update readme.md, main.cpp, pedestrian_tracker_demo.hpp

demos/pedestrian_tracker_demo/cpp/README.md

@@ -116,6 +116,7 @@ Options:
     -limit "<num>"               Optional. Number of frames to store in output. If 0 is set, all frames are stored.
     -m_det "<path>"              Required. Path to the Pedestrian Detection Retail model (.xml) file.
     -m_reid "<path>"             Required. Path to the Pedestrian Reidentification Retail model (.xml) file.
+	-at \"<type>\"               Required. Required. Architecture type for detector model: centernet, ssd or yolo.
     -l "<absolute_path>"         Optional. For CPU custom layers, if any. Absolute path to a shared library with the kernels implementation.
           Or
     -c "<absolute_path>"         Optional. For GPU custom kernels, if any. Absolute path to the .xml file with the kernels description.
@@ -126,6 +127,15 @@ Options:
     -delay                       Optional. Delay between frames used for visualization. If negative, the visualization is turned off (like with the option 'no_show'). If zero, the visualization is made frame-by-frame.
     -out "<path>"                Optional. The file name to write output log file with results of pedestrian tracking. The format of the log file is compatible with MOTChallenge format.
     -u                           Optional. List of monitors to show initially.
+	-t                           Optional. Probability threshold for detections.
+    -auto_resize                 Optional. Enables resizable input with support of ROI crop & auto resize.
+    -iou_t                       Optional. Filtering intersection over union threshold for overlapping boxes.
+    -yolo_af                     Optional. Use advanced postprocessing/filtering algorithm for YOLO.
+    -labels \"<path>\"           Optional. Path to a file with labels mapping.
+    -nireq \"<integer>\"         Optional. Number of infer requests for detector model. If this option is omitted, number of infer requests is determined automatically.
+    -nstreams                    Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode for detector model(for HETERO and MULTI device cases use format <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>)
+    -nthreads \"<integer>\"      Optional. Number of threads for detector model.
+    -person_label                Optional. Label of class person for detector. Default -1 for tracking all objects
 ```
 
 For example, to run the application with the OpenVINO&trade; toolkit pre-trained models with inferencing pedestrian detector on a GPU and pedestrian reidentification on a CPU, run the following command:

demos/pedestrian_tracker_demo/cpp/include/pedestrian_tracker_demo.hpp

@@ -40,16 +40,16 @@ static const char delay_message[] = "Optional. Delay between frames used for vis
 static const char output_log_message[] = "Optional. The file name to write output log file with results of pedestrian tracking. "
                                           "The format of the log file is compatible with MOTChallenge format.";
 static const char utilization_monitors_message[] = "Optional. List of monitors to show initially.";
-static const char at_message[] = "Required. Architecture type: centernet, ssd or yolo";
+static const char at_message[] = "Required. Architecture type for detector model: centernet, ssd or yolo.";
 static const char thresh_output_message[] = "Optional. Probability threshold for detections.";
 static const char input_resizable_message[] = "Optional. Enables resizable input with support of ROI crop & auto resize.";
 static const char iou_thresh_output_message[] = "Optional. Filtering intersection over union threshold for overlapping boxes.";
 static const char yolo_af_message[] = "Optional. Use advanced postprocessing/filtering algorithm for YOLO.";
 static const char labels_message[] = "Optional. Path to a file with labels mapping.";
-static const char nireq_message[] = "Optional. Number of infer requests. If this option is omitted, number of infer requests is determined automatically.";
-static const char num_threads_message[] = "Optional. Number of threads.";
+static const char nireq_message[] = "Optional. Number of infer requests for detector model. If this option is omitted, number of infer requests is determined automatically.";
+static const char num_threads_message[] = "Optional. Number of threads for detector model.";
 static const char num_streams_message[] = "Optional. Number of streams to use for inference on the CPU or/and GPU in "
-"throughput mode (for HETERO and MULTI device cases use format "
+"throughput mode for detector model (for HETERO and MULTI device cases use format "
 "<device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>)";
 static const char person_label_message[] = "Optional. Label of class person for detector. Default -1 for tracking all objects";
 

demos/pedestrian_tracker_demo/cpp/main.cpp

@@ -27,8 +27,6 @@
 #include <models/detection_model_yolo.h>
 #include <pipelines/metadata.h>
 
-using namespace InferenceEngine;
-
 using ImageWithFrameIndex = std::pair<cv::Mat, int>;
 
 std::unique_ptr<PedestrianTracker>
@@ -109,6 +107,7 @@ bool ParseAndCheckCommandLine(int argc, char *argv[]) {
 
 int main(int argc, char **argv) {
     try {
+        PerformanceMetrics metrics;
 
         if (!ParseAndCheckCommandLine(argc, argv)) {
             return 0;
@@ -158,7 +157,7 @@ int main(int argc, char **argv) {
 
         std::vector<std::string> devices{detector_mode, reid_mode};
 
-        slog::info << *GetInferenceEngineVersion() << slog::endl;
+        slog::info << *InferenceEngine::GetInferenceEngineVersion() << slog::endl;
         InferenceEngine::Core ie;
 
         auto execNet = detectionModel->loadExecutableNetwork(
@@ -176,6 +175,7 @@ int main(int argc, char **argv) {
             video_fps = 60.0;
         }
 
+        auto startTime = std::chrono::steady_clock::now();
         cv::Mat frame = cap->read();
         if (!frame.data) throw std::runtime_error("Can't read an image from the input");
         cv::Size firstFrameSize = frame.size();
@@ -205,11 +205,13 @@ int main(int argc, char **argv) {
 
                 auto blobPtr = req->GetBlob(outName);
 
-                if (Precision::I32 == blobPtr->getTensorDesc().getPrecision()) {
-                    res.outputsData.emplace(outName, std::make_shared<TBlob<int>>(*as<TBlob<int>>(blobPtr)));
+                if (InferenceEngine::Precision::I32 == blobPtr->getTensorDesc().getPrecision()) {
+                    res.outputsData.emplace(outName,
+                        std::make_shared<InferenceEngine::TBlob<int>>(*InferenceEngine::as<InferenceEngine::TBlob<int>>(blobPtr)));
                 }
                 else {
-                    res.outputsData.emplace(outName, std::make_shared<TBlob<float>>(*as<TBlob<float>>(blobPtr)));
+                    res.outputsData.emplace(outName,
+                        std::make_shared<InferenceEngine::TBlob<float>>(*InferenceEngine::as<InferenceEngine::TBlob<float>>(blobPtr)));
                 }
             }
 
@@ -250,6 +252,7 @@ int main(int argc, char **argv) {
             tracker->Process(frame, detections, cur_timestamp);
 
             presenter.drawGraphs(frame);
+            metrics.update(startTime, frame, { 10, 22 }, cv::FONT_HERSHEY_COMPLEX, 0.65);
             // Drawing colored "worms" (tracks).
             frame = tracker->DrawActiveTracks(frame);
 
@@ -284,6 +287,7 @@ int main(int argc, char **argv) {
                 DetectionLog log = tracker->GetDetectionLog(true);
                 SaveDetectionLogToTrajFile(detlog_out, log);
             }
+            startTime = std::chrono::steady_clock::now();
             frame = cap->read();
             if (!frame.data) break;
             if (frame.size() != firstFrameSize)
@@ -299,6 +303,8 @@ int main(int argc, char **argv) {
                 PrintDetectionLog(log);
         }
 
+        slog::info << "Metrics report:" << slog::endl;
+        metrics.logTotal();
         slog::info << presenter.reportMeans() << slog::endl;
     }
     catch (const std::exception& error) {