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updated docs
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Davis King
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@ -120,6 +120,7 @@
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<section>
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<name>Feature Extraction</name>
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<item>get_surf_points</item>
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<item>shape_predictor</item>
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<item nolink="true">
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<name>SURF Tools</name>
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<sub>
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@ -207,23 +208,23 @@
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</section>
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<section>
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<name>Colormaps</name>
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<name>Visualization</name>
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<item>randomly_color_image</item>
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<item>heatmap</item>
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<item>jet</item>
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</section>
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<section>
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<name>Miscellaneous</name>
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<item>cv_image</item>
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<item>toMat</item>
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<item>render_face_detections</item>
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<item>draw_line</item>
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<item>draw_surf_points</item>
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<item>draw_rectangle</item>
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<item>tile_images</item>
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<item>draw_fhog</item>
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<item>fill_rect</item>
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</section>
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<section>
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<name>Miscellaneous</name>
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<item>cv_image</item>
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<item>toMat</item>
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<item>assign_image</item>
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<item>assign_image_scaled</item>
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<item>assign_all_pixels</item>
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@ -362,6 +363,41 @@
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>shape_predictor</name>
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<file>dlib/image_processing.h</file>
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<spec_file link="true">dlib/image_processing/shape_predictor_abstract.h</spec_file>
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<description>
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This object is a tool that takes in an image region containing some object
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and outputs a "shape" or set of point locations that define the pose of the
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object. The classic example of this is human face pose prediction, where
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you take an image of a human face as input and are expected to identify the
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locations of important facial landmarks such as the corners of the mouth
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and eyes, tip of the nose, and so forth. For example, here is the output
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of dlib's <a href="http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2">68-face-landmark shape_predictor</a> on an image from the HELEN dataset: <br/><br/>
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<img src='face_landmarking_example.png'/>
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<br/><br/>
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To create useful instantiations of this object you need to use the
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<a href="ml.html#shape_predictor_trainer">shape_predictor_trainer</a> object to train a
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shape_predictor using a set of training images, each annotated with shapes you want to predict.
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To do this, the shape_predictor_trainer uses the state-of-the-art method from the
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paper:
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<blockquote>
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One Millisecond Face Alignment with an Ensemble of Regression Trees
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by Vahid Kazemi and Josephine Sullivan, CVPR 2014
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</blockquote>
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</description>
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<examples>
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<example>face_landmark_detection_ex.cpp.html</example>
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<example>train_shape_predictor_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -1020,6 +1056,31 @@
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>render_face_detections</name>
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<file>dlib/image_processing/render_face_detections.h</file>
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<spec_file link="true">dlib/image_processing/render_face_detections_abstract.h</spec_file>
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<description>
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This function takes a set of <a href="#full_object_detection">full_object_detections</a>
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which represent human faces annotated with 68 facial landmarks (according to the iBUG 300-W
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scheme) and converts them into a form suitable for display on an
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<a href="dlib/gui_widgets/widgets_abstract.h.html#image_window">image_window</a>.
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<p>
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For example, it will take the output of a <a href="#shape_predictor">shape_predictor</a>
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that uses this facial landmarking scheme and will produce visualizations like this:
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</p>
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<img src='face_landmarking_example.png'/>
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</description>
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<examples>
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<example>face_landmark_detection_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -104,6 +104,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>structural_track_association_trainer</item>
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<item>structural_graph_labeling_trainer</item>
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<item>svm_rank_trainer</item>
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<item>shape_predictor_trainer</item>
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</section>
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<section>
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<name>Clustering</name>
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@ -163,6 +164,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>test_track_association_function</item>
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<item>test_graph_labeling_function</item>
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<item>test_ranking_function</item>
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<item>test_shape_predictor</item>
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<item>average_precision</item>
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</section>
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@ -1315,6 +1317,30 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>shape_predictor_trainer</name>
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<file>dlib/image_processing.h</file>
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<spec_file link="true">dlib/image_processing/shape_predictor_abstract.h</spec_file>
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<description>
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This object is a tool for training <a href="imaging.html#shape_predictor">shape_predictors</a>
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based on annotated training images. Its implementation uses the algorithm described in:
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<blockquote>
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One Millisecond Face Alignment with an Ensemble of Regression Trees
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by Vahid Kazemi and Josephine Sullivan, CVPR 2014
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</blockquote>
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It is capable of learning high quality shape models. For example, this is an example output
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for one of the faces in the HELEN face dataset: <br/><br/>
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<img src='face_landmarking_example.png'/>
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</description>
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<examples>
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<example>train_shape_predictor_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -2684,6 +2710,25 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>test_shape_predictor</name>
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<file>dlib/image_processing.h</file>
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<spec_file link="true">dlib/image_processing/shape_predictor_abstract.h</spec_file>
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<description>
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Tests a <a href="imaging.html#shape_predictor">shape_predictor</a>'s ability to correctly
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predict the part locations of objects. The output is the average distance (measured in pixels) between
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each part and its true location. You can optionally normalize each distance using a
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user supplied scale. For example, when performing face landmarking, you might want to
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normalize the distances by the interocular distance.
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</description>
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<examples>
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<example>train_shape_predictor_ex.cpp.html</example>
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</examples>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -343,6 +343,10 @@
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<term file="ml.html" name="svm_c_linear_trainer" include="dlib/svm.h"/>
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<term file="ml.html" name="svm_c_linear_dcd_trainer" include="dlib/svm.h"/>
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<term file="ml.html" name="svm_rank_trainer" include="dlib/svm.h"/>
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<term file="ml.html" name="shape_predictor_trainer" include="dlib/image_processing.h"/>
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<term file="ml.html" name="test_shape_predictor" include="dlib/image_processing.h"/>
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<term file="imaging.html" name="shape_predictor" include="dlib/image_processing.h"/>
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<term file="imaging.html" name="render_face_detections" include="dlib/image_processing/render_face_detections.h"/>
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<term file="ml.html" name="ranking_pair" include="dlib/svm.h"/>
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<term file="ml.html" name="is_ranking_problem" include="dlib/svm.h"/>
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<term file="ml.html" name="count_ranking_inversions" include="dlib/svm.h"/>
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