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Commit f0e6a0af authored by Davis King's avatar Davis King
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updated docs

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docs/docs/linear_algebra.xml
View file @ f0e6a0af
......@@ -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
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......@@ -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
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......@@ -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
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......@@ -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"/>
......
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