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Last Modified:
Nov 13, 2014

Release notes

Release 18.11

Release date: Nov 13, 2014
Major Changes in this Release:
New Features:
   - Added save_jpeg()
   - Added the option to use an identity matrix prior to vector_normalizer_frobmetric.
   - Made the extract_image_chips() routine more flexible, in particular: Added
     get_mapping_to_chip(), get_face_chip_details(), map_det_to_chip(), and also
     upgraded chip_details so you can specify a chip extraction by a bunch of
     point correspondences between the chip and the original image.
   - Added a set of local-binary-pattern based feature extractors:
     make_uniform_lbp_image(), extract_histogram_descriptors(),
     extract_uniform_lbp_descriptors(), and extract_highdim_face_lbp_descriptors() 
   - Added compute_lda_transform() 
   - Added equal_error_rate()
   - Added cast_to() to the type_safe_union. This allows you to get the
     contents of a const type_safe_union.

Non-Backwards Compatible Changes:

Bug fixes:
   - Changed noncopyable.h to avoid a name clash with boost 1.56
   - On some platforms hostname_to_ip() would erroneously return 0.0.0.0. This
     has been fixed.

Other:

Release 18.10

Release date: Aug 28, 2014
Major Changes in this Release:
New Features:
   - Added find_similarity_transform()
   - Added the ability to upgrade a auto_mutex_readonly from a readonly lock to a write
     lock. 
   - Added an implementation of the paper "One Millisecond Face Alignment with an Ensemble
     of Regression Trees" by Vahid Kazemi and Josephine Sullivan which appeared in this
     year's CVPR conference.  Therefore, dlib now includes tools for learning shape models
     and also comes with a state-of-the-art face landmark locator.  See the
     face_landmark_detection_ex.cpp and train_shape_predictor_ex.cpp example programs for
     an introduction.

Non-Backwards Compatible Changes:
   - Made the interface to all the image processing routines more generic.  In particular, 
     it is now easier to use arbitrary image types with dlib.  The new generic image
     interface is defined in dlib/image_processing/generic_image.h and simply consists of
     seven user defined global functions and a traits template.  Any user code that was
     using array2d objects to represent images will still work.  However, if you had been
     using your own custom image object you will need to provide implementations of the
     seven functions.  Instructions for how to do this are in
     dlib/image_processing/generic_image.h.

Bug fixes:
   - Changed the murmur hash implementation to avoid any possibility of strict aliasing
     violations in user code, even when things get inlined in unfavorable ways.
   - Fixed a color space handling bug in resize_image() that caused bad looking outputs in
     some cases.
   - If "cmake" was a substring of the full path to your source code folder then the cmake
     scripts would fail. This has been fixed.
   - Fixed a compile time error that could occur when using find_max_single_variable().

Other:
   - load_image() now uses the internal file header information to detect the
     image format rather than looking at the file extension.
   - Renamed unit test program to dtest avoid warnings from CMake.
   - cross_validate_trainer() and cross_validate_trainer_threaded() no loner make copies
     of the training data.  This significantly reduces their RAM usage for large datasets.
   - Changed the serialization code for C-strings so that they don't save the null
     terminator byte. This makes their serialization format the same as the format for
     std::string.  The code should still be able to read all previously serialized data
     correctly, so the change is backwards compatible with previous versions of dlib. 
   - Changed the evaluate_detectors() routine so that it applies non-max suppression to
     each detector individually. This way one detector doesn't stomp on the output of
     another detector.
   - Made the version of draw_line() that draws onto a regular image use alpha blending
     for drawing diagonal lines.

Release 18.9

Release date: Jun 16, 2014
Major Changes in this Release:
New Features:

Non-Backwards Compatible Changes:

Bug fixes:
   - The new simplified serialization API that works like serialize("filename")<<object 
     was not opening files in binary mode and therefore didn't work properly on Windows.  
     This has been fixed.

Other:


Release 18.8

Release date: Jun 02, 2014
Major Changes in this Release:
New Features:
   - Added the ability to set a previously trained function as a prior to the
     svm_multiclass_linear_trainer, svm_c_linear_trainer, and svm_rank_trainer
     objects.
   - Added a user settable loss to the structural_assignment_trainer and
     structural_track_association_trainer objects.  
   - Added evaluate_detectors(), a function for efficiently running multiple fHOG
     based object detectors.
   - Added the new split_on_first() and split_on_last() string manipulation functions.
   - Added locally_change_current_dir, a RAII tool for switching between directories.
   - You can now make a 1x1 matrix containing a single value by calling mat() on a single
     scalar value.
   - The point transform functions and frobmetric_training_sample are now serializable.
   - Added a simplified operator << and >> based syntax for serializing to and
     from files.  So now you can serialize to a file using a syntax of:
       serialize("myfile.dat") << myobject << another_object;
     and then load those objects from disk via:
       deserialize("myfile.dat") >> myobject >> another_object;
     An arbitrary number of objects can be serialized or deserialized by
     chaining the << and >> operators.

Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a bug pointed out by Daniel Girardeau-Montaut. The covariance()
     function didn't work on non-double valued matrices.
   - Fixed a bug in the backtracking_line_search() function pointed out by
     Ping-Chang Shih. The function ignored the max_iter parameter.
   - Fixed a compiler error encountered when using clang 3.4 on Mac OS X 10.9.
     Thanks to Martin Fergie for reporting this problem.
   - Fixed a potential divide by zero in draw_fhog()

Other:
   - Added an example program showing how to set a custom logger output hook.
   - Made linear decision_functions which use sparse vectors much faster.


Release 18.7

Release date: Apr 09, 2014
Major Changes in this Release:
New Features:
   - Added a Python API for working with fHOG based object detectors.  See the
     new python example programs train_object_detector.py and face_detector.py for
     more details.
   - Added the ability to use a user supplied fHOG style feature extractor with 
     the scan_fhog_pyramid object.  So now you can define your own version of HOG
     for use with these tools.
   - The oca solver now supports taking a user supplied prior vector. That is,
     it lets you use a regularizer like ||w-prior||^2 instead of the usual
     ||w||^2 regularizer.
   - Added the structural_track_association_trainer object.  It is a structural
     SVM tool for creating multi-target tracking algorithms.  See the
     learning_to_track_ex.cpp example program for an introduction.
   - Added the following minor utility functions: nearest_center(),
     add_image_rotations(), set_aspect_ratio(), and tile_images().

Non-Backwards Compatible Changes:
   - Refactored the load_image_dataset() routines so they are easier to use and
     more flexible. This introduces a slight backwards incompatibility in that
     the version that loads full_object_detection objects now returns an ignore
     rectangle set instead of a parts name list.  Other than that the changes
     are backwards compatible with previous versions of dlib.
   - Added a bias term to the assignment_function's model so the user doesn't
     need to remember, or even understand, that they should add it themselves.
     However, this change breaks backwards compatibility with the previous
     serialization format for assignment_function objects.

Bug fixes:
   - Fixed a number of compile time errors that could occur in rare cases.
   - The stopping condition for the svr_linear_trainer was too tight, causing it
     to take an excessive amount of time to converge in some cases. 
   - Disabled use of XIM for X11 windowing since it makes programs hang on some
     systems. However, this means the wide character input methods won't work on
     X11 systems anymore.
   - Fixed a bug in randomize_samples() which caused the outputs to be not as
     random as they should be. 
   - Fixed dlib's CMakeLists.txt file so that the "use FFTW" option actually
     causes the build to use FFTW.
   - Fixed a compile time error that triggered when trying to link with FFTW.
   - mat() did not work correctly when used with std::vector<bool> objects.
     This has been fixed.

Other:


Release 18.6

Release date: Feb 03, 2014
Major Changes in this Release:
New Features:
   - Object Detection Tools:
      - Added scan_fhog_pyramid, a tool for creating Histogram of Oriented Gradient (HOG)
        based object detectors. 
      - Added get_frontal_face_detector(), a HOG based face detector.  
      - Added an option to include "ignore/don't care" truth boxes to the
        structural_object_detection_trainer.  This allows a user to tell the trainer that
        they don't care if certain objects are detected or not detected.  
   - Image Processing Tools:
      - Added extract_image_chips()
      - Added a version of draw_rectangle() for drawing on images.
      - The spatial filtering routines now support even sized filters.
      - Added flip_image_dataset_left_right(), upsample_image_dataset(), and
        rotate_image_dataset().
   - Machine Learning Tools:
      - Added a nuclear norm regularization option to the structural SVM solver.
      - Added the option to learn only non-negative weights to the
        svm_multiclass_linear_trainer.
   - Speed Improvements:
      - The svm_multiclass_linear_trainer, one_vs_one_trainer, and one_vs_all_trainer
        objects are now multithreaded.  This also means you have to #include
        dlib/svm_threaded.h instead of dlib/svm.h to use these tools. 
      - A number of image processing tools can now optionally use SSE and AVX instructions
        and are therefore considerably faster.  In particular, the following tools have been
        accelerated: extract_fhog_features, resize_image, pyramid_down, pyramid_up,
        spatially_filter_image_separable, and spatially_filter_image.
   - Added an inv() routine that inverts point transformation functions.
   - Added a sign() routine for matrix objects.

