root
131 lines
4.0 KiB
C++
131 lines
4.0 KiB
C++
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#include <iostream>
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#include <signal.h>
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#include <stdlib.h> /* srand, rand */
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#ifdef __linux__
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#include <unistd.h>
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#endif
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#include <mutex>
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#include "utils.h"
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "Yolo3Detection.h"
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//#include "CenternetDetection.h"
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//#include "MobilenetDetection.h"
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#include "evaluation.h"
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#include <chrono>
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#include <cstdint>
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#include <iostream>
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uint64_t timeSinceEpochMillisec() {
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using namespace std::chrono;
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return duration_cast<milliseconds>(system_clock::now().time_since_epoch()).count();
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}
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int baggage() {
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std::cout << timeSinceEpochMillisec() << std::endl;
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char ntype = 'y';
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const char *config_filename = "../demo/config.yaml";
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const char * net = "../demo/yolo4_fp32.rt";
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const char * img_path = "../demo/demo.jpg";
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bool show = false;
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bool verbose;
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int classes, map_points, map_levels;
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float map_step, IoU_thresh, conf_thresh;
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//read parameters
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tk::dnn::readmAPParams(config_filename, classes, map_points, map_levels, map_step,
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IoU_thresh, conf_thresh, verbose);
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//extract network name from rt path
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std::string net_name;
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removePathAndExtension(net, net_name);
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std::cout<<"Network: "<<net_name<<std::endl;
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//open files (if needed)
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std::ofstream times, memory, coco_json;
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// instantiate detector
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tk::dnn::Yolo3Detection yolo;
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// tk::dnn::CenternetDetection cnet;
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// tk::dnn::MobilenetDetection mbnet;
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tk::dnn::DetectionNN *detNN;
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int n_classes = classes;
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// float conf_threshold=0.001;
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detNN = &yolo;
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detNN->init(net, n_classes, 1, conf_thresh);
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//read images
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// std::ifstream all_labels(labels_path);
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std::cout << timeSinceEpochMillisec() << std::endl;
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std::string l_filename;
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std::vector<tk::dnn::Frame> images;
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std::vector<tk::dnn::box> detected_bbox;
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std::cout<<"Reading groundtruth and generating detections"<<std::endl;
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if(show)
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cv::namedWindow("detection", cv::WINDOW_NORMAL);
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// int images_done;
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// for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
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// std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
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tk::dnn::Frame f;
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// f.lFilename = l_filename;
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// f.iFilename = l_filename;
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//convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
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// read frame
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//if(!fileExist(f.iFilename.c_str()))
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// FatalError("Wrong image file path.");
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cv::Mat frame = cv::imread(img_path, cv::IMREAD_COLOR);
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std::vector<cv::Mat> batch_frames;
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batch_frames.push_back(frame);
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int height = frame.rows;
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int width = frame.cols;
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// if(!frame.data)
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// break;
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std::vector<cv::Mat> batch_dnn_input;
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batch_dnn_input.push_back(frame.clone());
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std::cout<<"test1"<<"\n";
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//inference
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detected_bbox.clear();
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detNN->update(batch_dnn_input,1);
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detNN->draw(batch_frames);
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detected_bbox = detNN->detected;
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std::cout<<"test2"<<"\n";
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// save detections labels
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for(auto d:detected_bbox){
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//convert detected bb in the same format as label
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//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
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tk::dnn::BoundingBox b;
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b.x = (d.x + d.w/2) / width;
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b.y = (d.y + d.h/2) / height;
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b.w = d.w / width;
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b.h = d.h / height;
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b.prob = d.prob;
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b.cl = d.cl;
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f.det.push_back(b);
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std::cout<< d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
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if(show)// draw rectangle for detection
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cv::rectangle(batch_frames[0], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);
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}
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//images.push_back(f);
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if(show){
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cv::imshow("detection", batch_frames[0]);
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cv::waitKey(0);
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}
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std::cout << timeSinceEpochMillisec() << std::endl;
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return 0;
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}
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