mmr/tkDNN
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memory-fix

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This commit is contained in:
mohit
2021-06-24 20:08:33 +05:30
parent 961e1c236e
commit ed41be6358
12 changed files with 162 additions and 485 deletions
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handler.cpp
+123 -61
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@@ -26,6 +26,7 @@
//#include "CenternetDetection.h"
//#include "MobilenetDetection.h"
#include "evaluation.h"
#include "tkdnn.h"
#include <chrono>
#include <cstdint>
#include <iostream>
@@ -35,6 +36,12 @@ using namespace cv;
#include <iostream>
#include <string>
#include "image.h"
void free_image(image m)
{
if(m.data){
free(m.data);
}
}
image make_empty_image(int w, int h, int c)
{
image out;
@@ -106,21 +113,38 @@ cv::Mat image_to_mat(image img)
int h=0;
int w=0;
int channels;
char ntype = 'y';
const char *config_filename = "../demo/config.yaml";
const char * net = "../demo/yolo4_fp32.rt";
const char *config_filename = "../config/config.yaml";
const char * net1="../config/yolo4x_fp16.rt";
const char * net2="../config/yolo4x_fp16.rt";
const char * net3="../config/yolo4x_fp16.rt";
const char * net4="../config/yolo4x_fp16.rt";
const char * net5="../config/yolo4x_fp16.rt";
int classes1 , classes2 , classes3 , classes4 , classes5,len;
char * img_data;
bool show = false;
bool verbose;
int classes, map_points, map_levels ,len;
string ustring;
float map_step, IoU_thresh, conf_thresh;
tk::dnn::Yolo3Detection yolo;
tk::dnn::DetectionNN *detNN;
int n_classes = classes;
float conf_thresh1 , conf_thresh2 , conf_thresh3 , conf_thresh4 , conf_thresh5;
tk::dnn::Yolo3Detection yolo1;
tk::dnn::DetectionNN *detNN1;
tk::dnn::Yolo3Detection yolo2;
tk::dnn::DetectionNN *detNN2;
tk::dnn::Yolo3Detection yolo3;
tk::dnn::DetectionNN *detNN3;
tk::dnn::Yolo3Detection yolo4;
tk::dnn::DetectionNN *detNN4;
unsigned char * sockData;
std::vector<cv::Mat> batch_frames;
std::vector<cv::Mat> batch_dnn_input;
std::vector<std::string> classesNames1;
std::vector<std::string> classesNames2;
std::vector<std::string> classesNames3;
std::vector<std::string> classesNames4;
std::vector<tk::dnn::Frame> images;
std::vector<tk::dnn::box> detected_bbox;
tk::dnn::Frame f;
std::vector<tk::dnn::box> detected_bbox1;
std::vector<tk::dnn::box> detected_bbox2;
std::vector<tk::dnn::box> detected_bbox3;
std::vector<tk::dnn::box> detected_bbox4;
//read parametersi
handler::handler(utility::string_t url):m_listener(url)
{
@@ -132,18 +156,21 @@ string name_from_path(string path)
{
return path.substr(path.find_last_of("/\\")+1);
}
void handler::init_bag(){tk::dnn::readmAPParams(config_filename, classes, map_points, map_levels, map_step,
IoU_thresh, conf_thresh, verbose);
void handler::init_bag(){tk::dnn::readmAPParams(config_filename, classes1,conf_thresh1, classes2,conf_thresh2
, classes3,conf_thresh3, classes4,conf_thresh4,classes5,conf_thresh5);
//extract network name from rt path
std::string net_name;
removePathAndExtension(net, net_name);
std::cout<<"Network: "<<net_name<<std::endl;
int n_classes = classes;
detNN = &yolo;
detNN->init(net, n_classes, 1, conf_thresh);
detNN1 = &yolo1;
detNN1->init(net1, classes1, 1, conf_thresh1);
classesNames1=detNN1-> classesNames;
detNN2 = &yolo2;
detNN2->init(net2, classes2, 1, conf_thresh2);
classesNames2=detNN2-> classesNames;
detNN3 = &yolo3;
detNN3->init(net3, classes3, 1, conf_thresh4);
classesNames3=detNN3-> classesNames;
detNN4 = &yolo4;
detNN4->init(net4, classes4, 1, conf_thresh4);
classesNames4=detNN4-> classesNames;
return;}
@@ -168,52 +195,89 @@ string name_from_path(string path)
}).wait();
//printSize(ustring);
BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << "Detection Started";
unsigned char * sockData = (unsigned char *)ustring.c_str();
unsigned char *data = stbi_load_from_memory(sockData, len, &w, &h, &channels, 0);
im = load_image_file(data, channels, 0, 0, w, h);
// free(data);
//free(sockData);
unsigned char *idata;
sockData = (unsigned char *)ustring.c_str();
