mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

Add conf_thresh parameter to detectors, add batches handling in map demo

Browse Source 
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-07-08 09:52:36 +02:00
parent 65e2074dda
commit e7779ad773
10 changed files with 113 additions and 80 deletions
Download Patch File Download Diff File Expand all files Collapse all files
demo/demo/demo.cpp
+2 -1
View File
@@ -69,7 +69,8 @@ int main(int argc, char *argv[]) {
FatalError("Network type not allowed (3rd parameter)\n");
}
detNN->init(net, n_classes, n_batch);
float conf_thresh = 0.3;
detNN->init(net, conf_thresh, n_classes, n_batch);
gRun = true;
demo/demo/map.cpp
+99 -72
View File
@@ -31,6 +31,8 @@ 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;
@@ -53,6 +55,12 @@ 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))
@@ -105,7 +113,7 @@ int main(int argc, char *argv[])
default:
FatalError("Network type not allowed (3rd parameter)\n");
}
detNN->init(net, n_classes);
detNN->init(net, confidence_thresh, n_classes, n_batches);
//read images
std::ifstream all_labels(labels_path);
@@ -118,90 +126,109 @@ 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;
@@ -218,11 +245,11 @@ 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::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);
if(write_res_on_file){
memory<<vm_total/images_done/1024.0<<";"<<rss_total/images_done/1024.0<<"\n";
include/tkDNN/CenternetDetection.h
+1 -1
View File
@@ -73,7 +73,7 @@ public:
CenternetDetection() {};
~CenternetDetection() {};
bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1);
bool init(const std::string& tensor_path, const float conf_threshold, const int n_classes=80, const int n_batches=1);
void preprocess(cv::Mat &frame, const int bi=0);
void postprocess(const int bi=0,const bool mAP=false);
};
include/tkDNN/DetectionNN.h
+1 -1
View File
@@ -84,7 +84,7 @@ class DetectionNN {
* @param n_batches maximum number of batches to use in inference
* @return true if everything is correct, false otherwise.
*/
virtual bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1) = 0;
virtual bool init(const std::string& tensor_path, const float conf_threshold, const int n_classes=80, const int n_batches=1) = 0;
/**
* This method performs the whole detection of the NN.
include/tkDNN/MobilenetDetection.h
+1 -1
View File
@@ -65,7 +65,7 @@ public:
MobilenetDetection() {};
~MobilenetDetection() {};
bool init(const std::string& tensor_path, const int n_classes, const int n_batches=1);
bool init(const std::string& tensor_path, const float conf_threshold, const int n_classes, const int n_batches=1);
void preprocess(cv::Mat &frame, const int bi=0);
void postprocess(const int bi=0,const bool mAP=false);
};
include/tkDNN/Yolo3Detection.h
+1 -1
View File
@@ -24,7 +24,7 @@ public:
Yolo3Detection() {};
~Yolo3Detection() {};
bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1);
bool init(const std::string& tensor_path, const float conf_threshold, const int n_classes=80, const int n_batches=1);
void preprocess(cv::Mat &frame, const int bi=0);
void postprocess(const int bi=0,const bool mAP=false);
};
include/tkDNN/evaluation.h
+2
View File
@@ -18,6 +18,8 @@ struct Frame
std::string iFilename;
std::vector<BoundingBox> gt;
std::vector<BoundingBox> det;
int width;
int height;
void print() const;
};
src/CenternetDetection.cpp
+2 -1
View File
@@ -3,11 +3,12 @@
namespace tk { namespace dnn {
bool CenternetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches){
bool CenternetDetection::init(const std::string& tensor_path, const float conf_threshold, const int n_classes, const int n_batches){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
classes = n_classes;
nBatches = n_batches;
confThreshold = conf_threshold;
dim = netRT->input_dim;
src/MobilenetDetection.cpp
+2 -1
View File
@@ -126,12 +126,13 @@ float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b){
return iou;
}
bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes, const int n_batches){
bool MobilenetDetection::init(const std::string& tensor_path, const float conf_threshold, const int n_classes, const int n_batches){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
imageSize = netRT->input_dim.h;
classes = n_classes;
nBatches = n_batches;
confThreshold = conf_threshold;
SSDSpec specs[N_SSDSPEC];
src/Yolo3Detection.cpp
+2 -1
View File
@@ -3,13 +3,14 @@
namespace tk { namespace dnn {
bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, const int n_batches) {
bool Yolo3Detection::init(const std::string& tensor_path, const float conf_threshold, const int n_classes, const int n_batches) {
//convert network to tensorRT
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
nBatches = n_batches;
confThreshold = conf_threshold;
tk::dnn::dataDim_t idim = netRT->input_dim;
idim.n = nBatches;