Non-Backwards Compatible Changes:
   - The spatial image filtering functions have the following changes:
      - They no longer zero the image borders when you set the add_to parameter to true.  
      - The spatially_filter_image_separable_down() routine now only allows grayscale
        output images.
   - Changed the default parameters of the test_box_overlap object. Now it defaults to
     using exactly the PASCAL VOC match criterion.
   - To use the svm_multiclass_linear_trainer, one_vs_one_trainer, or one_vs_all_trainer
     objects you now have to #include dlib/svm_threaded.h instead of dlib/svm.h. 
   - pyramid_up() no longer has a levels option.

Bug fixes:
   - Fixed a compile time bug that could occur when wide character strings were
     serialized.
   - Fixed a compile time bug occurring in gcc 4.7.1 on SUSE Linux.  Thanks to Volker
     Härtel for finding this.
   - Fixed compile time errors that occurred when using gcc on cygwin.
   - Fixed a compile time bug that could occur when serializing mlp objects.
   - Fixed a bug in the bigint object that caused division to sometimes produce incorrect
     results.
   - Fixed a bug which sometimes caused load_image_dataset() to erroneously report that
     the dataset file could not be found.
   - Fixed a bug in the structural_object_detection_trainer that caused it to erroneously
     throw a impossible_labeling_error exception in certain rare cases.
   - Updated find_max_factor_graph_nmplp() to use the improved version of the algorithm
     from the 2011 paper Introduction to dual decomposition for inference by David Sontag,
     Amir Globerson, and Tommi Jaakkola.  The original algorithm presented in their 2008
     paper had an error that negatively affected its convergence.  Thanks to James Gunning
     for pointing this out.

Other:
   - Fixed many compiler warnings in gcc 4.8.
   - Made many of the mat() converters bind the resulting matrix expressions into BLAS
     functions.
   - libpng and libjpeg are now included in the dlib/external folder to enable easy static
     linking to these libraries on platforms that typically don't have them (e.g. Windows).
     Moreover, dlib's cmake files will automatically perform this static linking when no
     copy of these libraries is found on the system. 

Release 18.5

Release date: Oct 22, 2013
Major Changes in this Release:
New Features:
   - Added routines for performing BFGS and L-BFGS optimization with box constraints.  
     See the new find_min_box_constrained() and find_max_box_constrained() routines.
   - Added vector_normalizer_frobmetric.  This is a tool for learning a
     Mahalanobis distance metric.
   - The user can now set different loss values for false alarming vs. getting a
     correct detection when using the structural_sequence_segmentation_trainer.
   - Added an overload of clamp() that lets you use matrix valued lower/upper bounds.
   - New image processing tools:
      - Added the scan_image_custom object, split_array(), and add_image_left_right_flips().
      - Added extract_fhog_features(), this is a function for computing
        Felzenszwalb's 31 channel HOG image representation.  

Non-Backwards Compatible Changes:
   - Refactored the image pyramid code. Now there is just one templated object called
     pyramid_down and you give it the downsampling amount as a template argument.  To make
     old code work with this change use the following substitutions:
       change pyramid_down to pyramid_down<2>
       change pyramid_down_3_2 to pyramid_down<3>
       change pyramid_down_4_3 to pyramid_down<4>
       change pyramid_down_5_4 to pyramid_down<5>

Bug fixes:

Other:
   - Made the structural SVM solver slightly faster.
   - Moved the python C++ utility headers from tools/python/src into dlib/python.
   - The PNG loader is now able to load grayscale images with an alpha channel.
   - Removed checks that prevented users from using references to functions with the
     optimization code and forced the use of function pointers. This was to avoid
     triggering a bug in gcc 4.0.  Since that compiler is no longer officially supported
     by dlib I've removed these checks to increase usability.
   - Made resize_image() use bilinear interpolation by default and also added a special
     version of it that is optimized for this case.
   - Dlib's cmake files will now automatically link to the Intel MKL on MS Windows
     platforms if the MKL is installed.

Release 18.4

Release date: Aug 14, 2013
Major Changes in this Release:
New Features:
   - Added Python interfaces to dlib's structural support vector machine solver and
     Hungarian algorithm implementation.
   - Added running_cross_covariance
   - Added order_by_descending_distance()
   - Added is_finite()
   - Added the csv IO manipulator that lets you print a matrix in comma separated value
     format.

Non-Backwards Compatible Changes:
   - Changed the object detector testing functions to output average precision instead of
     mean average precision.
   - Added an option to weight the features from a hashed_feature_image relative to the
     number of times they occur in an image.  I also made it the default behavior to use
     this relative weighting and changed the serialization format to accommodate this.

Bug fixes:
   - Fixed typo in learn_platt_scaling(). The method wasn't using the exact prior
     suggested by Platt's paper.
   - Fixed a bug in running_scalar_covariance that caused the covariance() and
     correlation() methods to output the wrong answer if the covariance was negative.

