Added a version of test_object_detection_function() for the DNN based MMOD detector.

dlib/dnn.h

@@ -19,6 +19,7 @@
 #include "dnn/cpu_dlib.h"
 #include "dnn/tensor_tools.h"
 #include "dnn/utilities.h"
+#include "dnn/validation.h"
 
 #endif // DLIB_DNn_
 

dlib/dnn/validation.h

+// 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 "validation_abstract.h"
+#include "../svm/cross_validate_object_detection_trainer.h"
+#include "layers.h"
+
+namespace dlib
+{
+
+    template <
+        typename SUBNET,
+        typename image_array_type
+        >
+    const matrix<double,1,3> test_object_detection_function (
+        loss_binary_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
+    )
+    {
+        // 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);
+
+
+            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
+                    truth_boxes.push_back(full_object_detection(b.rect));
+            }
+            for (auto&& b : hits)
+                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);
+            total_true_targets += truth_boxes.size();
+        }
+
+        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_
+

dlib/dnn/validation_abstract.h

+// Copyright (C) 2016  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_DNn_VALIDATION_ABSTRACT_H_
+#ifdef DLIB_DNn_VALIDATION_ABSTRACT_H_
+
+#include "../svm/cross_validate_object_detection_trainer_abstract.h"
+#include "layers_abstract.h"
+
+namespace dlib
+{
+
+    template <
+        typename SUBNET,
+        typename image_array_type
+        >
+    const matrix<double,1,3> test_object_detection_function (
+        loss_binary_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
+    );
+    /*!
+        requires
+            - is_learning_problem(images,truth_dets)
+            - image_array_type must be an implementation of dlib/array/array_kernel_abstract.h 
+              and it must contain objects which can be accepted by detector().
+        ensures
+            - This function is just like the test_object_detection_function() for
+              object_detector's except it runs on CNNs that use loss_binary_mmod.
+            - Tests the given detector against the supplied object detection problem and
+              returns the precision, recall, and average precision.  Note that the task is
+              to predict, for each images[i], the set of object locations given by
+              truth_dets[i].  Additionally, any detections on image[i] that match a box in
+              truth_dets[i] that are marked ignore are ignored.  That is, detections
+              matching an ignore box do not count as a false alarm and similarly if any
+              ignored box in truth_dets goes undetected it does not count as a missed
+              detection.  
+            - In particular, returns a matrix M such that:  
+                - M(0) == the precision of the detector object.  This is a number
+                  in the range [0,1] which measures the fraction of detector outputs
+                  which correspond to a real target.  A value of 1 means the detector
+                  never produces any false alarms while a value of 0 means it only
+                  produces false alarms.
+                - M(1) == the recall of the detector object.  This is a number in the
+                  range [0,1] which measures the fraction of targets found by the detector.
+                  A value of 1 means the detector found all the non-ignore targets in
+                  truth_dets while a value of 0 means the detector didn't locate any of the
+                  targets.
+                - M(2) == the average precision of the detector object.  This is a number
+                  in the range [0,1] which measures the overall quality of the detector.
+                  We compute this by taking all the detections output by the detector and
+                  ordering them in descending order of their detection scores.  Then we use
+                  the average_precision() routine to score the ranked listing and store the
+                  output into M(2).
+                - This function considers a detector output D to match a rectangle T if and
+                  only if overlap_tester(T,D) returns true. 
+                - Note that you can use the adjust_threshold argument to raise or lower the
+                  detection threshold.  This value is passed into the identically named
+                  argument to the detector object and therefore influences the number of
+                  output detections.  It can be useful, for example, to lower the detection
+                  threshold because it results in more detections being output by the
+                  detector, and therefore provides more information in the ranking,
+                  possibly raising the average precision.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_DNn_VALIDATION_ABSTRACT_H_
+