idata = stbi_load_from_memory(sockData, len, &w, &h, &channels, 0);
im = load_image_file(idata, channels, 0, 0, w, h);
batch_dnn_input.clear();
batch_frames.clear();
gray=image_to_mat(im);
// free(im);
cv::Mat in[] = {gray, gray,gray};
cv::merge(in, 3, frame);
std::vector<cv::Mat> batch_frames;
batch_frames.push_back(frame);
int height = frame.rows;
int width = frame.cols;
std::vector<cv::Mat> batch_dnn_input;
batch_dnn_input.push_back(frame.clone());
//inference
detected_bbox.clear();
detNN->update(batch_dnn_input,1);
detNN->draw(batch_frames);
detected_bbox = detNN->detected;
try{
json::value response;
vector<json::value> jsonArray;
// 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);
json::value detection;
detection["label"] = json::value::string(string("battery"));
detection["x"] = json::value::number(d.x);
detection["y"] = json::value::number(d.y);
detection["w"] = json::value::number(d.w);
detection["h"] = json::value::number(d.h);
detection["prob"] = json::value::number(b.prob);
detected_bbox1.clear();
detNN1->update(batch_dnn_input,1);
std::cout<<batch_dnn_input.rows<<" "<<batch_dnn_input.cols<<"\n";
detected_bbox1 = detNN1->detected;
for(auto d1:detected_bbox1){
json::value detection;
std::cout<<"1"<<" "<< d1.cl << " "<< d1.prob << " "<< d1.x << " "<< d1.y << " "<< d1.w << " "<< d1.h <<"\n";
detection["label"] = json::value::string(classesNames1[d1.cl]);
detection["x"] = json::value::number(d1.x);
detection["y"] = json::value::number(d1.y);
detection["w"] = json::value::number(d1.w);
detection["h"] = json::value::number(d1.h);
detection["prob"] = json::value::number(d1.prob);
jsonArray.push_back(detection);
std::cout<< d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
}
detected_bbox2.clear();
detNN2->update(batch_dnn_input,1);
std::cout<<batch_dnn_input.rows<<" "<<batch_dnn_input.cols<<"\n";
detected_bbox2 = detNN2->detected;
for(auto d2:detected_bbox2){
std::cout<<"2"<<" "<< d2.cl << " "<< d2.prob << " "<< d2.x << " "<< d2.y << " "<< d2.w << " "<< d2.h <<"\n";
json::value detection;
detection["label"] = json::value::string(classesNames2[d2.cl]);
detection["x"] = json::value::number(d2.x);
detection["y"] = json::value::number(d2.y);
detection["w"] = json::value::number(d2.w);
detection["h"] = json::value::number(d2.h);
detection["prob"] = json::value::number(d2.prob);
jsonArray.push_back(detection);
}
detected_bbox3.clear();
detNN3->update(batch_dnn_input,1);
detected_bbox3 = detNN3->detected;
for(auto d3:detected_bbox3){
std::cout<< "3"<<" "<<d3.cl << " "<< d3.prob << " "<< d3.x << " "<< d3.y << " "<< d3.w << " "<< d3.h <<"\n";
json::value detection;
detection["label"] = json::value::string(classesNames3[d3.cl]);
detection["x"] = json::value::number(d3.x);
detection["y"] = json::value::number(d3.y);
detection["w"] = json::value::number(d3.w);
detection["h"] = json::value::number(d3.h);
detection["prob"] = json::value::number(d3.prob);
jsonArray.push_back(detection);
}
detected_bbox4.clear();
detNN4->update(batch_dnn_input,1);
detected_bbox4 = detNN4->detected;
for(auto d4:detected_bbox4){
std::cout<<"4"<<" "<<d4.cl<< " "<< d4.prob << " "<< d4.x << " "<< d4.y << " "<< d4.w << " "<< d4.h <<"\n";
json::value detection;
detection["label"] = json::value::string(classesNames4[d4.cl]);
detection["x"] = json::value::number(d4.x);
detection["y"] = json::value::number(d4.y);
detection["w"] = json::value::number(d4.w);
detection["h"] = json::value::number(d4.h);
detection["prob"] = json::value::number(d4.prob);
jsonArray.push_back(detection);
}
try{
response["detections"] = json::value::array(jsonArray); //JSON Response
// free(jsonArray);
request.reply(status_codes::OK,response.serialize());
@@ -224,11 +288,9 @@ string name_from_path(string path)
BOOST_LOG_TRIVIAL(error) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << e.what();
request.reply(status_codes::BadRequest, e.what());
}
// free(data);
// free(sockData);
frame.release();
gray.release();
// std::cout << timeSinceEpochMillisec() << std::endl;
free(idata);
free_image(im);
return ;
}