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2021-06-04 05:24:13 +00:00
parent 8cc5ebf832
commit 8c2282a6fb
84 changed files with 1486 additions and 19657 deletions
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demo/config.yaml
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classes : 80 #number of classes
map_points : 101 #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
classes : 13 #number of classes
map_points : 0 #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
map_levels : 10 #number of IoU step for the AP
map_step : 0.05 #step of IoU
IoU_thresh : 0.5 #starting IoU threshold
demo/config_tetra.yaml
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classes : 3 #number of classes
map_points : 101 #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
map_levels : 10 #number of IoU step for the AP
map_step : 0.05 #step of IoU
IoU_thresh : 0.5 #starting IoU threshold
conf_thresh : 0.0 #threshold on the condifence of the bbox
verbose : false #print on screen information
demo/demo.jpg
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demo/demo/demo.cpp
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#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
//#include <unistd.h>
#include <mutex>
#include "CenternetDetection.h"
#include "MobilenetDetection.h"
#include "Yolo3Detection.h"
bool gRun;
bool SAVE_RESULT = false;
void sig_handler(int signo) {
std::cout<<"request gateway stop\n";
gRun = false;
}
int main(int argc, char *argv[]) {
std::cout<<"detection\n";
signal(SIGINT, sig_handler);
std::string net = "yolo4tiny_fp32.rt";
if(argc > 1)
net = argv[1];
#ifdef __linux__
std::string input = "../demo/yolo_test.mp4";
#elif _WIN32
std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
#endif
if(argc > 2)
input = argv[2];
char ntype = 'y';
if(argc > 3)
ntype = argv[3][0];
int n_classes = 80;
if(argc > 4)
n_classes = atoi(argv[4]);
int n_batch = 1;
if(argc > 5)
n_batch = atoi(argv[5]);
bool show = true;
if(argc > 6)
show = atoi(argv[6]);
float conf_thresh=0.3;
if(argc > 7)
conf_thresh = atof(argv[7]);
if(n_batch < 1 || n_batch > 64)
FatalError("Batch dim not supported");
if(!show)
SAVE_RESULT = true;
tk::dnn::Yolo3Detection yolo;
tk::dnn::CenternetDetection cnet;
tk::dnn::MobilenetDetection mbnet;
tk::dnn::DetectionNN *detNN;
switch(ntype)
{
case 'y':
detNN = &yolo;
break;
case 'c':
detNN = &cnet;
break;
case 'm':
detNN = &mbnet;
n_classes++;
break;
default:
FatalError("Network type not allowed (3rd parameter)\n");
}
detNN->init(net, n_classes, n_batch, conf_thresh);
gRun = true;
cv::VideoCapture cap(input);
if(!cap.isOpened())
gRun = false;
else
std::cout<<"camera started\n";
cv::VideoWriter resultVideo;
if(SAVE_RESULT) {
int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
}
cv::Mat frame;
if(show)
cv::namedWindow("detection", cv::WINDOW_NORMAL);
std::vector<cv::Mat> batch_frame;
std::vector<cv::Mat> batch_dnn_input;
while(gRun) {
batch_dnn_input.clear();
batch_frame.clear();
for(int bi=0; bi< n_batch; ++bi){
cap >> frame;
if(!frame.data)
break;
batch_frame.push_back(frame);
// this will be resized to the net format
batch_dnn_input.push_back(frame.clone());
}
if(!frame.data)
break;
//inference
detNN->update(batch_dnn_input, n_batch);
detNN->draw(batch_frame);
if(show){
for(int bi=0; bi< n_batch; ++bi){
cv::imshow("detection", batch_frame[bi]);
cv::waitKey(1);
}
}
if(n_batch == 1 && SAVE_RESULT)
resultVideo << frame;
}
std::cout<<"detection end\n";
double mean = 0;
std::cout<<COL_GREENB<<"\n\nTime stats:\n";
std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
std::cout<<"Avg: "<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n"<<COL_END;
return 0;
}
demo/demo/map.cpp
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#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#ifdef __linux__
#include <unistd.h>
#endif
#include <mutex>
#include "utils.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "Yolo3Detection.h"
#include "CenternetDetection.h"
#include "MobilenetDetection.h"
#include "evaluation.h"
#include <map>
void convertFilename(std::string &filename,const std::string l_folder, const std::string i_folder, const std::string l_ext,const std::string i_ext)
{
