Refactoring and modularization

Signed-off-by: Micaela Verucchi <micaela.verucchi@unimore.it>

CMakeLists.txt

@@ -38,12 +38,20 @@ cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
 file(GLOB tkdnn_SRC "src/*.cpp")
 set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} -lcudnn -lnvinfer ${OpenCV_LIBS} -lgdal)
 
+file(GLOB class_SRC "src/class_src/*.cpp")
+set(class_LIBS ${OpenCV_LIBS} -lgdal yaml-cpp python2.7)
+
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -std=c++11")
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES} "~/repos/cereal/include" ${CMAKE_CURRENT_SOURCE_DIR}/tracker_CLASS/c++/src)
 include_directories( BEFORE ${MY_SOURCE_DIR}/src /usr/include/python2.7 )
+
 add_library(tkDNN SHARED ${tkdnn_SRC})
 target_link_libraries(tkDNN ${tkdnn_LIBS})
 
+add_library(CLASS SHARED ${class_SRC})
+target_link_libraries(CLASS ${class_LIBS})
+
+
 #static 
 #add_library(tkDNN_static STATIC ${tkdnn_SRC})
 #target_link_libraries(tkDNN_static ${tkdnn_LIBS})
@@ -104,8 +112,7 @@ add_executable(yolo3_demo demo/demo/demo.cpp
                         tracker_CLASS/c++/src/tracker.cpp )
 
                         
-target_link_libraries(yolo3_demo tkDNN)
+target_link_libraries(yolo3_demo tkDNN CLASS)
-target_link_libraries(yolo3_demo python2.7 yaml-cpp)
 
 
 

demo/demo/demo.cpp

@@ -1,89 +1,34 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h> /* srand, rand */
-#include <unistd.h>
-#include <mutex>
-#include <ctime>
-#include <pthread.h>
-
 #include <time.h>
-#include <chrono>
-#include <math.h>
-#include <typeinfo>
 
 #include "utils.h"
-#include "BoxDetection.h"
-
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
-
-//saliency
-#include <opencv2/core/utility.hpp>
-#include <opencv2/saliency.hpp>
-#include <opencv2/highgui.hpp>
-
 #include "Yolo3Detection.h"
-#include "classutils.h"
+#include "message.h"
+#include "visualization.h"
+
+#include "tracker.h"
 #include "../masa_protocol/include/send.hpp"
 #include "../masa_protocol/include/serialize.hpp"
 
-#include "ekf.h"
+// #include <assert.h>
-#include "trackutils.h"
+// #include <unistd.h>
-#include "plot.h"
+// #include <mutex>
-#include "tracker.h"
+// #include <ctime>
-#include <assert.h> 
+// #include <pthread.h>
+// #include <signal.h>
+// #include <chrono>
+// #include <math.h>
+// #include <typeinfo>
+// #include <iostream>
 
 #define MAX_DETECT_SIZE 100
 
-std::chrono::steady_clock::time_point local_clock_start;
-
-std::mutex mutexgRun;
-
 bool gRun;
+std::chrono::steady_clock::time_point local_clock_start;
+std::mutex mutexgRun;
 std::string obj_class[10]{"person", "car", "truck", "bus", "motor", "bike", "rider", "traffic light", "traffic sign", "train"};
-
 //mutex for some opencv operations
 std::mutex mutex_cv;
-
+Show_t updates;
-struct ModFrame_t{
-    std::vector<Tracker> trackers;
-    geodetic_converter::GeodeticConverter gc;
-    double adfGeoTransform[6];
-    cv::Mat H;
-    cv::Mat original_frame;
-    tk::dnn::Yolo3Detection yolo;
-    cv::Mat mask;
-    // sem for mainthread, detectionthread and topviewthread
-    std::mutex sem;
-};
-
-struct Frame_t{
-    char *input;
-    cv::Mat frame;
-    int frame_nbr;
-    // sem_vc for mainthread, videocapturethread, originalthread and disparitythread
-    std::mutex sem_vc;
-};
-
-struct Camera_t{ 
-    int CAM_IDX;
-    char *input;
-    char *pmatrix;
-    char *maskfile;
-    char *cameraCalib;
-    char *maskFileOrient;
-    bool to_show;
-    tk::dnn::Yolo3Detection yolo;
-    double adfGeoTransform[6];
-};
-
-struct Show_t{
-    cv::Mat original, detection, topview, disparity;
-    bool update_o, update_de, update_t, update_di; 
-    // a single mutex for each operation - the show_updates function must get all mutex
-    std::mutex mutex_o, mutex_de, mutex_t, mutex_di;
-}updates;
 
 void sig_handler(int signo)
 {
@@ -113,7 +58,6 @@ void *readVideoCapture(void *x_void_ptr)
     else
         std::cout << "camera started\n";
 
-
     // cap.set(cv::CAP_PROP_BUFFERSIZE,3);
     // std::cout<<"buf size: "<<cap.get(CV_CAP_PROP_BUFFERSIZE)<<std::endl;
     auto start_t = std::chrono::steady_clock::now();
@@ -190,7 +134,6 @@ void *readVideoCapture(void *x_void_ptr)
         // CAP_PROP_FRAME_COUNT Number of frames in the video file.
         std::cout << "id: " << cap.get(cv::CAP_PROP_FRAME_COUNT) << std::endl;
 
-        
         if (!frame_loc.data)
         {
             usleep(1000000);
@@ -218,384 +161,6 @@ void *readVideoCapture(void *x_void_ptr)
     return (void *)0;
 }
 
