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Current Release

- Change Log
- Release Notes
- Version: 18.4

Last Modified: Aug 28, 2013

# Release notes

# Release 18.4

Release date: Aug 28, 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.

# Release 18.1

Release date: Mar 25, 2013
Major Changes in this Release:

    New Features:
       - Added svd_fast(), a routine for computing a singular value decomposition of very
         large matrices.
       - Added cca(), a routine for doing canonical correlation analysis on very large
         and high-dimensional datasets.
       - Added tools for creating parallel for loops, see parallel_for().
       - Added some features to the image display widgets to let the user easily
         get information about where the user is clicking.  This is the new
         get_next_double_click() routine.
       - Added an operator>> for matrix objects which allows you to read in ASCII
         matrices using the format used by operator<<.
       - Added serialization support for std::vector<bool>.
       - Added the following new minor objects and routines: average_precision(),
         make_sparse_vector_inplace(), orthogonalize(), count_bits(), draw_surf_points(),
         hamming_distance(), cosine_distance, and negative_dot_product_distance.

    Non-Backwards Compatible Changes:
       - Changed ranking evaluation functions to return the mean average precision
         in addition to just raw ranking accuracy. This changes their return types
         from double to matrix<double,1,2>.
       - Generalized segment_image() so it works on any pixel type or array of
         vectors.  I also changed its interface slightly.  In particular, I removed
         the min_diff parameter and replaced it with an explicit min_size parameter.
       - Changed how the SURF descriptor is computed slightly to improve its
         accuracy.  The interface to the user has not been changed, however, the
         number and position of detected SURF points might be different than in
         previous dlib versions.

    Bug fixes:
       - Fixed an endianness bug in the PNG I/O functions which occurred when 16bit
         grayscale PNGs were used.
       - Fixed a bug which could potentially occur when empty std::vector<char> or
         std::vector<unsigned char> were serialized.
       - There was a bug in the version of draw_line() that draws directly onto an
         array2d type image (not the one that draws onto a GUI canvas object). The
         bug triggered whenever a perfectly horizontal or vertical line that extended
         outside the image was drawn. This has been fixed.
       - Fixed a bug in the Windows implementation of the signaler object, which
         was found by Isaac Peterson. The bug caused the program to deadlock if
         signal() or broadcast() was called at exactly the same time a
         wait_or_timeout() function timed out.
       - Fixed a bug in the image_window and image_display GUI tools which caused
         them to not redraw overlay lines correctly in certain cases involving
         non-default zoom levels.
       - Switched randomly_color_image() to use the non-pointer based version of
         murmur_hash3() to avoid violation of the strict aliasing rule. In
         particular, the previous version didn't work correctly in gcc 4.7.2 when
         optimizations were enabled.
       - Visual Studio 2012's iostreams library has a bug which caused the
         iosockstream to crash on use.  This version of dlib has been changed to
         avoid triggering this bug.

    Other:
       - Refactored the Platt scaling code a little. Now there is a function,
         learn_platt_scaling(), that allows you to directly call the Platt scaling
         code without supplying a trainer object.
       - Optimized the oca and structural SVM solvers.  They are now a little bit faster
         than in previous dlib releases.

# Release 18.0

Release date: Feb 04, 2013
Major Changes in this Release:

    New Features:
       - Machine Learning
          - Added svm_rank_trainer, an optimized implementation of the SVM-Rank algorithm.
          - Added rank_unlabeled_training_samples(), an implementation of the SVM Active
            Learning algorithm.
          - Added svm_c_linear_dcd_trainer, a warm-startable SVM solver using the dual
            coordinate descent algorithm used by liblinear.
          - Added the ability to force the last element of a weight vector to 1 to the
            following objects: svm_c_linear_trainer, svm_c_linear_dcd_trainer,
            svm_rank_trainer, and oca.
          - Added the ability to learn non-negative weight vectors to the
            structural_sequence_labeling_trainer object.
       - Networking
          - Added an iosockstream object.
          - Added a method to the server object that lets a user set the graceful close timeout
            time to something other than the default of 500ms.
       - Linear Algebra
          - Added the gaussian_randm() function.
          - Added the find_affine_transform() function.
          - Added the mat() function.  It combines the array_to_matrix(), vector_to_matrix(),
            pointer_to_column_vector(), and pointer_to_matrix() methods all into one convenient
            interface.   mat() also works for Armadillo and Eigen matrices.
          - Added STL style begin() and end() methods to matrix and matrix_exp.
          - Added an overload of sparse_matrix_vector_multiply() that multiplies a dense matrix
            with a sparse vector.
          - Made toMat() work with the matrix object in addition to array2d style images.
       - Graphical User Interface Tools
          - Added draw_solid_convex_polygon().
          - Added an overload of draw_image() that's useful for drawing images and doing
            interpolation at the same time.
          - Added the on_view_changed() callback to zoomable_region and scrollable_region widgets.
       - Added parse_trees_to_string() and parse_trees_to_string_tagged().
       - Added lambda function support to the timeout object.
       - Added the vectorstream object.
       - Added the parse_xml() routines.
       - Added a group name feature to the command line parser.  Now it is possible to make
         print_options() print related options in named groups.
       - Added the following new hashing functions: murmur_hash3_128bit_3(),
         murmur_hash3_2(), murmur_hash3_3(), uniform_random_hash(), gaussian_random_hash()
         as well as hash() overloads for uint32, uint64, and std::pair.

