fix doc.

CMakeLists.txt

@@ -32,7 +32,7 @@ endif()
 option(ENABLE_PIC "Generate position independent code (necessary for shared libraries)" TRUE)
 set(CMAKE_POSITION_INDEPENDENT_CODE ${ENABLE_PIC})
 
-# Following block can break build in case of cross-compilng
+# Following block can break build in case of cross-compiling
 # but CMAKE_CROSSCOMPILING variable will be set only on project(OpenCV) command
 # so we will try to detect cross-compiling by the presence of CMAKE_TOOLCHAIN_FILE
 if(NOT DEFINED CMAKE_INSTALL_PREFIX)
@@ -43,17 +43,17 @@ if(NOT DEFINED CMAKE_INSTALL_PREFIX)
     else()
       set(CMAKE_INSTALL_PREFIX "/usr/local" CACHE PATH "Installation Directory")
     endif()
-  else(NOT CMAKE_TOOLCHAIN_FILE)
+  else()
     #Android: set output folder to ${CMAKE_BINARY_DIR}
-    set( LIBRARY_OUTPUT_PATH_ROOT ${CMAKE_BINARY_DIR} CACHE PATH "root for library output, set this to change where android libs are compiled to" )
+    set(LIBRARY_OUTPUT_PATH_ROOT ${CMAKE_BINARY_DIR} CACHE PATH "root for library output, set this to change where android libs are compiled to" )
     # any cross-compiling
     set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Installation Directory")
-  endif(NOT CMAKE_TOOLCHAIN_FILE)
+  endif()
 endif()
 
 if(CMAKE_SYSTEM_NAME MATCHES WindowsPhone OR CMAKE_SYSTEM_NAME MATCHES WindowsStore)
   set(WINRT TRUE)
-endif(CMAKE_SYSTEM_NAME MATCHES WindowsPhone OR CMAKE_SYSTEM_NAME MATCHES WindowsStore)
+endif()
 
 if(WINRT)
   add_definitions(-DWINRT -DNO_GETENV)

cmake/FindCUDA.cmake

@@ -1042,7 +1042,7 @@ function(CUDA_COMPUTE_BUILD_PATH path build_path)
   # Only deal with CMake style paths from here on out
   file(TO_CMAKE_PATH "${path}" bpath)
   if (IS_ABSOLUTE "${bpath}")
-    # Absolute paths are generally unnessary, especially if something like
+    # Absolute paths are generally unnecessary, especially if something like
     # file(GLOB_RECURSE) is used to pick up the files.
 
     string(FIND "${bpath}" "${CMAKE_CURRENT_BINARY_DIR}" _binary_dir_pos)
@@ -1065,7 +1065,7 @@ function(CUDA_COMPUTE_BUILD_PATH path build_path)
   # Avoid spaces
   string(REPLACE " " "_" bpath "${bpath}")
 
-  # Strip off the filename.  I wait until here to do it, since removin the
+  # Strip off the filename.  I wait until here to do it, since removing the
   # basename can make a path that looked like path/../basename turn into
   # path/.. (notice the trailing slash).
   get_filename_component(bpath "${bpath}" PATH)
@@ -1362,7 +1362,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
       # Bring in the dependencies.  Creates a variable CUDA_NVCC_DEPEND #######
       cuda_include_nvcc_dependencies(${cmake_dependency_file})
 
-      # Convience string for output ###########################################
+      # Convenience string for output ###########################################
       if(CUDA_BUILD_EMULATION)
         set(cuda_build_type "Emulation")
       else()
@@ -1563,7 +1563,7 @@ macro(CUDA_ADD_LIBRARY cuda_target)
     ${_cmake_options} ${_cuda_shared_flag}
     OPTIONS ${_options} )
 
-  # Compute the file name of the intermedate link file used for separable
+  # Compute the file name of the intermediate link file used for separable
   # compilation.
   CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
 
@@ -1607,7 +1607,7 @@ macro(CUDA_ADD_EXECUTABLE cuda_target)
   # Create custom commands and targets for each file.
   CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources} OPTIONS ${_options} )
 
-  # Compute the file name of the intermedate link file used for separable
+  # Compute the file name of the intermediate link file used for separable
   # compilation.
   CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
 
@@ -1723,7 +1723,7 @@ endmacro()
 ###############################################################################
 ###############################################################################
 macro(CUDA_BUILD_CLEAN_TARGET)
-  # Call this after you add all your CUDA targets, and you will get a convience
+  # Call this after you add all your CUDA targets, and you will get a convenience
   # target.  You should also make clean after running this target to get the
   # build system to generate all the code again.
 