-/* Thread function to show the updated images 
-**/
-void *show_updates(void *x_void_ptr)
-{
-    cv::namedWindow("original", cv::WINDOW_NORMAL);
-    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-    cv::namedWindow("topview", cv::WINDOW_NORMAL);
-    cv::namedWindow("disparity", cv::WINDOW_NORMAL);
-    cv::Mat original_loc, detection_loc, topview_loc, disparity_loc;
-    bool update_o_loc, update_de_loc, update_t_loc, update_di_loc;
-    
-    while (gRun)
-    {
-        TIMER_START
-        // critical section: copy the struct in local variable
-        // in this way we can unlock the sem for the main thread
-        if(updates.mutex_o.try_lock())
-            {
-            update_o_loc = updates.update_o;
-            updates.update_o = false;
-            if(update_o_loc)
-                original_loc = updates.original.clone();
-            updates.mutex_o.unlock();
-            }
-
-        if(updates.mutex_de.try_lock())
-            {    
-            update_de_loc = updates.update_de;
-            updates.update_de = false;
-            if(update_de_loc)
-                detection_loc = updates.detection.clone();
-            updates.mutex_de.unlock();
-            }
-
-        if(updates.mutex_t.try_lock())
-            {  
-            update_t_loc = updates.update_t;
-            updates.update_t = false;
-            if(update_t_loc)
-                topview_loc = updates.topview.clone();
-            updates.mutex_t.unlock();
-            }
-
-        if(updates.mutex_di.try_lock())
-            {  
-            update_di_loc = updates.update_di;
-            updates.update_di = false;
-            if(update_di_loc)
-                disparity_loc = updates.disparity.clone();
-            updates.mutex_di.unlock();
-            }
-
-        if(update_o_loc)
-            cv::imshow("original", original_loc);
-        if(update_de_loc)
-            cv::imshow("detection", detection_loc);
-        if(update_t_loc)
-            cv::imshow("topview", topview_loc);
-        if(update_di_loc)
-            cv::imshow("disparity", disparity_loc);
-        cv::waitKey(1);
-        // usleep(20000);  //sleep 20 msec
-        std::cout<<"show_updates: ";
-        TIMER_STOP
-    }
-    return (void *)0;
-}
-
-void *originalFrame(void *x_void_ptr)
-{
-    Frame_t *info_show_orig = (Frame_t *) x_void_ptr;
-    cv::Mat frame_loc;
-    int frame_nbr_loc = 0;
-    while (gRun)
-    {
-        TIMER_START
-        // critical section: copy the struct in local variable
-        // in this way we can unlock the sem for the main thread
-        info_show_orig->sem_vc.lock();
-        frame_loc = info_show_orig->frame.clone();
-        frame_nbr_loc = info_show_orig->frame_nbr;
-        info_show_orig->sem_vc.unlock();
-        if (frame_nbr_loc == 0)
-        {
-            usleep(1000000);
-            printf("no frame received\n");
-            continue;
-        }
-        updates.mutex_o.lock();
-        updates.original = frame_loc.clone();
-        updates.update_o = true;
-        updates.mutex_o.unlock();
-        usleep(10000);  //sleep 10 msec
-        std::cout<<"originalFrame: ";
-        TIMER_STOP
-    }
-    return (void *)0;
-}
-
-void *detectionFrame(void *x_void_ptr)
-{
-    ModFrame_t *info_show = (ModFrame_t *) x_void_ptr;
-    double lat, lon, alt;
-    int pix_x, pix_y;
-    cv::Mat original_frame_loc;
-    std::vector<Tracker> trackers;
-
-    geodetic_converter::GeodeticConverter gc;
-    double adfGeoTransform[6];
-    cv::Mat H;
-    tk::dnn::Yolo3Detection yolo;
-    int num_detected;
-    cv::Mat mask;
-
-    // box variable
-    tk::dnn::box b;
-    int x0, w, x1, y0, h, y1;
-    int objClass;
-    std::string det_class;;
-    // float prob; 
-    cv::Scalar intensity;
-    std::vector<cv::Point2f> map_p, camera_p;
-    int baseline = 0;
-    float fontScale = 0.5;
-    int thickness = 2;
-
-    while (gRun)
-    {
-        TIMER_START
-        // critical section: copy the struct in local variable
-        // in this way we can unlock the sem for the main thread
-        info_show->sem.lock();
-        original_frame_loc = info_show->original_frame.clone();
-        // std::vector<Tracker> trackers;
-        trackers = info_show->trackers;
-        // geodetic_converter::GeodeticConverter gc;
-        gc = info_show->gc;
-        for(int i = 0; i < 6; i++ )
-            adfGeoTransform[i] = info_show->adfGeoTransform[i];
-        // cv::Mat H;
-        H = info_show->H.clone();
-        yolo = info_show->yolo;
-        mask = info_show->mask.clone();
-        info_show->sem.unlock();
-        
-        if (trackers.empty())
-        {
-            usleep(1000000);
-            printf("no data available\n");
-            continue;
-        }
-
-        num_detected = yolo.detected.size();
-        for (int i = 0; i < num_detected; i++)
-        {
-            b = yolo.detected[i];
-            x0 = b.x;
-            w = b.w;
-            x1 = b.x + w;
-            y0 = b.y;
-            h = b.h;
-            y1 = b.y + h;
-            objClass = b.cl;
-            det_class = obj_class[b.cl];
-            // prob = b.prob;
-
-            intensity = mask.at<uchar>(cv::Point(int(x0 + b.w / 2), y1));
-            
-            if (intensity[0] && objClass < 6)
-            {
-                //std::cout<<objClass<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
-                cv::rectangle(original_frame_loc, cv::Point(x0, y0), cv::Point(x1, y1), yolo.colors[objClass], 2);
-                // draw label
-                cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
-                cv::rectangle(original_frame_loc, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), yolo.colors[b.cl], -1);
-                cv::putText(original_frame_loc, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness); 
-            }
-        }
-
-        for (auto t : trackers)
-        {
-            for (size_t p = 1; p < t.pred_list_.size(); p++)
-            {
-                gc.enu2Geodetic(t.pred_list_[p].x_, t.pred_list_[p].y_, 0, &lat, &lon, &alt);
-                coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
-
-                map_p.clear();
-                camera_p.clear();
-                map_p.push_back(cv::Point2f(pix_x, pix_y));
-
-                //transform camera pixel to map pixel
-                cv::perspectiveTransform(map_p, camera_p, H.inv());
-                // std::cout<<"x,y: "<<pix_x<<", "<<pix_y<<std::endl;
-                // std::cout<<"map_p: "<<map_p<<std::endl;
-                // std::cout<<"camera_p: "<<camera_p<<std::endl;
-                // std::cout<<"size original_frame_loc: "<<original_frame_loc.cols<<", "<<original_frame_loc.rows<<std::endl;
-                // assert (camera_p[0].x < original_frame_loc.cols);
-                // assert (camera_p[0].y < original_frame_loc.rows);
-                if (camera_p[0].x < original_frame_loc.cols && camera_p[0].y < original_frame_loc.rows && camera_p[0].x >= 0 && camera_p[0].y >= 0)
-                    cv::circle(original_frame_loc, cv::Point(camera_p[0].x, camera_p[0].y), 3.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
-            }
-        }
-
-        updates.mutex_de.lock();
-        updates.detection = original_frame_loc.clone();
-        updates.update_de = true;
-        updates.mutex_de.unlock();
-
-        std::cout<<"detectionFrame: ";
-        TIMER_STOP
-
-    }
-    return (void *)0;
-}
-
-void *topviewFrame(void *x_void_ptr)
-{
-    ModFrame_t *info_show = (ModFrame_t *) x_void_ptr;
-    double lat, lon, alt;
-    int pix_x, pix_y;
-    cv::Mat frame_top;
-    cv::Mat original_frame_top;
-    // original_frame_top = cv::imread("../demo/demo/data/map/map_geo.jpg");
-    original_frame_top = cv::imread("../demo/demo/data/map/MASA_4670.png");
-    // original_frame_top = cv::imread("../demo/demo/data/map/MASA_4670_V.png");
-    
-    std::vector<Tracker> trackers;
-    geodetic_converter::GeodeticConverter gc;
-    double adfGeoTransform[6];
-    cv::Mat H;
-    while (gRun)
-    {
-        TIMER_START
-        // critical section: copy the struct in local variable
-        // in this way we can unlock the sem for the main thread
-        info_show->sem.lock();
-        // std::vector<Tracker> trackers;
-        trackers = info_show->trackers;
-        // geodetic_converter::GeodeticConverter gc;
-        gc = info_show->gc;
-        for(int i = 0; i < 6; i++ )
-            adfGeoTransform[i] = info_show->adfGeoTransform[i];
-        // cv::Mat H;
-        H = info_show->H.clone();
-        info_show->sem.unlock();
-        if (trackers.empty())
-        {
-            usleep(1000000);
-            printf("no data available\n");
-            continue;
-        }
-        frame_top = original_frame_top.clone();
-        for (auto t : trackers)
-        {
-            for (size_t p = 1; p < t.pred_list_.size(); p++)
-            {
-                gc.enu2Geodetic(t.pred_list_[p].x_, t.pred_list_[p].y_, 0, &lat, &lon, &alt);
-                coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
-                if (pix_x < frame_top.cols && pix_y < frame_top.rows && pix_x >= 0 && pix_y >= 0)
-                    cv::circle(frame_top, cv::Point(pix_x, pix_y), 7.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
-
-            }
-        }
-        //outputVideo<< frame_top;
-        // ------------------------------------------------
-        updates.mutex_t.lock();
-        updates.topview = frame_top.clone();
-        updates.update_t = true;
-        updates.mutex_t.unlock();
-
-        std::cout<<"topviewFrame: ";
-        TIMER_STOP
-
-    }    
-    return (void *)0;
-}
-
-void *disparityFrame(void *x_void_ptr)
-{
-    Frame_t *info_show_disparity = (Frame_t *) x_void_ptr;
-    bool first_iteration = true;
-    cv::Mat frame_loc;
-    int frame_nbr_loc = 0, pre_frame_nbr_loc = 0;
-    auto start_t = std::chrono::steady_clock::now();
-    auto step_t = std::chrono::steady_clock::now();
-    auto end_t = std::chrono::steady_clock::now();
-
-    // information for the disparity map
-    cv::Mat canny, pre_canny, canny_RGB, pre_canny_RGB;
-    cv::Mat canny_img;
-    cv::Mat disparity_frame;
-    while (gRun)
-    {
-        start_t = std::chrono::steady_clock::now();
-        step_t = start_t;
-        // critical section: copy the struct in local variable
-        // in this way we can unlock the sem for the main thread
-        info_show_disparity->sem_vc.lock();
-        frame_loc = info_show_disparity->frame.clone();
-        frame_nbr_loc = info_show_disparity->frame_nbr;
-        info_show_disparity->sem_vc.unlock();
-        if (frame_nbr_loc == 0)
-        {
-            usleep(1000000);
-            printf("no frame received\n");
-            continue;
-        }
-        // compute frame disparity only in there is a new frame
-        if(frame_nbr_loc - pre_frame_nbr_loc > 0)
-        {
-            pre_frame_nbr_loc = frame_nbr_loc;
-            //preprocessing frame
-            step_t = std::chrono::steady_clock::now();
-            // src_gray
-            canny_img = img_laplacian(frame_loc,0);
-            cv::Canny(canny_img, canny, 100, 100*2 );
-            // sprintf(buf_frame_crop_name,"../demo/demo/data/img_disparity/%d_%d_canny.jpg",frame_nbr_loc, 999);
-            // cv::imwrite(buf_frame_crop_name, canny);
-            end_t = std::chrono::steady_clock::now();
-            std::cout << " TIME END pre canny : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms"<<std::endl;
-            step_t = end_t;
-            // std::cout<<"o: "<<frame_loc.cols<<" - "<<frame_loc.rows<<std::endl;
-            // std::cout<<"canny: "<<canny.cols<<" - "<<canny.rows<<std::endl;
-            // std::cout<<"pre: "<<pre_canny.cols<<" - "<<pre_canny.rows<<std::endl;
-            if(!first_iteration)
-            {
-                // backtorgb = cv::cvtColor(pre_canny,cv::COLOR_GRAY2RGB) 
-                cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
-                cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
-                disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr_loc, 999, 0);
-                // std::cout<<"size: "<<disparity_frame.rows<<" - "<<disparity_frame.cols<<std::endl;
-                // if (disparity_frame.rows == 0 || disparity_frame.cols == 0)
-                //     return -1;
-                // if (disparity_frame.empty()) 
-                // { // only fools don't check...
-                //     std::cout << "image not loaded !" << std::endl;
-                //     return -1;
-                // }
-                end_t = std::chrono::steady_clock::now();
-                std::cout << " TIME canny : frame_disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms"<<std::endl;
-                step_t = end_t;
-
-                // //--------------------------------
-                // //frame box disparity on the original image
-                // step_t_segmentation = std::chrono::steady_clock::now();
-                // frame_box_disparity(pre_frame, frame, pre_rois, frame_nbr_loc);
-                // // reset pre_rois for the new roi of the current frame
-                // // pre_rois.erase(pre_rois.begin(), pre_rois.end());
-                // end_t_segmentation = std::chrono::steady_clock::now();
-                // std::cout << " TIME Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
-                // step_t_segmentation = end_t_segmentation;
-
-                // //frame box disparity on the preprocessed image
-                // cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
-                // cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
-                // frame_box_disparity(pre_canny_RGB, canny_RGB, pre_rois, frame_nbr_loc);
-                // // reset pre_rois for the new roi of the current frame
-                // pre_rois.erase(pre_rois.begin(), pre_rois.end());
-                // end_t_segmentation = std::chrono::steady_clock::now();
-                // std::cout << " TIME Canny Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
-                // step_t_segmentation = end_t_segmentation;
-                // //---------------------------------
-
-                updates.mutex_di.lock();
-                updates.disparity = disparity_frame.clone();
-                updates.update_di = true;
-                updates.mutex_di.unlock();
-            }
-            pre_canny = canny.clone();
-            if(first_iteration)
-                first_iteration = false;
-            end_t = std::chrono::steady_clock::now();
-            std::cout<<"disparityFrame : TIME END pre canny : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count() << " ms"<<std::endl;
-        }
-    }
-    return (void *)0;
-}
-
 void *computationTask(void *x_void_ptr)
 {
     Camera_t *camera = (Camera_t *)x_void_ptr;
@@ -731,7 +296,8 @@ void *computationTask(void *x_void_ptr)
     tk::dnn::box b;
     int x0, h, y1; //w, x1, y0;
     int objClass;
-    std::string det_class;;
+    std::string det_class;
+    ;
     // float prob;
     cv::Scalar intensity;
 
@@ -878,8 +444,7 @@ void *computationTask(void *x_void_ptr)
                     // segmentation(frame(roi), frame(roi), frame_nbr, i, 1);
 
                     /////
-                    convert_coords(coords, x0 + b.w / 2, y1, objClass, H, adfGeoTransform, frame_nbr);
+                    convert_coords(coords, x0 + b.w / 2, y1, objClass, H, adfGeoTransform);
-                    
 
                     // //std::cout<<objClass<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
                     // cv::rectangle(frame, cv::Point(x0, y0), cv::Point(x1, y1), camera->yolo.colors[objClass], 2);
@@ -1038,7 +603,6 @@ int main(int argc, char *argv[])
     if (check_visualization > 1)
         return -1;
 
-    
     tk::dnn::Yolo3Detection yolo[n_cameras];
     for (int i = 0; i < n_cameras; i++)
     {
@@ -1064,7 +628,6 @@ int main(int argc, char *argv[])
             cameras[i].adfGeoTransform[j] = adfGeoTransform[j];
         cameras[i].yolo = yolo[i];
         // cameras[i].yolo = yolo;
-        
     }
     std::cout << "INIZIA:\n";
     pthread_t camera_task[n_cameras];

include/Layer.h

@@ -5,9 +5,13 @@
 #include "utils.h"
 #include "Network.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-enum layerType_t {
+enum layerType_t
+{
     LAYER_DENSE,
     LAYER_CONV2D,
     LAYER_ACTIVATION,
@@ -26,14 +30,16 @@ enum layerType_t {
 /** 
     Simple layer Father class
 */
-class Layer {
+class Layer
+{
 
 public:
     Layer(Network *net);
     virtual ~Layer();
     virtual layerType_t getLayerType() = 0;
 
-    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData) {
+    virtual dnnType *infer(dataDim_t &dim, dnnType *srcData)
+    {
         std::cout << "No infer action for this layer\n";
         return NULL;
     }
@@ -41,37 +47,52 @@ public:
     dataDim_t input_dim, output_dim;
     dnnType *dstData; //where results will be putted
 
-    std::string getLayerName() {
+    std::string getLayerName()
+    {
         layerType_t type = getLayerType();
-        switch(type) {
+        switch (type)
-            case LAYER_DENSE:       return "Dense";
+        {
-            case LAYER_CONV2D:      return "Conv2d";
+        case LAYER_DENSE:
-            case LAYER_ACTIVATION:  return "Activation";
+            return "Dense";
-            case LAYER_FLATTEN:     return "Flatten";
+        case LAYER_CONV2D:
-            case LAYER_MULADD:      return "MulAdd";
+            return "Conv2d";
-            case LAYER_POOLING:     return "Pooling";
+        case LAYER_ACTIVATION:
-            case LAYER_SOFTMAX:     return "Softmax";
+            return "Activation";
-            case LAYER_ROUTE:       return "Route";            
+        case LAYER_FLATTEN:
-            case LAYER_REORG:       return "Reorg";
+            return "Flatten";
-            case LAYER_SHORTCUT:    return "Shortcut";
+        case LAYER_MULADD:
-            case LAYER_UPSAMPLE:    return "Upsample";
+            return "MulAdd";
-            case LAYER_REGION:      return "Region";
+        case LAYER_POOLING:
-            case LAYER_YOLO:        return "Yolo";
+            return "Pooling";
-            default:                return "unknown";
+        case LAYER_SOFTMAX:
+            return "Softmax";
+        case LAYER_ROUTE:
+            return "Route";
+        case LAYER_REORG:
+            return "Reorg";
+        case LAYER_SHORTCUT:
+            return "Shortcut";
+        case LAYER_UPSAMPLE:
+            return "Upsample";
+        case LAYER_REGION:
+            return "Region";
+        case LAYER_YOLO:
+            return "Yolo";
+        default:
+            return "unknown";
         }
     }
 
 protected:
     Network *net;
     cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
-
 };
 
-
 /**
     Father class of all layer that need to load trained weights
 */
-class LayerWgs : public Layer {
+class LayerWgs : public Layer
+{
 
 public:
     LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kt,
@@ -101,11 +122,11 @@ public:
     __half *variance16_h, *variance16_d;
 };
 
-
 /**
     Dense (full interconnection) layer
 */
-class Dense : public LayerWgs {
+class Dense : public LayerWgs
+{
 
 public:
     Dense(Network *net, int out_ch, const char *fname_weights);
@@ -115,11 +136,11 @@ public:
     virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
 };
 
-
 /**
     Avaible activation functions
 */
-typedef enum {
+typedef enum
+{
     ACTIVATION_ELU = 100,
     ACTIVATION_LEAKY = 101
 } tkdnnActivationMode_t;
@@ -127,7 +148,8 @@ typedef enum {
 /**
     Activation layer (it doesnt need weigths)
 */
-class Activation : public Layer {
+class Activation : public Layer
+{
 
 public:
     int act_mode;
@@ -142,11 +164,11 @@ protected:
     cudnnActivationDescriptor_t activDesc;
 };
 
-
 /**
     Convolutional 2D layer
 */
-class Conv2d : public LayerWgs {
+class Conv2d : public LayerWgs
+{
 
 public:
     Conv2d(Network *net, int out_ch, int kernelH, int kernelW,
@@ -169,12 +191,12 @@ protected:
     size_t ws_sizeInBytes;
 };
 
-
 /**
     Flatten layer
     is actually a matrix transposition
 */
-class Flatten : public Layer {
+class Flatten : public Layer
+{
 
 public:
     Flatten(Network *net);
@@ -184,12 +206,12 @@ public:
     virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
 };
 
-
 /**
     MulAdd layer
     apply a multiplication and then an addition for each data
 */
-class MulAdd : public Layer {
+class MulAdd : public Layer
+{
 
 public:
     MulAdd(Network *net, dnnType mul, dnnType add);
@@ -203,12 +225,11 @@ protected:
     dnnType *add_vector;
 };
 
-
-
 /**
     Avaible pooling functions (padding on tkDNN is not supported)
 */
-typedef enum {
+typedef enum
+{
     POOLING_MAX = 0,
     POOLING_AVERAGE = 1,                // count for average includes padded values
     POOLING_AVERAGE_EXCLUDE_PADDING = 2 // count for average does not include padded values
@@ -218,7 +239,8 @@ typedef enum {
     Pooling layer
     currenty supported only 2d pooing (also on 3d input)
 */
-class Pooling : public Layer {
+class Pooling : public Layer
+{
 
 public:
     int winH, winW;
@@ -233,7 +255,6 @@ public:
     virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
 
 protected:
-
     cudnnPoolingDescriptor_t poolingDesc;
     tkdnnPoolingMode_t pool_mode;
     dnnType *tmpInputData, *tmpOutputData;
@@ -243,7 +264,8 @@ protected:
 /**
     Softmax layer
 */
-class Softmax : public Layer {
+class Softmax : public Layer
+{
 
 public:
     Softmax(Network *net);
@@ -257,7 +279,8 @@ public:
     Route layer
     Merge a list of layers
 */
-class Route : public Layer {
+class Route : public Layer
+{
 
 public:
     Route(Network *net, Layer **layers, int layers_n);
@@ -271,12 +294,12 @@ public:
     int layers_n;   //number of layers
 };
 
-
 /**
     Reorg layer
     Mantain same dimension but change C*H*W distribution
 */
-class Reorg : public Layer {
+class Reorg : public Layer
+{
 
 public:
     Reorg(Network *net, int stride);
@@ -292,7 +315,8 @@ public:
     Shortcut layer
     sum with stride another layer
 */
-class Shortcut : public Layer {
+class Shortcut : public Layer
+{
 
 public:
     Shortcut(Network *net, Layer *backLayer);
@@ -309,7 +333,8 @@ public:
     Upsample layer
     Mantain same dimension but change C*H*W distribution
 */
-class Upsample : public Layer {
+class Upsample : public Layer
+{
 
 public:
     Upsample(Network *net, int stride);
@@ -322,12 +347,14 @@ public:
     bool reverse;
 };
 
-struct box {
+struct box
+{
     int cl;
     float x, y, w, h;
     float prob;
 };
-struct sortable_bbox {
+struct sortable_bbox
+{
     int index;
     int cl;
     float **probs;
@@ -336,14 +363,17 @@ struct sortable_bbox {
 /**
     Yolo3 layer
 */
-class Yolo : public Layer {
+class Yolo : public Layer
+{
 
 public:
-    struct box {
+    struct box
+    {
         float x, y, w, h;
     };
 
-    struct detection{
+    struct detection
+    {
         Yolo::box bbox;
         int classes;
         float *prob;
@@ -373,7 +403,8 @@ public:
 /**
     Region layer
 */
-class Region : public Layer {
+class Region : public Layer
+{
 
 public:
     Region(Network *net, int classes, int coords, int num);
@@ -385,7 +416,8 @@ public:
     virtual dnnType *infer(dataDim_t &dim, dnnType *srcData);
 };
 
-class RegionInterpret {
+class RegionInterpret
+{
 
 public:
     RegionInterpret(dataDim_t input_dim, dataDim_t output_dim,
@@ -397,7 +429,6 @@ public:
     int classes, coords, num;
     float thresh;
 
-    
     box *boxes;
     float **probs;
     sortable_bbox *s;
@@ -415,5 +446,6 @@ public:
     static float box_iou(box a, box b);
 };
 
-}}
+} // namespace dnn
+} // namespace tk
 #endif //LAYER_H

include/Network.h

@@ -3,7 +3,10 @@
 
 #include "utils.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
 /**
     Data rapresentation beetween layers
@@ -13,20 +16,22 @@ namespace tk { namespace dnn {
     w = width  (rows)
     l = lenght (3rd dimension)
 */
-struct dataDim_t {
+struct dataDim_t
+{
 
     int n, c, h, w, l;
 
     dataDim_t() : n(1), c(1), h(1), w(1), l(1){};
 
-    dataDim_t(int _n, int _c, int _h, int _w, int _l = 1) :
+    dataDim_t(int _n, int _c, int _h, int _w, int _l = 1) : n(_n), c(_c), h(_h), w(_w), l(_l){};
-        n(_n), c(_c), h(_h), w(_w), l(_l) {};
 
-    void print() {
+    void print()
+    {
         std::cout << "Data dim: " << n << " " << c << " " << h << " " << w << " " << l << "\n";
     }
 
-    int tot() {
+    int tot()
+    {
         return n * c * h * w * l;
     }
 };
@@ -34,7 +39,8 @@ struct dataDim_t {
 class Layer;
 const int MAX_LAYERS = 256;
 
-class Network {
+class Network
+{
 
 public:
     Network(dataDim_t input_dim);
@@ -62,5 +68,6 @@ public:
     bool fp16, dla;
 };
 
-}}
+} // namespace dnn
+} // namespace tk
 #endif //NETWORK_H

include/NetworkRT.h

@@ -7,15 +7,20 @@
 #include "Layer.h"
 #include "NvInfer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-template<typename T> void writeBUF(char*& buffer, const T& val)
+template <typename T>
+void writeBUF(char *&buffer, const T &val)
 {
     *reinterpret_cast<T *>(buffer) = val;
     buffer += sizeof(T);
 }
 
-template<typename T> T readBUF(const char*& buffer)
+template <typename T>
+T readBUF(const char *&buffer)
 {
     T val = *reinterpret_cast<const T *>(buffer);
     buffer += sizeof(T);
@@ -41,9 +46,8 @@ public:
     virtual IPlugin *createPlugin(const char *layerName, const void *serialData, size_t serialLength);
 };
 
-
+class NetworkRT
-
+{
-class NetworkRT {
 
 public:
     nvinfer1::DataType dtRT;
@@ -90,5 +94,6 @@ public:
     bool deserialize(const char *filename);
 };
 
-}}
+} // namespace dnn
+} // namespace tk
 #endif //NETWORKRT_H

include/Yolo3Detection.h

@@ -1,3 +1,6 @@
+#ifndef YOLO3DDETECTION_H
+#define YOLO3DDETECTION_H
+
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h> /* srand, rand */
@@ -11,13 +14,17 @@
 
 #include "tkdnn.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
 /**
  * 
  * @author Francesco Gatti
  */
-class Yolo3Detection {
+class Yolo3Detection
+{
 
 private:
     tk::dnn::NetworkRT *netRT = nullptr;
@@ -30,8 +37,6 @@ class Yolo3Detection {
     cv::Mat imageF;
     cv::Mat bgr[3];
 
-        
-
 public:
     int classes = 0;
     int num = 0;
@@ -46,14 +51,16 @@ class Yolo3Detection {
     virtual ~Yolo3Detection() {}
 
     /**
-         * Method used for inizialize the class
+         * Method used to inizialize the class
          * 
          * @return Success of the initialization
          */
     bool init(std::string tensor_path);
     void addBorders(cv::Mat &imageORIG, cv::Mat &imageWBorders, int &top, int &left);
     void update(cv::Mat &frame);
-
 };
 
-}}
+} // namespace dnn
+} // namespace tk
+
+#endif /*YOLO3DDETECTION_H*/

include/calibration.h

@@ -0,0 +1,32 @@
+#ifndef CALIBRATION_H
+#define CALIBRATION_H
+
+#include "gdal.h"
+#include <gdal_priv.h>
+#include <gdal/gdal.h>
+#include "gdal/gdal_priv.h"
+#include "gdal/cpl_conv.h"
+
+#include <yaml-cpp/yaml.h>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/core.hpp>
+
+#include <iostream>
+#include <cstring>
+
+struct ObjCoords
+{
+    double lat_;
+    double long_;
+    int class_;
+};
+
+void readTiff(char *filename, double *adfGeoTransform);
+void readCameraCalibrationYaml(const std::string &cameraCalib, cv::Mat &cameraMat, cv::Mat &distCoeff);
+void pixel2coord(int x, int y, double &lat, double &lon, double *adfGeoTransform);
+void coord2pixel(double lat, double lon, int &x, int &y, double *adfGeoTransform);
+void fillMatrix(cv::Mat &H, double *matrix, bool show = false);
+void read_projection_matrix(cv::Mat &H, char *path);
+void convert_coords(std::vector<ObjCoords> &coords, int x, int y, int detected_class, cv::Mat H, double *adfGeoTransform);
+
+#endif /*CALIBRATION_H*/

include/cameraUtils.h

@@ -0,0 +1,45 @@
+#ifndef CAMERAUTILS_H
+#define CAMERAUTILS_H
+
+#include <vector>
+#include <mutex>
+#include <opencv2/core/core.hpp>
+#include "tracker.h"
+#include "Yolo3Detection.h"
+
+struct Camera_t
+{
+    int CAM_IDX;
+    char *input;
+    char *pmatrix;
+    char *maskfile;
+    char *cameraCalib;
+    char *maskFileOrient;
+    bool to_show;
+    tk::dnn::Yolo3Detection yolo;
+    double adfGeoTransform[6];
+};
+
+struct Frame_t
+{
+    char *input;
+    cv::Mat frame;
+    int frame_nbr;
+    // sem_vc for mainthread, videocapturethread, originalthread and disparitythread
+    std::mutex sem_vc;
+};
+
+struct ModFrame_t
+{
+    std::vector<Tracker> trackers;
+    geodetic_converter::GeodeticConverter gc;
+    double adfGeoTransform[6];
+    cv::Mat H;
+    cv::Mat original_frame;
+    tk::dnn::Yolo3Detection yolo;
+    cv::Mat mask;
+    // sem for mainthread, detectionthread and topviewthread
+    std::mutex sem;
+};
+
+#endif /*CAMERAUTILS_H*/