    Non-Backwards Compatible Changes:
       - Made the svm_c_linear_trainer use the risk gap to decide when to stop.  This was done
         because it is how all the other OCA based SVM tools in dlib decide when to stop.
         However, it might cause the outputs to be slightly different in this version of dlib.
       - It is now illegal to call unlock() on a mutex when the mutex is not owned by the
         calling thread.  The most likely reason for doing this was to unlock early in an area
         locked by an auto_mutex.  Old code that does this can be fixed by calling auto_mutex's
         unlock() function instead.
       - Removed the structural_assignment_trainer::learns_nonnegative_weights() routine
         and moved its functionality into the feature extraction interface used by this object.

    Bug fixes:
       - Fixed a bug in find_max_factor_graph_nmplp() which caused it to not work properly on
         some compilers.
       - Fixed a bug pointed out by Joel Nelson in the version of md5() that took an istream.
         The bug caused the function to crash on strings longer than 56 characters.

    Other:
       - dlib now has an excellent new logo thanks to Yasser Asmi.
       - Added a new documentation page for the various linear algebra tools.
       - The following objects were turned into single implementation components:
         sockstreambuf, timeout, member_function_pointer, xml_parser, linker,
         bound_function_pointer, and timer.

# Release 17.49

Release date: Dec 18, 2012
Major Changes in this Release:

    New Features:
       - Machine Learning
          - Added the ability to learn non-negative weight vectors to the
            structural_assignment_trainer object.
          - Added two new graph clustering algorithms: Chinese Whispers and Newman's modularity
            clustering.
          - Added a number of new graph manipulation tools: sparse_matrix_vector_multiply(),
            is_ordered_by_index(), find_neighbor_ranges(), convert_unordered_to_ordered(),
            remove_duplicate_edges(), and the ordered_sample_pair object.
       - Networking
          - Added a set of tools for creating applications using the Bulk Synchronous Parallel
            computing model.  See the new bsp_ex.cpp example program for an introduction.
          - Added a routine that lets a user disable Nagle's algorithm on a TCP connection.
          - Added an asynchronous start routine to the server object.  This is the new
            start_async() method.
          - Added the network_address object.
          - Added connect_to() to the bridge interface.
       - Added find_max_parse_cky(), a method implementing the well known CKY algorithm for
         parsing probabilistic context free grammars.
       - Added the ability to label parts of objects with the mouse to the image_display
         widget.
       - Added the ability to put overlay circles and full_object_detections into the
         image_window widget.
       - Added a stddev() for matrix objects.
       - Added operator+() for running_stats and running_scalar_covariance.
       - Added an overload of murmur_hash3_128bit() that takes 4 integers instead of a block of
         memory.
       - Added rand::get_random_64bit_number().

    Non-Backwards Compatible Changes:
       - Changed the image_dataset_metadata XML reading tools to use a map of strings to points
         to represent object parts. This change removes the old head point from a box since
         this information can now be represented in the parts map.
       - The syntax for passing order_by_distance and order_by_index to std::sort() is now
         slightly different since these functions are now templates.  However, this change
         allows them to work on any kind of sample_pair or ordered_sample_pair object.
       - The default distance value of a sample_pair is now initialized to 1 instead of
         infinity.

    Bug fixes:
       - Added a patch, contributed by Martin Müllenhaupt, to fix a minor bug in the SQLite
         bindings.
       - Fixed a typo which would prevent code that called running_stats::max_n() from
         compiling.

