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Refactoring & documentation

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-04-08 11:20:48 +02:00
parent 326c7e0940
commit ae1d8cd9e6
9 changed files with 133 additions and 168 deletions
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demo/demo/map.cpp
+1 -1
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@@ -34,7 +34,7 @@ int main(int argc, char *argv[])
bool show = false;
bool write_dets = false;
bool write_res_on_file = true;
int n_images = 50;
int n_images = 5000;
bool verbose;
int classes, map_points, map_levels;
include/tkDNN/DetectionNN.h
+8 -13
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@@ -24,12 +24,6 @@
namespace tk { namespace dnn {
enum networkType_t{
NETWORK_YOLO3,
NETWORK_MOBILENETSSDLITE,
NETWORK_CENTERNET
};
class DetectionNN {
protected:
@@ -52,7 +46,7 @@ class DetectionNN {
/**
* This method preprocess the image, before feeding it to the NN.
*
* @param original frame to adapt for inference.
* @param frame original frame to adapt for inference.
*/
virtual void preprocess(cv::Mat &frame) = 0;
@@ -78,7 +72,8 @@ class DetectionNN {
* Method used to inialize the class, allocate memory and compute
* needed data.
*
* @param path to the rt file og the NN.
* @param tensor_path path to the rt file og the NN.
* @param n_classes number of classes for the given dataset.
* @return true if everything is correct, false otherwise.
*/
virtual bool init(const std::string& tensor_path, const int n_classes=80) = 0;
@@ -86,9 +81,10 @@ class DetectionNN {
/**
* This method performs the whole detection of the NN.
*
* @param frame to run detection on.
* @param if set to true, preprocess, inference and postprocess times
* @param frame frame to run detection on.
* @param save_times if set to true, preprocess, inference and postprocess times
* are saved on a csv file, otherwise not.
* @param times pointer to the output stream where to write times
*/
void update(cv::Mat &frame, bool save_times=false, std::ofstream *times=nullptr){
if(!frame.data)
@@ -129,11 +125,10 @@ class DetectionNN {
/**
* Method to draw boundixg boxes and labels on a frame.
*
* @param orginal frame to draw bounding box on.
* @param frame orginal frame to draw bounding box on.
* @return frame with boundig boxes.
*/
cv::Mat draw(cv::Mat &frame)
{
cv::Mat draw(cv::Mat &frame) {
tk::dnn::box b;
int x0, w, x1, y0, h, y1;
int objClass;
include/tkDNN/evaluation.h
+74 -22
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@@ -8,20 +8,9 @@
#include <yaml-cpp/yaml.h>
#include "tkdnn.h"
#include "BoundingBox.h"
namespace tk { namespace dnn {
struct BoundingBox : public tk::dnn::box
{
friend std::ostream& operator<<(std::ostream& os, const BoundingBox& bb);
int uniqueTruthIndex = -1;
int truthFlag = 0;
float maxIoU = 0;
void clear();
};
std::ostream& operator<<(std::ostream& os, const BoundingBox& bb);
bool boxComparison (const BoundingBox& a,const BoundingBox& b) ;
struct Frame
{
@@ -42,19 +31,82 @@ struct PR
void print();
};
float boxOverlap(float x1, float w1, float x2, float w2);
float boxIntersection(const BoundingBox &a, const BoundingBox &b);
float boxUnion(const BoundingBox &a, const BoundingBox &b);
float boxIoU(const BoundingBox &a, const BoundingBox &b);
void readmAPParams( char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose);
void readmAPParams(char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose);
/**
* This method computes the mean Average Precision for a set of detections and
* groundtruths. It returns the mAP for a given IoU threshold, and a given
* confidence threshold over all the classes.
*
* @param images collection of frames on which to compute the metrics
* @param classes number of classes of the considered dataset
* @param IoU_thresh threshold used to compute Intersection over Union
* @param conf_thresh threshold used to filter bounding boxes based on their
* confidence (or probability)
* @param map_points number of point used to compute the mAP. if 0 is given,
* all the recall levels are evaluated, otherwise only
* map_point recall levels are used. For COCO evaluation
* 101 points are used.
