDE = stride(); InIt t = beg + flattenedThreadId(); @@ -117,7 +117,7 @@ namespace cv { namespace cuda { namespace device } template - static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) { int STRIDE = stride(); InIt1 t1 = beg1 + flattenedThreadId(); diff --git a/modules/core/include/opencv2/core/types_c.h b/modules/core/include/opencv2/core/types_c.h index 4f04d0c06f..65f354e070 100644 --- a/modules/core/include/opencv2/core/types_c.h +++ b/modules/core/include/opencv2/core/types_c.h @@ -1361,7 +1361,7 @@ CvGraph; /** @} */ -/*********************************** Chain/Countour *************************************/ +/*********************************** Chain/Contour *************************************/ typedef struct CvChain { diff --git a/modules/core/misc/java/gen_dict.json b/modules/core/misc/java/gen_dict.json index 36afe4e57c..44505b6b8d 100644 --- a/modules/core/misc/java/gen_dict.json +++ b/modules/core/misc/java/gen_dict.json @@ -108,13 +108,13 @@ " return result;", "\n", " } catch(const cv::Exception& e) {", - " LOGD(\"Core::n_1minMaxLoc() catched cv::Exception: %s\", e.what());", + " LOGD(\"Core::n_1minMaxLoc() caught cv::Exception: %s\", e.what());", " jclass je = env->FindClass(\"org/opencv/core/CvException\");", " if(!je) je = env->FindClass(\"java/lang/Exception\");", " env->ThrowNew(je, e.what());", " return NULL;", " } catch (...) {", - " LOGD(\"Core::n_1minMaxLoc() catched unknown exception (...)\");", + " LOGD(\"Core::n_1minMaxLoc() caught unknown exception (...)\");", " jclass je = env->FindClass(\"java/lang/Exception\");", " env->ThrowNew(je, \"Unknown exception in JNI code {core::minMaxLoc()}\");", " return NULL;", diff --git a/modules/flann/include/opencv2/flann/dist.h b/modules/flann/include/opencv2/flann/dist.h index 9e2c5129fa..eedaefff9c 100644 --- a/modules/flann/include/opencv2/flann/dist.h +++ b/modules/flann/include/opencv2/flann/dist.h @@ -843,7 +843,7 @@ typename Distance::ResultType ensureSquareDistance( typename Distance::ResultTyp /* * ...and a template to ensure the user that he will process the normal distance, - * and not squared distance, without loosing processing time calling sqrt(ensureSquareDistance) + * and not squared distance, without losing processing time calling sqrt(ensureSquareDistance) * that will result in doing actually sqrt(dist*dist) for L1 distance for instance. */ template diff --git a/modules/flann/include/opencv2/flann/kmeans_index.h b/modules/flann/include/opencv2/flann/kmeans_index.h index 98ad0c86e0..7da4e26240 100644 --- a/modules/flann/include/opencv2/flann/kmeans_index.h +++ b/modules/flann/include/opencv2/flann/kmeans_index.h @@ -1053,7 +1053,7 @@ private: /** - * Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize + * Helper function the descends in the hierarchical k-means tree by splitting those clusters that minimize * the overall variance of the clustering. * Params: * root = root node diff --git a/modules/js/test/test_imgproc.js b/modules/js/test/test_imgproc.js index 214a073666..b2180c3b87 100644 --- a/modules/js/test/test_imgproc.js +++ b/modules/js/test/test_imgproc.js @@ -555,7 +555,7 @@ QUnit.test('test_filter', function(assert) { cv._free(dataPtr2); } - // Arithmatic operations + // Arithmetic operations { let data1 = new Uint8Array([0, 1, 2, 3, 4, 5, 6, 7, 8]); let data2 = new Uint8Array([0, 2, 4, 6, 8, 10, 12, 14, 16]); diff --git a/modules/ml/doc/ml_intro.markdown b/modules/ml/doc/ml_intro.markdown index b7a3d4059f..3bc9b068f2 100644 --- a/modules/ml/doc/ml_intro.markdown +++ b/modules/ml/doc/ml_intro.markdown @@ -441,8 +441,8 @@ Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see discriminative classifier (see for more details). Logistic Regression is implemented as a C++ class in LogisticRegression. -In Logistic Regression, we try to optimize the training paramater \f$\theta\f$ such that the -hypothesis \f$0 \leq h_\theta(x) \leq 1\f$ is acheived. We have \f$h_\theta(x) = g(h_\theta(x))\f$ +In Logistic Regression, we try to optimize the training parameter \f$\theta\f$ such that the +hypothesis \f$0 \leq h_\theta(x) \leq 1\f$ is achieved. We have \f$h_\theta(x) = g(h_\theta(x))\f$ and \f$g(z) = \frac{1}{1+e^{-z}}\f$ as the logistic or sigmoid function. The term "Logistic" in Logistic Regression refers to this