include/classutils.h

@@ -1,316 +0,0 @@
-#ifndef CLASSUTILS_H
-#define CLASSUTILS_H
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <sys/time.h>
-#include <sys/socket.h> //socket
-#include <arpa/inet.h>  //inet_addr
-#include <unistd.h>     //write
-
-#include <opencv2/calib3d.hpp>
-#include <opencv2/core.hpp>
-#include "gdal.h"
-#include <gdal_priv.h>
-#include <gdal/gdal.h>
-#include "gdal/gdal_priv.h"
-#include "gdal/cpl_conv.h"
-
-#include "tracker.h"
-
-#include <yaml-cpp/yaml.h>
-
-#include "../masa_protocol/include/send.hpp"
-#include "../masa_protocol/include/serialize.hpp"
-
-struct ObjCoords
-{
-    double lat_;
-    double long_;
-    int class_;
-};
-
-void readTiff(char *filename, double *adfGeoTransform)
-{
-    GDALDataset *poDataset;
-    GDALAllRegister();
-    poDataset = (GDALDataset *)GDALOpen(filename, GA_ReadOnly);
-    if (poDataset != NULL)
-    {
-        poDataset->GetGeoTransform(adfGeoTransform);
-    }
-}
-
-void readCameraCalibrationYaml(const std::string &cameraCalib, cv::Mat &cameraMat, cv::Mat &distCoeff)
-{
-    YAML::Node config = YAML::LoadFile(cameraCalib);
-    const YAML::Node &node_test1 = config["camera_matrix"];
-
-    float data_cm[9];
-    for (std::size_t i = 0; i < node_test1["data"].size(); i++)
-        data_cm[i] = node_test1["data"][i].as<float>();
-    cv::Mat cameraMat_ = cv::Mat(3, 3, CV_32F, data_cm);
-    cameraMat = cameraMat_.clone();
-    std::cout << cameraMat << std::endl;
-    const YAML::Node &node_test2 = config["distortion_coefficients"];
-
-    float data_dc[5];
-    for (std::size_t i = 0; i < node_test2["data"].size(); i++)
-        data_dc[i] = node_test2["data"][i].as<float>();
-    cv::Mat distCoeff_ = cv::Mat(5, 1, CV_32F, data_dc);
-    distCoeff = distCoeff_.clone();
-    std::cout << distCoeff << std::endl;
-}
-
-void pixel2coord(int x, int y, double &lat, double &lon, double *adfGeoTransform)
-{
-    //Returns global coordinates from pixel x, y coordinates
-    double xoff, a, b, yoff, d, e;
-    xoff = adfGeoTransform[0];
-    a = adfGeoTransform[1];
-    b = adfGeoTransform[2];
-    yoff = adfGeoTransform[3];
-    d = adfGeoTransform[4];
-    e = adfGeoTransform[5];
-
-    //printf("%f %f %f %f %f %f\n",xoff, a, b, yoff, d, e );
-
-    lon = a * x + b * y + xoff;
-    lat = d * x + e * y + yoff;
-}
-void coord2pixel(double lat, double lon, int &x, int &y, double *adfGeoTransform)
-{
-    x = int(round((lon - adfGeoTransform[0]) / adfGeoTransform[1]));
-    y = int(round((lat - adfGeoTransform[3]) / adfGeoTransform[5]));
-}
-
-void fillMatrix(cv::Mat &H, double *matrix, bool show = false)
-{
-    double *vals = (double *)H.data;
-    for (int i = 0; i < 9; i++)
-    {
-        vals[i] = matrix[i];
-    }
-    if (show)
-        std::cout << H << "\n";
-}
-
-//FILE *out_file = fopen("prova_pixel.txt", "w");
-
-void convert_coords(std::vector<ObjCoords> &coords, int x, int y, int detected_class, cv::Mat H, double *adfGeoTransform, int frame_nbr)
-{
-    double latitude, longitude;
-    std::vector<cv::Point2f> x_y, ll;
-    x_y.push_back(cv::Point2f(x, y));
-    //transform camera pixel to map pixel
-    cv::perspectiveTransform(x_y, ll, H);
-    //tranform to map pixel to map gps
-    pixel2coord(ll[0].x, ll[0].y, latitude, longitude, adfGeoTransform);
-    //printf("lat: %f, long:%f \n", latitude, longitude);
-
-    ObjCoords coord;
-    coord.lat_ = latitude;
-    coord.long_ = longitude;
-    coord.class_ = detected_class;
-    coords.push_back(coord);
-
-    /*if (detected_class == 0)
-    {
-
-        struct timeval tv;
-        gettimeofday(&tv, NULL);
-        unsigned long long t_stamp_ms = (unsigned long long)(tv.tv_sec) * 1000 + (unsigned long long)(tv.tv_usec) / 1000;
-
-        //printf(out_file, "%d %lld %d %d\n",frame_nbr, t_stamp_ms, int(ll[0].x), int(ll[0].y));
-        fprintf(out_file, "%d %lld %f %f\n", frame_nbr, t_stamp_ms, coord.LAT, coord.LONG);
-        //printf( "%d %lld %f %f\n", frame_nbr, t_stamp_ms, coord.LAT, coord.LONG);
-    }*/
-}
-
-void read_projection_matrix(cv::Mat &H, char *path)
-{
-    FILE *fp;
-    char *line = NULL;
-    size_t len = 0;
-    ssize_t read;
-
-    // float *proj_matrix = (float *)malloc(9 * sizeof(float));
-    double proj_matrix[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
-    int i = 0;
-    fp = fopen(path, "r");
-    if (fp == NULL)
-        exit(EXIT_FAILURE);
-        
-    while ((read = getline(&line, &len, fp)) != -1)
-    {
-        std::cout<<line<<std::endl;
-        std::stringstream ss(line);
-        while (ss >> proj_matrix[i])
-            i++;
-    }
-    fclose(fp);
-    fillMatrix(H, proj_matrix);
-    free(line);
-    // free(proj_matrix);
-}
-
-void draw_arrow(float angleRad, float vel, cv::Scalar color, cv::Point center, cv::Mat &frame)
-{
-    int angle = angleRad * 180.0 / CV_PI;
-    auto length = 10 * vel;
-    auto direction = cv::Point(length * cos(angleRad), length * sin(angleRad)); // calculate direction
-    double tipLength = .2 + 0.4 * (angle % 180) / 360;
-    int lineType = 8;
-    int thickness = 2;
-    cv::arrowedLine(frame, center, center + direction, color, thickness, lineType, 0, tipLength); // draw arrow!
-}
-
-unsigned long long time_in_ms()
-{
-    struct timeval tv;
-    gettimeofday(&tv, NULL);
-    unsigned long long t_stamp_ms = (unsigned long long)(tv.tv_sec) * 1000 + (unsigned long long)(tv.tv_usec) / 1000;
-    return t_stamp_ms;
-}
-
-void addRoadUserfromTracker(const std::vector<Tracker> &trackers, Message *m, geodetic_converter::GeodeticConverter &gc, const cv::Mat& maskOrient, double *adfGeoTransform, cv::Mat H)
-{
-    m->t_stamp_ms = time_in_ms();
-    m->objects.clear();
-    double lat, lon, alt;
-
-    for (auto t : trackers)
-    {
-        if (t.pred_list_.size() > 0)
-        {
-            Categories cat;
-            switch (t.class_)
-            {
-            case 0:
-                cat = Categories::C_person;
-                break;
-            case 1:
-                cat = Categories::C_car;
-                break;
-            case 2:
-                cat = Categories::C_car;
-                break;
-            case 3:
-                cat = Categories::C_bus;
-                break;
-            case 4:
-                cat = Categories::C_motorbike;
-                break;
-            case 5:
-                cat = Categories::C_bycicle;
-                break;
-            }
-            //std::cout << t.pred_list_.size() << std::endl;
-            gc.enu2Geodetic(t.pred_list_.back().x_, t.pred_list_.back().y_, 0, &lat, &lon, &alt);
-
-            int pix_x, pix_y;
-            coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
-
-            // TODO: test correctness - added perspective transform call to converter pix_x and pix_y
-            // sometimes some values are wrong. float ok? 
-            // std::vector<cv::Point2f> map_p, camera_p;
-            // std::cout<<"--- pix_x, pix_y: "<<pix_x<<", "<<pix_y<<std::endl;
-            // map_p.push_back(cv::Point2f(pix_x, pix_y));
-            // std::cout<<"map_p: "<<map_p<<std::endl;
-            // //transform camera pixel to map pixel
-            // cv::perspectiveTransform(map_p, camera_p, H.inv());
-            // std::cout<<"size H: "<<H.cols<<", "<<H.rows<<std::endl;
-            // std::cout<<"camera_p: "<<camera_p<<std::endl;    
-            // // TODO: in some cases these lines causes seg fault!
-            // std::cout<<"y, x :"<<camera_p[0].y<<",  "<<camera_p[0].x<<std::endl;
-            // std::cout<<"size maskorient: "<<maskOrient.cols<<", "<<maskOrient.rows<<std::endl;
-            // // std::cout<<"vec3b: "<<(cv::Vec3b)(pix_y,pix_x);
-            // assert (camera_p[0].x < maskOrient.cols);
-            // assert (camera_p[0].y < maskOrient.rows);
-            // uint8_t maskOrientPixel = maskOrient.at<cv::Vec3b>(camera_p[0].y,camera_p[0].x)[0]; 
-            // std::cout<<"boo: "<<maskOrient.at<cv::Vec3b>(camera_p[0].y,camera_p[0].x)<<std::endl;
-            // uint8_t orientation;
-            // if(maskOrientPixel != 0)
-            // {
-            //     orientation = maskOrientPixel;
-            //     // std::cout<<"orientation given by the mask "<< int(orientation)<<std::endl;
-            // }
-            // else
-            // {
-            //     orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
-            //     //std::cout<<"orientation given by the tracker "<< int(orientation)<<std::endl;
-            // }
-
-            // TODO: to validate -> it works for grayscale image (see demo.cpp, row: "cv::Mat maskOrient = cv::imread(camera->maskFileOrient, 0);")
-            // TODO: include perspective transform
-            // std::cout<<"y, x :"<<pix_y<<", "<<pix_x<<std::endl;
-            // std::cout<<"size maskorient: "<<maskOrient.cols<<", "<<maskOrient.rows<<std::endl;
-            // std::cout<<"point: "<<(cv::Point)(pix_y,pix_x);
-            // uint8_t maskOrientPixel = maskOrient.at<uchar>(pix_y,pix_x);
-            // uint8_t orientation;
-            // if(maskOrientPixel != 0)
-            // {
-            //     orientation = maskOrientPixel;
-            //     // std::cout<<"orientation given by the mask "<< int(orientation)<<std::endl;
-            // }
-            // else
-            // {
-            //     orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
-            //     //std::cout<<"orientation given by the tracker "<< int(orientation)<<std::endl;
-            // }
-
-            uint8_t orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
-            // std::cout<<"orient: "<<unsigned(orientation)<<std::endl;
-            //std::cout << "lat: " << lat << " lon: " << lon << std::endl;
-            uint8_t velocity = uint8_t(std::abs(t.pred_list_.back().vel_ * 3.6 / 2));
-            // std::cout<<"vel: "<<unsigned(velocity)<<std::endl;
-            RoadUser r{static_cast<float>(lat), static_cast<float>(lon), velocity, orientation, cat};
-            //std::cout << std::setprecision(10) << r.latitude << " , " << r.longitude << " " << int(r.speed) << " " << int(r.orientation) << " " << r.category << std::endl;
-            m->objects.push_back(r);
-        }
-    }
-    m->num_objects = m->objects.size();
-}
-
-void prepare_message(Message *m, const std::vector<ObjCoords> &coords, int idx)
-{
-    m->cam_idx = idx;
-    m->t_stamp_ms = time_in_ms();
-    m->num_objects = coords.size();
-
-    m->objects.clear();
-    for (unsigned int i = 0; i < coords.size(); i++)
-    {
-        Categories cat;
-
-        switch (coords[i].class_)
-        {
-        case 0:
-            cat = Categories::C_person;
-            break;
-        case 1:
-            cat = Categories::C_car;
-            break;
-        case 2:
-            cat = Categories::C_car;
-            break;
-        case 3:
-            cat = Categories::C_bus;
-            break;
-        case 4:
-            cat = Categories::C_motorbike;
-            break;
-        case 5:
-            cat = Categories::C_bycicle;
-            break;
-        }
-        RoadUser r{static_cast<float>(coords[i].lat_), static_cast<float>(coords[i].long_), 0, 1, cat};
-        std::cout << std::setprecision(10) << r.latitude << " , " << r.longitude << " " << cat << std::endl;
-        m->objects.push_back(r);
-    }
-
-    m->lights.clear();
-}
-
-#endif /*CLASSUTILS_H*/

include/message.h

@@ -0,0 +1,22 @@
+#ifndef MESSAGE_H
+#define MESSAGE_H
+
+#include <iostream>
+#include <cstdlib>
+#include <ctime>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/core.hpp>
+// #include <sys/socket.h> //socket
+// #include <arpa/inet.h>  //inet_addr
+// #include <unistd.h>     //write
+
+#include "tracker.h"
+
+#include "../masa_protocol/include/send.hpp"
+#include "../masa_protocol/include/serialize.hpp"
+
+unsigned long long time_in_ms();
+
+void addRoadUserfromTracker(const std::vector<Tracker> &trackers, Message *m, geodetic_converter::GeodeticConverter &gc, const cv::Mat &maskOrient, double *adfGeoTransform, cv::Mat H);
+
+#endif /*MESSAGE_H*/

include/utils.h

@@ -32,13 +32,17 @@
 #define COL_CYANB "\033[1;36m"
 
 // Simple Timer
-#define TIMER_START timespec start, end;                               \
+#define TIMER_START    \
+  timespec start, end; \
   clock_gettime(CLOCK_MONOTONIC, &start);
 
-#define TIMER_STOP_C(col)  clock_gettime(CLOCK_MONOTONIC, &end);       \
+#define TIMER_STOP_C(col)                                         \
+  clock_gettime(CLOCK_MONOTONIC, &end);                           \
   double t_ns = ((double)(end.tv_sec - start.tv_sec) * 1.0e9 +    \
-                  (double)(end.tv_nsec - start.tv_nsec))/1.0e6;        \
+                 (double)(end.tv_nsec - start.tv_nsec)) /         \
-    std::cout<<col<<"Time:"<<std::setw(16)<<t_ns<<" ms\n"<<COL_END; 
+                1.0e6;                                            \
+  std::cout << col << "Time:" << std::setw(16) << t_ns << " ms\n" \
+            << COL_END;
 
 #define TIMER_STOP TIMER_STOP_C(COL_CYANB)
 
@@ -47,42 +51,52 @@
  * ******************************************************/
 #define EXIT_WAIVED 0
 
-#define FatalError(s) {                                                \
+#define FatalError(s)                                 \
+  {                                                   \
     std::stringstream _where, _message;               \
     _where << __FILE__ << ':' << __LINE__;            \
-    _message << std::string(s) + "\n" << __FILE__ << ':' << __LINE__;\
+    _message << std::string(s) + "\n"                 \
+             << __FILE__ << ':' << __LINE__;          \
     std::cerr << _message.str() << "\nAborting...\n"; \
     cudaDeviceReset();                                \
     exit(EXIT_FAILURE);                               \
   }
 