cmake/OpenCVCompilerOptions.cmake

 if("${CMAKE_CXX_COMPILER};${CMAKE_C_COMPILER};${CMAKE_CXX_COMPILER_LAUNCHER}" MATCHES "ccache")
-  set(CMAKE_COMPILER_IS_CCACHE 1)  # FIXIT Avoid setting of CMAKE_ variables
+  set(CMAKE_COMPILER_IS_CCACHE 1)  # TODO: FIXIT Avoid setting of CMAKE_ variables
   set(OPENCV_COMPILER_IS_CCACHE 1)
 endif()
 function(access_CMAKE_COMPILER_IS_CCACHE)

cmake/OpenCVFindLibsPerf.cmake

@@ -43,7 +43,7 @@ endif(WITH_IPP_A)
 if(WITH_CUDA)
   include("${OpenCV_SOURCE_DIR}/cmake/OpenCVDetectCUDA.cmake")
   if(NOT HAVE_CUDA)
-    message(WARNING "OpenCV is not able to find/confidure CUDA SDK (required by WITH_CUDA).
+    message(WARNING "OpenCV is not able to find/configure CUDA SDK (required by WITH_CUDA).
 CUDA support will be disabled in OpenCV build.
 To eliminate this warning remove WITH_CUDA=ON CMake configuration option.
 ")

cmake/OpenCVModule.cmake

@@ -455,7 +455,7 @@ function(__ocv_sort_modules_by_deps __lst)
   set(${__lst} "${result};${result_extra}" PARENT_SCOPE)
 endfunction()
 
-# resolve dependensies
+# resolve dependencies
 function(__ocv_resolve_dependencies)
   foreach(m ${OPENCV_MODULES_DISABLED_USER})
     set(HAVE_${m} OFF CACHE INTERNAL "Module ${m} will not be built in current configuration")
@@ -727,7 +727,7 @@ macro(ocv_set_module_sources)
     endif()
   endforeach()
 
-  # the hacky way to embeed any files into the OpenCV without modification of its build system
+  # the hacky way to embed any files into the OpenCV without modification of its build system
   if(COMMAND ocv_get_module_external_sources)
     ocv_get_module_external_sources()
   endif()
@@ -958,7 +958,7 @@ macro(_ocv_create_module)
     target_compile_definitions(${the_module} PRIVATE CVAPI_EXPORTS)
   endif()
 
-  # For dynamic link numbering convenions
+  # For dynamic link numbering conventions
   if(NOT ANDROID)
     # Android SDK build scripts can include only .so files into final .apk
     # As result we should not set version properties for Android

cmake/OpenCVPCHSupport.cmake

@@ -383,7 +383,7 @@ MACRO(ADD_NATIVE_PRECOMPILED_HEADER _targetName _input)
         # For Xcode, cmake needs my patch to process
         # GCC_PREFIX_HEADER and GCC_PRECOMPILE_PREFIX_HEADER as target properties
 
-        # When buiding out of the tree, precompiled may not be located
+        # When building out of the tree, precompiled may not be located
         # Use full path instead.
         GET_FILENAME_COMPONENT(fullPath ${_input} ABSOLUTE)
 

doc/tutorials/viz/launching_viz/launching_viz.markdown

@@ -37,7 +37,7 @@ Here is the general structure of the program:
     the same with **myWindow**. If the name does not exist, a new window is created.
     @code{.cpp}
     /// Access window via its name
-    viz::Viz3d sameWindow = viz::get("Viz Demo");
+    viz::Viz3d sameWindow = viz::getWindowByName("Viz Demo");
     @endcode
 -   Start a controlled event loop. Once it starts, **wasStopped** is set to false. Inside the while
     loop, in each iteration, **spinOnce** is called to prevent event loop from completely stopping.

modules/dnn/include/opencv2/dnn.hpp

@@ -42,7 +42,7 @@
 #ifndef OPENCV_DNN_HPP
 #define OPENCV_DNN_HPP
 
-// This is an umbrealla header to include into you project.
+// This is an umbrella header to include into you project.
 // We are free to change headers layout in dnn subfolder, so please include
 // this header for future compatibility
 
@@ -52,10 +52,10 @@
     This module contains:
         - API for new layers creation, layers are building bricks of neural networks;
         - set of built-in most-useful Layers;
-        - API to constuct and modify comprehensive neural networks from layers;
+        - API to construct and modify comprehensive neural networks from layers;
         - functionality for loading serialized networks models from different frameworks.
 
