Use yaml config file for the demo instead of param list

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

demo/demo/demo.cpp

@@ -9,7 +9,6 @@
 #include "Yolo3Detection.h"
 
 bool gRun;
-bool SAVE_RESULT = false;
 
 void sig_handler(int signo) {
     std::cout<<"request gateway stop\n";
@@ -18,43 +17,53 @@ void sig_handler(int signo) {
 
 int main(int argc, char *argv[]) {
 
-    std::cout<<"detection\n";
     signal(SIGINT, sig_handler);
 
-
-    std::string net = "yolo4tiny_fp32.rt";
-    if(argc > 1)
-        net = argv[1]; 
+    // get config file path and read it
     #ifdef __linux__ 
-        std::string input = "../demo/yolo_test.mp4";
+        std::string config_file = "../demo/demoConfig.yaml";
     #elif _WIN32
-        std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
+        std::string config_file = "..\\..\\..\\demo\\demoConfig.yaml";
     #endif
+    if(argc > 1)
+        config_file = config_file[1]; 
+    
+    YAML::Node conf =  YAMLloadConf(config_file);
+    if(!conf)
+        FatalError("Problem with config file");
 
-    if(argc > 2)
-        input = argv[2]; 
-    char ntype = 'y';
-    if(argc > 3)
-        ntype = argv[3][0]; 
-    int n_classes = 80;
-    if(argc > 4)
-        n_classes = atoi(argv[4]); 
-    int n_batch = 1;
-    if(argc > 5)
-        n_batch = atoi(argv[5]); 
-    bool show = true;
-    if(argc > 6)
-        show = atoi(argv[6]); 
-    float conf_thresh=0.3;
-    if(argc > 7)
-        conf_thresh = atof(argv[7]);     
+    // read settings from config file
+    std::string net = YAMLgetConf<std::string>(conf, "net", "yolo4tiny_fp32.rt");
+    if(!fileExist(net.c_str()))
+        FatalError("The given network does not exist. Create the rt first.");
 
+    #ifdef __linux__ 
+        std::string input = YAMLgetConf<std::string>(conf, "input", "../demo/yolo_test.mp4");
+    #elif _WIN32
+        std::string input = YAMLgetConf(conf, "win_input", "..\\..\\..\\demo\\yolo_test.mp4");
+    #endif
+    if(!fileExist(input.c_str()))
+        FatalError("The given input video does not exist.");
+    
+    char ntype          = YAMLgetConf<char>(conf, "ntype", 'y');
+    int n_classes       = YAMLgetConf<int>(conf, "n_classes", 80);
+    int n_batch         = YAMLgetConf<int>(conf, "n_batch", 1);
     if(n_batch < 1 || n_batch > 64)
         FatalError("Batch dim not supported");
+    float conf_thresh   = YAMLgetConf<float>(conf, "conf_thresh", 0.3);
+    bool show           = YAMLgetConf<bool>(conf, "show", true);
+    bool save           = YAMLgetConf<bool>(conf, "save", false);
 
-    if(!show)
-        SAVE_RESULT = true;
-
+    std::cout   <<"Net settings - net: "<< net
+                <<", ntype: "<< ntype
+                <<", n_classes: "<< n_classes
+                <<", n_batch: "<< n_batch
+                <<", conf_thresh: "<< conf_thresh<<"\n"; 
+    std::cout   <<"Demo settings - input: "<< input
+                <<", show: "<< show
+                <<", save: "<< save<<"\n\n"; 
+    
+    // create detection network
     tk::dnn::Yolo3Detection yolo;
     tk::dnn::CenternetDetection cnet;
     tk::dnn::MobilenetDetection mbnet;  
@@ -79,8 +88,7 @@ int main(int argc, char *argv[]) {
 
     detNN->init(net, n_classes, n_batch, conf_thresh);
 