    Other:
       - Added a new documentation page for the various graph tools in dlib.
       - Added support for Visual Studio 2012.
       - Switched the sample_pair object to use double to store its distance value instead of
         float.
       - Added William Sobel's patch to the web server that improves its flexibility and
         security.
       - Changed the server object so you don't have to use the server::kernel_1a syntax to
         declare it anymore.  Now you just say server, server_iostream, or server_http
         depending on which one you want.
       - Changed the cmd_line_parser so you don't have to use the ::kernel_1a syntax anymore.
         Now it is declared like a normal single implementation object.
       - Set the default max number of connections a server will accept at a time to 1000
         rather than the previous default of infinity.

# Release 17.48

Release date: Oct 18, 2012
Major Changes in this Release:

    New Features:
      - Added more overloads of find_max_factor_graph_potts() to make applying it
        over a Markov random field of image pixels really simple.
      - Added overloads of serialize()/deserialize() so that they can serialize
        Google protocol buffer objects.
      - Image Processing:
          - Added find_points_above_thresh()
          - Added max_filter()
          - Added scan_image_movable_parts()
          - Added sum_filter_assign()
          - Added the full_object_detection object.
          - Added the ability to model objects with movable parts into the
            scan_image_pyramid object.  This update also includes all the needed tools
            to train movable part models using the structural_object_detection_trainer.
      - Machine Learning:
          - Added a per node loss option to the structural_svm_graph_labeling_problem's
            interface.
          - Added Emanuele Cesena's implementation of Sammon's nonlinear dimensionality
            reduction method.

    Non-Backwards Compatible Changes:
      - To support movable part models, the serialization format of scan_image_pyramid
        objects was modified.  This breaks backwards compatibility with the previous
        format for scan_image_pyramid objects as well as object_detector instances
        that use the scan_image_pyramid.

    Bug fixes:
      - Fixed a bug in auto_threshold_image() that caused it to give bad outputs
        when used with very large images.

    Other:
      - Updated find_max_factor_graph_potts() to correctly say you can use infinite
        weights for the factor_value_disagreement() values since the code actually
        supports this.
      - Made integer serialization about 3 times faster.

# Release 17.47

Release date: Jun 15, 2012
Major Changes in this Release:

    New Features:
       - Improvements to linear algebra tools:
          - Added the lowerbound() and upperbound() routines for thresholding dense
            matrices.
          - Refined the tools for working with sparse vectors.  In particular,
            the following functions were added: min(), max(), make_sparse_vector(),
            add(), and subtract().  A number of existing routines were also updated
            to work with both sparse and dense vectors so that templated code which
            works on both vector types is simpler to write.
          - Added the += and -= operators to the set_subm(), set_rowm(), and set_colm()
            tools for operating on submatrices.
       - Optimization:
          - Added a new quadratic program solver, solve_qp4_using_smo().  This new
            solver is useful for solving quadratic programs corresponding to
            non-negative constrained primal quadratic programs.
          - Added an optional non-negativity constraint to the oca optimizer.
          - Added the min_cut object.  It provides a method to find the minimum weight
            cut on a graph.
          - Added tools for finding the maximum probability assignment in a Potts
            style Markov random field.  See the find_max_factor_graph_potts() routine
            for details.
       - Machine Learning:
          - Added structural SVM tools for learning the parameters of a Potts style
            Markov random field.  See the structural_graph_labeling_trainer and
            graph_labeler objects as well as their associated example program for
            details.
          - Added the ability to learn only non-negative weights to the
            svm_c_linear_trainer.
       - Improved Integration with OpenCV:
          - Updated the cv_image object so it works with cv::Mat as well as IplImage.
          - Added the toMat() routine for converting from a dlib style image to an
            OpenCV cv::Mat image.

    Non-Backwards Compatible Changes:
       - Removed the dlib::sparse_vector namespace.  Everything from this namespace
         was moved into the normal dlib:: namespace so that code which works with
         both sparse and dense vectors is more cohesive.

    Bug fixes:
       - Fixed a bug in find_max_factor_graph_viterbi() which sometimes occurred when
         the model order was larger than the number of variables.
       - Fixed a bug which caused a compiler error if you tried to call dot() on two
         1x1 matrices which were statically dimensioned.

    Other:
       - Improved existing documentation: added pictures of the gui widgets,
         added documentation of the dlib::bridge protocol, and other minor
         usability improvements.