* @param verbose is set to true, prints on screen additional info
*
* @return mAP computed
*/
double computeMap( std::vector<Frame> &images,const int classes,
const float IoU_thresh, const float conf_thresh=0.3,
const int map_points=101, const bool verbose=false);
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh=0.3, const int map_points=101, const bool verbose=false);
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false, const bool write_on_file = false, std::string net = "");
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false, const bool write_on_file=false, std::string net="");
/**
* This method computes the mean Average Precision for a set of detections and
* groundtruths on several IoU thresholds. It is used to compute, for example,
* the most used metric in Object Detection, namely the mAP 0.5:0.95, which is
* the average among the mAP for IoU level from 0.5 to 0.95 with a step of 0.05.
*
* @param images collection of frames on which to compute the metrics
* @param classes number of classes of the considered dataset
* @param IoU_thresh starting threshold used to compute Intersection over Union
* @param conf_thresh threshold used to filter bounding boxes based on their
* confidence (or probability)
* @param map_points number of point used to compute the mAP. if 0 is given,
* all the recall levels are evaluated, otherwise only
* map_point recall levels are used. For COCO evaluation
* 101 points are used.
* @param map_step step used to increment IoU theshold
* @param map_levels number of IoU step to perform
* @param verbose is set to true, prints on screen additional info
* @param write_on_file if set to true, the results produced by this function
* are written on file
* @param net name of the considerd neural network
*
* @return mAP IoU_tresh:IoU_tresh+map_step*map_levels (e.g. mAP 0.5:0.95 when
* map_step=0.05 and map_levels=10)
*/
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
const float i_IoU_thresh=0.5, const float conf_thresh=0.3,
const int map_points=101, const float map_step=0.05,
const int map_levels=10, const bool verbose=false,
const bool write_on_file = false, std::string net = "");
/**
* This method computes the numper of True Positive (TP), False Positive (FP),
* False Negative (FN), precision, recall and f1-score.
* Those values are computer over all the detections, over all the classes.
*
* @param images collection of frames on which to compute the metrics
* @param classes number of classes of the considered dataset
* @param IoU_thresh threshold used to compute Intersection over Union
* @param conf_thresh threshold used to filter bounding boxes based on their
* confidence (or probability)
* @param verbose is set to true, prints on screen additional info
* @param write_on_file if set to true, the results produced by this function
* are written on file
* @param net name of the considerd neural network
*/
void computeTPFPFN( std::vector<Frame> &images,const int classes,
const float IoU_thresh=0.5, const float conf_thresh=0.3,
bool verbose=false, const bool write_on_file=false,
std::string net="");
}}
#endif /*EVALUATION_H*/
src/CenternetDetection.cpp
+3 -6
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@@ -3,8 +3,7 @@
namespace tk { namespace dnn {
bool CenternetDetection::init(const std::string& tensor_path, const int n_classes)
{
bool CenternetDetection::init(const std::string& tensor_path, const int n_classes){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
classes = n_classes;
@@ -121,8 +120,7 @@ bool CenternetDetection::init(const std::string& tensor_path, const int n_classe
}
void CenternetDetection::preprocess(cv::Mat &frame)
{
void CenternetDetection::preprocess(cv::Mat &frame){
// -----------------------------------pre-process ------------------------------------------
// auto start_t = std::chrono::steady_clock::now();
@@ -262,8 +260,7 @@ void CenternetDetection::preprocess(cv::Mat &frame)
#endif
}
void CenternetDetection::postprocess()
{
void CenternetDetection::postprocess(){
dnnType *rt_out[4];
rt_out[0] = (dnnType *)netRT->buffersRT[1];
