mmr/tkDNN
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Merge remote-tracking branch 'origin/master' into cnet

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This commit is contained in:
Davide Sapienza
2021-07-22 17:03:23 +02:00
parent 2bb70859da f4e71a5a28
commit 0292e21c33
59 changed files with 7640 additions and 209 deletions
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demo/demo/map.cpp
+101 -74
View File
@@ -34,10 +34,12 @@ int main(int argc, char *argv[])
const char *config_filename = "../demo/config.yaml";
const char * net = "yolo3.rt";
const char * labels_path = "../demo/COCO_val2017/all_labels.txt";
int n_batches = 1;
float confidence_thresh = 0.3;
bool show = false;
bool write_dets = false;
bool write_res_on_file = true;
bool write_coco_json = true;
bool write_coco_json = false;
int n_images = 5000;
bool verbose;
@@ -56,6 +58,12 @@ int main(int argc, char *argv[])
labels_path = argv[3];
if(argc > 4)
config_filename = argv[4];
if(argc > 5)
n_batches = atoi(argv[5]);
if(argc > 6)
confidence_thresh = atof(argv[6]);
std::cout<<"conf t: "<<confidence_thresh<<std::endl;
//check if files needed exist
if(!fileExist(config_filename))
@@ -83,9 +91,9 @@ int main(int argc, char *argv[])
}
if(write_res_on_file){
times.open("times_"+net_name+".csv");
times.open("times_"+net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)+".csv");
memory.open("memory.csv", std::ios_base::app);
memory<<net<<";";
memory<<net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh)<<";";
}
// instantiate detector
@@ -121,90 +129,109 @@ int main(int argc, char *argv[])
if(show)
cv::namedWindow("detection", cv::WINDOW_NORMAL);
bool file_ok = false;
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;
for (images_done=0 ; images_done < n_images ;) {
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);
int cur_batches = 0;
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::vector<tk::dnn::Frame> cur_frames;
for(;cur_batches<n_batches && images_done < n_images;cur_batches++, ++images_done){
std::getline(all_labels, l_filename);
file_ok = all_labels ? true : false ;
if (!file_ok)
break;
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);
batch_frames.push_back(frame);
f.height = frame.rows;
f.width = frame.cols;
if(!frame.data)
break;
batch_dnn_input.push_back(frame.clone());
// read and save groundtruth labels
if(fileExist(f.lFilename.c_str()))
{
std::ifstream labels(f.lFilename);
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[cur_batches], cv::Point((b.x-b.w/2)*f.width, (b.y-b.h/2)*f.height), cv::Point((b.x+b.w/2)*f.width,(b.y+b.h/2)*f.height), cv::Scalar(0, 255, 0), 2);
}
}
cur_frames.push_back(f);
}
if (!file_ok)
break;
//inference
detected_bbox.clear();
detNN->update(batch_dnn_input,1,write_res_on_file, &times, write_coco_json);
detNN->update(batch_dnn_input,cur_batches,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);
for(int j=0;j<cur_frames.size(); ++j){
if(write_coco_json)
printJsonCOCOFormat(&coco_json, cur_frames[j].iFilename.c_str(), detNN->batchDetected[j], classes, cur_frames[j].width, cur_frames[j].height);
std::ofstream myfile;
if(write_dets)
myfile.open ("det/"+f.lFilename.substr(f.lFilename.find("labels/") + 7));
std::ofstream myfile;
if(write_dets)
myfile.open ("det/"+cur_frames[j].lFilename.substr(cur_frames[j].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);
// save detections labels
for(auto d:detNN->batchDetected[j]){
//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) / cur_frames[j].width;
b.y = (d.y + d.h/2) / cur_frames[j].height;
b.w = d.w / cur_frames[j].width;
b.h = d.h / cur_frames[j].height;
b.prob = d.prob;
b.cl = d.cl;
cur_frames[j].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[j], 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 << 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);
}
}
myfile.close();
images.push_back(f);
images.push_back(cur_frames[j]);
if(show){
cv::imshow("detection", batch_frames[0]);
cv::waitKey(0);
if(show){
cv::imshow("detection", batch_frames[j]);
cv::waitKey(0);
}
}
std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\tcur batch:\t"<<cur_batches<< "\n"<<COL_END;
getMemUsage(vm, rss);
vm_total += vm;
rss_total += rss;