-#define checkCUDNN(status) {                                           \
+#define checkCUDNN(status)                                        \
+  {                                                               \
     std::stringstream _error;                                     \
-    if (status != CUDNN_STATUS_SUCCESS) {                              \
+    if (status != CUDNN_STATUS_SUCCESS)                           \
+    {                                                             \
       _error << "CUDNN failure: " << cudnnGetErrorString(status); \
       FatalError(_error.str());                                   \
     }                                                             \
   }
 
-#define checkCuda(status) {                                            \
+#define checkCuda(status)                                       \
+  {                                                             \
     std::stringstream _error;                                   \
-    if (status != 0) {                                                 \
+    if (status != 0)                                            \
+    {                                                           \
       _error << "Cuda failure: " << cudaGetErrorString(status); \
       FatalError(_error.str());                                 \
     }                                                           \
   }
 
-#define checkERROR(status) {                                           \
+#define checkERROR(status)                     \
+  {                                            \
     std::stringstream _error;                  \
-    if (status != 0) {                                                 \
+    if (status != 0)                           \
+    {                                          \
       _error << "Generic failure: " << status; \
       FatalError(_error.str());                \
     }                                          \
   }
 
-#define checkNULL(ptr) {                                               \
+#define checkNULL(ptr)          \
+  {                             \
     std::stringstream _error;   \
-    if (ptr == nullptr) {                                              \
+    if (ptr == nullptr)         \
+    {                           \
       _error << "Null pointer"; \
       FatalError(_error.str()); \
     }                           \

include/visualization.h

@@ -0,0 +1,42 @@
+#ifndef VIZUALIZATION_H
+#define VIZUALIZATION_H
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+//saliency
+#include <opencv2/core/utility.hpp>
+#include <opencv2/saliency.hpp>
+#include <opencv2/highgui.hpp>
+
+#include <chrono>
+#include <iostream>
+#include <cstring>
+
+#include "tracker.h"
+#include "cameraUtils.h"
+#include "calibration.h"
+#include "boxDetection.h"
+
+struct Show_t
+{
+    cv::Mat original, detection, topview, disparity;
+    bool update_o, update_de, update_t, update_di;
+    // a single mutex for each operation - the show_updates function must get all mutex
+    std::mutex mutex_o, mutex_de, mutex_t, mutex_di;
+};
+
+extern Show_t updates;
+extern bool gRun;
+extern std::string obj_class[10];
+
+/* Thread function to show the updated images 
+**/
+void *show_updates(void *x_void_ptr);
+void *originalFrame(void *x_void_ptr);
+void *detectionFrame(void *x_void_ptr);
+void *topviewFrame(void *x_void_ptr);
+void *disparityFrame(void *x_void_ptr);
+
+#endif /*VIZUALIZATION_H*/

src/Activation.cpp

@@ -3,15 +3,19 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Activation::Activation(Network *net, int act_mode) : 
+Activation::Activation(Network *net, int act_mode) : Layer(net)
-    Layer(net) {
+{
 
     this->act_mode = act_mode;
     checkCuda(cudaMalloc(&dstData, input_dim.tot() * sizeof(dnnType)));
 
-    if(int(act_mode) < 100) {
+    if (int(act_mode) < 100)
+    {
 
         checkCUDNN(cudnnSetTensor4dDescriptor(srcTensorDesc,
                                               net->tensorFormat,
@@ -26,7 +30,6 @@ Activation::Activation(Network *net, int act_mode) :
                                               input_dim.c,
                                               input_dim.h, input_dim.w));
 
-
         checkCUDNN(cudnnCreateActivationDescriptor(&activDesc));
         checkCUDNN(cudnnSetActivationDescriptor(activDesc,
                                                 (cudnnActivationMode_t)act_mode,
@@ -35,7 +38,8 @@ Activation::Activation(Network *net, int act_mode) :
     }
 }
 
-Activation::~Activation() {
+Activation::~Activation()
+{
 
     checkCuda(cudaFree(dstData));
 
@@ -43,12 +47,15 @@ Activation::~Activation() {
         checkCUDNN(cudnnDestroyActivationDescriptor(activDesc));
 }
 
-dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *Activation::infer(dataDim_t &dim, dnnType *srcData)
+{
 
-    if(act_mode == ACTIVATION_LEAKY) {
+    if (act_mode == ACTIVATION_LEAKY)
+    {
         activationLEAKYForward(srcData, dstData, dim.tot());
-
+    }
-    } else {
+    else
+    {
         dnnType alpha = dnnType(1);
         dnnType beta = dnnType(0);
         checkCUDNN(cudnnActivationForward(net->cudnnHandle,
@@ -63,4 +70,5 @@ dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/Conv2d.cpp

@@ -2,14 +2,18 @@
 
 #include "Layer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
 Conv2d::Conv2d(Network *net, int out_ch, int kernelH, int kernelW,
                int strideH, int strideW, int paddingH, int paddingW,
                const char *fname_weights, bool batchnorm) :
 
                 LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1,
-             fname_weights, batchnorm) {
+                            fname_weights, batchnorm)
+{
 
     this->kernelH = kernelH;
     this->kernelW = kernelW;
@@ -59,16 +63,15 @@ Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
                                                        srcTensorDesc, filterDesc, convDesc, dstTensorDesc,
                                                        algo, &ws_sizeInBytes));
 
-    if (ws_sizeInBytes!=0) {
+    if (ws_sizeInBytes != 0)
+    {
         checkCuda(cudaMalloc(&workSpace, ws_sizeInBytes));
     }
 
-
     checkCUDNN(cudnnSetTensor4dDescriptor(biasTensorDesc,
                                           net->tensorFormat, net->dataType,
                                           1, out_ch, 1, 1));
 
-
     output_dim.n = n;
     output_dim.c = c;
     output_dim.h = h;
@@ -79,7 +82,8 @@ Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
     checkCuda(cudaMalloc(&dstData, output_dim.tot() * sizeof(dnnType)));
 }
 
-Conv2d::~Conv2d() {
+Conv2d::~Conv2d()
+{
 
     checkCUDNN(cudnnDestroyFilterDescriptor(filterDesc));
     checkCUDNN(cudnnDestroyConvolutionDescriptor(convDesc));
@@ -91,8 +95,8 @@ Conv2d::~Conv2d() {
     checkCuda(cudaFree(dstData));
 }
 
-dnnType* Conv2d::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *Conv2d::infer(dataDim_t &dim, dnnType *srcData)
-
+{
 
     // convolution
     dnnType alpha = dnnType(1);
@@ -102,14 +106,17 @@ dnnType* Conv2d::infer(dataDim_t &dim, dnnType* srcData) {
                                        data_d, convDesc, algo, workSpace, ws_sizeInBytes,
                                        &beta, dstTensorDesc, dstData));
 
-    if(!batchnorm) {
+    if (!batchnorm)
+    {
         // bias
         alpha = dnnType(1);
         beta = dnnType(1);
         checkCUDNN(cudnnAddTensor(net->cudnnHandle,
                                   &alpha, biasTensorDesc, bias_d,
                                   &beta, dstTensorDesc, dstData));
-    } else {
+    }
+    else
+    {
         float one = 1;
         float zero = 0;
         cudnnBatchNormalizationForwardInference(net->cudnnHandle,
@@ -125,4 +132,5 @@ dnnType* Conv2d::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/Dense.cpp

@@ -2,10 +2,13 @@
 
 #include "Layer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Dense::Dense(Network *net, int out_ch, const char* fname_weights) : 
+Dense::Dense(Network *net, int out_ch, const char *fname_weights) : LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights)
-    LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights) {
+{
 
     output_dim.n = 1;
     output_dim.c = out_ch;
@@ -17,12 +20,14 @@ Dense::Dense(Network *net, int out_ch, const char* fname_weights) :
     checkCuda(cudaMalloc(&dstData, output_dim.tot() * sizeof(dnnType)));
 }
 
-Dense::~Dense() {
+Dense::~Dense()
+{
 
     checkCuda(cudaFree(dstData));
 }
 
-dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *Dense::infer(dataDim_t &dim, dnnType *srcData)
+{
 
     if (dim.n != 1)
         FatalError("Not Implemented");
@@ -55,4 +60,5 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/Flatten.cpp

@@ -3,9 +3,13 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Flatten::Flatten(Network *net) : Layer(net) {
+Flatten::Flatten(Network *net) : Layer(net)
+{
 
     checkCuda(cudaMalloc(&dstData, input_dim.tot() * sizeof(dnnType)));
 
@@ -14,15 +18,16 @@ Flatten::Flatten(Network *net) : Layer(net) {
     output_dim.h = 1;
     output_dim.w = 1;
     output_dim.l = 1;
-
 }
 
-Flatten::~Flatten() {
+Flatten::~Flatten()
+{
 
     checkCuda(cudaFree(dstData));
 }
 
-dnnType* Flatten::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *Flatten::infer(dataDim_t &dim, dnnType *srcData)
+{
 
     //transpose per channel
     matrixTranspose(net->cublasHandle, srcData, dstData, dim.c, dim.h * dim.w * dim.l);
@@ -33,4 +38,5 @@ dnnType* Flatten::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/Layer.cpp

@@ -2,13 +2,18 @@
 
 #include "Layer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Layer::Layer(Network *net) {
+Layer::Layer(Network *net)
+{
 
     this->net = net;
 
-    if(net != nullptr) {
+    if (net != nullptr)
+    {
         this->input_dim = net->getOutputDim();
         this->output_dim = input_dim;
 
@@ -20,10 +25,12 @@ Layer::Layer(Network *net) {
     }
 }
 
-Layer::~Layer() {
+Layer::~Layer()
+{
 
     checkCUDNN(cudnnDestroyTensorDescriptor(srcTensorDesc));
     checkCUDNN(cudnnDestroyTensorDescriptor(dstTensorDesc));
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/LayerWgs.cpp

@@ -4,11 +4,15 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
 LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
                    int kh, int kw, int kl,
-                   const char* fname_weights, bool batchnorm) : Layer(net) {
+                   const char *fname_weights, bool batchnorm) : Layer(net)
+{
 
     this->inputs = inputs;
     this->outputs = outputs;
@@ -21,7 +25,8 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
     readBinaryFile(weights_path.c_str(), outputs, &bias_h, &bias_d, seek);
 
     this->batchnorm = batchnorm;
-    if(batchnorm) {
+    if (batchnorm)
+    {
         seek += outputs;
         readBinaryFile(weights_path.c_str(), outputs, &scales_h, &scales_d, seek);
         seek += outputs;
@@ -32,7 +37,8 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
         float eps = CUDNN_BN_MIN_EPSILON;
 
         power_h = new dnnType[outputs];
-        for(int i=0; i<outputs; i++) power_h[i] = 1.0f;
+        for (int i = 0; i < outputs; i++)
+            power_h[i] = 1.0f;
 
         for (int i = 0; i < outputs; i++)
             mean_h[i] = mean_h[i] / -sqrt(eps + variance_h[i]);
@@ -41,7 +47,6 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
             variance_h[i] = 1.0f / sqrt(eps + variance_h[i]);
     }
 
-
     if (!net->fp16)
         return;
 
@@ -58,7 +63,8 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
     float2half(bias_d, bias16_d, b_size);
     cudaMemcpy(bias16_h, bias16_d, b_size * sizeof(__half), cudaMemcpyDeviceToHost);
 
-    if(batchnorm) {
+    if (batchnorm)
+    {
 
         power16_h = new __half[b_size];
         mean16_h = new __half[b_size];
@@ -97,14 +103,16 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
     }
 }
 
-LayerWgs::~LayerWgs() {
+LayerWgs::~LayerWgs()
+{
 
     delete[] data_h;
     delete[] bias_h;
     checkCuda(cudaFree(data_d));
     checkCuda(cudaFree(bias_d));
 
-    if(batchnorm) {
+    if (batchnorm)
+    {
         delete[] scales_h;
         delete[] mean_h;
         delete[] variance_h;
@@ -114,4 +122,5 @@ LayerWgs::~LayerWgs() {
     }
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/MulAdd.cpp

@@ -3,9 +3,13 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net) {
+MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net)
+{
 
     this->mul = mul;
     this->add = add;
@@ -21,17 +25,18 @@ MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net) {
     checkCuda(cudaMemcpy(add_vector, add_vector_h, size * sizeof(dnnType), cudaMemcpyHostToDevice));
     delete[] add_vector_h;
 
-
     checkCuda(cudaMalloc(&dstData, input_dim.tot() * sizeof(dnnType)));
 }
 
-MulAdd::~MulAdd() {
+MulAdd::~MulAdd()
+{
 
     checkCuda(cudaFree(add_vector));
     checkCuda(cudaFree(dstData));
 }
 
-dnnType* MulAdd::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *MulAdd::infer(dataDim_t &dim, dnnType *srcData)
+{
 
     matrixMulAdd(net->cublasHandle, srcData, dstData, add_vector, input_dim.tot(), mul);
 
