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Last Modified:
Jun 15, 2012
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| Release notesRelease 17.47Release date: Jun 15, 2012
Major Changes in this Release:
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New Stuff:
- 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.
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Release 17.46Release date: Apr 11, 2012
Major Changes in this Release:
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New Stuff:
- 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.
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Release 17.45Release date: Jan 29, 2012
Major Changes in this Release:
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New Stuff:
- 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.
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Release 17.44Release date: Nov 21, 2011
Major Changes in this Release:
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New Stuff:
- Machine Learning
- Added the histogram intersection kernel for sparse and dense vectors.
- Added a set of tools to allow a user to easily learn to do sequence
labeling using dlib's structural SVM implementation. See the new
sequence_labeler object and its associated example program for an
introduction.
- Image processing:
- Added segment_image()
- Added randomly_color_image()
- Added the border_enumerator
- Added the disjoint_subsets object, it is an implementation of the
union-find algorithm/disjoint-set data structure.
- Added new matrix routines: conv(), conv_same(), conv_valid(), xcorr(),
xcorr_same(), xcorr_valid(), and flip().
Non-Backwards Compatible Changes:
- Changed find_max_factor_graph_viterbi() so you can use run-time
defined order and num_states parameters.
Bug fixes:
- The last dlib release added a max_iterations parameter to the
svm_c_linear_trainer and svm_c_ekm_trainer objects. However,
there was a bug which made them only do at most 16 iterations,
which is too few to solve many problems. This has been fixed.
- Fixed a bug in is_const_type. It didn't work for reference types.
- Fixed a bug in the SQLite binding routine statement::get_column_as_text().
It didn't work correctly if the column contained a NULL.
- Fixed a bug in find_max_factor_graph_viterbi() which occurred when a
zero order model had negative factor values.
Other:
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Release 17.43Release date: Oct 21, 2011
Major Changes in this Release:
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New Stuff:
- Two new routines for performing MAP inference in factor graphs:
- For chain-structured graphs: find_max_factor_graph_viterbi()
- For general graphs: find_max_factor_graph_nmplp()
- Image Processing
- Added more tools for creating image pyramids. See pyramid_down_5_4,
pyramid_down_4_3, and pyramid_down_3_2.
- Added more image filtering and morphology functions.
- Added a set of tools for creating sliding window classifiers:
- Added the scan_image() routine. It is a tool for sliding a set of
rectangles over an image space and finding the locations where the sum
of pixels in the rectangles exceeds a threshold. Also added
scan_image_pyramid, which is a tool for running scan_image() over an
image pyramid.
- Added the structural_object_detection_trainer. This is a tool which
formulates the sliding window classifier learning problem as an
instance of structural SVM learning.
- Added a variety of supporting tools and two object detection example
programs.
- Added the following functions for computing statistics on vectors:
mean_sign_agreement(), correlation(), covariance(), r_squared(),
and mean_squared_error()
- Added a C++ wrapper for SQLite (see the new database and statement objects)
Non-Backwards Compatible Changes:
- Changed the interface to the ridge regression trainer objects so that they
report the entire set of leave-one-out prediction values rather than a
summary statistic like mean squared error.
- Changed the serialization routine for bgr_pixels to store the pixels in BGR
order rather than RGB.
- Changed the interface for the spatially_filter_image() routine to take the
filter as a matrix rather than C-array. Also, now it won't force signed pixel
values to 0 if they go negative.
- Changed the test_regression_function() and cross_validate_regression_trainer()
routines so they return both the MSE and R-squared values rather than just the
MSE.
- Changed suppress_non_maximum_edges() to use the L2 norm instead of L1 norm
for measuring the strength of an edge since this produces a slightly better
result.
Bug fixes:
- The image_display didn't display overlay rectangles quite right. If you zoomed
in you could see that some of the pixels which are inside the rectangle were
outside the overlay. Specifically, the right column and bottom row was outside
the overlay rectangle. This has been fixed. Now all pixels which are supposed
to be part of a rectangle are drawn as being inside the overlay rectangle.
- Fixed a bug pointed out by Martin Müllenhaupt which caused the windows socket
code to not compile when used with the mingw-cross-env project.
- Fixed a bug in the png_loader. If you loaded an image with an alpha channel
into something without an alpha channel there were uninitialized values being
alpha blended into the image.
- Fixed a bug in the cpp_tokenizer that only shows up on newer versions of gcc.
It wasn't tokenizing double quoted strings right.
