1 files changed, 0 insertions, 122 deletions
diff --git a/ml/dlib/dlib/dnn/validation.h b/ml/dlib/dlib/dnn/validation.h
deleted file mode 100644
index c65cb4526..000000000
--- a/ml/dlib/dlib/dnn/validation.h
+++ /dev/null
@@ -1,122 +0,0 @@
-// Copyright (C) 2016  Davis E. King (davis@dlib.net)
-// License: Boost Software License   See LICENSE.txt for the full license.
-#ifndef DLIB_DNn_VALIDATION_H_
-#define DLIB_DNn_VALIDATION_H_
-
-#include "../svm/cross_validate_object_detection_trainer_abstract.h"
-#include "../svm/cross_validate_object_detection_trainer.h"
-#include "layers.h"
-#include <set>
-
-namespace dlib
-{
-    namespace impl
-    {
-        inline std::set<std::string> get_labels (
-            const std::vector<mmod_rect>& rects1,
-            const std::vector<mmod_rect>& rects2
-        )
-        {
-            std::set<std::string> labels;
-            for (auto& rr : rects1)
-                labels.insert(rr.label);
-            for (auto& rr : rects2)
-                labels.insert(rr.label);
-            return labels;
-        }
-    }
-
-    template <
-        typename SUBNET,
-        typename image_array_type
-        >
-    const matrix<double,1,3> test_object_detection_function (
-        loss_mmod<SUBNET>& detector,
-        const image_array_type& images,
-        const std::vector<std::vector<mmod_rect>>& truth_dets,
-        const test_box_overlap& overlap_tester = test_box_overlap(),
-        const double adjust_threshold = 0,
-        const test_box_overlap& overlaps_ignore_tester = test_box_overlap()
-    )
-    {
-        // make sure requires clause is not broken
-        DLIB_CASSERT( is_learning_problem(images,truth_dets) == true , 
-                    "\t matrix test_object_detection_function()"
-                    << "\n\t invalid inputs were given to this function"
-                    << "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
-                    << "\n\t images.size(): " << images.size() 
-                    );
-
-
-
-        double correct_hits = 0;
-        double total_true_targets = 0;
-
-        std::vector<std::pair<double,bool> > all_dets;
-        unsigned long missing_detections = 0;
-
-        resizable_tensor temp;
-
-        for (unsigned long i = 0; i < images.size(); ++i)
-        {
-            std::vector<mmod_rect> hits; 
-            detector.to_tensor(&images[i], &images[i]+1, temp);
-            detector.subnet().forward(temp);
-            detector.loss_details().to_label(temp, detector.subnet(), &hits, adjust_threshold);
-
-
-            for (auto& label : impl::get_labels(truth_dets[i], hits))
-            {
-                std::vector<full_object_detection> truth_boxes;
-                std::vector<rectangle> ignore;
-                std::vector<std::pair<double,rectangle>> boxes;
-                // copy hits and truth_dets into the above three objects
-                for (auto&& b : truth_dets[i])
-                {
-                    if (b.ignore)
-                    {
-                        ignore.push_back(b);
-                    }
-                    else if (b.label == label)
-                    {
-                        truth_boxes.push_back(full_object_detection(b.rect));
-                        ++total_true_targets;
-                    }
-                }
-                for (auto&& b : hits)
-                {
-                    if (b.label == label)
-                        boxes.push_back(std::make_pair(b.detection_confidence, b.rect));
-                }
-
-                correct_hits += impl::number_of_truth_hits(truth_boxes, ignore, boxes, overlap_tester, all_dets, missing_detections, overlaps_ignore_tester);
-            }
-        }
-
-        std::sort(all_dets.rbegin(), all_dets.rend());
-
-        double precision, recall;
-
-        double total_hits = all_dets.size();
-
-        if (total_hits == 0)
-            precision = 1;
-        else
-            precision = correct_hits / total_hits;
-
-        if (total_true_targets == 0)
-            recall = 1;
-        else
-            recall = correct_hits / total_true_targets;
-
-        matrix<double, 1, 3> res;
-        res = precision, recall, average_precision(all_dets, missing_detections);
-        return res;
-    }
-
-// ----------------------------------------------------------------------------------------
-
-}
-
-#endif // DLIB_DNn_VALIDATION_H_
-