rt_out[1] = (dnnType *)netRT->buffersRT[2];
src/MobilenetDetection.cpp
+6 -12
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@@ -6,8 +6,7 @@ bool boxProbCmp(const tk::dnn::box &a, const tk::dnn::box &b){
namespace tk{ namespace dnn{
void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp)
{
void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp){
nPriors = 0;
for (int i = 0; i < n_specs; i++){
nPriors += specs[i].featureSize * specs[i].featureSize * 6;
@@ -86,8 +85,7 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
}
}
void MobilenetDetection::convert_locatios_to_boxes_and_center()
{
void MobilenetDetection::convert_locatios_to_boxes_and_center(){
float cur_x, cur_y;
for (int i = 0; i < nPriors; i++){
locations_h[i * N_COORDS + 0] = locations_h[i * N_COORDS + 0] * centerVariance * priors[i * N_COORDS + 2] + priors[i * N_COORDS + 0];
@@ -105,8 +103,7 @@ void MobilenetDetection::convert_locatios_to_boxes_and_center()
}
}
float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b)
{
float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b){
float max_x = a.x > b.x ? a.x : b.x;
float max_y = a.y > b.y ? a.y : b.y;
float min_w = a.w < b.w ? a.w : b.w;
@@ -129,8 +126,7 @@ 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)
{
bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
imageSize = netRT->input_dim.h;
@@ -207,8 +203,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
return 1;
}
void MobilenetDetection::preprocess(cv::Mat &frame)
{
void MobilenetDetection::preprocess(cv::Mat &frame){
#ifdef OPENCV_CUDACONTRIB
//move original image on GPU
cv::cuda::GpuMat orig_img, frame_nomean;
@@ -243,8 +238,7 @@ void MobilenetDetection::preprocess(cv::Mat &frame)
#endif
}
void MobilenetDetection::postprocess()
{
void MobilenetDetection::postprocess(){
//get confidences and locations_h
dnnType *rt_out[2];
rt_out[0] = (dnnType *)netRT->buffersRT[3];
src/Reshape.cpp
+1 -4
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@@ -24,11 +24,8 @@ Reshape::~Reshape() {
dnnType* Reshape::infer(dataDim_t &dim, dnnType* srcData) {
//transpose per channel
//just copies the data and changes the output dim
checkCuda( cudaMemcpy(dstData, srcData, dim.n*dim.c*dim.h*dim.w*sizeof(dnnType), cudaMemcpyDeviceToDevice));
//update data dimensions
dim = output_dim;
return dstData;
src/Yolo3Detection.cpp
+3 -6
View File
@@ -48,8 +48,7 @@ bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes) {
return true;
}
void Yolo3Detection::preprocess(cv::Mat &frame)
{
void Yolo3Detection::preprocess(cv::Mat &frame){
#ifdef OPENCV_CUDACONTRIB
cv::cuda::GpuMat orig_img, img_resized;
orig_img = cv::cuda::GpuMat(frame);
@@ -84,8 +83,7 @@ void Yolo3Detection::preprocess(cv::Mat &frame)
#endif
}
void Yolo3Detection::postprocess()
{
void Yolo3Detection::postprocess(){
//get yolo outputs
dnnType *rt_out[netRT->pluginFactory->n_yolos];
for(int i=0; i<netRT->pluginFactory->n_yolos; i++) {
@@ -140,8 +138,7 @@ void Yolo3Detection::postprocess()
}
tk::dnn::Yolo* Yolo3Detection::getYoloLayer(int n)
{
tk::dnn::Yolo* Yolo3Detection::getYoloLayer(int n) {
if(n<3)
return yolo[n];
else
src/evaluation.cpp
+29 -88
View File
@@ -3,29 +3,7 @@
namespace tk { namespace dnn {
void BoundingBox::clear()
{
uniqueTruthIndex = -1;
truthFlag = 0;
maxIoU = 0;
}
bool boxComparison (const BoundingBox& a,const BoundingBox& b)
{
return (a.prob>b.prob);
}
std::ostream& operator<<(std::ostream& os, const BoundingBox& bb)
{
os <<"w: "<< bb.w << ", h: "<< bb.h << ", x: "<< bb.x << ", y: "<< bb.y <<
", cat: "<< bb.cl << ", conf: "<< bb.prob<< ", truth: "<<
bb.truthFlag<< ", assignedGT: "<< bb.uniqueTruthIndex<<
", maxIoU: "<< bb.maxIoU<<"\n";
return os;
}
void Frame::print() const
{
void Frame::print() const{
std::cout<<"labels filename: "<<lFilename<<std::endl;
std::cout<<"image filename: "<<iFilename<<std::endl;
std::cout<<"GT: "<<std::endl;
@@ -34,52 +12,13 @@ void Frame::print() const
for(auto d: det) std::cout<<d;
}
void PR::print()
{
void PR::print(){
std::cout<<"precision: "<<precision<<" recall: "<<recall<<" tp: "<<tp<<" fp:"<<fp<<" fn:"<<fn<<std::endl;
