micaela
117 lines
4.7 KiB
C++
117 lines
4.7 KiB
C++
#ifndef EVALUATION_H
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#define EVALUATION_H
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#include <iostream>
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#include <vector>
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#include <algorithm>
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#include <yaml-cpp/yaml.h>
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#include "tkdnn.h"
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#include "BoundingBox.h"
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namespace tk { namespace dnn {
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struct Frame
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{
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std::string lFilename;
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std::string iFilename;
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std::vector<BoundingBox> gt;
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std::vector<BoundingBox> det;
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void print() const;
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};
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struct PR
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{
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double precision = 0;
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double recall = 0;
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int tp = 0, fp = 0, fn = 0;
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void print();
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};
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void readmAPParams( const char* config_filename, int& classes, int& map_points,
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int& map_levels, float& map_step, float& IoU_thresh,
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float& conf_thresh, bool& verbose);
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/**
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* This method computes the mean Average Precision for a set of detections and
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* groundtruths. It returns the mAP for a given IoU threshold, and a given
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* confidence threshold over all the classes.
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*
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* @param images collection of frames on which to compute the metrics
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* @param classes number of classes of the considered dataset
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* @param IoU_thresh threshold used to compute Intersection over Union
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* @param conf_thresh threshold used to filter bounding boxes based on their
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* confidence (or probability)
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* @param map_points number of point used to compute the mAP. if 0 is given,
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* all the recall levels are evaluated, otherwise only
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* map_point recall levels are used. For COCO evaluation
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* 101 points are used.
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* @param verbose is set to true, prints on screen additional info
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*
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* @return mAP computed
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*/
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double computeMap( std::vector<Frame> &images,const int classes,
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const float IoU_thresh, const float conf_thresh=0.3,
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const int map_points=101, const bool verbose=false);
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/**
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* This method computes the mean Average Precision for a set of detections and
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* groundtruths on several IoU thresholds. It is used to compute, for example,
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* the most used metric in Object Detection, namely the mAP 0.5:0.95, which is
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* the average among the mAP for IoU level from 0.5 to 0.95 with a step of 0.05.
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*
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* @param images collection of frames on which to compute the metrics
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* @param classes number of classes of the considered dataset
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* @param IoU_thresh starting threshold used to compute Intersection over Union
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* @param conf_thresh threshold used to filter bounding boxes based on their
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* confidence (or probability)
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* @param map_points number of point used to compute the mAP. if 0 is given,
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* all the recall levels are evaluated, otherwise only
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* map_point recall levels are used. For COCO evaluation
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* 101 points are used.
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* @param map_step step used to increment IoU threshold
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* @param map_levels number of IoU step to perform
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* @param verbose is set to true, prints on screen additional info
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* @param write_on_file if set to true, the results produced by this function
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* are written on file
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* @param net name of the considered neural network
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*
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* @return mAP IoU_tresh:IoU_tresh+map_step*map_levels (e.g. mAP 0.5:0.95 when
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* map_step=0.05 and map_levels=10)
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*/
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double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
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const float i_IoU_thresh=0.5, const float conf_thresh=0.3,
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const int map_points=101, const float map_step=0.05,
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const int map_levels=10, const bool verbose=false,
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const bool write_on_file = false, std::string net = "");
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/**
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* This method computes the number of True Positive (TP), False Positive (FP),
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* False Negative (FN), precision, recall and f1-score.
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* Those values are computer over all the detections, over all the classes.
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*
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* @param images collection of frames on which to compute the metrics
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* @param classes number of classes of the considered dataset
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* @param IoU_thresh threshold used to compute Intersection over Union
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* @param conf_thresh threshold used to filter bounding boxes based on their
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* confidence (or probability)
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* @param verbose is set to true, prints on screen additional info
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* @param write_on_file if set to true, the results produced by this function
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* are written on file
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* @param net name of the considered neural network
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*/
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void computeTPFPFN( std::vector<Frame> &images,const int classes,
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const float IoU_thresh=0.5, const float conf_thresh=0.3,
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bool verbose=false, const bool write_on_file=false,
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std::string net="");
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void printJsonCOCOFormat(std::ofstream *out_file, const std::string image_path, std::vector<tk::dnn::box> bbox, const int classes, const int w, const int h);
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}}
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#endif /*EVALUATION_H*/
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