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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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include/tkDNN/DetectionNN.h
+8 -13
View File
@@ -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
View File
@@ -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*/