}
float boxOverlap(float x1, float w1, float x2, float w2)
{
float l1 = x1 - w1/2;
float l2 = x2 - w2/2;
float left = l1 > l2 ? l1 : l2;
float r1 = x1 + w1/2;
float r2 = x2 + w2/2;
float right = r1 < r2 ? r1 : r2;
return right - left;
}
float boxIntersection(const BoundingBox &a, const BoundingBox &b)
{
float w = boxOverlap(a.x, a.w, b.x, b.w);
float h = boxOverlap(a.y, a.h, b.y, b.h);
if(w < 0 || h < 0)
return 0;
float area = w*h;
return area;
}
float boxUnion(const BoundingBox &a, const BoundingBox &b)
{
float i = boxIntersection(a, b);
float u = a.w*a.h + b.w*b.h - i;
return u;
}
float boxIoU(const BoundingBox &a, const BoundingBox &b)
{
float I = boxIntersection(a, b);
float U = boxUnion(a, b);
if (I == 0 || U == 0)
return 0;
return I / U;
}
void readmAPParams(char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose)
{
void readmAPParams( char* config_filename, int& classes, int& map_points,
int& map_levels, float& map_step, float& IoU_thresh,
float& conf_thresh, bool& verbose) {
YAML::Node config = YAML::LoadFile(config_filename);
classes = config["classes"].as<int>();
map_points = config["map_points"].as<int>();
@@ -88,12 +27,12 @@ void readmAPParams(char* config_filename, int& classes, int& map_points,
IoU_thresh = config["IoU_thresh"].as<float>();
conf_thresh = config["conf_thresh"].as<float>();
verbose = config["verbose"].as<bool>();
}
/* Credits to https://github.com/AlexeyAB/darknet/blob/master/src/detector.c*/
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, const int map_points, const bool verbose)
{
double computeMap( std::vector<Frame> &images,const int classes,
const float IoU_thresh, const float conf_thresh,
const int map_points, const bool verbose) {
if(verbose)
for(auto img:images)
img.print();
@@ -132,15 +71,13 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
float maxIoU = 0;
int truth_index = -1;
for(size_t j=0; j<img.gt.size(); j++){
float currentIoU = boxIoU(img.det[i], img.gt[j]);
float currentIoU = img.det[i].IoU(img.gt[j]);
if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl){
maxIoU = currentIoU;
truth_index = j;
}
}
// std::cout<<"det i:"<<i<<" maxIoU:"<<maxIoU<<" tIndex:"<<truth_index<<std::endl;
if(truth_index > -1 && maxIoU > IoU_thresh){
// std::cout<<"(INSIDE) IoU thresh:"<<IoU_thresh<<" maxIoU:"<<maxIoU<<" maxIoU > IoU_thresh:"<<(maxIoU > IoU_thresh)<<std::endl;
img.det[i].uniqueTruthIndex = truth_index + gt_checked;
img.det[i].truthFlag = 1;
img.det[i].maxIoU = maxIoU;
@@ -262,8 +199,11 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
return mean_average_precision;
}
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose, const bool write_on_file, std::string net)
{
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
const float i_IoU_thresh, const float conf_thresh,
const int map_points, const float map_step,
const int map_levels, const bool verbose,
const bool write_on_file, std::string net) {
std::ofstream out_file;
if(write_on_file){
out_file.open("map.csv", std::ios_base::app);
@@ -272,15 +212,18 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
double AP = 0, cur_AP = 0;
float IoU_thresh = i_IoU_thresh;
for(int i=0; i<map_levels; ++i){
//clear detection-grounthuth matching
for(auto& img:images)
for(auto & d:img.det)
d.clear();
//compute mAP for the new IoU threshold
cur_AP = computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
if(write_on_file)
out_file<<cur_AP<<";";
AP += cur_AP;
IoU_thresh +=map_step;
if(write_on_file)
out_file<<cur_AP<<";";
AP += cur_AP;
IoU_thresh +=map_step;
}
AP/=map_levels;
@@ -291,8 +234,9 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const f
return AP;
}
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose, const bool write_on_file, std::string net)
{
void computeTPFPFN( std::vector<Frame> &images,const int classes,
const float IoU_thresh, const float conf_thresh,
bool verbose, const bool write_on_file, std::string net) {
std::ofstream out_file;
if(write_on_file){
@@ -304,11 +248,10 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
std::vector<int> dets_classes_count(classes,0);