- Fixed a bug in spatially_filter_image() which showed up when using non-square
filters. The bug would cause the edges of the output image to be incorrect.
- Fixed a bug in the matrix class. Expressions of the form mat *= mat(0) would
evaluate incorrectly because the *= operator took the right hand side by reference
and thus experienced an aliasing problem. The other op= operators had similar
problems and have also been fixed.
- Fixed a bug pointed out by Justin Solomon which could cause the svr_trainer and
svm_c_trainer to produce incorrect results in certain unusual cases.
Other:
- Added a more complete set of methods for converting between image space and
the downsampled hog grid used by hog_image. Now you can convert from image
to hog in addition to hog to image.
- Made the integral_image more general by making it templated on the type of
scalar used to store the sums.
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Release 17.42Release date: Jun 24, 2011
Major Changes in this Release:
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New Stuff:
- Added the check_sub_option() method to the command line parser check
object.
- Added match_endings to the dir_nav utils.
- Added a set_current_dir() function.
- Added the distance_to_rect_edge() routine.
- Added support for user drawn rectangle overlays and selectable overlays
to the image_display widget.
Non-Backwards Compatible Changes:
Bug fixes:
- Fixed a bug in the image_display widget. If you switched it between
images of a different size while any kind of zoom was in effect
it could cause a segmentation fault.
Other:
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Release 17.41Release date: Jun 12, 2011
Major Changes in this Release:
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New Stuff:
- You can now add tasks to a thread_pool by value, using the new
add_task_by_value() method.
Non-Backwards Compatible Changes:
Bug fixes:
- Fixed a bug which caused multiply defined symbol errors during linking
if the PNG saving routine was #included.
Other:
- Optimized the threaded and distributed structural svm solvers for the
case where there are many data samples and the separation oracle is
quick to evaluate.
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Release 17.40Release date: Jun 05, 2011
Major Changes in this Release:
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New Stuff:
- Added a function for saving to the PNG image format.
- Added Austin Appleby's excellent MurmurHash3 hashing code and setup some
additional convenience functions. These functions are murmur_hash3() and
various overloads of hash().
Non-Backwards Compatible Changes:
- Made get_pixel_intensity() more efficient. However, the value returned
is slightly different than it used to be for RGB pixel types.
Bug fixes:
- Setup proper error handling for libpng in the png_loader. Now if the PNG
file is corrupted in the middle it won't just print a message and abort
the program.
- Fixed a bug in assign_pixel_intensity() that happened when the target pixel
was an RGB pixel with an alpha channel.
Other:
- Added a Frequently Asked Questions page
- Changed the array2d object so you don't have to say array2d<type>::kernel_1a
anymore to declare it. Now you just say array2d<type>.
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Release 17.39Release date: May 22, 2011
Major Changes in this Release:
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New Stuff:
- Added tools for distributing the work involved in solving a structured
SVM problem over many computers and CPUs.
- Added the bridge. It allows a dlib::pipe to be used for networked
communication.
Non-Backwards Compatible Changes:
- Removed the DLIB_REVISION macro and replaced it with DLIB_MAJOR_VERSION and
DLIB_MINOR_VERSION.
Bug fixes:
Other:
- dlib's version control system has switched from Subversion to Mercurial.
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Release 17.38Release date: May 7, 2011
Major Changes in this Release:
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New Stuff:
- Added the max_sum_submatrix() function to the optimization tools.
- Upgraded the pyramid_down function object so it can create color pyramids.
Also, added some functions which define the coordinate transforms between
different layers in an image pyramid.
Non-Backwards Compatible Changes:
- Changed the oca_problem interface to the oca optimizer. Now the
optimization_status() function reports back the current risk and risk gap
in addition to the overall objective value and objective gap.
- Changed the stopping condition for the structured svm to the one suggested
by the Joachims paper. Now it stops when the risk gap is below a user
supplied epsilon.
Bug fixes:
Other:
- Various usability improvements.
- Improved the feature vector caching in the structural_svm_problem object.
- Some objects were setup as multi-implementation objects but only had one
implementation. I went through dlib and switched these to single implementation
objects. So for example, to use the dlib crc32 module you used to declare an
object of type "crc32::kernel_1a" but now you can just say "crc32". Note that
I did this change in a way that maintains backwards compatibility with previous
versions. So crc32::kernel_1a is still allowed but that form is officially
deprecated. The modified objects are as follows:
- base64
- byte_orderer
- config_reader
- crc32
- pipe
- rand
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