-    gRun = true;
-
+    // open video stream
     cv::VideoCapture cap(input);
     if(!cap.isOpened())
         gRun = false; 
@@ -88,19 +96,21 @@ int main(int argc, char *argv[]) {
         std::cout<<"camera started\n";
 
     cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
+    if(save) {
         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));
     }
 
-    cv::Mat frame;
     if(show)
         cv::namedWindow("detection", cv::WINDOW_NORMAL);
 
+    cv::Mat frame;
     std::vector<cv::Mat> batch_frame;
     std::vector<cv::Mat> batch_dnn_input;
 
+    // start detection loop
+    gRun = true;
     while(gRun) {
         batch_dnn_input.clear();
         batch_frame.clear();
@@ -128,19 +138,18 @@ int main(int argc, char *argv[]) {
                 cv::waitKey(1);
             }
         }
-        if(n_batch == 1 && SAVE_RESULT)
+        if(n_batch == 1 && save)
             resultVideo << frame;
     }
 
     std::cout<<"detection end\n";   
-    double mean = 0; 
     
+    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<<"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;
 }

demo/demoConfig.yaml

@@ -0,0 +1,14 @@
+# video input
+input     : "../demo/yolo_test.mp4"
+win_input : "..\\..\\..\\demo\\yolo_test.mp4"
+
+# network config
+net         : "yolo4tiny_fp32.rt"
+ntype       : 'y'
+n_classes   : 80
+n_batch     : 1
+conf_thresh : 0.3
+
+# demo config
+show  : true
+save  : true

docs/demo.md

@@ -32,21 +32,20 @@ make
 
 Once you have successfully created your rt file, run the demo: 
 ```
-./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
+./demo <path-to-config>
 ```
-In general the demo program takes 7 parameters:
-```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
-```
-where
+In general the demo program takes 1 parameter, the ```<path-to-config>``` that is the path to che configuration file. The parameter is optional and its default value is ```"../demo/demoConfig.yaml"```. 
 
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
-*  ```<number-of-classes>```is the number of classes the network is trained on
-*  ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
-*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
-*  ```<conf-thresh>``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
+The config file is a yaml file with the following attributes:
+*  ```net``` is the rt file generated by a test
+*  ```input``` is the path to a video file or a camera input  (on Linux)
+*  ```win_input``` is the path to a video file or a camera input  (on Windows)
+*  ```ntype``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
+*  ```n_classes``` is the number of classes the network is trained on
+*  ```n_batch``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
+*  ```conf_thresh``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
+*  ```show``` if set to 0 the demo will not show the visualization (if n-batches ==1)
+*  ```save``` if set to 1 the demo will save the video of the demo into result.mp4 (if n-batches ==1)
 
 N.B. By default it is used FP32 inference
 
@@ -61,7 +60,8 @@ To run the demo with FP16 inference follow these steps (example with yolov3):
 export TKDNN_MODE=FP16  # set the half floating point optimization
 rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
+# set net: yolo3_fp16.rt in the config-file
+./demo 
 ```
 N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). 
 
@@ -86,7 +86,8 @@ export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
 export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
 rm yolo3_int8.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
+# set net: yolo3_int8.rt in the config-file
+./demo 
 ```
 N.B. 
 

include/tkDNN/utils.h

@@ -21,6 +21,7 @@
 #include <ios>
 #include <chrono>
 
+#include <yaml-cpp/yaml.h>
 
 #define dnnType float
 
@@ -137,4 +138,19 @@ static inline bool isCudaPointer(void *data) {
   cudaPointerAttributes attr;
   return cudaPointerGetAttributes(&attr, data) == 0;
 }
+
+inline YAML::Node YAMLloadConf(const std::string& conf_file) {
+    std::cerr<<"Loading YAML: "<<conf_file<<"\n";
+    return YAML::LoadFile(conf_file);
+}
+
+template<typename T>
+inline T YAMLgetConf(YAML::Node conf, std::string key, T defaultVal) {
+    T val = defaultVal;
+    if(conf && conf[key]) {
+        val = conf[key].as<T>();
+    }
+    return val;
+}
+
 #endif //UTILS_H