Update demo.cpp

Added HTTP stream over multi-client MJPEG  sever, added ESC Key quit and resizable cv window

demo/demo/demo.cpp

@@ -1,8 +1,9 @@
 #include <iostream>
 #include <signal.h>
-#include <stdlib.h>     /* srand, rand */
+#include <stdlib.h> /* srand, rand */
 #include <unistd.h>
 #include <mutex>
+#include <https_stream.h> //https_stream
 
 #include "CenternetDetection.h"
 #include "MobilenetDetection.h"
@@ -11,61 +12,65 @@
 bool gRun;
 bool SAVE_RESULT = false;
 
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
+void sig_handler(int signo)
+{
+    std::cout << "request gateway stop\n";
     gRun = false;
 }
 
-int main(int argc, char *argv[]) {
+int main(int argc, char *argv[])
+{
 
-    std::cout<<"detection\n";
+    std::cout << "detection\n";
     signal(SIGINT, sig_handler);
 
-
     std::string net = "yolo3_berkeley.rt";
-    if(argc > 1)
-        net = argv[1]; 
+    if (argc > 1)
+        net = argv[1];
     std::string input = "../demo/yolo_test.mp4";
-    if(argc > 2)
-        input = argv[2]; 
+    if (argc > 2)
+        input = argv[2];
     char ntype = 'y';
-    if(argc > 3)
-        ntype = argv[3][0]; 
+    if (argc > 3)
+        ntype = argv[3][0];
     int n_classes = 80;
-    if(argc > 4)
-        n_classes = atoi(argv[4]); 
+    if (argc > 4)
+        n_classes = atoi(argv[4]);
     int n_batch = 1;
-    if(argc > 5)
-        n_batch = atoi(argv[5]); 
+    if (argc > 5)
+        n_batch = atoi(argv[5]);
     bool show = true;
-    if(argc > 6)
-        show = atoi(argv[6]); 
+    if (argc > 6)
+        show = atoi(argv[6]);
+    int port = 0;
+    if (argc > 7)
+        port = atoi(argv[7]);
 
-    if(n_batch < 1 || n_batch > 64)
+    if (n_batch < 1 || n_batch > 64)
         FatalError("Batch dim not supported");
 
-    if(!show)
+    if (!show)
         SAVE_RESULT = true;
 
     tk::dnn::Yolo3Detection yolo;
     tk::dnn::CenternetDetection cnet;
-    tk::dnn::MobilenetDetection mbnet;  
+    tk::dnn::MobilenetDetection mbnet;
 
-    tk::dnn::DetectionNN *detNN;  
+    tk::dnn::DetectionNN *detNN;
 
-    switch(ntype)
+    switch (ntype)
     {
-        case 'y':
-            detNN = &yolo;
-            break;
-        case 'c':
-            detNN = &cnet;
-            break;
-        case 'm':
-            detNN = &mbnet;
-            n_classes++;
-            break;
-        default:
+    case 'y':
+        detNN = &yolo;
+        break;
+    case 'c':
+        detNN = &cnet;
+        break;
+    case 'm':
+        detNN = &mbnet;
+        n_classes++;
+        break;
+    default:
         FatalError("Network type not allowed (3rd parameter)\n");
     }
 
@@ -74,66 +79,83 @@ int main(int argc, char *argv[]) {
     gRun = true;
 
     cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
+    if (!cap.isOpened())
+        gRun = false;
     else
-        std::cout<<"camera started\n";
+        std::cout << "camera started\n";
 
     cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
+    if (SAVE_RESULT)
+    {
         int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
         int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M', 'P', '4', 'V'), 30, cv::Size(w, h));
     }
 
     cv::Mat frame;
-    if(show)
+    if (show)
         cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    cv::moveWindow("detection", 0, 0);
+    cv::resizeWindow("detection", 1352, 1013);
 
     std::vector<cv::Mat> batch_frame;
     std::vector<cv::Mat> batch_dnn_input;
 
-    while(gRun) {
+    while (gRun)
+    {
         batch_dnn_input.clear();
         batch_frame.clear();
-        
-        for(int bi=0; bi< n_batch; ++bi){
-            cap >> frame; 
-            if(!frame.data) 
+
+        for (int bi = 0; bi < n_batch; ++bi)
+        {
+            cap >> frame;
+            if (!frame.data)
                 break;
-            
+
             batch_frame.push_back(frame);
 
             // this will be resized to the net format
             batch_dnn_input.push_back(frame.clone());
-        } 
-        if(!frame.data) 
+        }
+        if (!frame.data)
             break;
-    
+
         //inference
         detNN->update(batch_dnn_input, n_batch);
         detNN->draw(batch_frame);
 
-        if(show){
-            for(int bi=0; bi< n_batch; ++bi){
+        if (show)
+        {
+            for (int bi = 0; bi < n_batch; ++bi)
+            {
                 cv::imshow("detection", batch_frame[bi]);
-                cv::waitKey(1);
+                
             }
         }
-        if(n_batch == 1 && SAVE_RESULT)
+        if (cv::waitKey(1) == 27)
+        {
+            break;
+        }
+        if (n_batch == 1 && SAVE_RESULT)
             resultVideo << frame;
+       
+        if (port > 0)
+        {
+            send_mjpeg(batch_frame[0], port, 400000, 40);
+        }
     }
 
-    std::cout<<"detection end\n";   
-    double mean = 0; 
-    
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";    
-    for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
-    std::cout<<"Avg: "<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n"<<COL_END;   
-    
+    std::cout << "detection end\n";
+    double mean = 0;
+
+    std::cout << COL_GREENB << "\n\nTime stats:\n";
+    std::cout << "Min: " << *std::min_element(detNN->stats.begin(), detNN->stats.end()) / n_batch << " ms\n";
+    std::cout << "Max: " << *std::max_element(detNN->stats.begin(), detNN->stats.end()) / n_batch << " ms\n";
+    for (int i = 0; i < detNN->stats.size(); i++)
+        mean += detNN->stats[i];
+    mean /= detNN->stats.size();
+    std::cout << "Avg: " << mean / n_batch << " ms\t" << 1000 / (mean / n_batch) << " FPS\n"
+              << COL_END;
 
     return 0;
 }
-