-    Functionality of this module is designed only for forward pass computations (i. e. network testing).
+    Functionality of this module is designed only for forward pass computations (i.e. network testing).
     A network training is in principle not supported.
   @}
 */

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -58,7 +58,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
   You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
 
   Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
-  In partuclar, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
   - Convolution
   - Deconvolution
   - Pooling
@@ -108,13 +108,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
 
         For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$
-        (i.e. @f$W_x@f$ is vertical contacentaion of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
+        (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
         The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$
         and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$.
 
-        @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_h @f$)
-        @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_x @f$)
-        @param b  is bias vector (i.e. according to abovemtioned notation is @f$ b @f$)
+        @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
+        @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
+        @param b  is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
         */
         CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
 
@@ -148,7 +148,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
          * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
          *
-         * If setUseTimstampsDim() is set to fase then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
+         * If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
          * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
         */
 
@@ -550,7 +550,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
      * @f]
      *
-     * Where `x, y` - spatial cooridnates, `c` - channel.
+     * Where `x, y` - spatial coordinates, `c` - channel.
      *
      * An every sample in the batch is normalized separately. Optionally,
      * output is scaled by the trained parameters.
@@ -565,7 +565,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
     };
 
     /**
-     * @brief Resize input 4-dimensional blob by nearest neghbor strategy.
+     * @brief Resize input 4-dimensional blob by nearest neighbor strategy.
      *
      * Layer is used to support TensorFlow's resize_nearest_neighbor op.
      */

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -87,7 +87,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
 
     /** @brief This class provides all data needed to initialize layer.
      *
-     * It includes dictionary with scalar params (which can be readed by using Dict interface),
+     * It includes dictionary with scalar params (which can be read by using Dict interface),
      * blob params #blobs and optional meta information: #name and #type of layer instance.
     */
     class CV_EXPORTS LayerParams : public Dict
@@ -138,7 +138,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          * Initialize wrapper from another one. It'll wrap the same host CPU
          * memory and mustn't allocate memory on device(i.e. GPU). It might
          * has different shape. Use in case of CPU memory reusing for reuse
-         * associented memory on device too.
+         * associated memory on device too.
          */
         BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape& shape);
 
@@ -346,7 +346,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         /** @brief Create a network from Intel's Model Optimizer intermediate representation.
          *  @param[in] xml XML configuration file with network's topology.
          *  @param[in] bin Binary file with trained weights.
-         *  Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
+         *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
          *  backend.
          */
         CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
@@ -402,8 +402,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
 
         /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer.
          *  @param outLayerId identifier of the first layer
-         *  @param inpLayerId identifier of the second layer
          *  @param outNum number of the first layer output
+         *  @param inpLayerId identifier of the second layer
          *  @param inpNum number of the second layer input
          */
         void connect(int outLayerId, int outNum, int inpLayerId, int inpNum);
@@ -564,7 +564,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          */
         CV_WRAP int getLayersCount(const String& layerType) const;
 
-        /** @brief Computes bytes number which are requered to store
+        /** @brief Computes bytes number which are required to store
          * all weights and intermediate blobs for model.
          * @param netInputShapes vector of shapes for all net inputs.
          * @param weights output parameter to store resulting bytes for weights.
@@ -584,7 +584,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
                                           const MatShape& netInputShape,
                                           CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
 
-        /** @brief Computes bytes number which are requered to store
+        /** @brief Computes bytes number which are required to store
          * all weights and intermediate blobs for each layer.
          * @param netInputShapes vector of shapes for all net inputs.
          * @param layerIds output vector to save layer IDs.
@@ -727,7 +727,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @param[in] xml XML configuration file with network's topology.
      *  @param[in] bin Binary file with trained weights.
      *  @returns Net object.
-     *  Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
+     *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
      *  backend.
      */
     CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin);
@@ -745,7 +745,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
      *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
      *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
-     *  @returns 4-dimansional Mat with NCHW dimensions order.
+     *  @returns 4-dimensional Mat with NCHW dimensions order.
      */
     CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(),
                                    const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);

modules/dnn/misc/quantize_face_detector.py

@@ -223,9 +223,9 @@ with tf.Session() as sess:
 
     # By default, float16 weights are stored in repeated tensor's field called
     # `half_val`. It has type int32 with leading zeros for unused bytes.
-    # This type is encoded by Varint that means only 7 bits are used for value
+    # This type is encoded by Variant that means only 7 bits are used for value
     # representation but the last one is indicated the end of encoding. This way
-    # float16 might takes 1 or 2 or 3 bytes depends on value. To impove compression,
+    # float16 might takes 1 or 2 or 3 bytes depends on value. To improve compression,
     # we replace all `half_val` values to `tensor_content` using only 2 bytes for everyone.
     for node in graph_def.node:
         if 'value' in node.attr:

modules/dnn/src/dnn.cpp

@@ -541,7 +541,7 @@ public:
 
