coco
123 lines
4.0 KiB
C++
123 lines
4.0 KiB
C++
// Copyright (C) 2016 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_DNn_VALIDATION_H_
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#define DLIB_DNn_VALIDATION_H_
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#include "../svm/cross_validate_object_detection_trainer_abstract.h"
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#include "../svm/cross_validate_object_detection_trainer.h"
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#include "layers.h"
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#include <set>
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namespace dlib
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{
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namespace impl
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{
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inline std::set<std::string> get_labels (
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const std::vector<mmod_rect>& rects1,
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const std::vector<mmod_rect>& rects2
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)
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{
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std::set<std::string> labels;
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for (auto& rr : rects1)
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labels.insert(rr.label);
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for (auto& rr : rects2)
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labels.insert(rr.label);
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return labels;
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}
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}
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template <
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typename SUBNET,
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typename image_array_type
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>
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const matrix<double,1,3> test_object_detection_function (
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loss_mmod<SUBNET>& detector,
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const image_array_type& images,
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const std::vector<std::vector<mmod_rect>>& truth_dets,
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const test_box_overlap& overlap_tester = test_box_overlap(),
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const double adjust_threshold = 0,
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const test_box_overlap& overlaps_ignore_tester = test_box_overlap()
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)
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{
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// make sure requires clause is not broken
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DLIB_CASSERT( is_learning_problem(images,truth_dets) == true ,
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"\t matrix test_object_detection_function()"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
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<< "\n\t images.size(): " << images.size()
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);
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double correct_hits = 0;
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double total_true_targets = 0;
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std::vector<std::pair<double,bool> > all_dets;
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unsigned long missing_detections = 0;
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resizable_tensor temp;
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for (unsigned long i = 0; i < images.size(); ++i)
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{
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std::vector<mmod_rect> hits;
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detector.to_tensor(&images[i], &images[i]+1, temp);
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detector.subnet().forward(temp);
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detector.loss_details().to_label(temp, detector.subnet(), &hits, adjust_threshold);
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for (auto& label : impl::get_labels(truth_dets[i], hits))
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{
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std::vector<full_object_detection> truth_boxes;
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std::vector<rectangle> ignore;
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std::vector<std::pair<double,rectangle>> boxes;
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// copy hits and truth_dets into the above three objects
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for (auto&& b : truth_dets[i])
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{
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if (b.ignore)
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{
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ignore.push_back(b);
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}
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else if (b.label == label)
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{
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truth_boxes.push_back(full_object_detection(b.rect));
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++total_true_targets;
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}
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}
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for (auto&& b : hits)
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{
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if (b.label == label)
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boxes.push_back(std::make_pair(b.detection_confidence, b.rect));
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}
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correct_hits += impl::number_of_truth_hits(truth_boxes, ignore, boxes, overlap_tester, all_dets, missing_detections, overlaps_ignore_tester);
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}
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}
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std::sort(all_dets.rbegin(), all_dets.rend());
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double precision, recall;
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double total_hits = all_dets.size();
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if (total_hits == 0)
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precision = 1;
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else
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precision = correct_hits / total_hits;
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if (total_true_targets == 0)
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recall = 1;
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else
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recall = correct_hits / total_true_targets;
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matrix<double, 1, 3> res;
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res = precision, recall, average_precision(all_dets, missing_detections);
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return res;
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}
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_DNn_VALIDATION_H_
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