Release notes

Release 19.1

Release date: Aug 13, 2016
Major Changes in this Release:
New Features:
   - Support for cuDNN 5.1
   - dlib::async() and dlib::default_thread_pool().
   - rectangle_transform
   - imglab tool: added --resample, --ignore, --files, and --extract-chips
     command line options.  Also added convert_imglab_paths_to_relative and
     copy_imglab_dataset scripts.
   - Evgeniy Fominov made the shape_predictor trainer multi-threaded and faster.
   - sutr90 contributed support for the CIELab color space.  See the new lab_pixel.

Non-Backwards Compatible Changes:
   - All the cmake utility scripts were moved to dlib/cmake_utils.  
   - Code that #includes the shape_predictor can now only be compiled with
     compilers that support C++11 lambda functions.

Bug fixes:
   - Made CMake scripts work in a wider range of environments. 
   - Fixed compile time errors on various platforms.
   - Fixed bad multi-threading support in the MATLAB mex wrapper.
   - Fixed bug in cuDNN binding that could sometimes cause NaN outputs.
   - Fixed bad convergence testing in DNN tooling for very small datasets.

Other:

Release 19.0

Release date: Jun 25, 2016
Major Changes in this Release:
New Features:
   - A deep learning toolkit using CPU and/or GPU hardware.  Some major elements
     of this are:
      - Clean and fully documented C++11 API
      - Clean tutorials: see dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
      - Uses cuDNN v5.0
      - Multi-GPU support
      - Automatic learning rate adjustment
      - A pretrained 1000 class Imagenet classifier (see dnn_imagenet_ex.cpp)
   - Optimization Tools
      - Added find_optimal_parameters()
      - Added elastic_net class
      - Added the option to use the elastic net regularizer to the OCA solver.
      - Added an option to solve the L2-loss version of the SVM objective function to svm_c_linear_dcd_trainer.
      - Added solve_qp_box_constrained()
   - Image Processing
      - Added random_color_transform, disturb_colors(), and apply_random_color_offset().
      - load_image() now supports loading GIF files.
   - Many improvements to the MATLAB binding API  
      - Automatically link to MATLAB's Intel MKL when used on linux.
      - struct support
      - mex functions can have up to 20 arguments instead of 10.
      - In place operation.  Made column major matrices directly wrap MATLAB
        matrix objects when used inside mex files.  This way, if you use
        matrix_colmajor or fmatrix_colmajor in a mex file it will not do any
        unnecessary copying or transposing.
      - Catch ctrl+c presses in MATLAB console.  Allowing early termination of mex functions.
      - When used inside mex files, DLIB_ASSERTS won't kill the MATLAB process,
        just throw an exception.
      - Made cerr print in MATLAB as a red warning message.
   - load_mnist_dataset()
   - Added a constructor for seeding rand with a time_t.
   - Added subm_clipped()
   - Added unserialize.
   - Added running_gradient

Non-Backwards Compatible Changes:
   - Everything in dlib/matlab/call_matlab.h is now in the dlib namespace.
   - DLIB_TEST() and DLIB_TEST_MSG() macros now require you to terminate them with a ;

Bug fixes:
   - Fixed bug in 10 argument version of call_matlab() and also cleaned up a few
     minor things.
   - setup.py and CMake scripts work in a few more contexts.
   - Fixed compiler errors in visual studio 2015.
   - Fixed a bug in gaussian_blur() that caused messed up outputs when big
     sigma values were used on some pixel types.
   - Fixed minor bugs in join_rows() and join_cols(). They didn't work when one
     of the matrices was empty.

Other:
   - Made CMake scripts uniformly require CMake version 2.8.4.
   - Faster fHOG feature extraction / face detection
   - CMake scripts now enable C++11 by default
   - Gave array2d and matrix move constructors and move assignment operators.  Matrix
     can also now be created from initializer lists.

Release 18.18

Release date: Oct 28, 2015
Major Changes in this Release:
New Features:
   - Added the set_ptrm() routine for assigning dlib::matrix objects to arbitrary
     memory blocks.
      
Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a bug that caused cmake to not provide the correct preprocessor
     definitions until cmake was run twice. This was causing some projects to
     not build properly.

Other:
   - Improvements to build system:
      - Ehsan Azarnasab contributed a setup.py so the dlib Python API can be
        installed via the usual 'python setup.py install' command. 
      - Séverin Lemaignan upgraded dlib's CMake scripts so they include an 
        install target.  Now dlib can be installed system wide by executing 
        'cmake PATH_TO_DLIB; make install'.  This also includes installing the
        appropriate scripts for CMake's find_package(dlib) to work.

Release 18.17

Release date: Aug 15, 2015
Major Changes in this Release:
New Features:
   - More clustering tools:
      - Added bottom_up_cluster() and find_clusters_using_angular_kmeans()
        routines.
      - Added a --cluster option to the imglab tool.  This lets you cluster
        objects into groups of similar appearance/pose.
   - Improved the shape_predictor.  In particular, it can now be learned from
     datasets where some landmarks are missing.  The shape_predictor also now
     outputs a sparse feature vector that encodes which leafs are used on each
     tree to make a prediction.
      
Non-Backwards Compatible Changes:
   - extract_highdim_face_lbp_descriptors() produces slightly different output.