         {
             // if dst already has been allocated with total(shape) elements,
-            // it won't be recrreated and pointer of dst.data remains the same.
+            // it won't be recreated and pointer of dst.data remains the same.
             dst.create(shape, use_half ? CV_16S : CV_32F);
             addHost(lp, dst);
         }
@@ -1520,7 +1520,7 @@ struct Net::Impl
                     }
                 }
 
-                // fuse convlution layer followed by eltwise + relu
+                // fuse convolution layer followed by eltwise + relu
                 if ( IS_DNN_OPENCL_TARGET(preferableTarget) )
                 {
                     Ptr<EltwiseLayer> nextEltwiseLayer;
@@ -1649,7 +1649,7 @@ struct Net::Impl
 
             // the optimization #3. if there is concat layer that concatenates channels
             // from the inputs together (i.e. axis == 1) then we make the inputs of
-            // the concat layer to write to the concatetion output buffer
+            // the concat layer to write to the concatenation output buffer
             // (and so we eliminate the concatenation layer, because the channels
             // are concatenated implicitly).
             Ptr<ConcatLayer> concatLayer = ld.layerInstance.dynamicCast<ConcatLayer>();

modules/dnn/src/halide_scheduler.cpp

@@ -242,7 +242,7 @@ bool HalideScheduler::process(Ptr<BackendNode>& node)
     std::map<std::string, Halide::Func> funcsMap;  // Scheduled functions.
     // For every function, from top to bottom, we try to find a scheduling node.
     // Scheduling is successful (return true) if for the first function (top)
-    // node is respresented.
+    // node is represented.
     CV_Assert(!node.empty());
     std::vector<Halide::Func>& funcs = node.dynamicCast<HalideBackendNode>()->funcs;
     for (int i = funcs.size() - 1; i >= 0; --i)

modules/dnn/src/layers/convolution_layer.cpp

@@ -676,7 +676,7 @@ public:
                                         int j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
                                         int j1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
 
-                                        // here some non-continous sub-row of the row will not be
+                                        // here some non-continuous sub-row of the row will not be
                                         // filled from the tensor; we need to make sure that the uncovered
                                         // elements are explicitly set to 0's. the easiest way is to
                                         // set all the elements to 0's before the loop.

modules/dnn/src/layers/detection_output_layer.cpp

@@ -110,7 +110,7 @@ public:
 
     float _nmsThreshold;
     int _topK;
-    // Whenever predicted bounding boxes are respresented in YXHW instead of XYWH layout.
+    // Whenever predicted bounding boxes are represented in YXHW instead of XYWH layout.
     bool _locPredTransposed;
     // It's true whenever predicted bounding boxes and proposals are normalized to [0, 1].
     bool _bboxesNormalized;

modules/dnn/src/layers/eltwise_layer.cpp

@@ -79,7 +79,7 @@ public:
             else if (operation == "max")
                 op = MAX;
             else
-                CV_Error(cv::Error::StsBadArg, "Unknown operaticon type \"" + operation + "\"");
+                CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
         }
 
         if (params.has("coeff"))

modules/dnn/src/layers/prior_box_layer.cpp

@@ -366,7 +366,7 @@ public:
         kernel.set(13, (int)_imageWidth);
         kernel.run(1, &nthreads, NULL, false);
 
-        // clip the prior's coordidate such that it is within [0, 1]
+        // clip the prior's coordinate such that it is within [0, 1]
         if (_clip)
         {
             Mat mat = outputs[0].getMat(ACCESS_READ);
@@ -442,7 +442,7 @@ public:
                 }
             }
         }
-        // clip the prior's coordidate such that it is within [0, 1]
+        // clip the prior's coordinate such that it is within [0, 1]
         if (_clip)
         {
             int _outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
@@ -565,7 +565,7 @@ private:
     std::vector<float> _variance;
     std::vector<float> _offsetsX;
     std::vector<float> _offsetsY;
-    // Precomputed final widhts and heights based on aspect ratios or explicit sizes.
+    // Precomputed final widths and heights based on aspect ratios or explicit sizes.
     std::vector<float> _boxWidths;
     std::vector<float> _boxHeights;
 

modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

@@ -709,7 +709,7 @@ bool OCL4DNNConvSpatial<Dtype>::swizzleWeight(const UMat &weight,
             return false;
         }
     } else {
-        // assumption: kernel dimesion is 2
+        // assumption: kernel dimension is 2
         Mat weightMat = weight.getMat(ACCESS_READ);
         Dtype* cpu_weight = (Dtype *)weightMat.ptr<float>();
         Mat swizzledWeightMat;

modules/dnn/src/op_inf_engine.cpp

@@ -288,7 +288,7 @@ void InfEngineBackendNet::init(int targetId)
             }
             for (const InferenceEngine::DataPtr& out : l->outData)
             {
-                // TODO: Replace to uniquness assertion.
+                // TODO: Replace to uniqueness assertion.
                 if (internalOutputs.find(out->name) == internalOutputs.end())
                     internalOutputs[out->name] = out;
             }
@@ -305,7 +305,7 @@ void InfEngineBackendNet::init(int targetId)
             // Add all outputs.
             for (const InferenceEngine::DataPtr& out : l->outData)
             {
-                // TODO: Replace to uniquness assertion.
+                // TODO: Replace to uniqueness assertion.
                 if (unconnectedOuts.find(out->name) == unconnectedOuts.end())
                     unconnectedOuts[out->name] = out;
             }

modules/dnn/src/tensorflow/graph.proto

@@ -86,7 +86,7 @@ message NodeDef {
   //              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
   //
   // Valid values for this string include:
-  // * "@other/node"                         (colocate with "other/node")
+  // * "@other/node"                         (collocate with "other/node")
   // * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
   // * "/job:worker/gpu:3"                   (partial specification)
   // * ""                                    (no specification)

modules/dnn/src/torch/torch_importer.cpp

@@ -311,11 +311,11 @@ struct TorchImporter
                 int numModules = curModule->modules.size();
                 readTorchObject(index);
 
-                if (tensors.count(index)) //tensor was readed
+                if (tensors.count(index)) //tensor was read
                 {
                     tensorParams.insert(std::make_pair(key, std::make_pair(index, tensors[index])));
                 }
-                else if (storages.count(index)) //storage was readed
+                else if (storages.count(index)) //storage was read
                 {
                     Mat &matStorage = storages[index];
                     Mat matCasted;
@@ -399,7 +399,7 @@ struct TorchImporter
         size_t requireElems = (size_t)offset + (size_t)steps[0] * (size_t)sizes[0];
         size_t storageElems = storages[indexStorage].total();
         if (requireElems > storageElems)
-            CV_Error(Error::StsBadSize, "Storage has insufficent number of elemements for requested Tensor");
+            CV_Error(Error::StsBadSize, "Storage has insufficient number of elements for requested Tensor");
 
         //convert sizes
         AutoBuffer<int, 4> isizes(ndims);

modules/dnn/test/test_darknet_importer.cpp

@@ -143,7 +143,7 @@ TEST_P(Test_Darknet_nets, YoloVoc)
     std::vector<float> confidences(3);
     std::vector<Rect2d> boxes(3);
     classIds[0] = 6;  confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418);  // a car
-    classIds[1] = 1;  confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bycicle
+    classIds[1] = 1;  confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bicycle
     classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028);  // a dog
     double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 7e-3 : 8e-5;
     double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
@@ -182,7 +182,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
     std::vector<float> confidences(3);
     std::vector<Rect2d> boxes(3);
     classIds[0] = 7;  confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994);  // a truck
-    classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327);  // a bycicle
+    classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327);  // a bicycle
     classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752);  // a dog (COCO)
     double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
     double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;

modules/dnn/test/test_torch_importer.cpp

@@ -250,7 +250,7 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
     Mat out = net.forward();
     Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
     // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
-    // thresholds for ENet must be changed. Accuracy of resuults was checked on
+    // thresholds for ENet must be changed. Accuracy of results was checked on
     // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
     normAssert(ref, out, "", 0.00044, 0.44);
 

modules/viz/CMakeLists.txt

@@ -19,7 +19,7 @@ if(NOT BUILD_SHARED_LIBS)
     endif()
   endforeach()
   if(_conflicts)
-    message(STATUS "Disabling VIZ module due conflicts with VTK dependencies: ${_conflicts}")
+    message(STATUS "Disabling VIZ module due to conflicts with VTK dependencies: ${_conflicts}")
     ocv_module_disable(viz)
   endif()
 endif()

modules/viz/include/opencv2/viz/widgets.hpp

@@ -506,7 +506,7 @@ namespace cv
         };
 
         /////////////////////////////////////////////////////////////////////////////
-        /// Compond widgets
+        /// Compound widgets
 
         /** @brief This 3D Widget represents a coordinate system. :
         */