@@ -41,4 +46,5 @@ dnnType* MulAdd::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk

src/Network.cpp

@@ -5,9 +5,13 @@
 #include "Network.h"
 #include "Layer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Network::Network(dataDim_t input_dim) {
+Network::Network(dataDim_t input_dim)
+{
     this->input_dim = input_dim;
 
     float tk_ver = float(TKDNN_VERSION) / 1000;
@@ -25,10 +29,12 @@ Network::Network(dataDim_t input_dim) {
 
     fp16 = false;
     dla = false;
-    if(const char* env_p = std::getenv("TKDNN_MODE")) {
+    if (const char *env_p = std::getenv("TKDNN_MODE"))
+    {
         if (strcmp(env_p, "FP16") == 0)
             fp16 = true;
-	else if(strcmp(env_p, "DLA") == 0) {
+        else if (strcmp(env_p, "DLA") == 0)
+        {
             dla = true;
             fp16 = true;
         }
@@ -40,23 +46,27 @@ Network::Network(dataDim_t input_dim) {
         std::cout << COL_GREENB << "!! DLA INERENCE ENABLED !!" << COL_END << "\n";
 }
 
-Network::~Network() {
+Network::~Network()
+{
 
     checkCUDNN(cudnnDestroy(cudnnHandle));
     checkERROR(cublasDestroy(cublasHandle));
 }
 
-dnnType* Network::infer(dataDim_t &dim, dnnType* data) {
+dnnType *Network::infer(dataDim_t &dim, dnnType *data)
+{
 
     //do infer for every layer
-    for(int i=0; i<num_layers; i++) {
+    for (int i = 0; i < num_layers; i++)
+    {
         data = layers[i]->infer(dim, data);
     }
     checkCuda(cudaDeviceSynchronize());
     return data;
 }
 
-bool Network::addLayer(Layer *l) {
+bool Network::addLayer(Layer *l)
+{
     if (num_layers == MAX_LAYERS)
         return false;
 
@@ -64,7 +74,8 @@ bool Network::addLayer(Layer *l) {
     return true;
 }
 
-dataDim_t Network::getOutputDim() {
+dataDim_t Network::getOutputDim()
+{
 
     if (num_layers == 0)
         return input_dim;
@@ -72,39 +83,53 @@ dataDim_t Network::getOutputDim() {
         return layers[num_layers - 1]->output_dim;
 }
 
-void Network::print() {
+void Network::print()
+{
 
     printCenteredTitle(" NETWORK MODEL ", '=', 60);
-    std::cout.width(3); std::cout<<std::left<<"N.";
+    std::cout.width(3);
+    std::cout << std::left << "N.";
     std::cout << " ";
-    std::cout.width(17); std::cout<<std::left<<"Layer type";
+    std::cout.width(17);
-    std::cout.width(22); std::cout<<std::left<<"input (H*W,CH)";
+    std::cout << std::left << "Layer type";
-    std::cout.width(16); std::cout<<std::left<<"output (H*W,CH)";
+    std::cout.width(22);
+    std::cout << std::left << "input (H*W,CH)";
+    std::cout.width(16);
+    std::cout << std::left << "output (H*W,CH)";
     std::cout << "\n";
 
-    for(int i=0; i<num_layers; i++) {
+    for (int i = 0; i < num_layers; i++)
+    {
         dataDim_t in = layers[i]->input_dim;
         dataDim_t out = layers[i]->output_dim;
 
-        std::cout.width(3); std::cout<<std::right<<i;
+        std::cout.width(3);
+        std::cout << std::right << i;
         std::cout << " ";
-        std::cout.width(16); std::cout<<std::left<<layers[i]->getLayerName();
+        std::cout.width(16);
-        std::cout.width(4);  std::cout<<std::right<<in.h;
+        std::cout << std::left << layers[i]->getLayerName();
+        std::cout.width(4);
+        std::cout << std::right << in.h;
         std::cout << " x ";
-        std::cout.width(4);  std::cout<<std::right<<in.w;
+        std::cout.width(4);
+        std::cout << std::right << in.w;
         std::cout << ", ";
-        std::cout.width(4);  std::cout<<std::right<<in.c;
+        std::cout.width(4);
+        std::cout << std::right << in.c;
         std::cout << "  -> ";
-        std::cout.width(4);  std::cout<<std::right<<out.h;
+        std::cout.width(4);
+        std::cout << std::right << out.h;
         std::cout << " x ";
-        std::cout.width(4);  std::cout<<std::right<<out.w;
+        std::cout.width(4);
+        std::cout << std::right << out.w;
         std::cout << ", ";
-        std::cout.width(4);  std::cout<<std::right<<out.c;
+        std::cout.width(4);
+        std::cout << std::right << out.c;
         std::cout << "\n";
     }
     printCenteredTitle("", '=', 60);
     std::cout << "\n";
 }
 
-
+} // namespace dnn
-}}
+} // namespace tk

src/class_src/boxDetection.cpp

@@ -1,4 +1,4 @@
-#include "BoxDetection.h"
+#include "boxDetection.h"
 #include <string.h>
 char buf_frame_crop_name[200];
 
@@ -263,17 +263,20 @@ void image_segmentation(cv::Mat frame_crop, int frame_nbr, int i)
     auto end_t_segmentation = std::chrono::steady_clock::now();
     cv::Mat ret;
     // ret = img_threshold(frame_crop);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgthr.jpg", frame_nbr, i, img_threshold(frame_crop));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgthr.jpg", frame_nbr, i, img_threshold(frame_crop));
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME imgthr (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret =img_background(frame_crop);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgback.jpg", frame_nbr, i, img_background(frame_crop));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgback.jpg", frame_nbr, i, img_background(frame_crop));
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME imgback (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret = img_dist_transform(frame_crop);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgtrans.jpg", frame_nbr, i, img_dist_transform(frame_crop));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgtrans.jpg", frame_nbr, i, img_dist_transform(frame_crop));
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME imgtrans (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
@@ -291,25 +294,29 @@ void image_gradients(cv::Mat frame_crop, int frame_nbr, int i)
     cv::Mat ret;
     // sobel
     // ret = img_sobel_abssobel(frame_crop, 0);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_8U.jpgg", frame_nbr, i, img_sobel_abssobel(frame_crop, 0));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_8U.jpgg", frame_nbr, i, img_sobel_abssobel(frame_crop, 0));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME sobel0 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret = img_sobel_abssobel(frame_crop, 1);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_8U.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 1));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_8U.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 1));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME sobel1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret = img_sobel_abssobel(frame_crop, 2);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 2));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 2));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME sobel2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret = img_sobel_abssobel(frame_crop, 3);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 3));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 3));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME sobel3 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -317,19 +324,20 @@ void image_gradients(cv::Mat frame_crop, int frame_nbr, int i)
 
     // laplacian
     // ret = img_laplacian(frame_crop, 0);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_gr.jpg", frame_nbr, i, img_laplacian(frame_crop, 0));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_gr.jpg", frame_nbr, i, img_laplacian(frame_crop, 0));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME laplacian0 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // ret = img_laplacian(frame_crop, 1);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_dst.jpg", frame_nbr, i, img_laplacian(frame_crop, 1));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_dst.jpg", frame_nbr, i, img_laplacian(frame_crop, 1));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME laplacian1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     //////
-    
 }
 
 void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
@@ -337,7 +345,6 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // Finding contours in your image
     // https://docs.opencv.org/3.4/df/d0d/tutorial_find_contours.html
 
-    
     auto step_t_segmentation = std::chrono::steady_clock::now();
     auto end_t_segmentation = std::chrono::steady_clock::now();
     // plot lines on figure. 3 ways:
@@ -349,7 +356,8 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // src_gray
     cv::Mat img1 = img_laplacian(frame_crop, 0);
     cv::Canny(img1, canny_output1, 100, 100 * 2);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny1.jpg", frame_nbr, i, canny_output1);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny1.jpg", frame_nbr, i, canny_output1);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME canny1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -358,7 +366,8 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // cv::Mat img2 = img_laplacian(frame_crop, 2);
     // cv::Canny(img2, canny_output2, 100, 100*2 );
     cv::Canny(img1, canny_output2, 100, 100 * 2);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny2.jpg", frame_nbr, i, canny_output2);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny2.jpg", frame_nbr, i, canny_output2);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME canny2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -366,7 +375,8 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // dstx
     cv::Mat img3a = img_sobel_abssobel(frame_crop, 0);
     cv::Canny(img3a, canny_output3a, 100, 100 * 2);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3a.jpg", frame_nbr, i, canny_output3a);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3a.jpg", frame_nbr, i, canny_output3a);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME canny3a (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -374,7 +384,8 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // dsty
     cv::Mat img3b = img_sobel_abssobel(frame_crop, 1);
     cv::Canny(img3b, canny_output3b, 100, 100 * 2);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3b.jpg", frame_nbr, i, canny_output3b);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3b.jpg", frame_nbr, i, canny_output3b);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME canny3b (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -382,14 +393,16 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
 
     // 1 line
     // contours = find_contours(frame_crop, img1, canny_output1, 1);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_line1.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output1, 1));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line1.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output1, 1));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME line1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
     step_t_segmentation = end_t_segmentation;
     // 3 line
     // contours = find_contours(frame_crop, img1, canny_output2, 1);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_line2.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output2, 1));
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line2.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output2, 1));
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME line2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -398,7 +411,8 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     cv::Mat img_line = frame_crop.clone();
     img_line = find_contours(img_line, img3a, canny_output3a, 1);
     img_line = find_contours(img_line, img3b, canny_output3b, 1);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_line3.jpg", frame_nbr, i, img_line);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line3.jpg", frame_nbr, i, img_line);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME line3 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -410,7 +424,6 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // sprintf(buf_frame_crop_name,"../demo/demo/data/img_crop/%d_%d_line3bis.jpg",frame_nbr, i);
     // cv::imwrite(buf_frame_crop_name, img_line);
 
-    
     // cv::Mat canny_output4;
     // cv::Mat img4 = img_laplacian(frame_crop, 0);
     // cv::Canny(img4, canny_output4, 100, 100*2 );
@@ -418,7 +431,6 @@ void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
     // cv::imwrite(buf_frame_crop_name, fit_rectangular(frame_crop, img4, canny_output4));
 }
 
-
 void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
 {
     // https://github.com/opencv/opencv_contrib/blob/master/modules/saliency/samples/computeSaliency.cpp
@@ -434,7 +446,8 @@ void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
     cv::Mat spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 0);
     if (!spect_res.empty())
     {
-        if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_SpectralResidual.jpg", frame_nbr, i, spect_res);
+        if (SAVE)
+            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_SpectralResidual.jpg", frame_nbr, i, spect_res);
     }
     else
     {
@@ -449,7 +462,8 @@ void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
     spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 1);
     if (!spect_res.empty())
     {
-        if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinarySpectralResidual.jpg", frame_nbr, i, spect_res);
+        if (SAVE)
+            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinarySpectralResidual.jpg", frame_nbr, i, spect_res);
     }
     else
     {
@@ -465,7 +479,8 @@ void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
     spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 0);
     if (!spect_res.empty())
     {
-        if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_FineGrained.jpg", frame_nbr, i, spect_res);
+        if (SAVE)
+            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_FineGrained.jpg", frame_nbr, i, spect_res);
     }
     else
     {
@@ -509,7 +524,8 @@ void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
     saliencyAlgorithm.dynamicCast<cv::saliency::MotionSaliencyBinWangApr2014>()->init();
     cvtColor(frame_sal, frame_sal, cv::COLOR_BGR2GRAY);
     saliencyAlgorithm->computeSaliency(frame_sal, saliencyMap);
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinWangApr.jpg", frame_nbr, i, saliencyMap);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinWangApr.jpg", frame_nbr, i, saliencyMap);
 
     end_t_segmentation = std::chrono::steady_clock::now();
     std::cout << " - TIME BING WANG APR 2014(" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
@@ -552,7 +568,8 @@ cv::Mat frame_disparity(cv::Mat pre_frame, cv::Mat frame, int frame_nbr, int i,
             }
         }
     }
-    if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_dif.jpg", frame_nbr, i, foregroundMask);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_disparity/%d_%d_dif.jpg", frame_nbr, i, foregroundMask);
 
     return foregroundMask;
 }
@@ -569,7 +586,8 @@ void frame_box_disparity(cv::Mat pre_frame, cv::Mat frame, std::vector <cv::Rect
 
     for (auto r : pre_rois)
     {
-        if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_orig.jpg", frame_nbr, id, pre_frame(r));
+        if (SAVE)
+            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_orig.jpg", frame_nbr, id, pre_frame(r));
 
