Modify map demo, using abstract class. Move draw function in abstract class

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

include/tkDNN/CenternetDetection.h

@@ -19,8 +19,6 @@ namespace tk { namespace dnn {
 class CenternetDetection : public DetectionNN
 {
 private:
-    std::vector<std::string> classesNames;
-
     tk::dnn::dataDim_t dim;
     tk::dnn::dataDim_t dim2;
     tk::dnn::dataDim_t dim_hm; 
@@ -79,7 +77,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };
 
 

include/tkDNN/DetectionNN.h

@@ -49,6 +49,7 @@ class DetectionNN {
 
         std::vector<tk::dnn::box> detected; /*bounding boxes in output*/
         std::vector<double> stats; /*keeps track of inference times (ms)*/
+        std::vector<std::string> classesNames;
 
         DetectionNN() {};
         ~DetectionNN(){};
@@ -70,9 +71,9 @@ class DetectionNN {
         virtual void preprocess(cv::Mat &frame) = 0;
 
         /**
-         * This method performs the inference of the NN.
+         * This method performs the whole detection of the NN.
          * 
-         * @param frame to run inference on.
+         * @param frame to run detection on.
          */
         virtual void update(cv::Mat &frame) = 0;
 
@@ -91,7 +92,36 @@ class DetectionNN {
          * @param orginal frame to draw bounding box on.
          * @return frame with boundig boxes.
          */
-        virtual cv::Mat draw(cv::Mat &frame) = 0;
+        cv::Mat draw(cv::Mat &frame) 
+        {
+            tk::dnn::box b;
+            int x0, w, x1, y0, h, y1;
+            int objClass;
+            std::string det_class;
+
+            int baseline = 0;
+            float font_scale = 0.5;
+            int thickness = 2;   
+            // draw dets
+            for(int i=0; i<detected.size(); i++) {
+                b           = detected[i];
+                x0   		= b.x;
+                x1   		= b.x + b.w;
+                y0   		= b.y;
+                y1   		= b.y + b.h;
+                det_class 	= classesNames[b.cl];
+
+                // draw rectangle
+                cv::rectangle(frame, cv::Point(x0, y0), cv::Point(x1, y1), colors[b.cl], 2); 
+
+                // draw label
+                cv::Size text_size = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
+                cv::rectangle(frame, cv::Point(x0, y0), cv::Point((x0 + text_size.width - 2), (y0 - text_size.height - 2)), colors[b.cl], -1);                      
+                cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
+            }
+            return frame;
+        }
+
 };
 
 }}

include/tkDNN/Layer.h

@@ -475,7 +475,7 @@ public:
 
     dnnType *predictions;
 
-    static const int MAX_DETECTIONS = 1024;
+    static const int MAX_DETECTIONS = 2048;
     static Yolo::detection *allocateDetections(int nboxes, int classes);
     static void             mergeDetections(Yolo::detection *dets, int ndets, int classes);
 };

include/tkDNN/MobilenetDetection.h

@@ -53,7 +53,7 @@ private:
     int nPriors = 0;
     float *locations_h, *confidences_h;
 
-    std::vector<std::string> classesNames;
+    
 
     void generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp = true);
     void convert_locatios_to_boxes_and_center();
@@ -69,7 +69,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };
 
 

include/tkDNN/Yolo3Detection.h

@@ -26,7 +26,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };