Rebase Python example from ioir123ju/tkDNN/master/

demo/demo/darknetTR.cpp

@@ -0,0 +1,100 @@
+#include "darknetTR.h"
+
+bool gRun;
+bool SAVE_RESULT = false;
+
+void sig_handler(int signo) {
+    std::cout<<"request gateway stop\n";
+    gRun = false;
+}
+
+extern "C"
+{
+
+
+void copy_image_from_bytes(image im, unsigned char *pdata)
+{
+//    unsigned char *data = (unsigned char*)pdata;
+//    int i, k, j;
+    int w = im.w;
+    int h = im.h;
+    int c = im.c;
+//    for (k = 0; k < c; ++k) {
+//        for (j = 0; j < h; ++j) {
+//            for (i = 0; i < w; ++i) {
+//                int dst_index = i + w * j + w * h*k;
+//                int src_index = k + c * i + c * w*j;
+//                im.data[dst_index] = (float)data[src_index] / 255.;
+//            }
+//        }
+//    }
+    memcpy(im.data, pdata, h * w * c);
+
+}
+
+image make_empty_image(int w, int h, int c)
+{
+    image out;
+    out.data = 0;
+    out.h = h;
+    out.w = w;
+    out.c = c;
+    return out;
+}
+
+image make_image(int w, int h, int c)
+{
+    image out = make_empty_image(w,h,c);
+    out.data = (float*)xcalloc(h * w * c, sizeof(float));
+    return out;
+}
+
+tk::dnn::Yolo3Detection* load_network(char* net_cfg, int n_classes, int n_batch)
+{
+    std::string net;
+    net = net_cfg;
+    tk::dnn::Yolo3Detection *detNN = new tk::dnn::Yolo3Detection;
+    detNN->init(net, n_classes, n_batch);
+
+    return detNN;
+}
+#include <typeinfo>
+void do_inference(tk::dnn::Yolo3Detection *net, image im)
+{
+    std::vector<cv::Mat> batch_dnn_input;
+
+    cv::Mat frame(im.h, im.w, CV_8UC3, (unsigned char*)im.data);
+    batch_dnn_input.push_back(frame);
+    net->update(batch_dnn_input, 1);
+
+}
+
+
+detection* get_network_boxes(tk::dnn::Yolo3Detection *net, float thresh, int batch_num, int *pnum)
+{
+    std::vector<std::vector<tk::dnn::box>> batchDetected;
+    batchDetected = net->get_batch_detected();
+    int nboxes =0;
+    std::vector<std::string> classesName = net->get_classesName();
+    detection* dets = (detection*)xcalloc(batchDetected[batch_num].size(), sizeof(detection));
+    for (int i = 0; i < batchDetected[batch_num].size(); ++i)
+    {
+        if (batchDetected[batch_num][i].prob > thresh)
+        {
+            dets[nboxes].cl = batchDetected[batch_num][i].cl;
+            strcpy(dets[nboxes].name,classesName[dets[nboxes].cl].c_str());
+            dets[nboxes].bbox.x = batchDetected[batch_num][i].x;
+            dets[nboxes].bbox.y = batchDetected[batch_num][i].y;
+            dets[nboxes].bbox.w = batchDetected[batch_num][i].w;
+            dets[nboxes].bbox.h = batchDetected[batch_num][i].h;
+            dets[nboxes].prob = batchDetected[batch_num][i].prob;
+            nboxes += 1;
+        }
+    }
+    if (pnum) *pnum = nboxes;
+    return dets;
+}
+}
+
+
+

demo/demo/darknetTR.h

@@ -0,0 +1,37 @@
+#ifndef DEMO_H
+#define DEMO_H
+
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+#include <unistd.h>
+#include <mutex>
+#include <malloc.h>
+#include "CenternetDetection.h"
+#include "MobilenetDetection.h"
+#include "Yolo3Detection.h"
+#include "utils.h"
+extern "C"
+{
+typedef struct {
+    int w;
+    int h;
+    int c;
+    float *data;
+} image;
+
+typedef struct {
+    float x, y, w, h;
+}BOX;
+
+typedef struct {
+    int cl;
+    BOX bbox;
+    float prob;
+    char name[20];
+
+}detection;
+
+tk::dnn::Yolo3Detection* load_network(char* net_cfg, int n_classes, int n_batch);
+}
+#endif /* DETECTIONNN_H*/

demo/demo/demo.cpp

@@ -37,7 +37,7 @@ int main(int argc, char *argv[]) {
     int n_batch = 1;
     if(argc > 5)
         n_batch = atoi(argv[5]); 
-    bool show = true;
+    bool show = false;
     if(argc > 6)
         show = atoi(argv[6]); 
     float conf_thresh=0.3;
@@ -112,7 +112,7 @@ int main(int argc, char *argv[]) {
         } 
         if(!frame.data) 
             break;
-    
+
         //inference
         detNN->update(batch_dnn_input, n_batch);
         detNN->draw(batch_frame);