         //resize last roi with a tollerance
         dx = r.width / roi_tollerance;
@@ -588,8 +606,10 @@ void frame_box_disparity(cv::Mat pre_frame, cv::Mat frame, std::vector <cv::Rect
         //crop pre_frame and current frame
         pre_frame_crop = pre_frame(r);
         frame_crop = frame(r);
-        if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_cur.jpg", frame_nbr, id, frame_crop);
+        if (SAVE)
-        if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_pre.jpg", frame_nbr, id, pre_frame_crop);
+            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_cur.jpg", frame_nbr, id, frame_crop);
+        if (SAVE)
+            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_pre.jpg", frame_nbr, id, pre_frame_crop);
 
         // difference from two consecutive frame
         step_t_segmentation = std::chrono::steady_clock::now();
@@ -612,7 +632,8 @@ void segmentation(cv::Mat pre_frame, cv::Mat frame_crop, int frame_nbr, int i, i
     else
         sprintf(buf_str, "a box frame");
 
-    if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d.jpg", frame_nbr, i, frame_crop);
+    if (SAVE)
+        SAVE_TO("../demo/demo/data/img_crop/%d_%d.jpg", frame_nbr, i, frame_crop);
 
     auto step_t_segmentation = std::chrono::steady_clock::now();
     auto end_t_segmentation = std::chrono::steady_clock::now();
@@ -654,6 +675,4 @@ void segmentation(cv::Mat pre_frame, cv::Mat frame_crop, int frame_nbr, int i, i
         std::cout << " TIME " << buf_str << ": frame_disparity : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
         step_t_segmentation = end_t_segmentation;
     }
-    
-
 }

src/class_src/calibration.cpp

@@ -0,0 +1,108 @@
+#include "calibration.h"
+
+void readTiff(char *filename, double *adfGeoTransform)
+{
+    GDALDataset *poDataset;
+    GDALAllRegister();
+    poDataset = (GDALDataset *)GDALOpen(filename, GA_ReadOnly);
+    if (poDataset != NULL)
+    {
+        poDataset->GetGeoTransform(adfGeoTransform);
+    }
+}
+
+void readCameraCalibrationYaml(const std::string &cameraCalib, cv::Mat &cameraMat, cv::Mat &distCoeff)
+{
+    YAML::Node config = YAML::LoadFile(cameraCalib);
+    const YAML::Node &node_test1 = config["camera_matrix"];
+
+    float data_cm[9];
+    for (std::size_t i = 0; i < node_test1["data"].size(); i++)
+        data_cm[i] = node_test1["data"][i].as<float>();
+    cv::Mat cameraMat_ = cv::Mat(3, 3, CV_32F, data_cm);
+    cameraMat = cameraMat_.clone();
+    std::cout << cameraMat << std::endl;
+    const YAML::Node &node_test2 = config["distortion_coefficients"];
+
+    float data_dc[5];
+    for (std::size_t i = 0; i < node_test2["data"].size(); i++)
+        data_dc[i] = node_test2["data"][i].as<float>();
+    cv::Mat distCoeff_ = cv::Mat(5, 1, CV_32F, data_dc);
+    distCoeff = distCoeff_.clone();
+    std::cout << distCoeff << std::endl;
+}
+
+void pixel2coord(int x, int y, double &lat, double &lon, double *adfGeoTransform)
+{
+    //Returns global coordinates from pixel x, y coordinates
+    double xoff, a, b, yoff, d, e;
+    xoff = adfGeoTransform[0];
+    a = adfGeoTransform[1];
+    b = adfGeoTransform[2];
+    yoff = adfGeoTransform[3];
+    d = adfGeoTransform[4];
+    e = adfGeoTransform[5];
+
+    //printf("%f %f %f %f %f %f\n",xoff, a, b, yoff, d, e );
+
+    lon = a * x + b * y + xoff;
+    lat = d * x + e * y + yoff;
+}
+void coord2pixel(double lat, double lon, int &x, int &y, double *adfGeoTransform)
+{
+    x = int(round((lon - adfGeoTransform[0]) / adfGeoTransform[1]));
+    y = int(round((lat - adfGeoTransform[3]) / adfGeoTransform[5]));
+}
+
+void fillMatrix(cv::Mat &H, double *matrix, bool show)
+{
+    double *vals = (double *)H.data;
+    for (int i = 0; i < 9; i++)
+    {
+        vals[i] = matrix[i];
+    }
+    if (show)
+        std::cout << H << "\n";
+}
+
+void read_projection_matrix(cv::Mat &H, char *path)
+{
+    FILE *fp;
+    char *line = NULL;
+    size_t len = 0;
+    ssize_t read;
+
+    double proj_matrix[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
+    int i = 0;
+    fp = fopen(path, "r");
+    if (fp == NULL)
+        exit(EXIT_FAILURE);
+
+    while ((read = getline(&line, &len, fp)) != -1)
+    {
+        std::cout << line << std::endl;
+        std::stringstream ss(line);
+        while (ss >> proj_matrix[i])
+            i++;
+    }
+    fclose(fp);
+    fillMatrix(H, proj_matrix);
+    free(line);
+}
+
+void convert_coords(std::vector<ObjCoords> &coords, int x, int y, int detected_class, cv::Mat H, double *adfGeoTransform)
+{
+    double latitude, longitude;
+    std::vector<cv::Point2f> x_y, ll;
+    x_y.push_back(cv::Point2f(x, y));
+    //transform camera pixel to map pixel
+    cv::perspectiveTransform(x_y, ll, H);
+    //tranform to map pixel to map gps
+    pixel2coord(ll[0].x, ll[0].y, latitude, longitude, adfGeoTransform);
+
+    ObjCoords coord;
+    coord.lat_ = latitude;
+    coord.long_ = longitude;
+    coord.class_ = detected_class;
+    coords.push_back(coord);
+}

src/class_src/message.cpp

@@ -0,0 +1,109 @@
+#include "message.h"
+#include "calibration.h"
+
+unsigned long long time_in_ms()
+{
+    struct timeval tv;
+    gettimeofday(&tv, NULL);
+    unsigned long long t_stamp_ms = (unsigned long long)(tv.tv_sec) * 1000 + (unsigned long long)(tv.tv_usec) / 1000;
+    return t_stamp_ms;
+}
+
+void addRoadUserfromTracker(const std::vector<Tracker> &trackers, Message *m, geodetic_converter::GeodeticConverter &gc, const cv::Mat &maskOrient, double *adfGeoTransform, cv::Mat H)
+{
+    m->t_stamp_ms = time_in_ms();
+    m->objects.clear();
+    double lat, lon, alt;
+
+    for (auto t : trackers)
+    {
+        if (t.pred_list_.size() > 0)
+        {
+            Categories cat;
+            switch (t.class_)
+            {
+            case 0:
+                cat = Categories::C_person;
+                break;
+            case 1:
+                cat = Categories::C_car;
+                break;
+            case 2:
+                cat = Categories::C_car;
+                break;
+            case 3:
+                cat = Categories::C_bus;
+                break;
+            case 4:
+                cat = Categories::C_motorbike;
+                break;
+            case 5:
+                cat = Categories::C_bycicle;
+                break;
+            }
+            //std::cout << t.pred_list_.size() << std::endl;
+            gc.enu2Geodetic(t.pred_list_.back().x_, t.pred_list_.back().y_, 0, &lat, &lon, &alt);
+
+            int pix_x, pix_y;
+            coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
+
+            // TODO: test correctness - added perspective transform call to converter pix_x and pix_y
+            // sometimes some values are wrong. float ok?
+            // std::vector<cv::Point2f> map_p, camera_p;
+            // std::cout<<"--- pix_x, pix_y: "<<pix_x<<", "<<pix_y<<std::endl;
+            // map_p.push_back(cv::Point2f(pix_x, pix_y));
+            // std::cout<<"map_p: "<<map_p<<std::endl;
+            // //transform camera pixel to map pixel
+            // cv::perspectiveTransform(map_p, camera_p, H.inv());
+            // std::cout<<"size H: "<<H.cols<<", "<<H.rows<<std::endl;
+            // std::cout<<"camera_p: "<<camera_p<<std::endl;
+            // // TODO: in some cases these lines causes seg fault!
+            // std::cout<<"y, x :"<<camera_p[0].y<<",  "<<camera_p[0].x<<std::endl;
+            // std::cout<<"size maskorient: "<<maskOrient.cols<<", "<<maskOrient.rows<<std::endl;
+            // // std::cout<<"vec3b: "<<(cv::Vec3b)(pix_y,pix_x);
+            // assert (camera_p[0].x < maskOrient.cols);
+            // assert (camera_p[0].y < maskOrient.rows);
+            // uint8_t maskOrientPixel = maskOrient.at<cv::Vec3b>(camera_p[0].y,camera_p[0].x)[0];
+            // std::cout<<"boo: "<<maskOrient.at<cv::Vec3b>(camera_p[0].y,camera_p[0].x)<<std::endl;
+            // uint8_t orientation;
+            // if(maskOrientPixel != 0)
+            // {
+            //     orientation = maskOrientPixel;
+            //     // std::cout<<"orientation given by the mask "<< int(orientation)<<std::endl;
+            // }
+            // else
+            // {
+            //     orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
+            //     //std::cout<<"orientation given by the tracker "<< int(orientation)<<std::endl;
+            // }
+
+            // TODO: to validate -> it works for grayscale image (see demo.cpp, row: "cv::Mat maskOrient = cv::imread(camera->maskFileOrient, 0);")
+            // TODO: include perspective transform
+            // std::cout<<"y, x :"<<pix_y<<", "<<pix_x<<std::endl;
+            // std::cout<<"size maskorient: "<<maskOrient.cols<<", "<<maskOrient.rows<<std::endl;
+            // std::cout<<"point: "<<(cv::Point)(pix_y,pix_x);
+            // uint8_t maskOrientPixel = maskOrient.at<uchar>(pix_y,pix_x);
+            // uint8_t orientation;
+            // if(maskOrientPixel != 0)
+            // {
+            //     orientation = maskOrientPixel;
+            //     // std::cout<<"orientation given by the mask "<< int(orientation)<<std::endl;
+            // }
+            // else
+            // {
+            //     orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
+            //     //std::cout<<"orientation given by the tracker "<< int(orientation)<<std::endl;
+            // }
+
+            uint8_t orientation = uint8_t((int((t.pred_list_.back().yaw_ * 57.29 + 360)) % 360) * 17 / 24);
+            // std::cout<<"orient: "<<unsigned(orientation)<<std::endl;
+            //std::cout << "lat: " << lat << " lon: " << lon << std::endl;
+            uint8_t velocity = uint8_t(std::abs(t.pred_list_.back().vel_ * 3.6 / 2));
+            // std::cout<<"vel: "<<unsigned(velocity)<<std::endl;
+            RoadUser r{static_cast<float>(lat), static_cast<float>(lon), velocity, orientation, cat};
+            //std::cout << std::setprecision(10) << r.latitude << " , " << r.longitude << " " << int(r.speed) << " " << int(r.orientation) << " " << r.category << std::endl;
+            m->objects.push_back(r);
+        }
+    }
+    m->num_objects = m->objects.size();
+}

