Add json detection creation for codalab check

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

include/tkDNN/CenternetDetection.h

@@ -75,7 +75,7 @@ public:
 
     bool init(const std::string& tensor_path, const int n_classes=80);
     void preprocess(cv::Mat &frame);
-    void postprocess();
+    void postprocess(const bool mAP=false);
 };
 
 

include/tkDNN/DetectionNN.h

@@ -14,7 +14,7 @@
 
 #include "tkdnn.h"
 
-//#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
 
 #ifdef OPENCV_CUDACONTRIB
 #include <opencv2/cudawarping.hpp>
@@ -55,11 +55,11 @@ class DetectionNN {
          * boundig boxes. 
          * 
          */
-        virtual void postprocess() = 0;
+        virtual void postprocess(const bool mAP=false) = 0;
 
     public:
         int classes = 0;
-        float confThreshold = 0.3; /*threshold on the confidence of the boxes*/
+        float confThreshold = 0.05; /*threshold on the confidence of the boxes*/
 
         std::vector<tk::dnn::box> detected; /*bounding boxes in output*/
         std::vector<double> stats; /*keeps track of inference times (ms)*/
@@ -86,7 +86,7 @@ class DetectionNN {
          *        are saved on a csv file, otherwise not.
          * @param times pointer to the output stream where to write times
          */
-        void update(cv::Mat &frame, bool save_times=false, std::ofstream *times=nullptr){
+        void update(cv::Mat &frame, bool save_times=false, std::ofstream *times=nullptr, const bool mAP=false){
             if(!frame.data)
                 FatalError("No image data feed to detection");
 
@@ -116,7 +116,7 @@ class DetectionNN {
 
             {
                 TIMER_START
-                postprocess();
+                postprocess(mAP);
                 TIMER_STOP
                 if(save_times) *times<<t_ns<<"\n";
             }

include/tkDNN/Layer.h

@@ -535,6 +535,7 @@ struct box {
     int cl;
     float x, y, w, h;
     float prob;
+    std::vector<float> probs;
 
     void print() 
     {
@@ -581,7 +582,7 @@ public:
 
     dnnType *predictions;
 
-    static const int MAX_DETECTIONS = 2048;
+    static const int MAX_DETECTIONS = 8192;
     static Yolo::detection *allocateDetections(int nboxes, int classes);
     static void             mergeDetections(Yolo::detection *dets, int ndets, int classes);
 };

include/tkDNN/MobilenetDetection.h

@@ -67,7 +67,7 @@ public:
 
     bool init(const std::string& tensor_path, const int n_classes);
     void preprocess(cv::Mat &frame);
-    void postprocess();
+    void postprocess(const bool mAP=false);
 };
 
 

include/tkDNN/Yolo3Detection.h

@@ -26,7 +26,7 @@ public:
 
     bool init(const std::string& tensor_path, const int n_classes=80);
     void preprocess(cv::Mat &frame);
-    void postprocess();
+    void postprocess(const bool mAP=false);
 };
 
 

include/tkDNN/evaluation.h

@@ -108,6 +108,9 @@ void computeTPFPFN( std::vector<Frame> &images,const int classes,
                     bool verbose=false, const bool write_on_file=false,    
                     std::string net="");
 
+
+void printJsonCOCOFormat(std::ofstream *out_file, const std::string image_path, std::vector<tk::dnn::box> bbox, const int classes, const int w, const int h);
+
 }}
 #endif /*EVALUATION_H*/