updated docs

docs/docs/linear_algebra.xml

@@ -666,6 +666,7 @@
          <item>angle_between_lines</item> 
          <item>is_convex_quadrilateral</item> 
          <item>find_convex_quadrilateral</item> 
+         <item>polygon_area</item> 
       </section>
 
 
@@ -1366,6 +1367,18 @@
                                  
       </component>
 
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>polygon_area</name>
+         <file>dlib/geometry.h</file>
+         <spec_file link="true">dlib/geometry/vector_abstract.h</spec_file>
+         <description>        
+            When given a set of points defining the vertices of a polygon this routine
+            returns the area of the polygon.
+         </description>
+      </component>
+
    <!-- ************************************************************************* -->
 
       <component>

docs/docs/ml.xml

@@ -51,6 +51,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
          <item>rvm_trainer</item> 
          <item>svm_pegasos</item> 
          <item>train_probabilistic_decision_function</item> 
+         <item>auto_train_rbf_classifier</item> 
       </section>
       <section>
          <name>Multiclass Classification</name>
@@ -1994,6 +1995,23 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
       </component>
       
    <!-- ************************************************************************* -->
+      <component>
+         <name>auto_train_rbf_classifier</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/auto_abstract.h</spec_file>
+         <description>
+            This routine trains a radial basis function SVM on the given binary
+            classification training data.  It uses the <a
+            href="#svm_c_trainer">svm_c_trainer</a> to do this.  It also uses <a
+            href="optimization.html#global_function_search">global_function_search</a> and
+            6-fold cross-validation to automatically determine the best settings of the
+            SVM's hyper parameters.  Therefore, it takes no parameters. You just give it a dataset and it
+            returns a good binary classifier for that dataset.
+         </description>
+      </component>
+      
+   <!-- ************************************************************************* -->
+
 
       <component>
          <name>svm_c_linear_trainer</name>

docs/docs/release_notes.xml

@@ -11,6 +11,55 @@
 <!-- **************************************************************************************  -->
 
 <current>
+New Features and Improvements:
+   - Added a --split-train-test option to imglab.
+   - Added a max runtime option to the oca solver and structural object
+     detection trainer.
+   - Added an option to do translational jittering of the bounding boxes in the
+     shape_predictor_trainer.  Using this option allows you to train
+     shape_predictors that are somewhat more robust against variation in
+     detection box placement. 
+   - Added an optional thread_pool argument to find_max_global() and
+     find_min_global() to allow for parallel execution of objective function calls.
+   - Added auto_train_rbf_classifier(), a tool that trains a RBF-SVM without requiring the user
+     to supply any hyperparameters.
+   - Added polygon_area()
+   - The image view objects now implement the generic image interface.
+   - Updates to the Python API:
+       - Added Python bindings for: extract_image_chip(),
+         extract_image_chips(), center(), get_histogram(), sub_image(),
+         polygon_area(), auto_train_rbf_classifier(), reduced(),
+         translate_rect(), spatially_filter_image(),
+         spatially_filter_image_separable(), num_separable_filters(),
+         threshold_filter_singular_values(), max_point(),
+         max_point_interpolated(), zero_border_pixels().  Also added an option
+         for training nuclear norm regularized HOG detectors.
+       - Added a bunch of new overloads and operators for existing Python types.
+       - Made decision functions more viewable from python.  You can now inspect
+         the contents of the functions rather than them being completely opaque.
+       - Made it so you can call the std::vector&lt;detectors&gt; version of
+         the object_detector constructor from python. So now you can pack
+         multiple detectors into one object via the python API.
+       - Allow batched face recognition for greater performance.
+
+Non-Backwards Compatible Changes:
+
+Bug fixes:
+   - Fixed the point and dpoint operator + doing subtraction in the Python API.
+   - Fixed label_connected_blobs_watershed() not giving contiguous labels as outputs. 
+   - Fixed AVX detection not working correctly on some systems.
+   - Fixed dlib.threshold_image() ignoring the optional thresh argument in the Python API.
+   - Fixed hough_transform::find_pixels_voting_for_lines(). It would sometimes
+     include a pixel multiple times in the output lists.
+   - Made the routines that rotate image datasets use the rectangle_transform
+     instead of the bad old way that only really worked for square boxes.  This
+     improves placement of such rotated rectangular boxes.
+
+</current>
+
+<!-- **************************************************************************************  -->
+
+<old name="19.13" date="May 26, 2018">
 New Features and Improvements:
    - Added a lot of new Python bindings.  You can now use these things from Python:
       - gaussian_blur(), label_connected_blobs(), randomly_color_image(), jet(),
@@ -49,7 +98,7 @@ Bug fixes:
      conversions are attempted.
    - Fixed some python functions not taking as wide a range of image types as they did in
      previous dlib versions.
-</current>
+</old>
 
 <!-- **************************************************************************************  -->
 

docs/docs/term_index.xml

@@ -107,7 +107,11 @@
          <term file="dlib/cuda/tensor_abstract.h.html" name="alias_tensor_const_instance" include="dlib/cuda/tensor.h"/>
          <term file="dlib/cuda/tensor_abstract.h.html" name="alias_tensor" include="dlib/cuda/tensor.h"/>
          <term file="dlib/cuda/tensor_abstract.h.html" name="image_plane" include="dlib/cuda/tensor.h"/>
-         <term file="dlib/cuda/tensor_abstract.h.html" name="have_same_dimensions" include="dlib/cuda/tensor.h"/>
+         <term name="have_same_dimensions">
+            <term link="dlib/cuda/tensor_abstract.h.html#have_same_dimensions" name="for tensors" include="dlib/cuda/tensor.h"/>
+            <term link="dlib/image_processing/generic_image.h.html#have_same_dimensions" name="for images" include="dlib/image_processing/generic_image.h"/>
+         </term>
+
          <term file="dlib/cuda/gpu_data_abstract.h.html" name="gpu_data" include="dlib/cuda/gpu_data.h"/>
          <term name="memcpy">
             <term link="dlib/cuda/tensor_abstract.h.html#memcpy" name="for tensors"   include="dlib/cuda/tensor.h" />
@@ -435,6 +439,7 @@
          <term file="linear_algebra.html" name="count_points_between_lines"      include="dlib/geometry.h"/>
          <term file="linear_algebra.html" name="angle_between_lines"             include="dlib/geometry.h"/>
          <term file="linear_algebra.html" name="is_convex_quadrilateral"         include="dlib/geometry.h"/>
+         <term file="linear_algebra.html" name="polygon_area"                    include="dlib/geometry.h"/>
          <term file="linear_algebra.html" name="find_convex_quadrilateral"       include="dlib/geometry.h"/>
          <term file="dlib/geometry/line_abstract.h.html" name="no_convex_quadrilateral"       include="dlib/geometry.h"/>
 
@@ -661,6 +666,7 @@
          <term file="ml.html" name="polynomial_kernel"               include="dlib/svm.h"/>
          <term file="ml.html" name="sigmoid_kernel"                  include="dlib/svm.h"/>
          <term file="ml.html" name="radial_basis_kernel"             include="dlib/svm.h"/>
+         <term file="ml.html" name="auto_train_rbf_classifier"       include="dlib/svm.h"/>
 
 
          <term link="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors" name="unsorted sparse vectors"/>