function. For given data of a binary classification problem of classes 0 and 1, one can determine that the given data instance belongs to class 1 if \f$h_\theta(x) @@ -472,7 +472,7 @@ training error and ensuring high training accuracy: cv::ml::LogisticRegression::BATCH "LogisticRegression::BATCH" or @ref cv::ml::LogisticRegression::MINI_BATCH "LogisticRegression::MINI_BATCH". If training method is set to @ref cv::ml::LogisticRegression::MINI_BATCH "MINI_BATCH", the size of the mini batch has - to be to a postive integer set with @ref cv::ml::LogisticRegression::setMiniBatchSize + to be to a positive integer set with @ref cv::ml::LogisticRegression::setMiniBatchSize "setMiniBatchSize". A sample set of training parameters for the Logistic Regression classifier can be initialized as follows: diff --git a/modules/ml/include/opencv2/ml.hpp b/modules/ml/include/opencv2/ml.hpp index 0295a07650..aa25db961a 100644 --- a/modules/ml/include/opencv2/ml.hpp +++ b/modules/ml/include/opencv2/ml.hpp @@ -1269,7 +1269,7 @@ public: results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample. - @param samples Array containg the samples for which votes will be calculated. + @param samples Array containing the samples for which votes will be calculated. @param results Array where the result of the calculation will be written. @param flags Flags for defining the type of RTrees. */ @@ -1658,9 +1658,9 @@ public: */ CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; - /** @brief This function returns the trained paramters arranged across rows. + /** @brief This function returns the trained parameters arranged across rows. - For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of + For a two class classifcation 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; @@ -1854,7 +1854,7 @@ public: /****************************************************************************************\ -* Auxilary functions declarations * +* Auxiliary functions declarations * \****************************************************************************************/ /** @brief Generates _sample_ from multivariate normal distribution @@ -1921,7 +1921,7 @@ struct SimulatedAnnealingSolverSystem { /** Give energy value for a state of system.*/ double energy() const; - /** Function which change the state of system (random pertubation).*/ + /** Function which change the state of system (random perturbation).*/ void changeState(); /** Function to reverse to the previous state. Can be called once only after changeState(). */ void reverseState(); diff --git a/modules/ml/src/gbt.cpp b/modules/ml/src/gbt.cpp index 76baa00015..57f2eb176b 100644 --- a/modules/ml/src/gbt.cpp +++ b/modules/ml/src/gbt.cpp @@ -253,7 +253,7 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, } } - // inside gbt learning proccess only regression decision trees are built + // inside gbt learning process only regression decision trees are built data->is_classifier = false; // preproccessing sample indices diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp index 69c1bd8c9a..bc7042abe7 100644 --- a/modules/ml/src/svm.cpp +++ b/modules/ml/src/svm.cpp @@ -52,7 +52,7 @@ The code has been derived from libsvm library (version 2.6) (http://www.csie.ntu.edu.tw/~cjlin/libsvm). - Here is the orignal copyright: + Here is the original copyright: ------------------------------------------------------------------------------------------ Copyright (c) 2000-2003 Chih-Chung Chang and Chih-Jen Lin All rights reserved. @@ -287,7 +287,7 @@ public: double d = sample[k]-another[k]; double devisor = sample[k]+another[k]; /// if devisor == 0, the Chi2 distance would be zero, - // but calculation would rise an error because of deviding by zero + // but calculation would rise an error because of dividing by zero if (devisor != 0) { chi2 += d*d/devisor; diff --git a/modules/ml/test/test_emknearestkmeans.cpp b/modules/ml/test/test_emknearestkmeans.cpp index 5e72724514..6755c2e9e4 100644 --- a/modules/ml/test/test_emknearestkmeans.cpp +++ b/modules/ml/test/test_emknearestkmeans.cpp @@ -590,7 +590,7 @@ protected: if( errCaseCount > 0 ) { - ts->printf( cvtest::TS::LOG, "Different prediction results before writeing and after reading (errCaseCount=%d).\n", errCaseCount ); + ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errCaseCount=%d).