std::vector<PR> pr(classes);
//compute TP, FP, FN for each image, for each class
for(auto &img:images){
for(auto& tc: truth_classes_count)
tc = 0;
for(auto& dc: dets_classes_count)
dc = 0;
for(auto& tc: truth_classes_count) tc = 0;
for(auto& dc: dets_classes_count) dc = 0;
std::vector<bool> det_assigned(img.det.size(), false);
for(size_t j=0; j<img.gt.size(); j++){
@@ -317,7 +260,7 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
int det_index = -1;
for(size_t i=0; i<img.det.size(); i++){
if(img.det[i].prob > conf_thresh){
float currentIoU = boxIoU(img.det[i], img.gt[j]);
float currentIoU = img.det[i].IoU(img.gt[j]);
if(currentIoU > maxIoU && img.det[i].cl == img.gt[j].cl && !det_assigned[i]){
maxIoU = currentIoU;
det_index = i;
@@ -344,16 +287,14 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
}
}
//count all TP, FP, FN and compute precsion, recall and f1-score
double avg_precision = 0, avg_recall = 0, f1_score = 0;
int TP = 0, FP = 0, FN = 0;
for(size_t i=0; i<classes; i++){
pr[i].precision = (pr[i].tp + pr[i].fp) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fp) : 0;
pr[i].recall = (pr[i].tp + pr[i].fn) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fn) : 0;
if(verbose)
std::cout<<"Class "<<i<<"\tTP: "<<pr[i].tp<<"\tFP: "<<pr[i].fp<<"\tFN: "<<pr[i].fn<<"\tprecision: "<<pr[i].precision<<"\trecall: "<<pr[i].recall<<std::endl;
// std::cout<<i<<"\t"<<pr[i].tp<<"\t"<<pr[i].fp<<"\t"<<pr[i].fn<<"\t"<<pr[i].precision<<"\t"<<pr[i].recall<<std::endl;
avg_precision += pr[i].precision;
avg_recall += pr[i].recall;
src/utils.cpp
+8 -16
View File
@@ -20,10 +20,8 @@ bool fileExist(const char *fname) {
return true;
}
void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string& test_folder, const std::string& weights_url)
{
if(!fileExist(input_bin.c_str()))
{
void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string& test_folder, const std::string& weights_url){
if(!fileExist(input_bin.c_str())){
std::string wget_cmd = "wget " + weights_url + " -O " + test_folder + "/weights.zip";
std::string unzip_cmd = "unzip " + test_folder + "/weights.zip -d" + test_folder;
std::string rm_cmd = "rm " + test_folder + "/weights.zip";
@@ -34,8 +32,7 @@ void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string
}
void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek, bool skipLoad)
{
void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek, bool skipLoad){
int size_b = size*sizeof(dnnType);
*data_h = new dnnType[size];
@@ -65,8 +62,7 @@ void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** dat
checkCuda( cudaMemcpy(*data_d, *data_h, size_b, cudaMemcpyHostToDevice) );
}
void printDeviceVector(int size, dnnType* vec_d, bool device)
{
void printDeviceVector(int size, dnnType* vec_d, bool device){
dnnType *vec;
if(device) {
vec = new dnnType[size];
@@ -129,8 +125,7 @@ int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device) {
return diffs;
}
float getColor(const int c, const int x, const int max)
{
float getColor(const int c, const int x, const int max){
float _colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
float ratio = ((float)x/max)*5;
int i = floor(ratio);
@@ -141,8 +136,7 @@ float getColor(const int c, const int x, const int max)
}
void resize(int size, dnnType **data)
{
void resize(int size, dnnType **data){
if (*data != NULL)
checkCuda( cudaFree(*data) );
checkCuda( cudaMalloc(data, size*sizeof(dnnType)) );
@@ -170,8 +164,7 @@ void matrixMulAdd( cublasHandle_t handle, dnnType* srcData, dnnType* dstData,
}
void getMemUsage(double& vm_usage_kb, double& resident_set_kb)
{
void getMemUsage(double& vm_usage_kb, double& resident_set_kb){
using std::ios_base;
using std::ifstream;
using std::string;
@@ -202,8 +195,7 @@ void getMemUsage(double& vm_usage_kb, double& resident_set_kb)
resident_set_kb = rss * page_size_kb;
}
void removePathAndExtension(const std::string &full_string, std::string &name)
{
void removePathAndExtension(const std::string &full_string, std::string &name){
name = full_string;
std::string tmp_str = full_string;
std::string slash = "/";