Merge pull request #21775 from luzpaz:typos/gapi

* GAPI: fix various gapi related typos

Fixes source comments and documentation related to gapi code.

* Fix source typos

* Fixed typos requested

* Follow-up typo fix

modules/gapi/samples/face_detection_mtcnn.cpp

@@ -33,7 +33,7 @@ const std::string keys =
 "{ thrr             | 0.7                       | MTCNN R confidence threshold}"
 "{ thro             | 0.7                       | MTCNN O confidence threshold}"
 "{ half_scale       | false                     | MTCNN P use half scale pyramid}"
-"{ queue_capacity   | 1                         | Streaming executor queue capacity. Calculated automaticaly if 0}"
+"{ queue_capacity   | 1                         | Streaming executor queue capacity. Calculated automatically if 0}"
 ;
 
 namespace {
@@ -488,8 +488,8 @@ static inline std::string get_pnet_level_name(const cv::Size &in_size) {
 }
 
 int calculate_scales(const cv::Size &input_size, std::vector<double> &out_scales, std::vector<cv::Size> &out_sizes ) {
-    //calculate multi - scale and limit the maxinum side to 1000
-    //pr_scale: limit the maxinum side to 1000, < 1.0
+    //calculate multi - scale and limit the maximum side to 1000
+    //pr_scale: limit the maximum side to 1000, < 1.0
     double pr_scale = 1.0;
     double h = static_cast<double>(input_size.height);
     double w = static_cast<double>(input_size.width);
@@ -602,7 +602,7 @@ int main(int argc, char* argv[]) {
     cv::GArray<custom::Face> final_p_faces_for_bb2squares = custom::ApplyRegression::on(faces0, true);
     cv::GArray<custom::Face> final_faces_pnet0 = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares);
     total_faces[0] = custom::RunNMS::on(final_faces_pnet0, 0.5f, false);
-    //The rest PNet pyramid layers to accumlate all layers result in total_faces[PYRAMID_LEVELS - 1]]
+    //The rest PNet pyramid layers to accumulate all layers result in total_faces[PYRAMID_LEVELS - 1]]
     for (int i = 1; i < pyramid_levels; ++i)
     {
         std::tie(regressions[i], scores[i]) = run_mtcnn_p(in_transposedRGB, get_pnet_level_name(level_size[i]));

modules/gapi/samples/infer_single_roi.cpp

@@ -150,7 +150,7 @@ int main(int argc, char *argv[])
     auto networks = cv::gapi::networks(face_net);
 
     // Now build the graph. The graph structure may vary
-    // pased on the input parameters
+    // passed on the input parameters
     cv::GStreamingCompiled pipeline;
     auto inputs = cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
 

modules/gapi/samples/onevpl_infer_single_roi.cpp

@@ -43,7 +43,7 @@ const std::string keys =
     "{ facem                        | face-detection-adas-0001.xml              | Path to OpenVINO IE face detection model (.xml) }"
     "{ faced                        | AUTO                                      | Target device for face detection model (e.g. AUTO, GPU, VPU, ...) }"
     "{ cfg_params                   | <prop name>:<value>;<prop name>:<value>   | Semicolon separated list of oneVPL mfxVariants which is used for configuring source (see `MFXSetConfigFilterProperty` by https://spec.oneapi.io/versions/latest/elements/oneVPL/source/index.html) }"
-    "{ streaming_queue_capacity     | 1                                         | Streaming executor queue capacity. Calculated automaticaly if 0 }"
+    "{ streaming_queue_capacity     | 1                                         | Streaming executor queue capacity. Calculated automatically if 0 }"
     "{ frames_pool_size             | 0                                         | OneVPL source applies this parameter as preallocated frames pool size}"
     "{ vpp_frames_pool_size         | 0                                         | OneVPL source applies this parameter as preallocated frames pool size for VPP preprocessing results}"
     "{ roi                          | -1,-1,-1,-1                               | Region of interest (ROI) to use for inference. Identified automatically when not set }";
@@ -281,7 +281,7 @@ int main(int argc, char *argv[]) {
     const auto source_vpp_queue_capacity = cmd.get<uint32_t>("vpp_frames_pool_size");
     const auto device_id = cmd.get<std::string>("faced");
 
-    // check ouput file extension
+    // check output file extension
     if (!output.empty()) {
         auto ext = output.find_last_of(".");
         if (ext == std::string::npos || (output.substr(ext + 1) != "avi")) {
@@ -421,7 +421,7 @@ int main(int argc, char *argv[]) {
         } else {
             cap = cv::gapi::wip::make_onevpl_src(file_path, source_cfgs);
         }
-        std::cout << "oneVPL source desription: " << cap->descr_of() << std::endl;
+        std::cout << "oneVPL source description: " << cap->descr_of() << std::endl;
     } catch (const std::exception& ex) {
         std::cerr << "Cannot create source: " << ex.what() << std::endl;
         return -1;

modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp

@@ -70,7 +70,7 @@ bool DummySource::pull(cv::gapi::wip::Data& data) {
          *  update current seq_id correspondingly.
          *
          *  if drop_frames is enabled, wait for the next tick, otherwise
-         *  return last writen frame (+2 at the picture above) immediately.
+         *  return last written frame (+2 at the picture above) immediately.
          */
         int64_t num_frames =
             static_cast<int64_t>((curr_ts - m_next_tick_ts) / m_latency);