Other:
   - Gave the image_window the ability to tie the mouse and keyboard events together such
     that it is possible for a user to listen for both simultaneously.
   - A number of changes were made to the structural_svm_problem's code which make it 
     significantly faster in some cases.
   - Added Steven Van Ingelgem's patch to the HTTP server which makes operations on HTTP
     headers case-insensitive.


Release 18.3

Release date: June 21, 2013
Major Changes in this Release:
New Features:
   - Machine Learning:
      - Added the svr_linear_trainer, a tool for solving large scale support vector 
        regression problems.
      - Added a tool for working with BIO and BILOU style sequence taggers/segmenters.  
        This is the new sequence_segmenter object and its associated 
        structural_sequence_segmentation_trainer object.
      - Added a python interface to some of the machine learning tools.  These
        include the svm_c_trainer, svm_c_linear_trainer, svm_rank_trainer, and
        structural_sequence_segmentation_trainer objects as well as the cca()
        routine.  
   - Added point_transform_projective and find_projective_transform().
   - Added a function for numerically integrating arbitrary functions, this is the 
     new integrate_function_adapt_simpson() routine which was contributed by 
     Steve Taylor
   - Added jet(), a routine for coloring images with the jet color scheme.

Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a bug in hysteresis_threshold() that caused it to produce incorrect
     outputs in some cases.
   - Fixed a segmentation fault in the eigenvalue_decomposition object which
     could occur when NaN valued inputs were given.

Other:
   - Made image saving routines work on matrix objects in addition to array2d objects.
   - The machine learning page now contains a flow chart to help new users
     select a machine learning tool appropriate for their task.


Release 18.2

Release date: May 30, 2013
Major Changes in this Release:
New Features:
   - Object Detection Tools:
      - Added another image scanning tool similar to scan_image_pyramid.  This
        is the new scan_image_boxes object.  It allows a user to easily specify
        an arbitrary set of object boxes which should be checked by an object
        detector rather than scanning a fixed sized window over the image as is
        done by the scan_image_pyramid tool.  This allows more flexible scanning 
        strategies.  For example, it is now possible to use the selective search 
        method implemented by the new find_candidate_object_locations() routine.  
      - Added the binned_vector_feature_image.
      - Upgraded the object_detector so that you can use the adjust_threshold
        argument for all versions of the operator() method.
      - Added remove_unobtainable_rectangles()
   - Hashing Tools:
      - Added a set of new locality sensitive hashing functions meant for use
        with larger vectors and higher bit sizes than the current LSH tools.
        These are the new hash_similar_angles_xxx objects.
      - Added find_k_nearest_neighbors_lsh() and hash_samples()
      - Added create_max_margin_projection_hash()
   - New Matrix Routines: linpiece(), fft(), and ifft()
   - Added numeric constants and additional statistics to the running_stats
     object.  This code was contributed by Steve Taylor.
   - Added the image_window::get_next_keypress() routine.  This tool allows a
     user to easily find out which keyboard key the user is pressing.

Non-Backwards Compatible Changes:
   - Changed the object_detector interface slightly.  In particular, it no
     longer adds the adjust_threshold argument to the output scores.    
   - The object detector testing functions now output mean average precision in
     addition to precision and recall.
   - Changed how dlib does serialization in a number of ways:
      - The running_stats and scan_image_pyramid objects have had their
        serialization format changed in a way that breaks backwards
        compatibility.  This means serialized versions of these objects can't be
        loaded by the new version of dlib.
      - Changed the format dlib uses when it serializes floating point values.
        Previously, we used an ASCII based format.  Dlib now uses a much more
        efficient binary format. However, the deserialization routines have
        been made backwards compatible with the previous format. So dlib can
        still deserialize older data but older software won't be able to read
        the new format.
      - Changed the serialization formats for the matrix and array2d objects so
        that either object can be deserialized into the other. This was done in a
        way that is backwards compatible with previous versions of dlib. That is,
        we can still load data serialized by previous dlib versions. However,
        older versions of dlib can't load the new serialization format.

Bug fixes:
   - Fixed a bug in save_dng() that happened sometimes when saving images with
     unsigned char pixels.
   - The test_ranking_function() and cross_validate_ranking_trainer() routines
     computed incorrect MAP scores when the learned function output a constant
     value for all samples.  This has been fixed.

Other:
   - Gave load_image_dataset() the ability to skip images that don't have any
     ground truth boxes.
   - Changed average_precision() to use interpolated precision. So now it uses
     the same metric as the one used by the Pascal VOC challenge.
   - Upgraded the dng image format so it can natively store floating point
     pixel types without any information loss.




Old Release Notes