@@ -221,11 +248,11 @@ int main(int argc, char *argv[])
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);
double AP = tk::dnn::computeMapNIoULevels(images,classes,IoU_thresh,confidence_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net_name+"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));
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);
tk::dnn::computeTPFPFN(images,classes,IoU_thresh,confidence_thresh, verbose, write_res_on_file, net_name +"_"+ std::to_string(n_batches)+"_"+std::to_string(confidence_thresh));
if(write_res_on_file){
memory<<vm_total/images_done/1024.0<<";"<<rss_total/images_done/1024.0<<"\n";
demo/demo/seg_demo.cpp
+148
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@@ -0,0 +1,148 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#include <unistd.h>
#include <mutex>
#include "SegmentationNN.h"
bool gRun;
bool SAVE_RESULT = true;
void sig_handler(int signo) {
std::cout<<"request gateway stop\n";
gRun = false;
}
void writePred(const std::string& images_names, const std::string& gt_folder, const std::string& out_folder, tk::dnn::SegmentationNN& segNN, int& width, int& height, bool show=false){
std::ifstream all_gt(images_names);
std::string filename;
cv::Mat frame;
for (; std::getline(all_gt, filename); ) {
std::cout<<filename<<std::endl;
frame = cv::imread(gt_folder + filename);
height = frame.rows;
width = frame.cols;
segNN.updateOriginal(frame, false);
if(show)
segNN.draw();
cv::imwrite(out_folder + filename, segNN.segmented[0]);
}
}
int main(int argc, char *argv[]) {
std::cout<<"detection\n";
signal(SIGINT, sig_handler);
std::string net = "shelfnet_fp32.rt";
if(argc > 1)
net = argv[1];
std::string input = "../demo/yolo_test.mp4";
if(argc > 2)
input = argv[2];
int n_batch = 1;
if(argc > 3)
n_batch = atoi(argv[3]);
int n_classes = 19;
if(argc > 4)
n_classes = atoi(argv[4]);
bool resize = false;
if(argc > 5)
resize = atoi(argv[5]);
int baseline_resize = 1024;
if(argc > 6)
baseline_resize = atoi(argv[6]);
bool show = true;
if(argc > 7)
show = atoi(argv[7]);
bool write_pred = false;
if(argc > 8)
write_pred = atoi(argv[8]);
if(resize && (baseline_resize < 0 || baseline_resize > 5000))
FatalError("Problem with baseline resize")
if(n_batch < 1 || n_batch > 64)
FatalError("Batch dim not supported");
//net initialization
tk::dnn::SegmentationNN segNN;
segNN.init(net, n_classes, n_batch);
int height = 0, width = 0;
int basewidth=baseline_resize, hsize;
if(write_pred){
std::string gt_folder = "../demo/CityScapes_val/images/";
std::string images_names = "../demo/CityScapes_val/all_images.txt";
std::string out_folder = "seg/";
writePred(images_names, gt_folder, out_folder, segNN, width, height, show);
}
else{
if(!show)
SAVE_RESULT = true;
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,h;
if(resize){
w = basewidth;
h = int((float(cap.get(cv::CAP_PROP_FRAME_HEIGHT))*float(basewidth/float(cap.get(cv::CAP_PROP_FRAME_WIDTH)))));
}
else{
w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
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;
while(gRun) {
cap >> frame;
if(!frame.data)
break;
if(resize){
hsize = int((float(frame.rows)*float(basewidth/float(frame.cols))));
cv::resize(frame, frame, cv::Size(basewidth, hsize));
}
height = frame.rows;
width = frame.cols;
//inference
segNN.updateOriginal(frame, true);
if(show)
segNN.draw();
if(SAVE_RESULT)
resultVideo << segNN.segmented[0];
}
}
std::cout<<"segmentation end\n";
double mean = 0, mean_pre = 0, mean_post = 0;
std::cout<<COL_GREENB<<"\n\nTime stats for size ["<<width<<","<<height<<"] :\n";
for(int i=0; i<segNN.stats.size(); i++) mean += segNN.stats[i]; mean /= segNN.stats.size();
for(int i=0; i<segNN.stats_pre.size(); i++) mean_pre += segNN.stats_pre[i]; mean_pre /= segNN.stats_pre.size();
for(int i=0; i<segNN.stats_post.size(); i++) mean_post += segNN.stats_post[i]; mean_post /= segNN.stats_post.size();
std::cout<<"Avg pre:\t"<<mean_pre<<" ms\t"<<1000/(mean_pre)<<" FPS\n";
std::cout<<"Avg inf:\t"<<mean<<" ms\t"<<1000/(mean)<<" FPS\n";
std::cout<<"Avg post:\t"<<mean_post<<" ms\t"<<1000/(mean_post)<<" FPS\n\n";
std::cout<<"Avg tot:\t"<<(mean_pre + mean_post + mean) <<" ms\t"<<1000/((mean_pre + mean_post + mean))<<" FPS\n"<<COL_END;
return 0;
}