Moved all the test_object_detection_function() specs into one file so they are

organized in the HTML documentation.

dlib/dnn/validation.h

@@ -3,7 +3,7 @@
 #ifndef DLIB_DNn_VALIDATION_H_
 #define DLIB_DNn_VALIDATION_H_
 
-#include "validation_abstract.h"
+#include "../svm/cross_validate_object_detection_trainer_abstract.h"
 #include "../svm/cross_validate_object_detection_trainer.h"
 #include "layers.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_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_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_
-

dlib/svm/cross_validate_object_detection_trainer_abstract.h

@@ -7,6 +7,7 @@
 #include "../matrix.h"
 #include "../geometry.h"
 #include "../image_processing/full_object_detection_abstract.h"
+#include "../dnn/layers_abstract.h"
 
 namespace dlib
 {
@@ -130,6 +131,63 @@ namespace dlib
               given arguments and an empty set of ignore rectangles and returns the results.
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    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
+    );
+    /*!
+        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_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.
+    !*/
+
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------