# Release 17.46

Release date: Apr 11, 2012
Major Changes in this Release:

    New Features:
       - Image Processing:
          - Added the option to make the features generated by poly_image rotationally
            invariant.
          - Added a set of routines for warping, scaling, and resizing images.
            See the new "Scaling and Rotating" section of the image processing
            documentation for details.
          - Added the heatmap() routine for converting an image into a heatmap.
       - Machine Learning
          - Updated the sequence labeling trainer to allow the user to set different
            loss values for different labels.
          - Added the rls object.  It is an implementation of the linear recursive
            least squares algorithm.
       - Added the get_option() routines which slightly simplify option parsing
         from the command line and config files.
       - Added the 128bit version of Murmur hash.
       - Added the kalman_filter and rls_filter objects.  These are tools for
         performing Kalman filtering and recursive least squares filtering.
       - Added the circular_buffer object.

    Non-Backwards Compatible Changes:
       - The poly_image generates slightly different features in this new release.
         Therefore, classifiers trained using the previous version will need to be
         retrained if they are switched to the new version of poly_image.
       - Changed the xcorr() functions so they take the complex conjugate of the right
         hand arguments if they are complex numbers.  This way they do a proper
         cross-correlation and also mirror the behavior of MATLAB.  However, this
         breaks backwards compatibility with the previous behavior of xcorr().
       - Previously, dlib included two versions of dlib::array.  However, to
         simplify the user interface, dlib now includes only the contiguous
         memory implementation of dlib::array.  This change should only affect
         you if you wrote code which assumed dlib::array::set_max_size() only
         allocated a small amount of RAM.  The new behavior is similar to the
         std::vector::reserve() routine.  That is, dlib::array::set_max_size()
         will allocate the requested amount of memory immediately.

    Bug fixes:
       - Fixed a bug which caused certain matrix expressions to not compile
         when the BLAS bindings were enabled.  In particular, expressions which
         involved a 1x1 matrix sometimes didn't compile.

    Other:
       - Made the matrix routines min(), max(), sum() and mean() work with
         complex numbers.
       - Turned the array object into a single implementation object.  Now arrays
         can be created using the normal array<type> obj; syntax.  Additionally,
         all extensions were merged into the array object.
       - Added an example program which better documents how to create training
         data for the object detection tools as well as how this data can be used.
         See the train_object_detector.cpp example for details.

# Release 17.45

Release date: Jan 29, 2012
Major Changes in this Release:

    New Features:
       - Added tools for timing blocks of code
       - Machine Learning
          - Added a set of tools for learning to solve the assignment problem.
            See the structural_assignment_trainer and its associated example
            program for an introduction.
          - Added random projection based locality sensitive hashing tools.
          - Added tools to simplify the creation of scan_image_pyramid objects.
            See the object_detector_ex.cpp example program for details.
       - Image Processing
          - Added sum_filter() and spatially_filter_image_separable_down()
          - New feature extractors: poly_image, nearest_neighbor_feature_image, and
            fine_hog_image

    Non-Backwards Compatible Changes:
       - Changed the serialization format for rand objects.
       - Changed the order of arguments for the sequence_labeler's constructor.
       - Object Detection Changes
          - Some parts of the object detection tools have been refactored.  In particular,
            the interfaces of the scan_image_pyramid and structural_object_detection_trainer
            have been changed slightly to improve usability.
          - Made the test_box_overlap a little more flexible.  This change breaks
            backwards compatibility with the previous version though.
          - The hashed_feature_image object has been made more general.  It now
            uses a user supplied hashing function rather than its own hashing
            implementation.
          - Removed constness from the operator() member functions of the
            object_detector.
       - Fixed improper normalization in the gaussian() functions.  The
         normalization constant was being computed incorrectly.
       - Sequence labeling feature extractors must now define a sequence_type
         typedef instead of sample_type.  This change allows the user to use any
         type of sequence, not just std::vector objects.

    Bug fixes:
       - Changed the add_probability() method of joint_probability_table so
         it does a saturating add rather than a normal add.  This ensures the
         probability value stays exactly <= 1.  Previously, floating point
         rounding error could cause it to be slightly above 1 and would therefore
         cause some asserts to misfire during debugging mode.
       - The object_detector had code in it which limited the number of outputs
         to 100 rectangles.  This has been removed.
       - Fixed improper normalization in the gaussian() functions.  The
         normalization constant was being computed incorrectly.

    Other:
       - dlib::rand can now generate Gaussian random numbers.
       - The structural_object_detection_trainer will now automatically setup
         appropriate non-max suppression parameters if the user doesn't supply them.
       - The structural_object_detection_trainer has been optimized and now runs
         significantly faster than in previous dlib releases.
       - The tools folder containing htmlify, imglab, and mltool is now included
         in the dlib release archive files.  Previously, these tools were only
         available directly from source control.

Old Release Notes