Bug fixes:
   - Fixed a minor bug in extract_highdim_face_lbp_descriptors() which was
     pointed out by Yan Xu. One of the face locations was mistakenly used twice
     while another was skipped. This change breaks backwards compatibility with
     the previous feature extraction output but should slightly improve
     accuracy of classifiers trained using these features.
   - Fixed jet() and heatmap() so they work on empty images.
   - The SQLite transaction object did not function correctly when compiled 
     in a C++11 program.  Since its destructor can throw, an exception
     specification needed to be added indicating that this was possible since
     destructors are now noexcept by default in C++11.
   - Fixed a bug pointed out by Ernesto Tapia that could cause matrix
     expressions that involve sub matrix views (e.g. colm) to produce the wrong
     results when the BLAS bindings were enabled.
   - Added an if to avoid a possible division by zero inside spectral_cluster().
   - Fixed a bug in parse_xml(). It failed to check if the given input stream
     was valid before trying to parse it.

Other:


Release 18.16

Release date: Jun 3, 2015
Major Changes in this Release:
New Features:
   - Added a linear model predictive control solver.  See the mpc_ex.cpp example
     program for details.
   - Thanks to Patrick Snape, the correlation_tracker can now be used from Python.
      
Non-Backwards Compatible Changes:
   - The camera_transform's second operator() method now takes 3 arguments
     instead of 2.  This is to allow it to output the z distance in addition to
     scale.

Bug fixes:
   - Fixed a bug in the eigenvalue_decomposition which could occur when a
     symmetric matrix was used along with the LAPACK bindings.
   - Fixed a bug where the last column of data in a file wasn't loaded on some
     OS X machines when load_libsvm_formatted_data() was called.

Other:
   - Added a hard iteration limit to a number of the SVM solvers.
   - Adrian Rosebrock graciously setup an OS X machine for dlib testing, which
     resulted in improved CMake python scripts on OS X machines.
   - Improved the way overlapping points are rendered by the perspective_window.


Release 18.15

Release date: Apr 29, 2015
Major Changes in this Release:
New Features:
   - Added a number of tools for working with 3D data:
      - Added the perspective_window which is a tool for displaying 3D point clouds.
      - Added camera_transform.  It performs the 3D to 2D mapping needed to visualize 3D
        data.
      - Added point_transform_affine3d as well as functions for creating such transforms:
        rotate_around_x(), rotate_around_y(), rotate_around_z(), and translate_point().
   - Added draw_solid_circle() for drawing on images.
   - Added get_best_hough_point() to the hough_transform.
   - Thanks to Jack Culpepper, the python API for object detection now outputs detection
     confidences.  
   - Added lspi, an implementation of the least-squares policy iteration algorithm.
      
Non-Backwards Compatible Changes:
   - The shape_predictor and shape_predictor_trainer had a non-optimal behavior when used
     with objects that have non-square bounding boxes. This has been fixed but will cause
     models that were trained with the previous version of dlib to not work as accurately if
     they used non-square boxes. So you might have to retrain your models when updating dlib.

Bug fixes:
   - Fixed a bug which prevented add_image_rotations() from compiling.

Other:
   - The imglab tool now allows the user to click and drag annotations around by holding
     shift and right clicking.


Release 18.14

Release date: Mar 01, 2015
Major Changes in this Release:
New Features:
   - Added spectral_cluster()
   - Added sub_image() and sub_image_proxy
   - Added set_all_logging_headers()

Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a bug that caused the correlation_tracker to erroneously trigger an assert when
     run in debug mode.

Other:
   - Improved the usability of the new drectanle object.
   - Optimized extract_fhog_features() for the case where cell_size==1. This makes it about
     4x faster in that case.
   - Made it so you can compose point transform objects via operator *.


Release 18.13

Release date: Feb 03, 2015
Major Changes in this Release:
New Features:
   - Added the correlation_tracker object
   - Added the option to force the last weight to 1 to structural_assignment_trainer.
   - Added max_point_interpolated()
   - Added the drectangle object
   - New Python Tools:
      - Patrick Snape contributed a Python binding for the face landmarking tool and 
        the general purpose shape prediction/training tools.
      - Vinh Khuc contributed a Python binding for find_candidate_object_locations(),
        dlib's implementation of the selective search object location proposal method.

Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a bug in extract_image_chips() and get_mapping_to_chip() that caused
     incorrect outputs when the requested chip stretched the image unevenly
     vertically or horizontally.
   - Made CMake check that libpng and libjpeg actually contain the link symbols
     they are supposed to since, on some systems, these libraries aren't
     installed correctly and will cause linker errors if used.
   - Fixed assign_border_pixels(img, rect) so that it correctly zeros an image
     when an empty rectangle is supplied. Previously, it did nothing to the
     image in this case.
   - Fixed compute_lda_transform() so it works properly when the class
     covariance matrices are singular even after performing PCA.
   - Fixed a bug in find_similarity_transform(). When given just two points as
     inputs it would sometimes produce a reflection rather than a similarity
     transform.
   - Disabled all bindings to FFTW because FFTW isn't threadsafe.

Other:
   - Added an example program for dlib's SQLite API and made a few minor
     usability improvements to the API as well.


Release 18.12

Release date: Dec 20, 2014
Major Changes in this Release:
New Features:
   - Upgraded fft() and ifft() to support 2D matrices.
   - Added hough_transform
   - Added skeleton() for finding the skeletonization of a binary image.
   - Added distance_to_line(), clip_line_to_rectangle(), min_point(), and max_point().
   - Added a simple API for calling C++ from MATLAB.

Non-Backwards Compatible Changes:

Bug fixes:
   - Fixed a compile time error that could happen when calling fft() for
     certain input types.
   - Fixed a compile time error that prevented auto_threshold_image() from
     being used.
   - Fixed name clashes with new version of Boost. 
   - Changed Python pickling code so it works with Python 3.
   - Fixed CMake compile time error related to finding fftw.

Other:
   - Made extract_image_chips() much faster when extracting unscaled image chips.

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:



Old Release Notes