Misc. modules/ typos

Found via `codespell`

modules/dnn/src/caffe/opencv-caffe.proto

@@ -131,7 +131,7 @@ message PriorBoxParameter {
   // Variance for adjusting the prior bboxes.
   repeated float variance = 6;
   // By default, we calculate img_height, img_width, step_x, step_y based on
-  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitely
+  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitly
   // provided.
   // Explicitly provide the img_size.
   optional uint32 img_size = 7;

modules/dnn/src/dnn.cpp

@@ -58,7 +58,7 @@ namespace cv {
 namespace dnn {
 CV__DNN_EXPERIMENTAL_NS_BEGIN
 
-// this option is usefull to run valgrind memory errors detection
+// this option is useful to run valgrind memory errors detection
 static bool DNN_DISABLE_MEMORY_OPTIMIZATIONS = utils::getConfigurationParameterBool("OPENCV_DNN_DISABLE_MEMORY_OPTIMIZATIONS", false);
 
 using std::vector;
@@ -911,7 +911,7 @@ struct Net::Impl
         int id = getLayerId(layerName);
 
         if (id < 0)
-            CV_Error(Error::StsError, "Requsted layer \"" + layerName + "\" not found");
+            CV_Error(Error::StsError, "Requested layer \"" + layerName + "\" not found");
 
         return getLayerData(id);
     }
@@ -1897,7 +1897,7 @@ struct Net::Impl
         if ((size_t)pin.oid >= ld.outputBlobs.size())
         {
             CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, "
-                                           "the #%d was requsted", ld.name.c_str(),
+                                           "the #%d was requested", ld.name.c_str(),
                                            ld.outputBlobs.size(), pin.oid));
         }
         if (preferableTarget != DNN_TARGET_CPU)

modules/dnn/src/layers/concat_layer.cpp

@@ -88,7 +88,7 @@ public:
                 for (int curAxis = 0; curAxis < outputs[0].size(); curAxis++)
                 {
                     if (curAxis != cAxis && outputs[0][curAxis] != curShape[curAxis])
-                        CV_Error(Error::StsBadSize, "Inconsitent shape for ConcatLayer");
+                        CV_Error(Error::StsBadSize, "Inconsistent shape for ConcatLayer");
                 }
             }
 

modules/dnn/src/ocl4dnn/src/math_functions.cpp

@@ -185,7 +185,7 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
     int blockC_height = blocksize;
 
     int use_buffer_indicator = 8;
-    // To fix the edge problem casued by the sub group block read.
+    // To fix the edge problem caused by the sub group block read.
     // we have to pad the image if it's not multiple of tile.
     // just padding one line is enough as the sub group block read
     // will clamp to edge according to the spec.

modules/dnn/src/opencl/conv_layer_spatial.cl

@@ -188,7 +188,7 @@ __kernel void ConvolveBasic(
 #define VLOAD4(_v, _p) do { _v = vload4(0, _p); } while(0)
 
 // Each work-item computes a OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT region of one output map.
-// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the imput image.
+// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the input image.
 // NDRange:  (output_width+pad)/ OUT_BLOCK_WIDTH, (output_height+pad)/OUT_BLOCK_HEIGHT, NUM_FILTERS/OUT_BLOCK_DEPTH
 
 // NOTE: for beignet this reqd_work_group_size does not guarantee that SIMD16 mode will be used, the compiler could choose to use two SIMD8 threads, and if that happens the code will break.
@@ -220,7 +220,7 @@ convolve_simd(
 
   int in_addr;
 
-  // find weights adress of given neuron (lid is index)
+  // find weights address of given neuron (lid is index)
   unsigned int weight_addr = (fmg % (ALIGNED_NUM_FILTERS/SIMD_SIZE)) * INPUT_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE + lid;
 
   for(int i=0;i<OUT_BLOCK_SIZE;i++) {

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -1096,9 +1096,9 @@ void TFImporter::populateNet(Net dstNet)
             dstNet.setInputsNames(netInputs);
         }
         else if (type == "Split") {
-            // TODO: determing axis index remapping by input dimensions order of input blob
+            // TODO: determining axis index remapping by input dimensions order of input blob
             // TODO: slicing input may be Const op
-            // TODO: slicing kernels for convolutions - in current implenmentation it is impossible
+            // TODO: slicing kernels for convolutions - in current implementation it is impossible
             // TODO: add parsing num of slices parameter
             CV_Assert(layer.input_size() == 2);
             // num_split