src/class_src/visualization.cpp

@@ -0,0 +1,377 @@
+#include "visualization.h"
+
+/* Thread function to show the updated images 
+**/
+void *show_updates(void *x_void_ptr)
+{
+    cv::namedWindow("original", cv::WINDOW_NORMAL);
+    cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    cv::namedWindow("topview", cv::WINDOW_NORMAL);
+    cv::namedWindow("disparity", cv::WINDOW_NORMAL);
+    cv::Mat original_loc, detection_loc, topview_loc, disparity_loc;
+    bool update_o_loc, update_de_loc, update_t_loc, update_di_loc;
+
+    while (gRun)
+    {
+        TIMER_START
+        // critical section: copy the struct in local variable
+        // in this way we can unlock the sem for the main thread
+        if (updates.mutex_o.try_lock())
+        {
+            update_o_loc = updates.update_o;
+            updates.update_o = false;
+            if (update_o_loc)
+                original_loc = updates.original.clone();
+            updates.mutex_o.unlock();
+        }
+
+        if (updates.mutex_de.try_lock())
+        {
+            update_de_loc = updates.update_de;
+            updates.update_de = false;
+            if (update_de_loc)
+                detection_loc = updates.detection.clone();
+            updates.mutex_de.unlock();
+        }
+
+        if (updates.mutex_t.try_lock())
+        {
+            update_t_loc = updates.update_t;
+            updates.update_t = false;
+            if (update_t_loc)
+                topview_loc = updates.topview.clone();
+            updates.mutex_t.unlock();
+        }
+
+        if (updates.mutex_di.try_lock())
+        {
+            update_di_loc = updates.update_di;
+            updates.update_di = false;
+            if (update_di_loc)
+                disparity_loc = updates.disparity.clone();
+            updates.mutex_di.unlock();
+        }
+
+        if (update_o_loc)
+            cv::imshow("original", original_loc);
+        if (update_de_loc)
+            cv::imshow("detection", detection_loc);
+        if (update_t_loc)
+            cv::imshow("topview", topview_loc);
+        if (update_di_loc)
+            cv::imshow("disparity", disparity_loc);
+        cv::waitKey(1);
+        // usleep(20000);  //sleep 20 msec
+        std::cout << "show_updates: ";
+        TIMER_STOP
+    }
+    return (void *)0;
+}
+
+void *originalFrame(void *x_void_ptr)
+{
+    Frame_t *info_show_orig = (Frame_t *)x_void_ptr;
+    cv::Mat frame_loc;
+    int frame_nbr_loc = 0;
+    while (gRun)
+    {
+        TIMER_START
+        // critical section: copy the struct in local variable
+        // in this way we can unlock the sem for the main thread
+        info_show_orig->sem_vc.lock();
+        frame_loc = info_show_orig->frame.clone();
+        frame_nbr_loc = info_show_orig->frame_nbr;
+        info_show_orig->sem_vc.unlock();
+        if (frame_nbr_loc == 0)
+        {
+            usleep(1000000);
+            printf("no frame received\n");
+            continue;
+        }
+        updates.mutex_o.lock();
+        updates.original = frame_loc.clone();
+        updates.update_o = true;
+        updates.mutex_o.unlock();
+        usleep(10000); //sleep 10 msec
+        std::cout << "originalFrame: ";
+        TIMER_STOP
+    }
+    return (void *)0;
+}
+
+void *detectionFrame(void *x_void_ptr)
+{
+    ModFrame_t *info_show = (ModFrame_t *)x_void_ptr;
+    double lat, lon, alt;
+    int pix_x, pix_y;
+    cv::Mat original_frame_loc;
+    std::vector<Tracker> trackers;
+
+    geodetic_converter::GeodeticConverter gc;
+    double adfGeoTransform[6];
+    cv::Mat H;
+    tk::dnn::Yolo3Detection yolo;
+    int num_detected;
+    cv::Mat mask;
+
+    // box variable
+    tk::dnn::box b;
+    int x0, w, x1, y0, h, y1;
+    int objClass;
+    std::string det_class;
+    ;
+    // float prob;
+    cv::Scalar intensity;
+    std::vector<cv::Point2f> map_p, camera_p;
+    int baseline = 0;
+    float fontScale = 0.5;
+    int thickness = 2;
+
+    while (gRun)
+    {
+        TIMER_START
+        // critical section: copy the struct in local variable
+        // in this way we can unlock the sem for the main thread
+        info_show->sem.lock();
+        original_frame_loc = info_show->original_frame.clone();
+        // std::vector<Tracker> trackers;
+        trackers = info_show->trackers;
+        // geodetic_converter::GeodeticConverter gc;
+        gc = info_show->gc;
+        for (int i = 0; i < 6; i++)
+            adfGeoTransform[i] = info_show->adfGeoTransform[i];
+        // cv::Mat H;
+        H = info_show->H.clone();
+        yolo = info_show->yolo;
+        mask = info_show->mask.clone();
+        info_show->sem.unlock();
+
+        if (trackers.empty())
+        {
+            usleep(1000000);
+            printf("no data available\n");
+            continue;
+        }
+
+        num_detected = yolo.detected.size();
+        for (int i = 0; i < num_detected; i++)
+        {
+            b = yolo.detected[i];
+            x0 = b.x;
+            w = b.w;
+            x1 = b.x + w;
+            y0 = b.y;
+            h = b.h;
+            y1 = b.y + h;
+            objClass = b.cl;
+            det_class = obj_class[b.cl];
+            // prob = b.prob;
+
+            intensity = mask.at<uchar>(cv::Point(int(x0 + b.w / 2), y1));
+
+            if (intensity[0] && objClass < 6)
+            {
+                //std::cout<<objClass<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
+                cv::rectangle(original_frame_loc, cv::Point(x0, y0), cv::Point(x1, y1), yolo.colors[objClass], 2);
+                // draw label
+                cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
+                cv::rectangle(original_frame_loc, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), yolo.colors[b.cl], -1);
+                cv::putText(original_frame_loc, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
+            }
+        }
+
+        for (auto t : trackers)
+        {
+            for (size_t p = 1; p < t.pred_list_.size(); p++)
+            {
+                gc.enu2Geodetic(t.pred_list_[p].x_, t.pred_list_[p].y_, 0, &lat, &lon, &alt);
+                coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
+
+                map_p.clear();
+                camera_p.clear();
+                map_p.push_back(cv::Point2f(pix_x, pix_y));
+
+                //transform camera pixel to map pixel
+                cv::perspectiveTransform(map_p, camera_p, H.inv());
+                // std::cout<<"x,y: "<<pix_x<<", "<<pix_y<<std::endl;
+                // std::cout<<"map_p: "<<map_p<<std::endl;
+                // std::cout<<"camera_p: "<<camera_p<<std::endl;
+                // std::cout<<"size original_frame_loc: "<<original_frame_loc.cols<<", "<<original_frame_loc.rows<<std::endl;
+                // assert (camera_p[0].x < original_frame_loc.cols);
+                // assert (camera_p[0].y < original_frame_loc.rows);
+                if (camera_p[0].x < original_frame_loc.cols && camera_p[0].y < original_frame_loc.rows && camera_p[0].x >= 0 && camera_p[0].y >= 0)
+                    cv::circle(original_frame_loc, cv::Point(camera_p[0].x, camera_p[0].y), 3.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
+            }
+        }
+
+        updates.mutex_de.lock();
+        updates.detection = original_frame_loc.clone();
+        updates.update_de = true;
+        updates.mutex_de.unlock();
+
+        std::cout << "detectionFrame: ";
+        TIMER_STOP
+    }
+    return (void *)0;
+}
+
+void *topviewFrame(void *x_void_ptr)
+{
+    ModFrame_t *info_show = (ModFrame_t *)x_void_ptr;
+    double lat, lon, alt;
+    int pix_x, pix_y;
+    cv::Mat frame_top;
+    cv::Mat original_frame_top;
+    // original_frame_top = cv::imread("../demo/demo/data/map/map_geo.jpg");
+    original_frame_top = cv::imread("../demo/demo/data/map/MASA_4670.png");
+    // original_frame_top = cv::imread("../demo/demo/data/map/MASA_4670_V.png");
+
+    std::vector<Tracker> trackers;
+    geodetic_converter::GeodeticConverter gc;
+    double adfGeoTransform[6];
+    cv::Mat H;
+    while (gRun)
+    {
+        TIMER_START
+        // critical section: copy the struct in local variable
+        // in this way we can unlock the sem for the main thread
+        info_show->sem.lock();
+        // std::vector<Tracker> trackers;
+        trackers = info_show->trackers;
+        // geodetic_converter::GeodeticConverter gc;
+        gc = info_show->gc;
+        for (int i = 0; i < 6; i++)
+            adfGeoTransform[i] = info_show->adfGeoTransform[i];
+        // cv::Mat H;
+        H = info_show->H.clone();
+        info_show->sem.unlock();
+        if (trackers.empty())
+        {
+            usleep(1000000);
+            printf("no data available\n");
+            continue;
+        }
+        frame_top = original_frame_top.clone();
+        for (auto t : trackers)
+        {
+            for (size_t p = 1; p < t.pred_list_.size(); p++)
+            {
+                gc.enu2Geodetic(t.pred_list_[p].x_, t.pred_list_[p].y_, 0, &lat, &lon, &alt);
+                coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
+                if (pix_x < frame_top.cols && pix_y < frame_top.rows && pix_x >= 0 && pix_y >= 0)
+                    cv::circle(frame_top, cv::Point(pix_x, pix_y), 7.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
+            }
+        }
+        //outputVideo<< frame_top;
+        // ------------------------------------------------
+        updates.mutex_t.lock();
+        updates.topview = frame_top.clone();
+        updates.update_t = true;
+        updates.mutex_t.unlock();
+
+        std::cout << "topviewFrame: ";
+        TIMER_STOP
+    }
+    return (void *)0;
+}
+
+void *disparityFrame(void *x_void_ptr)
+{
+    Frame_t *info_show_disparity = (Frame_t *)x_void_ptr;
+    bool first_iteration = true;
+    cv::Mat frame_loc;
+    int frame_nbr_loc = 0, pre_frame_nbr_loc = 0;
+    auto start_t = std::chrono::steady_clock::now();
+    auto step_t = std::chrono::steady_clock::now();
+    auto end_t = std::chrono::steady_clock::now();
+
+    // information for the disparity map
+    cv::Mat canny, pre_canny, canny_RGB, pre_canny_RGB;
+    cv::Mat canny_img;
+    cv::Mat disparity_frame;
+    while (gRun)
+    {
+        start_t = std::chrono::steady_clock::now();
+        step_t = start_t;
+        // critical section: copy the struct in local variable
+        // in this way we can unlock the sem for the main thread
+        info_show_disparity->sem_vc.lock();
+        frame_loc = info_show_disparity->frame.clone();
+        frame_nbr_loc = info_show_disparity->frame_nbr;
+        info_show_disparity->sem_vc.unlock();
+        if (frame_nbr_loc == 0)
+        {
+            usleep(1000000);
+            printf("no frame received\n");
+            continue;
+        }
+        // compute frame disparity only in there is a new frame
+        if (frame_nbr_loc - pre_frame_nbr_loc > 0)
+        {
+            pre_frame_nbr_loc = frame_nbr_loc;
+            //preprocessing frame
+            step_t = std::chrono::steady_clock::now();
+            // src_gray
+            canny_img = img_laplacian(frame_loc, 0);
+            cv::Canny(canny_img, canny, 100, 100 * 2);
+            // sprintf(buf_frame_crop_name,"../demo/demo/data/img_disparity/%d_%d_canny.jpg",frame_nbr_loc, 999);
+            // cv::imwrite(buf_frame_crop_name, canny);
+            end_t = std::chrono::steady_clock::now();
+            std::cout << " TIME END pre canny : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
+            step_t = end_t;
+            // std::cout<<"o: "<<frame_loc.cols<<" - "<<frame_loc.rows<<std::endl;
+            // std::cout<<"canny: "<<canny.cols<<" - "<<canny.rows<<std::endl;
+            // std::cout<<"pre: "<<pre_canny.cols<<" - "<<pre_canny.rows<<std::endl;
+            if (!first_iteration)
+            {
+                // backtorgb = cv::cvtColor(pre_canny,cv::COLOR_GRAY2RGB)
+                cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+                cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+                disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr_loc, 999, 0);
+                // std::cout<<"size: "<<disparity_frame.rows<<" - "<<disparity_frame.cols<<std::endl;
+                // if (disparity_frame.rows == 0 || disparity_frame.cols == 0)
+                //     return -1;
+                // if (disparity_frame.empty())
+                // { // only fools don't check...
+                //     std::cout << "image not loaded !" << std::endl;
+                //     return -1;
+                // }
+                end_t = std::chrono::steady_clock::now();
+                std::cout << " TIME canny : frame_disparity : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - step_t).count() << " ms" << std::endl;
+                step_t = end_t;
+
+                // //--------------------------------
+                // //frame box disparity on the original image
+                // step_t_segmentation = std::chrono::steady_clock::now();
+                // frame_box_disparity(pre_frame, frame, pre_rois, frame_nbr_loc);
+                // // reset pre_rois for the new roi of the current frame
+                // // pre_rois.erase(pre_rois.begin(), pre_rois.end());
+                // end_t_segmentation = std::chrono::steady_clock::now();
+                // std::cout << " TIME Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+                // step_t_segmentation = end_t_segmentation;
+
+                // //frame box disparity on the preprocessed image
+                // cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+                // cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+                // frame_box_disparity(pre_canny_RGB, canny_RGB, pre_rois, frame_nbr_loc);
+                // // reset pre_rois for the new roi of the current frame
+                // pre_rois.erase(pre_rois.begin(), pre_rois.end());
+                // end_t_segmentation = std::chrono::steady_clock::now();
+                // std::cout << " TIME Canny Frame disparity : "<<std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms"<<std::endl;
+                // step_t_segmentation = end_t_segmentation;
+                // //---------------------------------
+
+                updates.mutex_di.lock();
+                updates.disparity = disparity_frame.clone();
+                updates.update_di = true;
+                updates.mutex_di.unlock();
+            }
+            pre_canny = canny.clone();
+            if (first_iteration)
+                first_iteration = false;
+            end_t = std::chrono::steady_clock::now();
+            std::cout << "disparityFrame : TIME END pre canny : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count() << " ms" << std::endl;
+        }
+    }
+    return (void *)0;
+}