filename.replace(filename.find(l_folder),l_folder.length(),i_folder);
filename.replace(filename.find(l_ext),l_ext.length(),i_ext);
}
int main(int argc, char *argv[])
{
char ntype = 'y';
const char *config_filename = "../demo/config.yaml";
const char * net = "yolo3.rt";
const char * labels_path = "../demo/COCO_val2017/all_labels.txt";
bool show = false;
bool write_dets = false;
bool write_res_on_file = true;
bool write_coco_json = true;
int n_images = 5000;
bool verbose;
int classes, map_points, map_levels;
float map_step, IoU_thresh, conf_thresh;
double vm_total = 0, rss_total = 0;
double vm, rss;
//read args
if(argc > 1)
net = argv[1];
if(argc > 2)
ntype = argv[2][0];
if(argc > 3)
labels_path = argv[3];
if(argc > 4)
config_filename = argv[4];
//check if files needed exist
if(!fileExist(config_filename))
FatalError("Wrong config file path.");
if(!fileExist(net))
FatalError("Wrong net file path.");
if(!fileExist(labels_path))
FatalError("Wrong labels file path.");
//read mAP parameters
tk::dnn::readmAPParams( config_filename, classes, map_points, map_levels, map_step,
IoU_thresh, conf_thresh, verbose);
//extract network name from rt path
std::string net_name;
removePathAndExtension(net, net_name);
std::cout<<"Network: "<<net_name<<std::endl;
//open files (if needed)
std::ofstream times, memory, coco_json;
if(write_coco_json){
coco_json.open(net_name+"_COCO_res.json");
coco_json << "[\n";
}
if(write_res_on_file){
times.open("times_"+net_name+".csv");
memory.open("memory.csv", std::ios_base::app);
memory<<net<<";";
}
// instantiate detector
tk::dnn::Yolo3Detection yolo;
tk::dnn::CenternetDetection cnet;
tk::dnn::MobilenetDetection mbnet;
tk::dnn::DetectionNN *detNN;
int n_classes = classes;
switch(ntype){
case 'y':
detNN = &yolo;
break;
case 'c':
detNN = &cnet;
break;
case 'm':
detNN = &mbnet;
n_classes++;
break;
default:
FatalError("Network type not allowed (3rd parameter)\n");
}
detNN->init(net, n_classes, 1, conf_thresh);
//read images
std::ifstream all_labels(labels_path);
std::string l_filename;
std::vector<tk::dnn::Frame> images;
std::vector<tk::dnn::box> detected_bbox;
std::cout<<"Reading groundtruth and generating detections"<<std::endl;
if(show)
cv::namedWindow("detection", cv::WINDOW_NORMAL);
int images_done;
for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
tk::dnn::Frame f;
f.lFilename = l_filename;
f.iFilename = l_filename;
convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
// read frame
if(!fileExist(f.iFilename.c_str()))
FatalError("Wrong image file path.");
cv::Mat frame = cv::imread(f.iFilename.c_str(), cv::IMREAD_COLOR);
std::vector<cv::Mat> batch_frames;
batch_frames.push_back(frame);
int height = frame.rows;
int width = frame.cols;
if(!frame.data)
break;
std::vector<cv::Mat> batch_dnn_input;
batch_dnn_input.push_back(frame.clone());
//inference
detected_bbox.clear();
detNN->update(batch_dnn_input,1,write_res_on_file, &times, write_coco_json);
detNN->draw(batch_frames);
detected_bbox = detNN->detected;
if(write_coco_json)
printJsonCOCOFormat(&coco_json, f.iFilename.c_str(), detected_bbox, classes, width, height);
std::ofstream myfile;
if(write_dets)
myfile.open ("det/"+f.lFilename.substr(f.lFilename.find("labels/") + 7));
// save detections labels
for(auto d:detected_bbox){
//convert detected bb in the same format as label
//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
tk::dnn::BoundingBox b;
b.x = (d.x + d.w/2) / width;
b.y = (d.y + d.h/2) / height;
b.w = d.w / width;
b.h = d.h / height;
b.prob = d.prob;
b.cl = d.cl;
f.det.push_back(b);
if(write_dets)
myfile << d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
if(show)// draw rectangle for detection
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);
}
if(write_dets)
myfile.close();
// read and save groundtruth labels
if(fileExist(f.lFilename.c_str()))
{
std::ifstream labels(l_filename);
for(std::string line; std::getline(labels, line); ){
std::istringstream in(line);
tk::dnn::BoundingBox b;
in >> b.cl >> b.x >> b.y >> b.w >> b.h;
b.prob = 1;
b.truthFlag = 1;
f.gt.push_back(b);
if(show)// draw rectangle for groundtruth
cv::rectangle(batch_frames[0], cv::Point((b.x-b.w/2)*width, (b.y-b.h/2)*height), cv::Point((b.x+b.w/2)*width,(b.y+b.h/2)*height), cv::Scalar(0, 255, 0), 2);
}
}