\n", errCaseCount ); code = cvtest::TS::FAIL_BAD_ACCURACY; } diff --git a/modules/ml/test/test_lr.cpp b/modules/ml/test/test_lr.cpp index d99b989beb..15d59d77fa 100644 --- a/modules/ml/test/test_lr.cpp +++ b/modules/ml/test/test_lr.cpp @@ -91,7 +91,7 @@ protected: void CV_LRTest::run( int /*start_from*/ ) { CV_TRACE_FUNCTION(); - // initialize varibles from the popular Iris Dataset + // initialize variables from the popular Iris Dataset string dataFileName = ts->get_data_path() + "iris.data"; Ptr tdata = TrainData::loadFromCSV(dataFileName, 0); @@ -153,7 +153,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ ) CV_TRACE_FUNCTION(); int code = cvtest::TS::OK; - // initialize varibles from the popular Iris Dataset + // initialize variables from the popular Iris Dataset string dataFileName = ts->get_data_path() + "iris.data"; Ptr tdata = TrainData::loadFromCSV(dataFileName, 0); @@ -205,7 +205,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ ) comp_learnt_mats = comp_learnt_mats/255; // compare difference in prediction outputs and stored inputs - // check if there is any difference between computed learnt mat and retreived mat + // check if there is any difference between computed learnt mat and retrieved mat float errorCount = 0.0; errorCount += 1 - (float)countNonZero(responses1 == responses2)/responses1.rows; diff --git a/modules/objdetect/include/opencv2/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect.hpp index 40ebf56f76..5c6c221afa 100644 --- a/modules/objdetect/include/opencv2/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect.hpp @@ -346,7 +346,7 @@ struct DetectionROI { //! scale(size) of the bounding box double scale; - //! set of requrested locations to be evaluated + //! set of requested locations to be evaluated std::vector locations; //! vector that will contain confidence values for each location std::vector confidences; @@ -497,11 +497,11 @@ public: @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. @param weights Vector that will contain confidence values for each detected object. @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding - @param searchLocations Vector of Point includes set of requrested locations to be evaluated. + @param searchLocations Vector of Point includes set of requested locations to be evaluated. */ CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, CV_OUT std::vector& weights, @@ -513,7 +513,7 @@ public: @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @@ -530,7 +530,7 @@ public: @param foundLocations Vector of rectangles where each rectangle contains the detected object. @param foundWeights Vector that will contain confidence values for each detected object. @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @@ -548,7 +548,7 @@ public: @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. @param foundLocations Vector of rectangles where each rectangle contains the detected object. @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @@ -632,7 +632,7 @@ public: @param foundLocations Vector of Point where each Point is detected object's top-left point. @param confidences confidences @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually - it is 0 and should be specfied in the detector coefficients (as the last free coefficient). But if + it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here @param winStride winStride @param padding padding @@ -646,7 +646,7 @@ public: @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. @param foundLocations Vector of rectangles where each rectangle contains the detected object. @param locations Vector of DetectionROI - @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specfied + @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. */ diff --git a/modules/objdetect/src/hog.cpp b/modules/objdetect/src/hog.cpp index 0c5cbedd7f..0964b7b633 100644 --- a/modules/objdetect/src/hog.cpp +++ b/modules/objdetect/src/hog.cpp @@ -3608,7 +3608,7 @@ void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector & const HOGCache::BlockData& bj = blockData[j]; Point pt = pt0 + bj.imgOffset; - // need to devide this into 4 parts! + // need to divide this into 4 parts! const float* vec = cache.getBlock(pt, &blockHist[0]); #if CV_SSE2 __m128 _vec = _mm_loadu_ps(vec); @@ -3699,7 +3699,7 @@ void HOGDescriptor::readALTModel(String modelfile) CV_THROW (Exception(Error::StsError, eerr, efile, efunc, __LINE__)); } if(strcmp(version_buffer,"V6.01")) { - String eerr("version doesnot match"); + String eerr("version does not match"); String efile(__FILE__); String efunc(__FUNCTION__); fclose(modelfl); @@ -3715,7 +3715,7 @@ void HOGDescriptor::readALTModel(String modelfile) } if (version < 200) { - String eerr("version doesnot match"); + String eerr("version does not match"); String efile(__FILE__); String efunc(__FUNCTION__); fclose(modelfl); diff --git a/modules/photo/include/opencv2/photo.hpp b/modules/photo/include/opencv2/photo.hpp index 6651ac80ac..487b720fe0 100644 --- a/modules/photo/include/opencv2/photo.hpp +++ b/modules/photo/include/opencv2/photo.hpp @@ -189,7 +189,7 @@ CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21); -/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been +/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see @@ -216,7 +216,7 @@ CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputA int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21); -/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been +/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see diff --git a/modules/shape/include/opencv2/shape/shape_transformer.hpp b/modules/shape/include/opencv2/shape/shape_transformer.hpp index ace104eddd..3c3ce20c12 100644 --- a/modules/shape/include/opencv2/shape/shape_transformer.hpp +++ b/modules/shape/include/opencv2/shape/shape_transformer.hpp @@ -92,7 +92,7 @@ public: /** @brief Definition of the transformation -ocupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by +occupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by F.L. Bookstein (PAMI 1989). : */ class CV_EXPORTS_W ThinPlateSplineShapeTransformer : public ShapeTransformer diff --git a/modules/shape/src/emdL1.cpp b/modules/shape/src/emdL1.cpp index cfb71ff126..8e5faaaf9d 100644 --- a/modules/shape/src/emdL1.cpp +++ b/modules/shape/src/emdL1.cpp @@ -294,8 +294,8 @@ bool EmdL1::greedySolution2() // move to up pBV = &(m_EdgesUp[r][c]); m_NBVEdges[nNBV++] = &(m_EdgesRight[r][c]); - D[r+1][c] += dFlow; // auxilary matrix maintanence - d1s[r+1] += dFlow; // auxilary matrix maintanence + D[r+1][c] += dFlow; // auxiliary matrix maintenance + d1s[r+1] += dFlow; // auxiliary matrix maintenance } else { @@ -304,8 +304,8 @@ bool EmdL1::greedySolution2() if(rpParent->pChild = pBV; pBV->flow = fabs(dFlow); @@ -318,7 +318,7 @@ bool EmdL1::greedySolution2() { dFlow = D[r][c]; pBV = &(m_EdgesUp[r][c]); - D[r+1][c] += dFlow; // auxilary matrix maintanence + D[r+1][c] += dFlow; // auxiliary matrix maintenance pBV->pParent->pChild= pBV; pBV->flow = fabs(dFlow); pBV->iDir = dFlow>0; // 1:outward, 0:inward @@ -402,7 +402,7 @@ bool EmdL1::greedySolution3() pBV = &(m_3dEdgesUp[i1][i2][i3]); // up if(i2 3. ? 0. : matches_info.confidence; - // extend H to represent linear tranformation in homogeneous coordinates + // extend H to represent linear transformation in homogeneous coordinates matches_info.H.push_back(Mat::zeros(1, 3, CV_64F)); matches_info.H.at(2, 2) = 1; } diff --git a/modules/stitching/src/motion_estimators.cpp b/modules/stitching/src/motion_estimators.cpp index 925bb44de1..8d36538912 100644 --- a/modules/stitching/src/motion_estimators.cpp +++ b/modules/stitching/src/motion_estimators.cpp @@ -89,7 +89,7 @@ struct CalcRotation /** - * @brief Functor calculating final tranformation by chaining linear transformations + * @brief Functor calculating final transformation by chaining linear transformations */ struct CalcAffineTransform { diff --git a/modules/superres/include/opencv2/superres.hpp b/modules/superres/include/opencv2/superres.hpp index 60d4faa6e6..e8bb5e5bc6 100644 --- a/modules/superres/include/opencv2/superres.hpp +++ b/modules/superres/include/opencv2/superres.hpp @@ -50,7 +50,7 @@ @defgroup superres Super Resolution The Super Resolution module contains a set of functions and classes that can be used to solve the -problem of resolution enhancement. There are a few methods implemented, most of them are descibed in +problem of resolution enhancement. There are a few methods implemented, most of them are described in the papers @cite Farsiu03 and @cite Mitzel09 . */