Fix spelling typos

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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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 = {};