images.push_back(f);
if(show){
cv::imshow("detection", batch_frames[0]);
cv::waitKey(0);
}
getMemUsage(vm, rss);
vm_total += vm;
rss_total += rss;
}
if(write_coco_json){
coco_json.seekp (coco_json.tellp() - std::streampos(2));
coco_json << "\n]\n";
coco_json.close();
}
std::cout << "Avg VM[MB]: " << vm_total/images_done/1024.0 << ";Avg RSS[MB]: " << rss_total/images_done/1024.0 << std::endl;
//compute mAP
double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net_name);
std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
//compute average precision, recall and f1score
tk::dnn::computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net_name);
if(write_res_on_file){
memory<<vm_total/images_done/1024.0<<";"<<rss_total/images_done/1024.0<<"\n";
times.close();
memory.close();
}
return 0;
}
demo/inf.cpp
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#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#ifdef __linux__
#include <unistd.h>
#endif
#include <mutex>
#include "utils.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "Yolo3Detection.h"
//#include "CenternetDetection.h"
//#include "MobilenetDetection.h"
#include "evaluation.h"
#include <chrono>
#include <cstdint>
#include <iostream>
uint64_t timeSinceEpochMillisec() {
using namespace std::chrono;
return duration_cast<milliseconds>(system_clock::now().time_since_epoch()).count();
}
int baggage() {
std::cout << timeSinceEpochMillisec() << std::endl;
char ntype = 'y';
const char *config_filename = "../demo/config.yaml";
const char * net = "../demo/yolo4_fp32.rt";
const char * img_path = "../demo/demo.jpg";
bool show = false;
bool verbose;
int classes, map_points, map_levels;
float map_step, IoU_thresh, conf_thresh;
//read parameters
tk::dnn::readmAPParams(config_filename, classes, map_points, map_levels, map_step,
IoU_thresh, conf_thresh, verbose);
//extract network name from rt path
std::string net_name;
removePathAndExtension(net, net_name);
std::cout<<"Network: "<<net_name<<std::endl;
//open files (if needed)
std::ofstream times, memory, coco_json;
// instantiate detector
tk::dnn::Yolo3Detection yolo;
// tk::dnn::CenternetDetection cnet;
// tk::dnn::MobilenetDetection mbnet;
tk::dnn::DetectionNN *detNN;
int n_classes = classes;
// float conf_threshold=0.001;
detNN = &yolo;
detNN->init(net, n_classes, 1, conf_thresh);
//read images
// std::ifstream all_labels(labels_path);
std::cout << timeSinceEpochMillisec() << std::endl;
std::string l_filename;
std::vector<tk::dnn::Frame> images;
std::vector<tk::dnn::box> detected_bbox;
std::cout<<"Reading groundtruth and generating detections"<<std::endl;
if(show)
cv::namedWindow("detection", cv::WINDOW_NORMAL);
// int images_done;
// for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
// std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
tk::dnn::Frame f;
// f.lFilename = l_filename;
// f.iFilename = l_filename;
//convertFilename(f.iFilename, "labels", "images", ".txt", ".jpg");
// read frame
//if(!fileExist(f.iFilename.c_str()))
// FatalError("Wrong image file path.");
cv::Mat frame = cv::imread(img_path, cv::IMREAD_COLOR);
std::vector<cv::Mat> batch_frames;
batch_frames.push_back(frame);
int height = frame.rows;
int width = frame.cols;
// if(!frame.data)
// break;
std::vector<cv::Mat> batch_dnn_input;
batch_dnn_input.push_back(frame.clone());
std::cout<<"test1"<<"\n";
//inference
detected_bbox.clear();
detNN->update(batch_dnn_input,1);
detNN->draw(batch_frames);
detected_bbox = detNN->detected;
std::cout<<"test2"<<"\n";
// save detections labels
for(auto d:detected_bbox){
//convert detected bb in the same format as label
//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
tk::dnn::BoundingBox b;
b.x = (d.x + d.w/2) / width;
b.y = (d.y + d.h/2) / height;
b.w = d.w / width;
b.h = d.h / height;
b.prob = d.prob;
b.cl = d.cl;
f.det.push_back(b);
std::cout<< d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
if(show)// draw rectangle for detection
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);
}
//images.push_back(f);
if(show){
cv::imshow("detection", batch_frames[0]);
cv::waitKey(0);
}
std::cout << timeSinceEpochMillisec() << std::endl;
return 0;
}
demo/yolo_test.mp4
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